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| "text": "Introduction to Applications based Internet of Things Network +t 7 T +t LE TRONG NHAN trongnhanle@hcmut.edu.vn trongnhanle85@gmail.com", |
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| "text": "Introduction to Applications based Internet of Things (loTs) Evaluation 20% Mid term exam (MCQs) 30% Final term exam (MCQs) 40% Lab + 10% Project 2", |
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| "text": "Introduction to Applications based Internet of Things (IoTs) Project Structure loT Server Adafruit lO TRC-S001 04/01/2022 08:39:22 27.1° 57.2% KhiC02 400 ppm OHu PM10 11 12 ppm ppm PM2.5 46 Gateway Sensor node S/cm 25.7 Dan Dien (EC) 43.1 0 STM32 Platform (Nucleo, PC or Raspberry Pi Microbit or ESP32 NhietDo Am Humidity E2E Protocol 0 Temperature and sensors (2 sensors) (MQTT) Wireless or wired communication Smartphone 0 0 Modbus 485 App: 3", |
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| "text": "Introduction to Applications based Internet of Things (IoTs) Project evaluation Group project of 3 students!!!l Extra points: Gateway: Implement the gateway on Raspberry PI Implement the Error Control for the gateway Implement the NB-IoT for the gateway (SIM7070) Adafruit server: Webhock service 4", |
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| "text": "Introduction to Applications based Internet of Things (IoTs) Labs Lab1 (1 week): Python and Adafruit server Lab 2 (2 weeks) Sensor integration 1 Lab 3 (2 weeks) Mobile apps Lab 4 (1 week) Al and Extra features 5", |
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| "text": "Introduction to Applications based Internet of Things (IoTs) Installed Software PyCharm + Python v3.8.5 Android Studio version 4 STM32Cube for ARM MakeCode for Microbit (or Yolo:Bit) platform 6", |
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| "text": "Introduction to Applications based Internet of Things (IoTs) Content Introduction Network and Internet and Internet of Things Internet of Things Architecture Edge Computing and Edge AI Demo: Teachable Machine with Google Proposed block diagram for loT Applications Sensing System based on Microcontroller Platform Gateway Processing using Raspberry PI or PC Potential Applications based loT Network Environment Monitoring Smart Street Light Autonomous Robots 7", |
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| "text": "Network, Internet and Internet of Things Application Layer loT TransportLayer InternetLayer Network Access Layer", |
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| "text": "Introduction to Applications based Internet of Things (IoTs) Communication Model SourceSystem Destination System Trans- Trans- Source Receiver Destination mission mitter System (a) General block diagram Workstation Modem Modem Server Public Telephone Network (b) Example 9", |
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| "text": "Introduction to Applications based Internet of Things (loTs) Conventional Communication Networks Switching Wide-area node network SourceSystem Destination System Trans- Trans- Source mission Receiver Destination mitter System Localarea network 1", |
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| "text": "Introduction to Applications based Internet of Things (IoTs) Local Area Networks (LANs) Characteristics Token- LAN Smaller scope 1 Ring Building or small campus Usually by owned same organization as attached devices Data rates much high-- Categories 192.168.1.2 Switched LANs 192.168.1.1 192.168.1.3 Ethernet SWITCH/ROUTER Wireless LANs D ATM LANs 192.168.1.4 192.168.1.6 (Asynchronous Transfer Mode) 192.168.1.5 1", |
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| "text": "Introduction to Applications based Internet of Things (IoTs) Wide Area Networks (WANs) Characteristics Span a large geographical area Cross public rights of way Switching Wide-area node network Technologies used include: Circuit switching Packet switching Frame relay Asynchronous Transfer Mode (ATM) 1", |
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| "text": "Introduction to Applications based Internet of Things (IoTs) Internet Network Subscriber connection High-speed link Residential (e. g. SONET) TCP/IP user Internet service provider (ISP) Architecture Application Router Internet Iransport ATM switch Internet High-speed Firewall link host ATM Network Data Link Ethernet Router switch Physical Private Information LAN PCs WAN server andworkstations 1", |
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| "text": "Introduction to Applications based Internet of Things (IoTs) Internet of Things (1oTs) # $ SMS SALE ? 1", |
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| "text": "Introduction to Applications based Internet of Things (IoTs) Internet of Things (loT) Smart Agriculture Open Data Internet of Things Smart Smart Home Retail Smart Mobility Education SMART CITY Smart Grid/ Smart Health Smart Energy + SmartGovernment 1", |
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| "text": "Introduction to Applications based Internet of Things (IoTs) Industrv 4.0 Figure 1: The four stages of the Industrial Revolution Em First programmable logic controller 4.industrialrevolution PLC.Modicon 084 based on Cyber-Physical 1969 Systemss 3.industrial revolution uses electronics and IT to First production line achieve further automation Cincinnati slaughterhouses of manufacturing 1870 2.industrial revolution follows introduction of electrically-poweredmass First mechanical loom production based on the 1784 division of labour 1.industrial revolution follows introduction of water-and steam-powered mechanical manufacturing time facilities End of Start of Start of 1970s today 18th century 20th century Source:DFKI2011 1", |
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| "text": "Introduction to Applications based Internet of Things_(loTs) loTs Services and Figure 4: Internet of People Social Web The Internet of Things and 106-108 Services - Networking people, objects and systems CPS- platforms Smart Grid Business Web Smart Factory 264 X Smart Building Smart Home Internet of Things Internet of Services 107-109 104-106 Source.BoschSotware lrnovations 2012 1", |
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| "text": "Internet of Things Architecture Networks Middleware Applications Ek ek Ek Smartthings Gateways N IOT ARCHITECTURE 0 Q https:// www.altexsoft.com/", |
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| "text": "Introduction to Applications based Internet of Things (loTs) Internet of Things (loT) (Timothy Chou) Do Learn OT LTEAdvanced Cellular 4G/LTE 3G-GPS/GPRS 2G/GSM/EDGE.CDMA.EVDO WEIGHTLESS VIMAX LICENSE-FREESPECTRUM Collect DASH7 wi Fi WiFi BLUETOOTH UWB Z-WAVE ZIGBEE 6LOWPAN NFC ANT RFID WAN Connect WideArea Network-802.20 POWERLINE ETHERNET MAN PRINTED MetropolitanAreaNetwork-8o2.16 LAN LocalAreaNetwork-802.11 P4IPv6UDP DTLSRPLTeinEtMQTT DDS CoAP XMPP HTTPSOCKETSRESTAP PAN PersonalArea Network 802.15 Ambient Light Touch Screen Proximity Fingerprint Attitude Things Accelerometer Gyroscope Moisture Magnetometer Gravity Barometer 1", |
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| "text": "Introduction to Applications based Internet of Things (IoTs) Things Layer Sensors are selected according to the applications: Easy configuration Low cost - Long lifetime Camera is used as a sensor Actuators [MQTT Protocol] Remote control Low latency Smart eHealth Water aOuallty. ENSDF SPIROMETER Smart Water ALERTPATIENT s.atet TEMPERATURE water control& managen Monitorin g drinkiing water quaililty MySignals/L ealty.pollution-1 ns for wildtit Ch-cmicalilcakcag risk monitoring 2", |
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| "text": "Introduction to Applications based Internet of Things (IoTs) Connect Layer Globalareanetwork Building area WIFD WirelessWide network iBS Area Network Metropolitan area Home area WIFD Neighborhood network network areanetwork Access area network 3G+ Personal area Bluetooth 4. ZigBee networks Campus area AAD Lte network xDSL Lte CableTV Near PLC NNFC wlmax CDMA450 communications RFID FTTx Indoor/in-home/intra communication Outdoor/inter-communication More than 60 protocols are proposed every year Mobility network 2", |
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| "text": "Introduction to Applications based Internet of Things (IoTs) Communication Protocol The combination of framing, flow control, and error control to achieve the delivery of data from one node to another. The protocols are normally implemented in software by using one of the common programming languages. Sender Receiver Protocols A Request Frame Arrival ACK Arrival- For noiseless For noisy Request Frame channel channel Arrival ACK Arrival Simplest Stop-and-Wait ARQ Time Time Stop-and-Wait Go-Back-N ARQ -Selective Repeat ARQ 2", |
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| "text": "Introduction to Applications based Internet of Things (IoTs) OMNeT++ 5.0 OMNeT++.5.0 3D Visualization Demo 2 88 Press Esc to exit full screen BostonPark#0OsgEarthNet Event#1 t=0s Msg stats20scheduled/20existing/20created Nextmove(omnetpp.cMessage.id=20 InOsgEart bleNode.id=RambleNode Atlastevent+Os OsaEarthNet. scheduled-eve.. OsgEarthNetO. base fields owned objects parameters.gates 0:32/3:51 cc HD 2", |
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| "text": "Edge Computing g in Internet of Things Applications N EDGE nVIDIA COMPUTING", |
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| "text": "Introduction to Applications based Internet of Things (IoTs) Edge Computing Operations Information Technology Technology THE EDGE Data The Things Sensors & Data Center & Aggregation Edge lT Actuators Cloud IT Wearables . Mobile Devices & Gateways Cars Meters Motors Robotics Buildings - Generators DATAGENERATION DATASENSING DATACOLLECTION EARLY DATA DEEP DATA AGGREGATION ANALYTICS ANALYTICS 2", |
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| "text": "Introduction to Applications based Internet of Things (IoTs) Combine e it with AI Edge AI Training MLmodel training Data ? Artificial Intelligence Connectivity IOT IntelligentEdge Backend Core Edge Al overcomes the latency of data and complex computations: Increase in Ievels of Automation Digital Twins for Advanced Analytics Real-Time Decision Making Edge Inference and Training 3", |
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| "text": "Introduction to Applications based Internet of Things (loTs) Edge AI Embedded Platform on Sample Code Nsight Developer Tools Multimedia APl TensorRT VisionWorks Vulkan libargus AX CuDNN OpenCV OpenGL GStreamer TF, PyTorch, .. NPP EGL/GLES V4L2 Deep Learning Computer Vision Graphics Media CUDA, Linux For Tegra, ROS Jetson AGX Xavier:Advanced GPU,64-bit CPU,Video CODEC, DLAs 384 NVIDlA CUDA cores and 48 Tensor Cores 3", |
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| "text": "Introduction to Applications based Internet of Things_(IoTs) PyCoral [https://coral.ai/products/] & Coral Models Trained TensorFlow models for the Edge TPU California Quail Image classification Object detection Semantic segmentation 0.9179 Moaeis that recognize the subject in an image Moceis that identify multiple objects and Modeis that identify specific pixels belonging to plus classification models for on-device provice their location. different objects transfer learning. See models See models See models 3", |
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| "text": "Introduction to Applications based Internet of Things (IoTs) Train Al Model an Application Get Data Train Model Dev 2 4 3 5 Clean,Prepare Test Data & Manipulate Data 3", |
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| "text": "Introduction to Applications based Internet of Things (IoTs) 1 Google Demo with TM https://teachablemachine.withgoogle.com/train New Project Open an existing project from Drive. Open an existing project from a file Image Project Audio Project Pose Project Teach based on images, from Teach based on one-second-long Teach based on images, from files or your webcam sounds, from files or your files or your webcam microphone. 3", |
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| "text": "Introduction to Applications based Internet of Things (IoTs) System Architecture. loT Server Adafruit lO Thingsboard Smartphone Android Gateway Sensor node Raspberry Pi Arduino Uno E2E Temperature sensor Protocol Wireless or wired (MQTT) communication Internet connection 3", |
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| "text": "Introduction to Applications based Internet of Things_(IoTs) Sensing System based Arduino b CXD UNO AREUZ 33723 @ Microcontroller platform: Device drivers are supported ONLINE SIMULATIONS!! Low cost Open source 3", |
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| "text": "Introduction to Applications based Internet of Things (IoTs) Gateway using Raspberry PI = New Load Save Run Debug Over Into Out Stop Zoom Quit gateway_iot.pyx 1 from Adafruit I0 import MQTTClient 2 import serial.tools.list ports 3 4 AIO USERNAME=\"NPNLab BBC 5 AI0 KEY = \"aio Uwbb13i76I30GhRak2sq6h5kdyE4\" 6 AIO FEED IDS= [\"bbc-led\",\"bbc-pump\"] 7 8 def connectedclient): 9 print\"Ket noi thanh cong\") 10 for feed in AIO FEED IDS: 11 client.subscribe(feed) 12 13 def subscribe(client, userdata, mid, granted qos) 14 print(\"Subscribe thanh cong\" 15 16 def disconnectedclient): 17 print\"Ngat ket noi\" 18 system.exit(l) 19 20 def message(client, feed id, payload): 21 print(\"Nhan du lieu: \" + payload) 22 23 client = MQTTClient(AI0 USERNAME, AI0 KEY) 24 client.on connect = connected 25 client.on disconnect = disconnected 26 rlinn+ Micro processor platform: Network connection loT services are supported 3", |
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| "text": "Potential Applications", |
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| "text": "Introduction to Applications based Internet of Things_(loTs) Smart Agriculture Temperature and Humidity pH, EC Light intensity Pump controllers 6AM 8AM Feed Wate AELONKIMOR Humidit 250 21C 6.0 60% 3", |
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| "text": "Introduction to Applications based Internet of Things (IoTs) Monitoring based AR/VR system Water Level 63% Sunligh 720% Plant Progress 8-10wecks 351 60% 6.0 25°C 21° VateCTEMPERATURE AIRTEMPERATURE 24C 4", |
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| "text": "Introduction to Applications based Internet of Things (IoTs) Monitoring Air Quality 58- 35 93 255 70 64 23 69 0 Temperature Humidity PM2.5 Air quality monitoring program assists us in improving and developing air pollution control s to reduce the effect of air pollution. programs PM2.5, PM10, CO2, C0 4", |
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| "text": "Introduction to Applications based Internet of Things_(loTs) Smart t Street Light Management S Street Light 1 999999 DO CH H 088808 Generic loT Platform Internet Street Connection Light 2 Wifi/3G/4G/5G 999999 Do PLC Monitoring 4", |
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| "text": "Introduction to Applications based Internet of Things (IoTs) Autonomous Robots micro:bit Smart warehouse applications 4", |
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| "CO3037-chapter-2-slide-043-0000": { |
| "id": "CO3037-chapter-2-slide-043-0000", |
| "text": "Introduction to Artificial Intelligence, Machine Learning and Deep Learning 2 Input Output Feature extraction Classification Input Output Neural Networks", |
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| "CO3037-chapter-2-slide-044-0000": { |
| "id": "CO3037-chapter-2-slide-044-0000", |
| "text": "Computer Program and Machine Learning A The Traditional Programming Paradigm Inputs (observations) Programmer -> Program - Computer > Outputs Machine Learning B Inputs Computer Program Outputs Machine Learning comes up with the decision making \"program\" (machine learning model) that optimizes the decision making according to a user- defined objective. 2", |
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| "CO3037-chapter-2-slide-045-0000": { |
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| "text": "Artificial lntelligence Overview ARTIFICIAL INTELLIGENCE Early artificial intelligence MACHINE stirs excitement LEARNING Machine learning begins DEEP to flourish. LEARNING Deep learning breakthroughs drive Al boom. 00012 00101 00100 110 0111 .0110 0010 010111 10101 010 110101 01 01011.9 1010 1950's 1960's 1970's 1980's 1990's 2000's 2010's Since an early flush of optimism in the 1950s.smaller subsets of artificial intelligence-first machine learning.ther deep learning,a subset of machine learning-have created ever larger disruptions. 3", |
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| "CO3037-chapter-2-slide-046-0000": { |
| "id": "CO3037-chapter-2-slide-046-0000", |
| "text": "Machine vs Deep Learning Learnina Machine Learning Input Output Feature extraction Classification Traditional machine learning uses hand-crafted features.which is tedious and costly to develop Deep Learning Input Output Neural Networks Deep learning learns hierarchical representation from the data itself.and scales with moredata Deep learning models are gaining popularity for the fact that they can achieve state-of-the-art accuracy which at times can outperform human 4", |
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| "CO3037-chapter-2-slide-047-0000": { |
| "id": "CO3037-chapter-2-slide-047-0000", |
| "text": "What is Machine Learning (ml? ML allows machines(computers) to learn from data or experience and make a prediction based on the experience ML enables the computers or the machines to make data-driven decisions rather than being explicitly programmed for carrying out a certain task These or algorithms designed in a programs are way that they learn and improve over time when are exposed to new data 5", |
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| "CO3037-chapter-2-slide-048-0000": { |
| "id": "CO3037-chapter-2-slide-048-0000", |
| "text": "Learning lypes of Machine Meaningful Structure Image Compression CustomerRetention Discovery Classification Big data Dimensionality Feature Idenity Fraud Classification Diagnostics Visualistaion Reduction Elicitation Detection Recommender Supervised Advertising Popularity Unsupervised Systems Prediction Learning Learning Weather Forecasting Clustering Regression Machine Targetted Population Marketing Market Growth Forecasting Prediction Customer Learning Estimating Segmentation life expectancy Supervised Unsupervised Real-time decisions Game Al Reinforcement Reinforcement Learning Robot Navigation SkillAcquisition Learning Tasks 6", |
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| "CO3037-chapter-2-slide-049-0000": { |
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| "text": "d Learning Supervised Find the mapping between the input variable(x) and output variable(Y) (input X and label Y) Supervised learning is divided into two types: Regression - The output variable is continuous and real value. For example price, weight, etc Classification - The output variable is a category such as \"red\" or \"blue\" or \"disease\" or \"no disease\" 7", |
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| "CO3037-chapter-2-slide-050-0000": { |
| "id": "CO3037-chapter-2-slide-050-0000", |
| "text": "Regression l Classification and A B 200 4 150 2 100 0 50 Class 1 (y = 1) 0 Linear regression fit Class 2 (y = 2) 6 -3 -2 -1 0 1 2 -8 -6 -4 -2 0 2 4 Feature variable (x) Feature variable 1 (x) llustrations of the two main categories of supervised learning, regression (A) and classification (B) 8", |
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| "CO3037-chapter-2-slide-051-0000": { |
| "id": "CO3037-chapter-2-slide-051-0000", |
| "text": "d Learning Unsupervised Unsupervised learning is where only have you input data (X) and corresponding no output variables. The goal of unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data. Unsupervised learning is divided into two types: Clustering: c discover the inherent 1 groupings in the data, such by purchasing as grouping customers behavior Association: discover rules that describe large portions of your data, such as people that buy X also tend to buy Y. 9", |
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| "CO3037-chapter-2-slide-052-0000": { |
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| "text": "Clustering Example of A B X1 X1 Cluster 3 Cluster 1 Cluster 2 X2 X2 It seems that there are 3 clusters for the example dataset 10", |
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| "text": "Reinforcement Learning Reinforcement learning, in the context of artificial intelligence, is a type of dynamic programming that trains algorithms using a system of r 1 rewaro and punishment The by receives performing agent rewards correctly penalties for performing incorrectly and The learns without intervention from agent a by maximizing its reward l and r minimizing human its penalty 11", |
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| "text": "Example of Reinforcement 2 Agent Action Reward: State: At Rt Environment 1 3 Al Games (Bot) Optimize the power consumption in a house 12", |
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| } |
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| "CO3037-chapter-2-slide-055-0000": { |
| "id": "CO3037-chapter-2-slide-055-0000", |
| "text": "What is Deep Learning? Deep Learning a subfield of r machine learning l is with algorithms inspired by the concerned structure and function of the brain called artificial neural networks. IBM Deep Learning Has Revolutionized Machine Learning Accuracy #of Searches for Deep Learning from 2011to 2017 100 Deep Learning 80 60 Traditional Machine Learning 40 20 Data 2011-022012-022013-022014-022015-022016-022017-02 Source:Google Trends.Search term \"Deep Learning Note:graph is representational only and does not depict actual data 13", |
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| "page_index": 55, |
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| "timestamp": "2025-10-31T18:33:14+07:00" |
| } |
| }, |
| "CO3037-chapter-2-slide-056-0000": { |
| "id": "CO3037-chapter-2-slide-056-0000", |
| "text": "Applications of [ Learning Deep Self-driving cars Voice search and virtual assistants Machine translation Image caption generation Real-time object recognition in the image (Google lens. AIAPPLICATIONS B c !c SHK Recammendation Image Classification Object Detec tion Vaice Recognitian Language Translatior Engines Sentiment Ana lysis NATURAL LANGUAGE COMPUTER VISION SPEECH&AUDIO PROCESSING TEDEREDE 14", |
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| "page_index": 56, |
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| "timestamp": "2025-10-31T18:33:19+07:00" |
| } |
| }, |
| "CO3037-chapter-2-slide-057-0000": { |
| "id": "CO3037-chapter-2-slide-057-0000", |
| "text": "Introduction to TensorFlow tri if(parameters.contai hq1 LCSO if(parameters.contains e if(parameters.contains er", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_2/slide_015.png", |
| "page_index": 57, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:33:23+07:00" |
| } |
| }, |
| "CO3037-chapter-2-slide-058-0000": { |
| "id": "CO3037-chapter-2-slide-058-0000", |
| "text": "What is s TensorFlow? W ProjectPro theano H20.ai SINGA fearn K Machine Learning C Frameworks m TensorFlow is one of the most popular machine learning frameworks that help engineers, deep neural scientists to create Al models (Machine Learning and Deep Learning) 16", |
| "metadata": { |
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| "page_index": 58, |
| "language": "en", |
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| "timestamp": "2025-10-31T18:33:27+07:00" |
| } |
| }, |
| "CO3037-chapter-2-slide-059-0000": { |
| "id": "CO3037-chapter-2-slide-059-0000", |
| "text": "TensorFlow Lite 1F TensorFlow vLite TensorFlow Deploy the Train a Make Convertthe Optimize modelat inferences model model the model Edge at Edge Linux Embedded Android devices-RaspberryPi ios Microcontrollers lensorFlow Lite is 1 an product ready, cross- open-source, l platform deep learning g framework that converts a pre-trained model in TensorFlow to a special format that can be optimized for speed or storage. 17", |
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| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_2/slide_017.png", |
| "page_index": 59, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:33:32+07:00" |
| } |
| }, |
| "CO3037-chapter-2-slide-060-0000": { |
| "id": "CO3037-chapter-2-slide-060-0000", |
| "text": "Convert TensorFlow and TensorFlow Lite File format Infrastructure TensorFlowAPls Data Type Backend tf.Keras LowlevelAP tf.Keras Concrete SavedModel NNAPI model Function(s) GPU TFLite TFLite TFLite CPU Converter Flatbuffer interpreter client server Edge Computing and Al 18", |
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| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_2/slide_018.png", |
| "page_index": 60, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:33:36+07:00" |
| } |
| }, |
| "CO3037-chapter-2-slide-061-0000": { |
| "id": "CO3037-chapter-2-slide-061-0000", |
| "text": "Build A Simple AI using Google Teachable Machine and Python try if(parameters.contai hq1 LCSO if(parameters.contains nu hql e 2 terl query.setParam eters.contains en LE TRONG NHAN trongnhanle@hcmut.edu.vn trongnhanle85@gmail.com", |
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| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_2/slide_019.png", |
| "page_index": 61, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:33:40+07:00" |
| } |
| }, |
| "CO3037-chapter-2-slide-062-0000": { |
| "id": "CO3037-chapter-2-slide-062-0000", |
| "text": "Content Build a simple Al model in Google Teachable Machine Implement Al inference using Python Extra work: Publish Al result to Adafruit IO server 20", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_2/slide_020.png", |
| "page_index": 62, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:33:43+07:00" |
| } |
| }, |
| "CO3037-chapter-2-slide-063-0000": { |
| "id": "CO3037-chapter-2-slide-063-0000", |
| "text": "Part 1: Build A Simple Al", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_2/slide_021.png", |
| "page_index": 63, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:33:45+07:00" |
| } |
| }, |
| "CO3037-chapter-2-slide-064-0000": { |
| "id": "CO3037-chapter-2-slide-064-0000", |
| "text": "Build simple Al a s model Use PC webcam to recognize simple objects, for instance: People is wearing masks or not wearing masks Ripe tomatoes (normally in red) or raw tomatoes (normally in green) Leaves are wilted or diseased Corn_Downy mildew Corn_Eyespot Corn_Healthy Corn_Northern leaf blight Corn_Southern rust Cotton_Alternaria leaf Cotton_Nutrient Cotton_Healthy Cotton_Powdery Cotton_Verticillium blight deficiency mildew wilt Stages of Ripening In Heirloom Tomatoes l.Immature Green 5.Pink-we usually pick 2.Mature Green at this stage 3.Breaker 6.Light Red 4.Turning 7.Red Cucumbers_Anthracno Cucumbers_Downy Cucumbers_Healthy Cucumbers_Nutrient Cucumbers_Powde se mildew deficiency rymildew 6 5 3 Grape_Black rot Grape_Chlorosis Grape_Healthy Grape_Powdery Grape_Esca mildew Wheat_Healthy Wheat_Powdery Wheat Black chaff Wheat_Brown rust mildew Wheat_Yellow rust 22", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_2/slide_022.png", |
| "page_index": 64, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:33:53+07:00" |
| } |
| }, |
| "CO3037-chapter-2-slide-065-0000": { |
| "id": "CO3037-chapter-2-slide-065-0000", |
| "text": "Step 1 Go to the Google Teachable Machine website: https://teachablemachine.withgoogle.com/train New Project Open an existing project from Drive Open an existing project from a file Image Project Audio Project Pose Project Teach based on images,from Teach based on one-second-long Teach based on images, from files or your webcam sounds,from files or your files or your webcam microphone. Select on the Image Project 23", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_2/slide_023.png", |
| "page_index": 65, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:33:58+07:00" |
| } |
| }, |
| "CO3037-chapter-2-slide-066-0000": { |
| "id": "CO3037-chapter-2-slide-066-0000", |
| "text": "Step 2 Provide images for each class. Note that each class should have a name. Unmaskable 46lmage Samples Webcam Upload Training Maskable Preview Export Model Train Model 61 lmage Samples You must train a model on the left before you can preview it here Advanced Webcam Upload Nobody 21 lmage Samples Webcam Upload Provide a class name \"background\", when there is no object need to be recognized 24", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_2/slide_024.png", |
| "page_index": 66, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:34:02+07:00" |
| } |
| }, |
| "CO3037-chapter-2-slide-067-0000": { |
| "id": "CO3037-chapter-2-slide-067-0000", |
| "text": "Step 3 Save your project to Google Drive Teachable Machine + New Project TM VGU 001 Open project from Drive Save project to Drive View project in Drive Make a copy in Drive Sign out of Drive 25", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_2/slide_025.png", |
| "page_index": 67, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:34:06+07:00" |
| } |
| }, |
| "CO3037-chapter-2-slide-068-0000": { |
| "id": "CO3037-chapter-2-slide-068-0000", |
| "text": "Step 4 Click on the Train Model button to train your Al Unmaskable 46 Image Samples t 818181818181 Webcam Upload Training Maskable Preview T Export Model Train Mode 61 lmage Samples You must train a model on the left before you can preview it here. Advanced Webcam Upload Nobody 21 Image Samples Webcam Upload Test your model in the Preview option. If the performance is not so good, provide more images and train the Al again 26", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_2/slide_026.png", |
| "page_index": 68, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:34:11+07:00" |
| } |
| }, |
| "CO3037-chapter-2-slide-069-0000": { |
| "id": "CO3037-chapter-2-slide-069-0000", |
| "text": "Step 4 Export the Al model: Click on Export Select Tensorflow, Keras and Download the Model Export your model to use it in projects. Tensorflow.js Fensorflow i Tensorflow Lite Model conversion type: Keras Savedmodel Download my mode your model to a keras.h5 model.Note the conv pens in the cloud,but your training data is uploaded,only your trained model. Code snippets to use your model: Keras Contribute on Github C Copy from keras.models import load_model from PIL import Image, ImageOps import numpy as np #Loadthemodel model=load_modelkeras_model.h5 # Create the array of the right shape to feed into the keras model # Thelength or number of imagesyou can put lnto the array 1s determinedby the first position in the shape tuple, ln this case 1 data=np.ndarrayshape=1,224,224,3.dtype=np.float32 # Replace this with the path to your image image=Image.openIMAGE_PATH> #resize the 1mage to a 224x224 with the same strategy as in TM2 #resizing the image to be at least 224x224 and then cropping from the center size=224,224 27", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_2/slide_027.png", |
| "page_index": 69, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:34:17+07:00" |
| } |
| }, |
| "CO3037-chapter-2-slide-070-0000": { |
| "id": "CO3037-chapter-2-slide-070-0000", |
| "text": "Step 5 Prepare materials for Python programming: Unzip the files Copy the code from Google Teachable to source Notepad Python Source Code Al Files (keras model.h5 and lables.t from keras.models import load model from PIL import Image, ImageOps import numpy as np # Load the model model = load model('keras model.h5') # Create the array of the right shape to feed into the keras model # The 'length' or number of images you can put into the array is # determined by the first position in the shape tuple, in this case 1 data = np.ndarray(shape=(1, 224, 224,3), dtype=np.float32) # Replace this with the path to your image image = Image.open('<IMAGE PATH>') #resize the image to a 224x224 with the same strategy as in TM2: #resizing the image to be at least 224x224 and then cropping from the center size =(224,224) image = ImageOps.fit(image, size, Image.ANTIALIAS) #turn the image into a numpy array image_array = np.asarray(image) # Normalize the image normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1 keras_model.h5 labels # Load the image into the array data[0] = normalized_image_array # run the inference prediction = model.predict(data) print(prediction) 28", |
| "metadata": { |
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| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_2/slide_028.png", |
| "page_index": 70, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:34:24+07:00" |
| } |
| }, |
| "CO3037-chapter-2-slide-071-0000": { |
| "id": "CO3037-chapter-2-slide-071-0000", |
| "text": "Part 2: Python Programming", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_2/slide_029.png", |
| "page_index": 71, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:34:26+07:00" |
| } |
| }, |
| "CO3037-chapter-2-slide-072-0000": { |
| "id": "CO3037-chapter-2-slide-072-0000", |
| "text": "Step 1 Create a new Python project: Save the project in an empty folder (e.g. Al_Python) Python version 3.8.5 (or older) is recommended Project main.py AIProject DTeachingVGUintro 1 print(\"Hello AI\") Al_Week1 myenv library root Al_Week1.zip keras_model.h5 main.py test.jpeg Illi External Libraries Scratches and Consoles main D:TeachingVGuIntroductionToDataScienceAI Hello AI Student should print to the console a simple string in order to test the Python Editor and Python Interpreter 30", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_2/slide_030.png", |
| "page_index": 72, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:34:31+07:00" |
| } |
| }, |
| "CO3037-chapter-2-slide-073-0000": { |
| "id": "CO3037-chapter-2-slide-073-0000", |
| "text": "Step 2 Install following libraries (required for Al processing) : tensorflow keras numpy pillow opencv-python In Pycharm Editor, these libraries can be installed through GUI: From menu File, select Setting Navigate to the Project Select Python Interpreter Click on Add icon, filter the library and click on Install 31", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_2/slide_031.png", |
| "page_index": 73, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:34:34+07:00" |
| } |
| }, |
| "CO3037-chapter-2-slide-074-0000": { |
| "id": "CO3037-chapter-2-slide-074-0000", |
| "text": "Install library by GUl In PyCharm PC Settings X PC Available Packages Q Project: AlProject> Python Interpreter tensorflow Appearance&Behavior Python Interpreter: y Python 3.8AIProjectDTeachingvGUntroductionToDataScienceAIProjectmyenvScriptspython.exe a Keymap G 3 > Editor O Plugins tensorflow age Version Latest version > Version Control processing 1.1.2 1.1.2 tensorflow-aarch64 V Project: AIProject 3.4.1 3.4.1 tensorflow-addons Python Interpreter MarkupSafe 2.1.1 2.1.1 Project Structure Pillow 9.2.0 tensorflow-ascend Build,Execution, Dep Werkzeug 2.2.2 2.2.2 absl-py 1.2.0 1.2.0 tensorflow-auto > Languages & Frameworks astunparse 1.6.3 1.6.3 >Tools tensorflow-auto-detect cachetools 5.2.0 5.2.0 Advanced Settings certifi 2022.9.14 2022.9.14 tensorflow-batchnorm-folding charset-normalizer 2.1.1 2.1.1 flatbuffers d:teachingvguintroductiontodatascienceaiprojectmyenvlibsite-packages tensorflow-determinism gast 0.4.0 0.5.3 google-auth 2.11.0 2.11.0 tensorflow-directml google-auth-oauthlib 0.4.6 0.5.3 google-pasta 0.2.0 0.2.0 tensorflow-directml-plugin grpcio 1.48.1 tensorflow-edwin h5py 3.7.0 idna 3.4 3.4 importlib-metadata 4.12.0 4.12.0 Install Package keras 2.10.0 2.10.0 libclang 14.0.6 14.0.6 numpy 1.23.3 oauthlib 3.2.1 OK Cancel Apply This option is only available in Pycharm IDE. Other IDEs normally use the pip command to install a package 32", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_2/slide_032.png", |
| "page_index": 74, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:34:47+07:00" |
| } |
| }, |
| "CO3037-chapter-2-slide-075-0000": { |
| "id": "CO3037-chapter-2-slide-075-0000", |
| "text": "Step 4 Copy the Al Files (keras model.h5 and lables.txt) to the same folder of main.py Copy an simple image to the same folder of main.py (the resolution should be higher than 224x224 pixel) Project AIProject D:TeachingVGUlntr myenv library root keras_model.h5 labels.txt main.py test.jpeg Illi External Libraries Scratches and Consoles 33", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_2/slide_033.png", |
| "page_index": 75, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:34:50+07:00" |
| } |
| }, |
| "CO3037-chapter-2-slide-076-0000": { |
| "id": "CO3037-chapter-2-slide-076-0000", |
| "text": "Step 5 Copy the whole source code and place in main.py: PC Eile Edit View Navigate Code Refactor Run Tools VCS Window Help AIProject-main.py AIProject main.py m Project main.py AlProject Daleachin 1 print(\"Hello AI\") > myenv library root keras_model.h5 labels.txt main.py 2 from keras.models import load_model test.jpeg External Libraries Scratches and Consoles 3 from PIL import Image, ImageOps import numpy as np 5 6 # Load the model model = load_model('keras_model.h5') 8 34", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_2/slide_034.png", |
| "page_index": 76, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:34:57+07:00" |
| } |
| }, |
| "CO3037-chapter-2-slide-077-0000": { |
| "id": "CO3037-chapter-2-slide-077-0000", |
| "text": "Step 6 Modify the line 13 or 14, change the name of the image to your image, which is placed in the same folder with main.py data = r np.ndarray(shape=(1, 224, 224, 3), dtype # Replace this with the path to your image image = Image.open('test.jpeg') #resize the image to a 224x224 with the same st #resizing the image to be at least 224x224 and (224, 224) PEP 8:E265 block comment should start with# size Reformat the fileAlt+Shift+Ente Moreactions...Alt+Ente image ImageOps.fit(image, size, Image.ANTIALl 35", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_2/slide_035.png", |
| "page_index": 77, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:35:02+07:00" |
| } |
| }, |
| "CO3037-chapter-2-slide-078-0000": { |
| "id": "CO3037-chapter-2-slide-078-0000", |
| "text": "Step 7 Run the python program. The output should be an (in percent) of all the labels trained in Google Edit View Navigate Code Refactor Run Iools VCS Window Help AIProject-main.py 6 + AIProject main.py main Q Project main.py AIProject A1 Notifications myenv library root 27 # run the inference ?keras_model.h5 labels.txt main.py 28 prediction = model.predict(data) test.jpeg Illi Extemal Libraries Scratches and Consoles 29 print(prediction) 30 Run: main a 1/1 ==1 - 1s 875ms/step 4 [1.6029941e-05 9.9972016e-01 2.6371540e-04] Process finished with exit code 0 . 36", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_2/slide_036.png", |
| "page_index": 78, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:35:08+07:00" |
| } |
| }, |
| "CO3037-chapter-2-slide-079-0000": { |
| "id": "CO3037-chapter-2-slide-079-0000", |
| "text": "Step 8 Find the maximum confidence and its index from 2D array #get the 1D array output = prediction[0] #assign default value for max confidence max index = 0 max confidence = output[0] #find the maximum confidence and its index for i in range(1, len(output)): if max confidence < output[i] max confidence = output[i] max index = i 37", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_2/slide_037.png", |
| "page_index": 79, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:35:12+07:00" |
| } |
| }, |
| "CO3037-chapter-2-slide-080-0000": { |
| "id": "CO3037-chapter-2-slide-080-0000", |
| "text": "Step 9 Map the labels to the maximum index file = open(\"labels.txt\",encoding=\"utf8 data = file.read().split(\"n\") print(\"Al Result: \" 1 datalmax < index]) 38", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_2/slide_038.png", |
| "page_index": 80, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:35:15+07:00" |
| } |
| }, |
| "CO3037-chapter-2-slide-081-0000": { |
| "id": "CO3037-chapter-2-slide-081-0000", |
| "text": "Step 10 Add live image from the camera import cv2 cam = cv2.VideoCapture(0) def image_capture() : ret,frame = cam.reado) cv2.imwrite (\"test.png\",frame) 39", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_2/slide_039.png", |
| "page_index": 81, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:35:18+07:00" |
| } |
| }, |
| "CO3037-chapter-2-slide-082-0000": { |
| "id": "CO3037-chapter-2-slide-082-0000", |
| "text": "Step 11 print(\"Hello Al\") from keras.models import load model from PIL import Image, ImageOps import numpy as np import cv2 Refactor the cam = cv2.VideoCapture(0) # Load the model model = load model('keras model.h5') python source def image_capture(): ret,frame = cam.read() code, having cv2.imwrite (\"test.png\",frame) def image detector(): image_capture() data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32) # Replace this with the path to your image image = Image.open('test.png') #resize the image to a 224x224 with the same strategy as in TM2: and #resizing the image to be at least 224x224 and then cropping from the center size = (224,224) image = ImageOps.fit(image, size, Image.ANTIALIAS) image detector() #turn the image into a numpy array image_array = np.asarray(image) # Normalize the image normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1 # Load the image into the array data[0] = normalized_image_array # run the inference prediction = model.predict(data) #get the 1D array output = prediction[0] #assign default value for max confidence max index = 0 max confidence = output[0] #find the maximum confidence and its index for i in range(1, len(output)): if max confidence < output[i] max confidence = output[i] max index = i print(max_index, max_confidence) file = open(\"labels.txt\",encoding=\"utf8\" data = file.read(.split(\"n\") print(\"Al Result: \", data[max_index] 40", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_2/slide_040.png", |
| "page_index": 82, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:35:25+07:00" |
| } |
| }, |
| "CO3037-chapter-2-slide-083-0000": { |
| "id": "CO3037-chapter-2-slide-083-0000", |
| "text": "Step 12 print(\"Hello Al\" from keras.models import load model from PIL import Image, ImageOps import numpy as np import cv2 import time Add an infinite cam = cv2.VideoCapture(0) #Load the mode model = load model('keras model.h5' loop to the def image_capture() : ret,frame = cam.read() cv2.imwrite (\"test.png\",frame) def image detector(): data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32) code. # Replace this with the path to your image image = Image.open('test.png') #resize the image to a 224x224 with the same strategy as in TM2: #resizing the image to be at least 224x224 and then cropping from the center Import time size = (224,224) image = ImageOps.fit(image, size, Image.ANTIALIAS) library #turn the image into a numpy array image_array = np.asarray(image) # Normalize the image normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1 # Load the image into the array Every 5 data[0] = normalized_image_array # run the inference prediction = model.predict(data) seconds, call #get the 1D array output = prediction[0] image_capture( #assign default value for max confidence max index = 0 max confidence = output[0] #find the maximum confidence and its index ) then call for i in range(1, len(output)): if max confidence < output[i]: max confidence = output[i] image_detector max index = i print(max_index, max_confidence) file = open(\"labels.txt\",encoding=\"utf8\") 0) data = file.read(.split(\"n\") print(\"Al Result: \", data[max_index]) while True: time.sleep(5) image_capture() image detector() 41", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_2/slide_041.png", |
| "page_index": 83, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:35:33+07:00" |
| } |
| }, |
| "CO3037-chapter-2-slide-084-0000": { |
| "id": "CO3037-chapter-2-slide-084-0000", |
| "text": "Part 3: Upload to Adafruit I0", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_2/slide_042.png", |
| "page_index": 84, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:35:35+07:00" |
| } |
| }, |
| "CO3037-chapter-2-slide-085-0000": { |
| "id": "CO3037-chapter-2-slide-085-0000", |
| "text": "Introduction The Al result can be published to the Adafruit IO for remote monitoring When Al is combined with loT, we form a kind of AloT application!!! 43", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_2/slide_043.png", |
| "page_index": 85, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:35:37+07:00" |
| } |
| }, |
| "CO3037-chapter-2-slide-086-0000": { |
| "id": "CO3037-chapter-2-slide-086-0000", |
| "text": "Step 1 Create an account in Adafruit IO Create a feed on Adafruit IO: Feed/ New Feed Provide the name for the Feed, e.g. \"ai\" BBC loT / Feeds New Group Q New Feed Default Feed Name Key Last value Recorded ai ai Khöng Khau Trang 8 days ago 44", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_2/slide_044.png", |
| "page_index": 86, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:35:41+07:00" |
| } |
| }, |
| "CO3037-chapter-2-slide-087-0000": { |
| "id": "CO3037-chapter-2-slide-087-0000", |
| "text": "Step 2 Create a simple Dashboard, have a textview to display the last data from feed ai BBC_loT / Dashboards / Al Dashboard Al Result Khöng Khau Trang 45", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_2/slide_045.png", |
| "page_index": 87, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:35:44+07:00" |
| } |
| }, |
| "CO3037-chapter-2-slide-088-0000": { |
| "id": "CO3037-chapter-2-slide-088-0000", |
| "text": "Step 3 Copy your account and your key, which are used for the Python programming YOUR ADAFRUIT IO KEY x Your Adafruit lO Key should be kept in a safe place and treated with the same care as your Adafruit username and password. People who have access to your Adafruit lO Key can view all of your data, create new feeds for your account, and manipulate your active feeds. If you need to regenerate a new Adafruit lO Key, all of your existing programs and scripts will need to be manually changed to the new key. Username BBC_loT Active Key aio_QYth850SuWaXh20PWdD20n0W5XSM REGENERATEKEY Hide Code Samples 46", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_2/slide_046.png", |
| "page_index": 88, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:35:49+07:00" |
| } |
| }, |
| "CO3037-chapter-2-slide-089-0000": { |
| "id": "CO3037-chapter-2-slide-089-0000", |
| "text": "Step 4 Install the adafruit-io library Import libs: Available Packages adafruit-ic import sys G Description adafrust Python client library for Adafruit IO (http://io.adafruit.com/) from Adafruit IO import MQTTClient Version 2.70 Author Adafruit Industries mailto.adafruitio@adafruit.com https://github.com/adafruit/Adafruit_IO_Python Specify version 2.7.0 Options Install Package 47", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_2/slide_047.png", |
| "page_index": 89, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:35:53+07:00" |
| } |
| }, |
| "CO3037-chapter-2-slide-090-0000": { |
| "id": "CO3037-chapter-2-slide-090-0000", |
| "text": "Step 5 Create an instance of the MQTT Client client = MQTTClient(\"your usr\",\"your key\") client.connecto client.loop background() Then, use client.publish(\"ai\", \"your ai result\") to the service 48", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_2/slide_048.png", |
| "page_index": 90, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:35:56+07:00" |
| } |
| }, |
| "CO3037-chapter-2-slide-091-0000": { |
| "id": "CO3037-chapter-2-slide-091-0000", |
| "text": "Step 6 print(\"Hello Al\") from keras.models import load_model from PIL import Image, ImageOps import numpy as np import cv2 Finalize the whole program import time from Adafruit_IO import MQTTClient cam = cv2.VideoCapture(0) #Load the model model = load model('keras model.h5') def image_capture(): ret,frame = cam.read() cv2.imwrite (\"test.png\",frame) def image_detector(): #Create the array of the right shape to feed into the keras mode # The 'length'or number of images you can put into the array is # determined by the firstposition in the shape tuple,in this case 1 data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32) # Replace this with the path to your image image = Image.open('test.png') #resize the image to a 224x224 with the same strategy as in TM2: #resizing the image to be at least 224x224 and then cropping from the center size =(224,224) image = ImageOps.fit(image, size, Image.ANTIALIAS) #turn the image into a numpy array image_array = np.asarray(image) # Normalize the image normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1 # Load the image into the array data[0] = normalized_image_array #run the inference prediction = model.predict(data) #get the 1D array output = prediction[0] #assign defaultvalue for max confidence max_index = 0 max confidence = output[0] #find the maximum confidence and its index for i in range(1, len(output)): if max confidence < output[i] max confidence = output[i] max index = i print(max_index, max_confidence) file = open(\"labels.txt\",encoding=\"utf8\") data = file.read().split(\"n\") print(\"Al Result: \", data[max_index]) client.publish(\"ai\", data[max_index]) client = MQTTClient(\"BBC_loT\",\"aio_QYth850SuWaXh20PWdD20n0W5XSN\" client.connecto) client.loop_background() while True: time.sleep(5) image_capture() image_detector() 49", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_2/slide_049.png", |
| "page_index": 91, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:36:03+07:00" |
| } |
| }, |
| "CO3037-chapter-3-slide-092-0000": { |
| "id": "CO3037-chapter-3-slide-092-0000", |
| "text": "Programming Platform for Internet of Things (loTs) Applications +t Go Battery-Less LoRaWANTExpLoRer Kit SimpleLink Ultra-low Power Wireless MCU Platform INSTRUMENTS NTERNET OF THINGS Biuetooth Smart 6LoWPAN ZigBeo Sub-1 GHz powered by RF4CE Microchip DCE DEPT. OF COMPUTER ENGINEERING", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_3/slide_001.png", |
| "page_index": 92, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:36:08+07:00" |
| } |
| }, |
| "CO3037-chapter-3-slide-093-0000": { |
| "id": "CO3037-chapter-3-slide-093-0000", |
| "text": "CO3037 - Lecture 2: Programming Platform for Internet of Things Applications Micro-Controller Platform Micro-Controller Unit (MCU) contains RAM HDD RAM, ROM and IO Micro-Processor Unit (MPU) only contains the CPU MCU MCUVS.MPU MPU System on Chip (SoC) refers to MCUs with RAM CPU ROM a greater number of onboard peripherals and functionality PERIPHERALS Go Battery-Less LoRaWANTMExpLoRer Kit SimpleLink Ultra-low Power Wireless MCU Platform TEXAS INSTRUMENTS MICROCHIP BluetoothSmart 6LoWPAN nttps://lpwa. igBee Sub-1GHz powered by RF4CE Microchip http:// http:// http:// 2 theairboard.cc www.ti.com www.microchip.com", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_3/slide_002.png", |
| "page_index": 93, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:36:15+07:00" |
| } |
| }, |
| "CO3037-chapter-3-slide-094-0000": { |
| "id": "CO3037-chapter-3-slide-094-0000", |
| "text": "CO3037 - Lecture 2: Programming Platform for Internet of Things Applications C Language: Header and c++ Files C ++ tinclude \"led.h' #ifndef LED H #define LED H int T on; int T off; #include <system lib.h> Int counter: #include \"user lib.h\" #include void setOn(long duration \"UserFolder/lib.h\" S l/TODO: set LED on extern int T on; here extern int T off; } void setoff(long duration) void setOn(long duratien): void setoff(long duration); l/TODO: set LED off here #endif } 3 irstianJ", |
| "metadata": { |
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| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_3/slide_003.png", |
| "page_index": 94, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:36:21+07:00" |
| } |
| }, |
| "CO3037-chapter-3-slide-095-0000": { |
| "id": "CO3037-chapter-3-slide-095-0000", |
| "text": "CO3037 - Lecture 2: Programming Platform for Internet of Things Applications C Language: File Main #include \"led.h\" Modules/ Libraries #tinclude \"timer.h are included #include \"gpio.h\" #include \"button.h\" ++ void mainO{ initGPIOO; LE Modules/ Libraries C ++ initTimerO; are initiated D .h initButtonO initLEDO TIME R ++ .h while(1){} GPI C ++ 0 void timer isrO) } System operations are implemented in void ext isrO{ BUTTO interrupt functions N 4", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_3/slide_004.png", |
| "page_index": 95, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:36:27+07:00" |
| } |
| }, |
| "CO3037-chapter-3-slide-096-0000": { |
| "id": "CO3037-chapter-3-slide-096-0000", |
| "text": "C03037 - Lecture 2: Programming Platform for Internet of Things Applications Finite State Machine (FSM) Finite-State Machine (FSM) or Deterministic Finite Automata (DFA), finite automaton, or simply a state machine, is a mathematical model of computation Current Input Next State State Locked Coin Unlocked Push Locked Unlocked Coin Unlocked A Push Locked turnstile Push Coin Un- Locked locked Push Coin 5", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_3/slide_005.png", |
| "page_index": 96, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:36:32+07:00" |
| } |
| }, |
| "CO3037-chapter-3-slide-097-0000": { |
| "id": "CO3037-chapter-3-slide-097-0000", |
| "text": "CO3037 - Lecture 2: Programming Platform for Internet of Things Applications Finite Machine Programming State while(1){ switch(status){ case LOCKED : lock_turnstile(); //operation in a state if(Coin == true) //transition condition status UNL0CKED; //next state break; CaSe UNLOCKED : unlock_turnstile); //operation in a state if(Push i == true //transition condition status = L0CKED; //next state break; default : break; 2 6", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_3/slide_006.png", |
| "page_index": 97, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:36:37+07:00" |
| } |
| }, |
| "CO3037-chapter-3-slide-098-0000": { |
| "id": "CO3037-chapter-3-slide-098-0000", |
| "text": "CO3037 - Lecture 2: Programming Platform for Internet of Things Applications Bai toän mo cüa Cua m6 thi loa sé keu Cüa d6ng thi loa sé khng keu nhien, néu cua m6 qua Tuy 3 giay, thi loa khong cüng kéu 7", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_3/slide_007.png", |
| "page_index": 98, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:36:41+07:00" |
| } |
| }, |
| "CO3037-chapter-3-slide-099-0000": { |
| "id": "CO3037-chapter-3-slide-099-0000", |
| "text": "C03037 - Lecture 2: Programming Platform for Internet of Things Applications Example 1 Given an LED turns on for T on and then turns off for T off Design an DFA for this LED Implement the DFA in Arduino digitalWrite(13, HIGH): turn on the LED digitalwrite(13, z L0W) : turn off the LED setTime(duration) : set a clock (timer_flag o), when the clock is expired, timer_f1ag 1 = 1; duration is in mili-seconds. 8", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_3/slide_008.png", |
| "page_index": 99, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:36:45+07:00" |
| } |
| }, |
| "CO3037-chapter-3-slide-100-0000": { |
| "id": "CO3037-chapter-3-slide-100-0000", |
| "text": "CO3037 - Lecture 2: Programming Platform for Internet of Things Applications Answer timer flag L INI LED LED 0 T ON FF timer flag 1 INIT: Set pin 13 to OUTPUT mode, set timer LED ON: Turn on the LED LED OFF: Turn off the LED 9", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_3/slide_009.png", |
| "page_index": 100, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:36:48+07:00" |
| } |
| }, |
| "CO3037-chapter-3-slide-101-0000": { |
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| "text": "CO3037 - Lecture 2: Programming Platform for Internet of Things Applications Code) Answer (Arduino void loop(){ void timer_run({ switch(status){ case INIT: if(timer_counter > o) pinMode(13, OUTPUT); timer_counter-- ; setTimer(T_on): 0) status = LED_ON; if(counter_timer == digitalWrite(13, HIGH); timer_flag 1 =1; break; } case LED ON: void setTimer(long duration){ if(timer_flag == 1){ status = LED_OFF: timer_counter = duration; setTimer(T_off) : timer_flag = 0; digitalwrite(13, L0w) ; } break; CaSe LED OFF: if(timer_flag == 1){ status = LED_ONj setTimer(T_on) : digitalWrite(13, HIGH); } break; default : break; } delay(10): } 1", |
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| "text": "CO3037 - Lecture 2: Programming Platform for Internet of Things Applications Example 2 Design a smart lock which accepts 4 digits as a secrete code. However, there is a time-out for each digit (e.g. T out = 5s). After this period, the system is reset 1", |
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| "text": "CO3037 - Lecture 2: Programming Platform for Internet of Things Applications Embedded Platform based on Operating System Embedded means something that is attached to another thing. \"Any sort of device which includes a programmable computer but itself is not intended to be a general-purpose computer\" [Marilyn Wolf] Embedded system has three components: Hardware Real Time Operating System (RTOS) Software 1", |
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| "CO3037-chapter-3-slide-104-0000": { |
| "id": "CO3037-chapter-3-slide-104-0000", |
| "text": "C03037 - Lecture 2: Programming Platform for Internet of Things Applications Classification of Embedded System AME MICROCHIP ATmega328 Small Scale Medium Sophisticated Scale Scale 1", |
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| "id": "CO3037-chapter-3-slide-105-0000", |
| "text": "CO3037 - Lecture 2: Programming Platform for Internet of Things Applications Processor Processor is the heart of the Embedded Syste General Purpose processor (GPP) Microprocessor Microcontroller Embedded Processor Digital Signal Processor Application Specific System Processor (ASSP) Multi Processor System using GPPs 1", |
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| "text": "C03037 - Lecture 2: Programming Platform for Internet of Things Applications Operating System: Linux Android and Connectivity and uI Two powerful operating used in most of the systems embedded systems Wireless connectivity and graphics interface: Android 0s Linux comes with a complex flow and it might be difficult for a beginner to understand it Power management Android and Linux effective supports power management compared to real time operating systems Responsiveness Cost 1", |
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| "text": "C03037 - Lecture 2: Programming Platform for Internet of Things Applications Android Operating System Component baseparamer-720P.img 07/08/20185:49PM Disc Image File 1 KB boot.img 07/08/2018 5:50 PM Disc Image File 10,656 KB kernel.img 07/08/20185:49PM Disc Image File 6,661 KB misc.img 07/08/20185:49PM Disc Image File 48 KB recovery.img 07/08/2018 5:50 PM Disc Image File 13,776 KB resource.img 07/08/20185:49PM Disc Image File 2,799 KB system.img 07/08/20185:51PM Disc Image File 778,396 KB uboot-rk3128.img 07/08/20185:49PM Disc Image File 2,048 KB 1", |
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| "text": "CO3037 - Lecture 2: Programming Platform for Internet of Things Applications Android Boot File mkdir /system mkdir /data 0771 system system mkdir /cache 0770 system cache mkdir /config 1 0500 r00t r00t mkdir /metadata 0770 root root service yunos_preinstall /system/bin/yunos_preinstall.sh user root group root class main disabled oneshot 1", |
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| "id": "CO3037-chapter-3-slide-109-0000", |
| "text": "CO3037 - Lecture 2: Programming Platform for Internet of Things Applications Android File Resource UBC tv IR Remote Tivi LED Cong Nghe Nhat Ban Data logo.bmp ir keyl rockchip,usercode = <0xdf00>; rockchip,key table = <0xe3 0x74 0xf7 0x71 Oxfe 0xf9 0xa0 0xb4 0x73 0xe7 0x8b 0xe8 0xfd 0xe5 0x67 0xb7 0x6c 0xb8 0x 0x3 0xea 0x4 0xaf 0x5 0xed 0x6 0xee 0x7 0xb3 0x8 0xf1 0x9 }; ir key2 { rockchip,usercode = <0xff00>; logo_kernel.bmp rockchip,key table = <0xeb 0x74 0xe 0x3b 0xd 0x3c 0xc 0x3 0xf9 0xf4 0x73 0xa7 0x72 0xb7 0x66 0xa3 0x9e 0xfc 0x67 0x 0xf6 0x2 0xe2 0x3 0xe0 0x4 0xf2 0x5 0xe6 0x6 0xe4 0x7 0xe }; ir key3 { rockchip,usercode = <0xldcc>; rockchip,key table = <0xee 0xe8 0xf0 0x9e0xf80x67 0xbb 0xb7 0xd9 0xff 0x74 0xf3 0x71 0xbf 0x8b 0xf9 0xl9l 0xf5 0x6 0xbl 0x7 0xfc 0x8 0xf8 0x9 0xb0 0xa 0xb6 0xb 0xb5 0xe } i }; rk-kernel.dts 1", |
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| "text": "CO3037 - Lecture 2: Programming Platform for Internet of Things Applications Android I System File 1 app 27/07/20188:46A File folder System 1 bin 27/07/20188:07A File folder 1 etc 27/07/20187:59A File folder apps 1 fake-libs 27/07/20187:59A File folder User apps 1 fonts 27/07/20187:59A File folder 1 framework 27/07/20187:59A File folder Launcher 1 lib 27/07/20187:59A File folder 1 lib64 27/07/20187:59A File folder apps 1 lost+found 27/07/20187:59A File folder Preinstall 1 media 27/07/20188:07A File folder 1 preinstall 27/07/20187:59A File folder apps 1 preinstall_del 27/07/20187:59A File folder Libs 1 priv-app 27/07/20188:45A File folder 1 tts 27/07/20187:59A File folder KeyLayout 1 usr 27/07/20188:00A File folder 1 vendor 27/07/20188:00A File folder 1 xbin 27/07/20188:00A File folder build.prop 27/07/20188:47A PROP File manifest.xml 07/06/20181049 XML Document 1", |
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| "CO3037-chapter-3-slide-111-0000": { |
| "id": "CO3037-chapter-3-slide-111-0000", |
| "text": "C03037 - Lecture 2: Programming Platform for Internet of Things Applications Python Programing Language High Level Simple Interpreted Object-Oriented Open Source Expressive Gui Prograimming Language Large Standard Library Al, Data science and Deep Learning Interpreter programing language 2", |
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| "CO3037-chapter-3-slide-112-0000": { |
| "id": "CO3037-chapter-3-slide-112-0000", |
| "text": "C03037 - Lecture 2: Programming Platform for Internet of Things Applications Class in Python class Student: def init (self, name, age): self.name = hame self.age = _age def setName(self, name): self.name = name Flexible and high level approach 2", |
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| "CO3037-chapter-3-slide-113-0000": { |
| "id": "CO3037-chapter-3-slide-113-0000", |
| "text": "CO3037 - Lecture 2: Programming Platform for Internet of Things Applications Model View Controller Controller Request Contacts Shows 5 Model Returns View Delivers lAbstraction Layerl Database A pattern for OOP program 2", |
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| "CO3037-chapter-3-slide-114-0000": { |
| "id": "CO3037-chapter-3-slide-114-0000", |
| "text": "MVC using l Python Controller Notify Update Model View Update User Action", |
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| "CO3037-chapter-3-slide-115-0000": { |
| "id": "CO3037-chapter-3-slide-115-0000", |
| "text": "CO3037 - Lecture 2: Programming Platform for Internet of Things Applications Step 1: Create project in Python a new Create Model folder: Student.py Create Controller folder: StudentController.py The main.py is the View of the project 2", |
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| "CO3037-chapter-3-slide-116-0000": { |
| "id": "CO3037-chapter-3-slide-116-0000", |
| "text": "C03037 - Lecture 2: Programming Platform for Internet of Things Applications Step 2: Implement the Student t Class class Student: def init (self, name, age): self.name = name self.age = _age def setName(self, name) : self.name = name def getName(self) : return self.name def setAge(self, age): self.age = _age def getAge(self) : return self.age The fields are highly related to the database properties 2", |
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| "CO3037-chapter-3-slide-117-0000": { |
| "id": "CO3037-chapter-3-slide-117-0000", |
| "text": "C03037 - Lecture 2: Programming Platform for Internet of Things Applications Step 3: Implement the Controller class StudentController: def insertStudent(self, student) : print(student.name, student.age) Data manipulation: insert, update or delete Data storage: database or file management system 2", |
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| "CO3037-chapter-3-slide-118-0000": { |
| "id": "CO3037-chapter-3-slide-118-0000", |
| "text": "CO3037 - Lecture 2: Programming Platform for Internet of Things Applications Blinky Android Things Lab 2: LED on Hardware connection BHCH fritzing 2", |
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| "CO3037-chapter-3-slide-119-0000": { |
| "id": "CO3037-chapter-3-slide-119-0000", |
| "text": "} cd Applications Android Things Create Project an Target Android Devices Select the form factors and minimum SDK Some devices reguire additional SDKs.Low API levels target more devices,but offer fewer API features. Phone and Tablet API 19:Android 4.4KitKat By targeting APl 19and later,your app will run on approximately 95.3%of devices. Include Android Instant App support Wear API 21:Android5.0Lollipop TV API21:Android 5.0Lollipop Android Auto Android Things AnIC.AnAAiAOn/ Previous Next Cancel Finish 2", |
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| "CO3037-chapter-3-slide-120-0000": { |
| "id": "CO3037-chapter-3-slide-120-0000", |
| "text": "CO3037 - Lecture 2: Programming Platform for Internet of Things Applications MainActivity.java Step 1: Declare a pin name wired to LED private static final String LED PIN NAME = iBCM26\" Step 2: Implement initGPIO function private void initGPIO(){ PeripheralManager manager PeripheralManager.getInstance() ; try { mLedGpio = manager.openGpio(LED PIN NAME); catch (IOException e) Log.d(TAG, \"Error on PeripheralIO API\") ; Step 3: Call initGPlOO on OnCreate() 2", |
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| } |
| }, |
| "CO3037-chapter-3-slide-121-0000": { |
| "id": "CO3037-chapter-3-slide-121-0000", |
| "text": "C03037 - Lecture 2: Programming Platform for Internet of Things Applications MainActivity.java Step 4: Release the pin when the app is closed @Override protected l void super.onDestroy() ; try mLedGpio.close) ; catch (IOException e) { GPIO\"); Log.e(TAG, \"Error closing 3", |
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| "CO3037-chapter-3-slide-122-0000": { |
| "id": "CO3037-chapter-3-slide-122-0000", |
| "text": "CO3037 - Lecture 2: Programming Platform for Internet of Things Applications MainActivity.java Step 5: Create a Timer void setupBinkyTimer(){ Timer aTimer = new TimerO : TimerTask aTask new TimerTaskO { @Override public void run() try { mLedGpio.setValue(!mLedGpio.getValue()) ; }catch (Exception e){} } i aTimer.schedule(aTask, 1000, 1000) ; 1 Step 6: Call this function on OnCreate( 3", |
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| } |
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| "CO3037-chapter-3-slide-123-0000": { |
| "id": "CO3037-chapter-3-slide-123-0000", |
| "text": "Software Development Framework NET Framework NET Core Mono for Xamarin Windows ASP.NET WPF ios Forms Core Android Mobile ASP.NET UWP macos Web Flutter .NET Standard Library Desktop Common Infrastructure Embedded Build Tools Languages Runtime Components", |
| "metadata": { |
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| "CO3037-chapter-3-slide-124-0000": { |
| "id": "CO3037-chapter-3-slide-124-0000", |
| "text": "C03037 - Lecture 2: Programming Platform for Internet of Things Applications What is Software Development Framework? Sets oflibraries or classes Built-in generic Reusable Working Can be functionalities software template modified by Dealswith enviroment application writing standardlow additionalcode level details Supported libraries, SDK, programming languages for software development and deployment 3", |
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| "CO3037-chapter-3-slide-125-0000": { |
| "id": "CO3037-chapter-3-slide-125-0000", |
| "text": "C03037 - Lecture 2: Programming Platform for Internet of Things Applications Dot Net Framework TOOLS 0 X DESKTOP WEB CLOUD MOBILE GAMING loT Al WPF ASP.NET Azure Xamarin Unity ARM32 ML.NET VISUAL STUDIO Windows Forms ARM64 .NET for Apache Spark UWP NET STANDARD VISUAL STUDIO FOR MAC X NET 5 VISUAL STUDIO CODE INFRASTRUCTURE RUNTIME COMPONENTS COMPILERS LANGUAGES COMMAND LINE INTERFACE Unify framework for Desktop Applications C, C++ and C# t are the most popular languages 1 Visual studio IDE: Drag and Drop!!! 3", |
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| "text": "CO3037 - Lecture 2: Programming Platform for Internet of Things Applications Framework 7 - React t js https://framework7.io/react/ Framework 7 Full FeaturedHTMLFramework SELI 4:21PM 100 For Building iOS7 Apps Framework7 http://idangero.us/framework7 WELCOMETO FRAMEWORK ReadAboutFramework7 Free &Open Source 1:1 System Modals FRAMEWORK7KITCHENSINK -Ultra Lightweight -SidePanels Modals 1:1Page Transitions -Ready To Use Form Elements -XHR+Caching -Data ListsTable View Popover -History - Tabs Tabs F7 -DynamicNavbar -Swipe To Delete -SplitViews -Flexible Layout Grid Side Panels - Easy to customize -And many more.. Data Lists 3", |
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| "text": "CO3037 - Lecture 2: Programming Platform for Internet of Things Applications Example of Framework 7 Examples Cards import React from react' import f Page, Navbar Simple Cards BlockTitle Card, This is a simple card with plain text CardHeader but cards can also contain their own CardContent header,footer,list view,image,or any CardFooter other element. Link, List, ListItem, Card header fromframework7-react import./cards.css': Card with header and footer.Card headers are used to display card titles export default => and footers for additional information orjust for custom actions. <Page> <Navbar title=\"Cards\" <BlockTitle>Simple Cards</BlockTitle: Card footer <Card content=\"This is a simple card with plain text, but cards can also contain their own header, footer, list view, image, or any other element.></Card> <Card Another card.Lorem ipsum dolor sit title=\"Card header\" amet,consectetur adipiscing elit content=\"Card with header and footer. Card headers are used to display card titles and Suspendisse feugiat sem est,nor footers for additional information or just for custom actions. tincidunt ligula volutpat sit amet. footer=\"Cardfooter\" Mauris aliquet magna justo. ></Card> <Card content=\"Another card. Lorem ipsum dolor sit amet, consectetur adipiscing elit Suspendisse feugiat sem est, non tincidunt ligula volutpat sit amet. Mauris aliquet magna Outline Cards justo.\"></Card> <BlockTitle>Outline Cards</BlockTitle> Framework 7 Web-App development environment 3", |
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| "CO3037-chapter-3-slide-128-0000": { |
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| "text": "CO3037 - Lecture 2: Programming Platform for Internet of Things Applications Android Studio Flutter and Mobile Web Flutter Desktop Embeddea Flutter is an Android Studio plug-in: Access to the hardware of the mobile device (Bluetooth, NFC", |
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| "CO3037-chapter-3-slide-129-0000": { |
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| "text": "CO3037 - Lecture 2: Programming Platform for Internet of Things Applications Unity 3 D AuBen 532 Esszimmer 13.8° 21.9° 84% 1.9 m/s 52% oC MäBig bewölkt Siemens LED-Protect 1.129 BFT1.159 1.249 23 21 TOTAL 1.219 1.129 1.249 Minuten Minuten A SHELL1.219 A 1.129 1.249 20 57.0 -c K 80 DSL WiFi HoWi 22 Tage 5 5.90 g/m3 D 5884/x 11:50 07:43 16:51 A product from Microsoft Not only for game developers, but also a cross-framework for software developemt C# programing language 3", |
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| "text": "C03037 - Lecture 2: Programming Platform for Internet of Things Applications Unity Asset Store https://assetstore.unity.com/templates https://www.youtube.com/watch?v=uZaFHx1daLU Data Visualization 01 22° Wed 2021/4/815:14:43 >>EventTitle01 Total Data: >Event Title 04 Address 714731876 8% 8% 8% Th Wet Tue Title01 Title 01 Title01 Mor 500 400 300 100 100 200 100 400 >>EventTitle02 >Event Title Os 66% 120 66% 94 O Legend 01 38.%5 25000 Event Title Event Title OLegend0221.84 Total XX UnityXX City.XX Stale Event Tille O Legend 03 39.51 120 66% 3500 66% 1107 Adress Event Title Event Title lel >>EventTitle03 >EventTitle 07 >EventTitle06 Event1 Event1 Event1 400 336d 350 847 300 Lexus Minnesota 2021-04-08 200 Janice Newyork 2021-04-09 100 Thomas Alabama 2021-04-10 100% 100% 100° Kevin Northcarolina 2021-04-12 2015 2016 2017 2018 2019 2020 Event rate Event rate Event rate lark Alahama 2021-04-12 3", |
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| "text": "Device Drivers for Embedded Internet of Things (loTs) Platforms Your Application Application IDE Editor Graphics Terminal and Converter for C Compile and Program GUl Builde Download Embedded Operating System Windows Operating System Real-Time Runtime Interactive Multitasking Graphical Program C-Language Operating Engine with AutoStart Debugger Systern Event-Driven and Menu Manager Error Handling (RTOS) 1 Firmware Libraries Easily Updated Arrays, Integer, Double Multiple Heap Matrices Data Analysis Driver Precision.and Device Memory Manager Kernel Extensions Data Structure Floating Point and Matrix Math and Strings Arithmetic DCE User Interface and l/O Device Drivers Keypad Extensive Serial Timekeeper. Touchscreen Analog and Pulse Counter Precoded Drivers Precoded Scanner and Digital /O Driver Communications Input Capture& forExtensibie/O Interrupt Graphics Drive Support Output Compare using WildCards Handlers DEPT. OF COMPUTER ENGINEERING", |
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| "text": "C03037 - Lecture 3: Device Driver for Embedded Internet of Things oTs) Platforms Content Device Driver Overview Serial Communication Device Driver Universal Asynchronous Receiver/Transmitter (UART) Serial Peripheral Interface (SPI) Inter-Integrated Circuit (I2C) Summary 2", |
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| "text": "C03037 - Lecture 3: Device Driver for Embedded Internet of Things loTs) Platforms What is Device Driver? Device driver is a particular software application that is designed to enable interaction with hardware devices. Device drivers operating are system-specific and hardware-dependent. For embedded platforms running OS, device drivers are considered as part of firmware. Firmware is s the software which executes on the embedded system's CPU (written in assembler and C), was complied and the binary burned onto an EPROM. 3", |
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| "text": "C03037 - Lecture 3: Device Driver for Embedded Internet of Things loTs Platforms Why Do We I Need Device Driver? Provide uniform APls to access hardware. Custom platforms Contain peripheral devices many and kerne supported CpU Get kernel to boot on the board Device drivers to allow applications to access peripheral devices 4", |
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| "id": "CO3037-chapter-4-slide-135-0000", |
| "text": "CO3037 - Lecture 3: Device Driver for Embedded Internet of Things loTs Platforms Device Driver Models Device drivers, over the years, have become very complex Drivers are separated into classes Serial, network, audio, video, touch panel, etc Layer approach is used To support a new device, a layer is modified instead of rewriting the entire driver Processing functions required for a given class often do not require modification 5", |
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| "id": "CO3037-chapter-4-slide-136-0000", |
| "text": "C03037 - Lecture 3: Device Driver for Embedded Internet of Things lloTs Platforms Device Driver Architecture Device Drivers nterrupt Service Threads User Mode Kernel Mode Interrrupt Service Routines Kerne HA L 6", |
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| "text": "C03037 - Lecture 3: Device Driver for Embedded Internet of Things loTs Platforms Serial Communication using UART The most popular connection in hardware e devices UART UART 2 Packet T x T x 0to1 start 1102 5to 9 data bits parity bit stop bits bits Rx Rx Data Frame Idle Start Data Parity Stop Idle Bit Bits Bit Bits Space Mark What is the Bit Time difference Character Frame between UART RS232 and 7", |
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| "text": "C03037 - Lecture 3: Device Driver for Embedded Internet of Things loTs Platforms Android Things UART Device Driver on UART stands for Universal Asynchronous Receiver Transmitter It is universal because both the data transfer speed and data byte format are configurable. It is asynchronous in that there are no clock signals present to synchronize the data transfer between the two devices UART data transfer is full-duplex, meaning data can be sent and received at the same time RX RX UART UART TX TX loT Device Peripheral Device GND GND 8", |
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| "id": "CO3037-chapter-4-slide-139-0000", |
| "text": "CO3037 - Lecture 3: Device Driver for Embedded Internet of Things loTs Platforms UART Implementation https://github.com/androidthinas/sample-uartloop back As cuses-permission android:name=\"com.google.android.things.permission.USE PERIPHERAL IO\" /> PeripheralManager manager = PeripheralManager.getinstanceO: List<String> deviceList = manager.getUartDeviceListO; if (deviceList.isEmpty() { Log.i(TAG, \"No UART port available on this device.\"); } else { Log.i(TAG, \"List of available devices: \" + deviceList); } 9", |
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| "id": "CO3037-chapter-4-slide-140-0000", |
| "text": "CO3037 - Lecture 3: Device Driver for Embedded Internet of Things loTs Platforms UART Implementation Open an UART Port private UartDevice mDevice; PeripheralManager manager = PeripheralManager.getlnstanceO; mDevice = manager.openUartDevice(UART DEVICE NAME); } catch (lOException e) { Log.w(TAG, \"Unable to access UART device\", e); } Close @Override protected void onDestroy( { super.onDestroyO; if (mDevice != nulI) { try { mDevice.closeO mDevice = null; } catch (IOException e) Log.w(TAG,\"Unable to close UART device\", e); } } 1", |
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| "id": "CO3037-chapter-4-slide-141-0000", |
| "text": "CO3037 - Lecture 3: Device Driver for Embedded Internet of Things loTs) Platforms UART Implementation Configure the frame format public void configureUartFrame(UartDevice uart) throws lOException { l/ Configure the UART port uart.setBaudrate(115200): uart.setDataSize(8); uart.setParity(UartDevice.PARlTY NONE); uart.setStopBits(1); Baudrate: Communication speed in baud. In computer, it is equivalent to bits per second (bps) 1", |
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| "id": "CO3037-chapter-4-slide-142-0000", |
| "text": "CO3037 - Lecture 3: Device Driver for Embedded Internet of Things loTs) Platforms UART Implementation Send a messaae public void writeUartData(UartDevice uart) throws lOException { byte[] buffer = {...}; int count = uart.write(buffer, buffer.length) Log.d(TAG, \"Wrote \" + count + \" bytes to peripheral\"); Receive a messaae private UartDeviceCallback mUartCallback = new public void readUartBuffer(UartDevice uart) throws UartDeviceCallbackO { I0Exception { @Override // Maximum amount of data to read at one time public boolean onUartDeviceDataAvailable(UartDevice final int maxCount = ...: uart) { byte[] buffer = new byte[maxCount] // Read available data from the UART device try { int count; readUartBuffer(uart): while ((count = uart.read(buffer, buffer.length) > 0) { } catch (lOException e) { Log.d(TAG, \"Read \" + count + \" bytes from Log.w(TAG, \"Unable to access UART device\", e); peripheral\"): } } return true; } @Override public void onUartDeviceError(UartDevice uart, int error) Log.w(TAG, uart + \": Error event \" + error) } }; 1", |
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| "id": "CO3037-chapter-4-slide-143-0000", |
| "text": "CO3037 - Lecture 3: Device Driver for Embedded Internet of Things loTs Platforms Wireless Modules using UART lnterface AT command (AT stands for attention) Wifi IPXANT. (ESP82 MICRO SIM 66) ED NET RING Vcc Vcc RING SIM800L D1 RST IME1862643031465390 OTR MIC RXD RXD FCC1D:DV-2013072402 MIC+ S2-1065J MIC TXD TXD -2142X MIC- SPK GND GND E0678 SPK SPK GSM/ GPRS LORA (SX172 Bluetoo XBe 1 O", |
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| "id": "CO3037-chapter-4-slide-144-0000", |
| "text": "CO3037 - Lecture 3: Device Driver for Embedded Internet of Things lloTs platforms Hyper Terminal https://www.dropbox.com/s/7xuwege5pjv6fis/Terminal.exe?dl=0 Terminalv1.9b-201301168-byBr@y++ X Baudrate Data bits Parity Stop bits Connect COM Port Handshaking 600 C 14400 C 57600 5 none ReScan 2COM5 C 1 none 1200 C 19200 115200 C odd CRTS/CTS Help C 2400 C 28800 128000 C even C 1.5 C XON/XOFF About. COMs 7 C 4800 C 38400 256000 C mark C RTS/CTS+XON/XOFF 8 c 2 Quit C 9600 C 56000 custom C space C RTS on TX invert 1. Set Baudrate Settings Auto Dis/Connect Time Stream log custom BR Rx Clear ASCll table Scripting OCTS CD Set Font AutoStart Script CR=LF Stay on Top 9600 .1 Graph Remote DSR RI 3. Select COM Port Receive C HEX DecBin 4. Click Connect CLEAR AutoScroll Reset Cnt 13 : Cnt= 6 StartLogStopLog Req/Resp ASCII Hex AT 5. Send a testing OK command (AT) Transmit CLEAR Send File CR=CR+LF BREAK ODTR CRTS Macros Set Macros M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12 M13 M14 M15 M16 M17 M18 M19 M20 M21 M22 M23 M24 5 AT +CR >Send AT Disconnected Rx: 18 Tx: 6 Rx OK 1", |
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| "text": "C03037 - Lecture 3: Device Driver for Embedded Internet of Things loTs Platforms Example AT Commands for GSM/GPRS Send a SMS message to a phone: AT+CMGF=1 AT+CSCS=\"GSM' AT+CMGS=\"84906362340' Send text message 0x1A Send a GET request AT+SAPBR=1,1 AT+HTTPINIT AT+HTTPPARA=\"CID\" 1 AT+HTTPPARA=\"URL\"\"http://www.iforce2d.net/test.php' AT+HTTPACTION=O AT+HTTPREAD AT+HTTPTERM Note: Every AT command ends with carry return o rn 0x0d 0x0a 1", |
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| "id": "CO3037-chapter-4-slide-146-0000", |
| "text": "C03037 - Lecture 3: Device Driver for Embedded Internet of Things loTs Platforms Serial Communication using SPI Serial Peripheral Interface (SPI) is an interface bus commonly to data used send between microcontrollers and small peripherals such l as shift registers, sensors, and SD cards Separate clock (SCK), data lines (MISO, MOSI) and chip select (Cs) are used. SCK SCK MOSI SDI SPI Slave MISO SDO Synchronous protocol SPI Master SSO CS SS1 SS2 SCK SDI SPI Slave SDO CS SCK SDI SPI Slave SDO CS 1", |
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| "id": "CO3037-chapter-4-slide-147-0000", |
| "text": "CO3037 - Lecture 3: Device Driver for Embedded Internet of Things loTs Platforms Asynchronous Synchronous Vs Protocol Asynchronous (UART) : TX RX There is no CLOCK data bits start stop ldloor idle bit bit next byte Clock drift issue -> low speec 0x53=ASCllS CLOCK CLOCK Synchronous (SPI) : DATA DATA There is a CLOCK line idle or idle next byte CLOCK High speed DATA 1001010 0x53=ASC1S 1", |
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| "CO3037-chapter-4-slide-148-0000": { |
| "id": "CO3037-chapter-4-slide-148-0000", |
| "text": "CO3037 - Lecture 3: Device Driver for Embedded Internet of Things lloTs platforms SPl Protocol MASTER SLAVE SCK SCK MOSI MOSI MISO MISO ss ss Master to Slave Slave to Master idle or Idle next byte SCK Clock from Master 2 3 2 3 4 5 MOSI Master-Out Slave-ln 11001010 0x53=ASClS MISO Master-In Slave-Out 01100010 0x46=ASCIIF ss after last byte sent Slave-Select or recelved Clock is generated by the master 1", |
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| "CO3037-chapter-4-slide-149-0000": { |
| "id": "CO3037-chapter-4-slide-149-0000", |
| "text": "C03037 - Lecture 3: Device Driver for Embedded Internet of Things loTs) Platforms Android Things SPl Device Driver on Serial Peripheral Interface (SPl) devices are typically found where fast data transfer rates are required (e.g. external non-volatile memory and graphical displays) MOSI MOSI ra MISO MISO SPI CLK CLK SPI Slave#1 Master Device GND GND CS1 cS CS2 MOSI MISO SPI CLK Slave#2 GND cS 1", |
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| "CO3037-chapter-4-slide-150-0000": { |
| "id": "CO3037-chapter-4-slide-150-0000", |
| "text": "CO3037 - Lecture 3: Device Driver for Embedded Internet of Things loTs Platforms Implement SPI Adding the required permission <uses-permission android:name=\"com.google.android.things.permission.USE PERIPHERAL IO\" /> Managing the device connection PeripheralManager manager = PeripheralManager.getlnstanceO; List<String> deviceList = manager.getSpiBusList(); if (deviceList.isEmpty() { Log.i(TAG, \"No SPI bus available on this device.\"); } else { Log.i(TAG, \"List of available devices: \" + deviceList); } 2", |
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| "CO3037-chapter-4-slide-151-0000": { |
| "id": "CO3037-chapter-4-slide-151-0000", |
| "text": "CO3037 - Lecture 3: Device Driver for Embedded Internet of Things loTs Platforms Implementation SPI Open SPI Port try { PeripheralManager manager = PeripheralManager.getlnstanceO; mDevice = manager.openSpiDevice(SPI DEVICE NAME); } catch (lOException e) { Log.w(TAG, \"Unable to access SPI device\", e); @Override protected void onDestroy() { Close SPl Port super.onDestroy(); if (mDevice != null) { try { mDevice.closeO; mDevice = null; } catch (IOException e) { Log.w(TAG, \"Unable to close SPI device\", e); } } } 2", |
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| "CO3037-chapter-4-slide-152-0000": { |
| "id": "CO3037-chapter-4-slide-152-0000", |
| "text": "CO3037 - Lecture 3: Device Driver for Embedded Internet of Things loTs Platforms Implement SP Configure SPI connection 2 MODE0: Clock signal idles low, data is transferred on the leading clock edge MODE1: Clock signal idles low, data is transferred on the trailing clock edge MODE2: Clock signal idles high, data is transferred on the leading clock edge MODE3: Clock signal idles high, data is transferred on the trailing clock edge public void configureSpiDevice(SpiDevice device) throws lOException { // Low clock,leading edge transfer device.setMode(SpiDevice.MODE0) l 16MHz,8BPW,MSB first device.setFrequency(16000000); device.setBitsPerWord(8); device.setBitJustification(SpiDevice.BlT JUSTIFICATlON MSB FlRST ): } 2", |
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| }, |
| "CO3037-chapter-4-slide-153-0000": { |
| "id": "CO3037-chapter-4-slide-153-0000", |
| "text": "CO3037 - Lecture 3: Device Driver for Embedded Internet of Things loTs Platforms Implement SPI Transferring and Receiving Data public void sendCommand(SpiDevice device, byte[] buffer) throws lOException { / Shift data out to slave device.write(buffer, buffer.length); / Read the response byte[] response = new byte[32]; device.read(response, response.length); If 2 bytes are sent and 2 bytes will be received, how many bytes for the buffer (the second parameter in sendCommand function)??? 2", |
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| "course_id": "CO3037", |
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| }, |
| "CO3037-chapter-4-slide-154-0000": { |
| "id": "CO3037-chapter-4-slide-154-0000", |
| "text": "C03037 - Lecture 3: Device Driver for Embedded Internet of Things loTs Platforms Serial Communication using 12C Inter-integrated Circuit (I2C) Protocol is a protocol intended to allow multiple \"slave\" digital integrated circuits (\"chips\") to communicate with one or more \"master\" chips. Like the Serial Peripheral Interface (SPI), it is only intended for short distance communications within a single device. Like Asynchronous Serial Interfaces (such as RS- 232 or UARTs), it only requires two signal wires to exchange information. 2", |
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| }, |
| "CO3037-chapter-4-slide-155-0000": { |
| "id": "CO3037-chapter-4-slide-155-0000", |
| "text": "CO3037 - Lecture 3: Device Driver for Embedded Internet of Things loTs Platforms I2C SPI VS Number of pins -> Difficult in tight MISO MISO PCB layout MOSI MOSI Master SCK SCK Slave 1 SPl only allows one master on the CS1 CS CS2 bus MISO >MOSI >SCK Slave 2 SPI is good for high data rate full- >CS duplex Only two pins, like asynchronous SDA SDA serial, but can support up to 128 Slave 1 Master 1 SCL SCL slave devices Support a multi-master system SDA SDA Slave 2 Master 2 Data rates is at 100kHz or 400kHz SCL -SCL 2", |
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| "timestamp": "2025-10-31T18:41:38+07:00" |
| } |
| }, |
| "CO3037-chapter-4-slide-156-0000": { |
| "id": "CO3037-chapter-4-slide-156-0000", |
| "text": "CO3037 - Lecture 3: Device Driver for Embedded Internet of Things loTs Platforms 2C Protocol X SDA! 78/9 SCL 7 1-8 g 1-8 9 START ADDR R/W ACK DATA ACK DATA ACK STOP Master sends START condition and controls the clock (SCL) Master sends a unique 7-bit slave address Master sends Read/Write bit: 0 write to slave, 1: read from slave SIave which address is matched send ACK bit Data (8bit) is transfered 2", |
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| "timestamp": "2025-10-31T18:41:43+07:00" |
| } |
| }, |
| "CO3037-chapter-4-slide-157-0000": { |
| "id": "CO3037-chapter-4-slide-157-0000", |
| "text": "C03037 - Lecture 3: Device Driver for Embedded Internet of Things (loTs Platforms Implement I2C I2C is a synchronous serial interface. The device in control of triggering the clock signal is known as the master. All other connected peripherals are known as slaves. Each device is connected to the same set of data signals to form a bus. SDA SDA 2c 12c Slave SCL SCL Master Device Address:0x3C GND GND SDA https://developer.andr 12c Slave SCL oid.com/things/sdk/pi Address:0x4C 0/i2c GND 2", |
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| "course_id": "CO3037", |
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| "timestamp": "2025-10-31T18:41:49+07:00" |
| } |
| }, |
| "CO3037-chapter-4-slide-158-0000": { |
| "id": "CO3037-chapter-4-slide-158-0000", |
| "text": "CO3037 - Lecture 3: Device Driver for Embedded Internet of Things loTs Platforms Summary SDA I2c RPi SCL Device 1 Device 2 Device 3 SCLK SCLK SCLK MOSI SPI MOSI RPi MOSI MISO MISO MISO CS CS2 CS3 CS2 CS3 Additional configuration reguired for more than 2 CS lines Tx Rx UART RPi Rx Tx Device GND GND MBTechWorks.com UART = Universal Asynchronous Receiver / Transmitter SPl = Serial Peripheral Interface I2C = Inter-Integrated Circuit 2", |
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| "doc_type": "slide", |
| "course_id": "CO3037", |
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| "page_index": 158, |
| "language": "en", |
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| "timestamp": "2025-10-31T18:41:54+07:00" |
| } |
| }, |
| "CO3037-chapter-4-slide-159-0000": { |
| "id": "CO3037-chapter-4-slide-159-0000", |
| "text": "C03037 - Lecture 3: Device Driver for Embedded Internet of Things loTs) Platforms Serial Communications Methods Name Description Function Half duplex, serial data transmission used for short- Inter-Integrated I2C distance between Circuit boards, modules and peripherals. Uses 2 pins. Full-duplex, serial data transmission Serial Peripheral used for short- SPI Interface bus distance between devices. Uses 4 pins. Full-duplex Universal Asynchronous Asynchronous serial data UART Receiver- transmission Transmitter between devices Jses 2 nins 2", |
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| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_4/slide_029.png", |
| "page_index": 159, |
| "language": "en", |
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| "timestamp": "2025-10-31T18:42:00+07:00" |
| } |
| }, |
| "CO3037-chapter-4-slide-160-0000": { |
| "id": "CO3037-chapter-4-slide-160-0000", |
| "text": "C03037 - Lecture 3: Device Driver for Embedded Internet of Things loTs Platforms Conclusion UART - simple; not high speed; no clock needed; limited to one device connected to the Pi. I2C - faster than UART, but not as fast as SPl; easier to chain many devices; Pi drives the clock so no sync issues. SPI - fastest of the three; Pi drives the clock so no sync issues; practical limit to number of devices on the Pi. 3", |
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| "page_index": 160, |
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| "timestamp": "2025-10-31T18:42:03+07:00" |
| } |
| }, |
| "CO3037-chapter-5-slide-161-0000": { |
| "id": "CO3037-chapter-5-slide-161-0000", |
| "text": "Wireless Communication in Internet of Things (loT) Task Table Main radio Add = Add Type Period(s) (MRF) 0x01 (a3) Add = A 0x00 ND 2 0x04 0x04 Base Station (BS) 0x01 ND 150 0x01 DT 10 Add = 0x02 DT 0x02 10 Add = 0x01 SoC 100 B Pfx 1 Add = 0x03,0x05 0x03 0x05 0x05 0x02 SoC 100 Main radio Network Topology Add = 0x03 (MRF) D E Fluorescent lights BS 001 0x04 ForWard Address Table (FWAT) 0x02 0x03 0x05 Energy flow Wake-up radio Microcontroller DClE controller (WUR) ND : Neighbor Discovery DT : Data request Energy storage SoC: State of Charge request DEPT. OF COMPUTER ENGINEERING", |
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| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_5/slide_001.png", |
| "page_index": 161, |
| "language": "en", |
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| "timestamp": "2025-10-31T18:42:11+07:00" |
| } |
| }, |
| "CO3037-chapter-5-slide-162-0000": { |
| "id": "CO3037-chapter-5-slide-162-0000", |
| "text": "37 - Lecture 5: Wireless Communications in Internet of Things (loTs) loTs based TCP/IP Architecture Name Host Network File E-Mail & wwW & Inter- System Config Mgmt Transfer News Gopher active Application DNS RFC822 Telnet BOOTP SNMP FTP /MIME HTTP Application layer 6 SMTP File Com- POP/ sharing mands DHCP RMON TFTP IMAP Gopher 5 NFS IRC NNTP User Datagram Protocol Transmission Control Protocol 4 Transport (UDP) (TCP) Transport layer IP Support IP Routing IP NAT Protocols Protocols ICMP/ICMPv4 Internet layer Internet Protocol IPSeC ICMPv6 RIP,OSPF, 3 Internet (IP/IPv4,IPv6) GGP,HELLO, IGRP,EIGRP Mobile Neighbor BGP,EGP IP Discovery (ND) Network access Address Resolution Reverse Address Resolution ProtocolARP ProtocolRARP) layer Network SerialLinelnterface Point-to-Point Protocol (LAN/WLAN/WAN 2 Interface ProtocolSLIP) (PPP) Hardware Drivers) Physical layer 2", |
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| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_5/slide_002.png", |
| "page_index": 162, |
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| "timestamp": "2025-10-31T18:42:21+07:00" |
| } |
| }, |
| "CO3037-chapter-5-slide-163-0000": { |
| "id": "CO3037-chapter-5-slide-163-0000", |
| "text": "37 - Lecture 5: Wireless Communications in Internet of Things (loTs) Medium Access Control (MAC) Protocol Protocol is the combination of framing, flow control, and error control to achieve the delivery of data from one node to another The protocols are normally implemented in software by using one of the common programming languages Flow control: Refers to a set of procedures used to restrict the amount of data that the sender can send before waiting for acknowledgment Error control: is both error detection and error correction 3", |
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| "page_index": 163, |
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| "timestamp": "2025-10-31T18:42:25+07:00" |
| } |
| }, |
| "CO3037-chapter-5-slide-164-0000": { |
| "id": "CO3037-chapter-5-slide-164-0000", |
| "text": "37 - Lecture 5: Wireless Communications in Internet of Things (loTs) Simplest Protocol Design message Sender Receiver Network Get data Deliverdata Network A Data link Data link Physical Send frame Receive frame Physical Data frames Request from Event: network layer Repeat forever Repeat forever Algorithm for sender site Algorithm forreceiversite Notification from Event: physical layer 4", |
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| "course_id": "CO3037", |
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| "timestamp": "2025-10-31T18:42:30+07:00" |
| } |
| }, |
| "CO3037-chapter-5-slide-165-0000": { |
| "id": "CO3037-chapter-5-slide-165-0000", |
| "text": "37 - Lecture 5: Wireless Communications in Internet of Things (loTs) Implementation 1 while(true){ 2 WaitForEventQ ; 3 if(Event(RequestToSend)){ GetDataQ ; 5 MakeFrame O i 6 SendFrame() ; 7 1 while(true) { 8 } 2 WaitForEventQ) ; 3 if(Event(ArrivalNotification)){ ReceiveFrame() ; 5 ExtractDataQ ; 6 DeliverDataQ; 7 } 8 }", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_5/slide_005.png", |
| "page_index": 165, |
| "language": "en", |
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| "timestamp": "2025-10-31T18:42:36+07:00" |
| } |
| }, |
| "CO3037-chapter-5-slide-166-0000": { |
| "id": "CO3037-chapter-5-slide-166-0000", |
| "text": "37 - Lecture 5: Wireless Communications in Internet of Things (loTs) Example Sender Receiver Propagation delay A B - Request Frame Arrival Request Frame Arrival Request Frame Arrival 1 1 Time Time even thinking about the receiver There is no error handler There is no synchronization (the receiver processing time is slower than the transmission speed) 6", |
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| }, |
| "CO3037-chapter-5-slide-167-0000": { |
| "id": "CO3037-chapter-5-slide-167-0000", |
| "text": "37 - Lecture 5: Wireless Communications in Internet of Things (loTs) MAC I Protocol in loTs Synchronous protocols The stream of data to be transferred is encoded as fluctuating voltage levels in one wire (the 'DATA' and a periodic pulse of voltage on a separate wire (called the \"CLOCK\") which tells the e receiver that the current r DATA bit is avaiIabIe at this moment in time Asynchronous protocols Data is transmitted at a random time. Normally, a start and stop conditions l to used begin are a \"rendez-vous\" to initiate a comunication 7", |
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| "page_index": 167, |
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| "timestamp": "2025-10-31T18:42:45+07:00" |
| } |
| }, |
| "CO3037-chapter-5-slide-168-0000": { |
| "id": "CO3037-chapter-5-slide-168-0000", |
| "text": "37 - Lecture 5: Wireless Communications in Internet of Things (loTs) I Disadvantage Advantage and Advantage Disadvantage Asynchronous Simple, doesn't reguire Large relative transmission synchronization of both overhead, a high communication sides proportion of the Cheap, asynchronous transmitted bits are requires less hardware uniquely for control Suitable for low data rate purposes and thus applications carry no useful information Synchronous Lower overhead and thus Slightly more transmission greater throughput complex Hardware is more expensive 8", |
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| } |
| }, |
| "CO3037-chapter-5-slide-169-0000": { |
| "id": "CO3037-chapter-5-slide-169-0000", |
| "text": "37 - Lecture 5: Wireless Communications in Internet of Things (loTs) Protocols: S-MAC Communication are synchronized in time with two state: Active: Carrier sensing, Request To Send, Clear To Send and Sync Packet sleep: Low power mode for enerav reservation sleep active sleep active sleep active sleep Sensor A active sleep active sleep active sleep active sleep Sensor B active iTime Drawback: Energy wasted for active period: When there is no packet to send????? 9", |
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| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_5/slide_009.png", |
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| "language": "en", |
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| "timestamp": "2025-10-31T18:42:57+07:00" |
| } |
| }, |
| "CO3037-chapter-5-slide-170-0000": { |
| "id": "CO3037-chapter-5-slide-170-0000", |
| "text": "37 - Lecture 5: Wireless Communications in Internet of Things (loTs) Protocols: T-MAC An extended version from S-MAC: Active period is adapted: if there is no RTS or CTS after a period the node go to sleep mode sleep active sleep sleep active sleep Sensor A active active @iTime High impact on low data rate networks 10", |
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| "timestamp": "2025-10-31T18:43:00+07:00" |
| } |
| }, |
| "CO3037-chapter-5-slide-171-0000": { |
| "id": "CO3037-chapter-5-slide-171-0000", |
| "text": "37 - Lecture 5: Wireless Communications in Internet of Things (loTs) Protocols Wake-up beacon (WUB Tr Data Reception (DR) Acknowledgement (ACK Receiver Clear Channel Assessment (CCA) Wait for data packet Calculation Before Transmission (CBT) Idle listening Data Transmission (DT) Transmitter RICER (Receiver Initiated Cycled Receiver): Receiver sends a BEACON Transmitter waits for a BEACON, before sending its DATA ACK is used to confirm a communication 11", |
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| "text": "37 - Lecture 5: Wireless Communications in Internet of Things (loTs) Asynchronous Protocols Extended version of RlCER: RlCER3,RlCER3b,RICE5 ODMAC: ACK plays the role of a new BEACON SymMAC: RlCER + TlCER PreamblesDAT LDAT Tx DLDAT Tx A A R-Mote T-Mote Wait for a DXIA Rx RX 12", |
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| "text": "37 - Lecture 5: Wireless Communications in Internet of Things (loTs) Routing Protocol in loTs Node(19) Node(17) Node(15 Node(8) Node(18) Node(9) Node(7) Node(16) Node(4) Node(0) Node(1) Node(2) Node(20) Base Station Node(6) Node(11 Node(12) Node(10) Node(5) Node(4) Node(3) Node(13) Energy efficient routing for sending a packet??? 13", |
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| "text": "37 - Lecture 5: Wireless Communications in Internet of Things (loTs) Neighbor Discovery Process Node(19) Node(17) Node(15) Node(8) Node(18) Node(9) Node(7) Node(16) Node(14) Node(0) Node(1) Node(2) Node(20) Base Station Node(6) Node(11) Node(12) Node(10) Node(5) Node(4) Node(3) Base station nodes send neighbor discovery packet: Node (1) (6) (7) are discovered 14", |
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| "text": "37 - Lecture 5: Wireless Communications in Internet of Things (loTs) Neighbor Discovery Process Forwa rd Table Node (8) Node(19) Node(17) Node(15) Node Node(8) Node(18) (9) Node(9) Node(7) Node(16) Node(14) Node(0) Node(1) Node(2) Node(20) Base Station Node(6) Node(11) Node(12) Node(10) Node(5) Node(4) Node(3) Node (7) sends neighbor discovery packet: Node (8) and (9) response (8) and (9) are added to the forward table of node (7) 15", |
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| "text": "37 - Lecture 5: Wireless Communications in Internet of Things (loTs) Data Forwa rd Table Node (8) Node(19) Node(17) Node(15) Node Node(8) Node(18) (9) Node(9) Node(7) Node(16) Node(14) Node(0) Node(1) Node(2) Node(20) Base Station Node(6) Node(11) Node(12) Node(10) Node(5) Node(4) Node(3) Node (0) send data request packet to Node 8. Node (7) forwards this packet Node (8) response to Node (7) and then, forward to Node (0) 16", |
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| "text": "Control in Error Communication Sender Receiver Protocols A B Request Frame Arrival ACK Arrival- For noiseless For noisy Request Frame channel channel Arrival ACK Arrival- Simplest Stop-and-Wait ARQ Time Time -Stop-and-Wait -Go-Back-NARQ DCE -Selective RepeatARQ DEPT. OF COMPUTER ENGINEERING", |
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| "text": "ror and Flow Control in Communications System Architecture AIO Feed +AddDete Downioed All Dete TFie Created at Value 2021/08/1012528AM 2021/08/1012906AM 0 2021/08/1012902AM NPNLab_BBC>Dashboards DHB_BBC_LED MICROBITLED AIO Dashboard Sensor Nodes Gateway IoT 2", |
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| "CO3037-chapter-6-slide-179-0000": { |
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| "text": "ror and Flow Control in Communications Communication Protocol The combination of framing, flow control, and error control to achieve the delivery of data from one node to another. The protocols are normally implemented in software by using one of the common programming languages. 3", |
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| "text": "ror and Flow Control in Communications classification I of Protocols Protocols For noiseless For noisy channel channel Simplest Stop-and-Wait ARQ Stop-and-Wait Go-Back-N ARQ -Selective Repeat ARQ 4", |
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| "CO3037-chapter-6-slide-181-0000": { |
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| "text": "ror and Flow Control in Communications NOISELESS CHANNELS No frames are lost, duplicated, or corrupted Simplest Protocol - has no flow or error control Stop-and-Wait Protocol - sender sends one frame, stops until it receives agree from receiver and then sends the next frame 5", |
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| "CO3037-chapter-6-slide-182-0000": { |
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| "text": "ror and Flow Control in Communications Simplest Protocol Unidirectional protocol: data frames are traveling in only one direction-from the sender to receiver. The receiver can immediately handle any frame it receives with a processing time that is small enough to be negligible. The data link layer of the receiver immediately removes the header from the frame and hands the data packet to network layer, which can also accept the packet immediately 6", |
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| "CO3037-chapter-6-slide-183-0000": { |
| "id": "CO3037-chapter-6-slide-183-0000", |
| "text": "ror and Flow Control in Communications Simplest Protocol Design message Sender Receiver Network Get data Deliverdata Network A Data link Data link A Physical Send frame Receive frame Physical Data frames Request from Event: network layer Repeat forever Repeat forever Algorithm for sender site Algorithm for receiver site Notification from Event: physical layer 7", |
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| "CO3037-chapter-6-slide-184-0000": { |
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| "text": "ror and Flow Control in Communications Implementation 1 while(true){ 2 WaitForEventQ ; 3 if(Event(RequestToSend)){ 4 GetDataQ ; 5 MakeFrame() ; 6 SendFrame() ; 7 } 1 Fwhile(true) { 8 } 2 WaitForEventQ) ; 3 if(Event(ArrivalNotification)) { ReceiveFrame() ; 5 ExtractDataQ ; 6 DeliverDataQ; 7 } 8 }", |
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| "CO3037-chapter-6-slide-185-0000": { |
| "id": "CO3037-chapter-6-slide-185-0000", |
| "text": "ror and Flow Control in Communications Example Sender Receiver Propagation delay A B - Request Frame Arrival Request Frame Arrival Request Frame Arrival 1 Time Time even thinking about the receiver There is no error handler There is no synchronization (the receiver processing time is slower than the transmission speed) 9", |
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| "CO3037-chapter-6-slide-186-0000": { |
| "id": "CO3037-chapter-6-slide-186-0000", |
| "text": "ror and Flow Control in Communications Stop l Wait Protocol and If data frames arrive at the receiver site faster than they can be processed, the frames s must be stored until their use Normally, the receiver does not have enough storage space, especially if it is receiving data from many sources The sender sends one frame, stops until it receives agreement the receiver (okay to go ahead), and then sends the next frame AcK frames (simple tokens of acknowledgment) travel from the other direction 10", |
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| "CO3037-chapter-6-slide-187-0000": { |
| "id": "CO3037-chapter-6-slide-187-0000", |
| "text": "ror and Flow Control in Communications d Wait Protocol Design Stop and Sender Receiver Deliver Network Get data data Network A Data link Data link Receive Send Physical Receive Send Physical frame frame frame frame Data frame ACKframe Request from Event: networklayer Repeat forever Repeat forever Algorithm for sendersite Algorithm for receiver site Notificationfrom Notification from Event: Event: physical layer physicallayer 11", |
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| "CO3037-chapter-6-slide-188-0000": { |
| "id": "CO3037-chapter-6-slide-188-0000", |
| "text": "ror and Flow Control in Communications Example Sender Receiver A B 1 - - Request Frame Arrival ACK 1 Arrival 1 Request Frame Arrival ACK Arrival< 1 1 Time Time The sender sends one frame and waits for feedback from the receiver before sending the next frame Four events at the Sender and two events at the Receiver 12", |
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| "CO3037-chapter-6-slide-189-0000": { |
| "id": "CO3037-chapter-6-slide-189-0000", |
| "text": "ror and Flow Control in Communications NOISY CHANNELS Although the Stop-and-Wait Protocol gives us an idea of how to add flow control to its predecessor noiseless s channels are nonexistent. Stop-and-Wait Automatic Repeat Request(ARQ) Go-Back-N Automatic Repeat Request Selective Repeat Automatic Repeat Request 13", |
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| "CO3037-chapter-6-slide-190-0000": { |
| "id": "CO3037-chapter-6-slide-190-0000", |
| "text": "ror and Flow Control in Communications Principles Stop I Wait ARQ and A copy of a frame (sent to the receiver) is kept in the buffer Retransmitting this frame when the timer expires meaning that ACK is not received Seguence numbers are used to index the frames. The acknowledgment number always announces the sequence number of the next frame expected 14", |
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| "CO3037-chapter-6-slide-191-0000": { |
| "id": "CO3037-chapter-6-slide-191-0000", |
| "text": "ror and Flow Control in Communications Stop I Wait ARQ Protocol and Next frame Next frame to send to receive 0 0 1 0 0 0 Sender Receiver Data frame ACKframe Deliver Get data Network data Network seqNo ackNo A Data link Data link A Physical Receive Send Receive Send Physical frame frame frame frame Request from Event: networklayer Repeatforever Repeat forever Algorithmfor sender site Time-out Algorithmforreceiversite Event: Notificationfrom Notificationfrom Event: Event: physicallayer physicallayer 15", |
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| "CO3037-chapter-6-slide-192-0000": { |
| "id": "CO3037-chapter-6-slide-192-0000", |
| "text": "ror and Flow Control in Communications Example Sender Receiver A B Start 101:01 Rn Request 0 Frame0 Sn 010101Arrival - Stop ACK1 Arrival:010:1:0:1: Sn - Frame is los Request000 Frame1 Lost Sn Time-out Time-out:00:1:0:1: Rn L Frame1(resent) restart Sn 0101Arrival - ACKO Stop Arrival0101 Start Request011o1 Rn Frame0 - Sn 01001Arrival ACK1 Lost Sn Time-out Time-out0:1101 Frame 0(resent) Rn restart 01:00:1Arrival Sn I ACK1 Discard,duplicate Stop Arrival0:1001 . I . ACK is lost Duplicate reception at the receiver 16", |
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| "CO3037-chapter-6-slide-193-0000": { |
| "id": "CO3037-chapter-6-slide-193-0000", |
| "text": "ror and Flow Control in Communications Go-Back-N Automatic Repeat Request Multiple frames must be in transition while waiting for acknowledgment to maximize the efficiency Protocol principles: Several frames are sent before receiving ACKs A copy of these frames are kept until the ACKs arrive 17", |
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| "CO3037-chapter-6-slide-194-0000": { |
| "id": "CO3037-chapter-6-slide-194-0000", |
| "text": "ror and Flow Control in Communications Send Windows for Go Back N (m=4) Sf Send window, Sn Send window first outstanding frame next frame to send 131415 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15i 0 Frames already Frames sent,but not Frames that can be sent, Frames that acknowledged acknowledged(outstanding) but not receivedfrom upperlayer cannot be sent Send window, size Ssize = 2m - 1 a.Send window before sliding Sf Sn 1 13 2 3 4 5 6 7 8 9 10 11 1 12 13 14 15 4 0 0 1 b.Send window after sliding 18", |
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| "CO3037-chapter-6-slide-195-0000": { |
| "id": "CO3037-chapter-6-slide-195-0000", |
| "text": "ror and Flow Control in Communications Definitions S.: the sequence number of the first (oldest) outstanding frame S,: the sequence number that will be assigned to the next frame to be sent. S protocol. S, can slide one or more slots when a valid ACK arrives. 19", |
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| "CO3037-chapter-6-slide-196-0000": { |
| "id": "CO3037-chapter-6-slide-196-0000", |
| "text": "ror and Flow Control in Communications windows for Go Back Receive N Ro Receive window,nextframe expected 13 14 15 6 8 9 4 15 0 Framesalready received Frames that cannotbe received andacknowledged until the window slides a.Receive window 6 8 9 10 14 15 b.Window after sliding The window slides one slot when a correct frame has arrived; 20", |
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| "CO3037-chapter-6-slide-197-0000": { |
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| "text": "ror and Flow Control in Communications Design Go Back N First Next Next St S outstanding to send to receive Sender Receiver Data frame ACKframe Deliver Network Get data data Network seqNo ackNo A Y Datalink Datalink Receive Send Receive Send Physical Physical frame frame frame frame Request from Event: networklayer Repeatforever Repeat forever Algorithm for sender site Time-out Algorithm forreceiver site Event: Notification from Notificationfrom Event: Event: physicallayer physicallayer 21", |
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| "text": "Software Development Framework NET Framework NET Core Mono for Xamarin Windows ASP.NET WPF ios Forms Core Android Mobile ASP.NET UWP macos Web Flutter .NET Standard Library Desktop Common Infrastructure Embedded Build Tools Languages Runtime Components", |
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| "text": "C/C++ Programming Language The best performance program C ++ Compiler programing language tinclude \"led.h\" #ifndef LED H #define LED H int T on; int T off; #include <system lib.h> Int counter; #include \"user lib.h\" #include void setOn(long duration) \"UserFolder/lib.h\" /TODO: set LED on extern int T on; here extern int T off: } void setoff(long duration) void setOn(long duratien): S void setoff(long duration) ; l/TODO: set LED off here #endif } 21", |
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| "CO3037-chapter-7-slide-224-0000": { |
| "id": "CO3037-chapter-7-slide-224-0000", |
| "text": "Finite State Machine (FSM) Finite-State Machine (FSM) or Deterministic Finite Automata (DFA), finite automaton, or simply a state machine, is a mathematical model of computation Current Input Next State State Locked Coin Unlocked Push Locked Unlockea Coin Unlocked A turnstile Push Locked Push Coin Un- Locked locked Push Coin 22", |
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| } |
| }, |
| "CO3037-chapter-7-slide-225-0000": { |
| "id": "CO3037-chapter-7-slide-225-0000", |
| "text": "Finite State Machine Programming while(1){ switch(status){ case LOCKED : lock_turnstile(); //operation in a state if(Coin == true) //transition condition status UNLOCKED; //next state break; CaSe UNLOCKED : unlock_turnstile(): //operation in a state if(Push i == true) //transition condition status = L0CKED; //next state break; default : break; 2 23", |
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| "CO3037-chapter-7-slide-226-0000": { |
| "id": "CO3037-chapter-7-slide-226-0000", |
| "text": "Python Programing Language High Level Simple Interpreted Object-Oriented Open Source Expressive Gui Programming Language Large Standard Library - Al, Data science and Deep Learning Interpreter programing language 24", |
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| "CO3037-chapter-7-slide-227-0000": { |
| "id": "CO3037-chapter-7-slide-227-0000", |
| "text": "Class in Python class Student: def F init_(self, _name,_age): self.name = name self.age = _age def setName(self, name): self.name = name Flexible and high level approach 25", |
| "metadata": { |
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| "CO3037-chapter-7-slide-228-0000": { |
| "id": "CO3037-chapter-7-slide-228-0000", |
| "text": "Model View Controller 2 Controller Request Contacts 2 Shows 5 Model Returns View Delivers Abstraction Layer Database A pattern for OOP program 26", |
| "metadata": { |
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| }, |
| "CO3037-chapter-7-slide-229-0000": { |
| "id": "CO3037-chapter-7-slide-229-0000", |
| "text": "MVC using l Python Controller Notify Update Model View Update User Action", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_7/slide_027.png", |
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| } |
| }, |
| "CO3037-chapter-7-slide-230-0000": { |
| "id": "CO3037-chapter-7-slide-230-0000", |
| "text": "Step 1: Create a new project in Python Create Model folder: Student.py Create Controller folder: StudentController.py The main.py is the View of the project 28", |
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| } |
| }, |
| "CO3037-chapter-7-slide-231-0000": { |
| "id": "CO3037-chapter-7-slide-231-0000", |
| "text": "Step 2: Implement the Student Class class Student: def init (self, name, age): self.name = name self.age = _age def setName(self, name): self.name = name def getName(self) : return self.name def setAge(self, age): self.age = _age def getAge(self) : return self.age - The fields are highly related to the database properties 29", |
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| } |
| }, |
| "CO3037-chapter-7-slide-232-0000": { |
| "id": "CO3037-chapter-7-slide-232-0000", |
| "text": "Step 3: Implement the Controller class StudentController: def insertStudent(self, student): print(student.name, student.age) Data manipulation: insert, update or delete Data storage: database or file management system 30", |
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| } |
| }, |
| "CO3037-chapter-8-slide-233-0000": { |
| "id": "CO3037-chapter-8-slide-233-0000", |
| "text": "Introduction to Algorithm 19 unsigned int lev 20 Start const size_t len1=s1.size7 21 Move forward 100 pixels,ther vector<unsigned int> col(len2+1), prevcoll prevcol.size1 22 turn clockwise 90 degrees 23 len11 Do this a total of 4 times. for (unsigned int 24 prevCol[i]=i len2J 25 Moveforward for (unsigned int 100 26 col[@]=1+1; pen Down Turn clockwise 27 for (unsigned int j =std::minstd count <- 0 28 col[j+1] prevcolljl 29 repeat until count = 4 30 col.swap(prevcol): forward 1o0 31 clockwise 9o° count=4? prevColllen2l: 32 count = count +1 eturn 3 Stop", |
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| "CO3037-chapter-8-slide-234-0000": { |
| "id": "CO3037-chapter-8-slide-234-0000", |
| "text": "What is an algorithm? An algorithm is s\"a finite set of precise Instructions for performing a or for solving computation j a I problem A program is one type of algorithm All programs are algorithms Not all algorithms are programs! Design a scheduler for RTOs is an algorithm 2", |
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| "page_index": 234, |
| "language": "en", |
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| "timestamp": "2025-10-31T18:48:06+07:00" |
| } |
| }, |
| "CO3037-chapter-8-slide-235-0000": { |
| "id": "CO3037-chapter-8-slide-235-0000", |
| "text": "Some algorithms are e harder than others Some algorithms are easy Finding the largest (or smallest) value in a list Finding a specific value in a list Some algorithms are a bit harder Sorting a list Some algorithms are very hard Finding the shortest path between Miami and Seattle Some algorithms are essentially impossible Factoring large composite numbers Algorithm complexity needs to be considered 3", |
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| "page_index": 235, |
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| "timestamp": "2025-10-31T18:48:10+07:00" |
| } |
| }, |
| "CO3037-chapter-8-slide-236-0000": { |
| "id": "CO3037-chapter-8-slide-236-0000", |
| "text": "Algorithm 1: Maximum Element Given a list, how do we find the maximum element in the list? To express the algorithm, Pseudocode can be used procedure max(a1, a2, ..., an: integers max := a1 for i := 2 to n if max < a; then max := a 4", |
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| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_8/slide_004.png", |
| "page_index": 236, |
| "language": "en", |
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| } |
| }, |
| "CO3037-chapter-8-slide-237-0000": { |
| "id": "CO3037-chapter-8-slide-237-0000", |
| "text": "element running Maximum time How long does this take? If the list has n elements, worst case scenario is that it takes n \"steps\" Here, a step is considered a single step through the list 5", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_8/slide_005.png", |
| "page_index": 237, |
| "language": "en", |
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| "timestamp": "2025-10-31T18:48:15+07:00" |
| } |
| }, |
| "CO3037-chapter-8-slide-238-0000": { |
| "id": "CO3037-chapter-8-slide-238-0000", |
| "text": "Properties of Algorithms Algorithms generally share a set of properties: Input: what the algorithm takes in as input Output: what the algorithm produces as output Definiteness: the steps are defined precisely Correctness: should produce the correct output Finiteness: the steps required should be finite Effectiveness: each step be able to be must performed in a finite amount of time Generality: the algorithm shou/d be applicable to all problems of a similar form 6", |
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| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_8/slide_006.png", |
| "page_index": 238, |
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| } |
| }, |
| "CO3037-chapter-8-slide-239-0000": { |
| "id": "CO3037-chapter-8-slide-239-0000", |
| "text": "Searching Algorithms Given a list, find a specific element in the list We will see two types Linear search a.k.a. sequential search Binary search 7", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_8/slide_007.png", |
| "page_index": 239, |
| "language": "en", |
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| "timestamp": "2025-10-31T18:48:22+07:00" |
| } |
| }, |
| "CO3037-chapter-8-slide-240-0000": { |
| "id": "CO3037-chapter-8-slide-240-0000", |
| "text": "Algorithm 2: Linear Search Given a list, find a specific element in the list List does NOT have to be sorted! i := 1 while (i n and x a;) i:=i+ 1 if i < n then location := i else /ocation := 0 {/ocation is the subscript of the term that equals x, or it is 0 if x is not found} 8", |
| "metadata": { |
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| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_8/slide_008.png", |
| "page_index": 240, |
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| "timestamp": "2025-10-31T18:48:25+07:00" |
| } |
| }, |
| "CO3037-chapter-8-slide-241-0000": { |
| "id": "CO3037-chapter-8-slide-241-0000", |
| "text": "Search Running Linear Time How long does this take? If the list has n elements, worst case scenario is that it takes n \"steps\" Here, a step is considered a single step through the list Complexity is O(N) 9", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_8/slide_009.png", |
| "page_index": 241, |
| "language": "en", |
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| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:48:28+07:00" |
| } |
| }, |
| "CO3037-chapter-8-slide-242-0000": { |
| "id": "CO3037-chapter-8-slide-242-0000", |
| "text": "Algorithm 3: Binary Search Given a list, find a specific element in the list List MUST be sorted! Each time it iterates through, it cuts the list in integers) i := 1 { i is left endpoint of search interval } j := n { j is right endpoint of search interval } while i<j begin L(i+j)/2]{ m is the point in the middle } m := if x > am then i := m+1 else j := m end if x = a; then location := i else location := 0 10", |
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| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_8/slide_010.png", |
| "page_index": 242, |
| "language": "en", |
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| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:48:32+07:00" |
| } |
| }, |
| "CO3037-chapter-8-slide-243-0000": { |
| "id": "CO3037-chapter-8-slide-243-0000", |
| "text": "Binary Search Running Time How long does this take (worst case)? f the list has 8 elements It takes 3 steps If the list has 16 elements It takes 4 steps If the list has n elements It takes Iogz n steps 11", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_8/slide_011.png", |
| "page_index": 243, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:48:35+07:00" |
| } |
| }, |
| "CO3037-chapter-8-slide-244-0000": { |
| "id": "CO3037-chapter-8-slide-244-0000", |
| "text": "Sorting Algorithms Given a list, put it into some order Numerical, lexicographic, etc. We will see two types Bubble sort Insertion sort 12", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_8/slide_012.png", |
| "page_index": 244, |
| "language": "en", |
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| "timestamp": "2025-10-31T18:48:38+07:00" |
| } |
| }, |
| "CO3037-chapter-8-slide-245-0000": { |
| "id": "CO3037-chapter-8-slide-245-0000", |
| "text": "Algorithm 4: Bubble Sort One of the most simple sorting algorithms Also one of the least efficient It takes successive elements and \"bubbles\" them up the list procedure e bubble sort (ay az for i := 1 to n-1 for i := 1 to n-i if a;> a;+1 then interchange a; and a;+1 { a,, ..., a, are in increasing order } 13", |
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| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_8/slide_013.png", |
| "page_index": 245, |
| "language": "en", |
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| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:48:41+07:00" |
| } |
| }, |
| "CO3037-chapter-8-slide-246-0000": { |
| "id": "CO3037-chapter-8-slide-246-0000", |
| "text": "Bubble Sort Running Time Outer for loop does n-1 iterations Inner for loop does n-1 iterations the first time n-2 iterations the second time 1 iteration the last time Total: (n-1) + (n-2) + (n-3) + ... + 2 + 1 = (n- n)/2 We can say that's \"about\" n2 time 14", |
| "metadata": { |
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| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_8/slide_014.png", |
| "page_index": 246, |
| "language": "en", |
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| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:48:44+07:00" |
| } |
| }, |
| "CO3037-chapter-8-slide-247-0000": { |
| "id": "CO3037-chapter-8-slide-247-0000", |
| "text": "Algorithm 5: Insertion Sort Another simple (and inefficient) algorithm It starts with a list with one element, and inserts new elements into their proper place in the sorted part of the list an) for /:= 2 to n begin i:= 1 while a; > ai i:= i+1 m := aj for k := 0 to j-i-1 aj-k := ajk-1 aj:= m 15", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_8/slide_015.png", |
| "page_index": 247, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:48:48+07:00" |
| } |
| }, |
| "CO3037-chapter-8-slide-248-0000": { |
| "id": "CO3037-chapter-8-slide-248-0000", |
| "text": "Insertion Sort Running Time Outer for loop runs n-1 times In the inner for Ioop: Worst case is when the while keeps i at 1, and the for loop runs lots of times If i is 1, the inner for loop runs 1 time (k goes from 0 to 0) on the first iteration, 1 time on the second, up to n-2 times on the Iast iteration Total is 1 + 2 + .. + n-2 = (n-1)(n-2)/2 We can say that's \"about\" n2 time 16", |
| "metadata": { |
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| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_8/slide_016.png", |
| "page_index": 248, |
| "language": "en", |
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| "timestamp": "2025-10-31T18:48:51+07:00" |
| } |
| }, |
| "CO3037-chapter-8-slide-249-0000": { |
| "id": "CO3037-chapter-8-slide-249-0000", |
| "text": "Comparison of Running Times Searches Linear: n steps Binary: log, n steps Binary search is about as fast as you can get Sorts Bubble: n2 steps Insertion: n2 steps There are other, more efficient, sorting technigues In principle, the fastest are heap sort, quick sort, and merge sort These each take take n * log, n steps In practice, quick sort is the fastest, followed by merge sort 17", |
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| "CO3037-chapter-8-slide-250-0000": { |
| "id": "CO3037-chapter-8-slide-250-0000", |
| "text": "RTOS 'update' function void SCH_Update(void) { tByte Index; NOTE: calculations are in *TICKS* (not milliseconds) for (Index = 0;Index < SCH MAX TASKS;Index++) { / Check if there is a task at this location if(SCH tasks G[Index].pTask) { if (SCH_tasks_G[Index].Delay == 0) { The task is due to run SCH tasks G[Index].RunMe += 1; // Inc. the 'RunMe'flag if (SCH tasks G[Index].Period) { // Schedule periodic tasks to run again SCH tasks G[Index].Delay = SCH tasks G[Index].Period; } } else { // Not yet ready to run: just decrement the delay SCH tasks G[lndex].Delay -= 1; } 18", |
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| "timestamp": "2025-10-31T18:48:59+07:00" |
| } |
| }, |
| "CO3037-chapter-8-slide-251-0000": { |
| "id": "CO3037-chapter-8-slide-251-0000", |
| "text": "MCQ The word comes from the name of a Persian mathematician a) Flowchart b) Flow c) Algorithm d) Syntax 19", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_8/slide_019.png", |
| "page_index": 251, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:49:02+07:00" |
| } |
| }, |
| "CO3037-chapter-8-slide-252-0000": { |
| "id": "CO3037-chapter-8-slide-252-0000", |
| "text": "MCQ The time that depends on the input: an already sorted sequence that is easier to sort. a) Process b) Evaluation Running c) d) Input 20", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_8/slide_020.png", |
| "page_index": 252, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:49:05+07:00" |
| } |
| }, |
| "CO3037-chapter-8-slide-253-0000": { |
| "id": "CO3037-chapter-8-slide-253-0000", |
| "text": "MCQ Algorithms can be represented (select incorrect) : a) as pseudo codes b) as syntax c) a as programs d) as flowcharts 21", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_8/slide_021.png", |
| "page_index": 253, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:49:08+07:00" |
| } |
| }, |
| "CO3037-chapter-8-slide-254-0000": { |
| "id": "CO3037-chapter-8-slide-254-0000", |
| "text": "MCQ When an algorithm is written in the form of a programming language, it becomes a a) Flowchart b) Program c) Pseudo code d) Syntax 22", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_8/slide_022.png", |
| "page_index": 254, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:49:10+07:00" |
| } |
| }, |
| "CO3037-chapter-8-slide-255-0000": { |
| "id": "CO3037-chapter-8-slide-255-0000", |
| "text": "MCQ Any algorithm is a program. a) True b) False Any program is an algorithm a) True b) False 23", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_8/slide_023.png", |
| "page_index": 255, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:49:12+07:00" |
| } |
| }, |
| "CO3037-chapter-8-slide-256-0000": { |
| "id": "CO3037-chapter-8-slide-256-0000", |
| "text": "MCQ A system wherein items are added from one and removed from the other end a) Stack b) Queue c) Linked List d) Array 24", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_8/slide_024.png", |
| "page_index": 256, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:49:15+07:00" |
| } |
| }, |
| "CO3037-chapter-8-slide-257-0000": { |
| "id": "CO3037-chapter-8-slide-257-0000", |
| "text": "MCQ Another name for 1-D arrays. a) Linear arrays b) Lists c) Horizontal array d) Vertical array 25", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_8/slide_025.png", |
| "page_index": 257, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:49:17+07:00" |
| } |
| }, |
| "CO3037-chapter-8-slide-258-0000": { |
| "id": "CO3037-chapter-8-slide-258-0000", |
| "text": "https:l/www.youtube.com/watch?v=01sAkU_NvOY nport cv2 nport mediapipe as mp nport time ap = cv2.VideoCapture(1) ipHands = mp.solutions.hands ands = mpHands.HandsO pDraw = mp.solutions.drawing utils hile True: success, img = cap.read() imgRGB = cv2.cvtColor(img, cv2.COLOR BGR2RGB) results = hands.process(imgRGB) if results.multi hand landmarks: for handlms in results.multi hand Iandmarks: #21 points in handlms mpDraw.draw landmarks(img, handIms, mpHands.HAND CONNECTIONS) cv2.imshow(\"Image\", img cv2.waitKey(1) 26", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_8/slide_026.png", |
| "page_index": 258, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:49:21+07:00" |
| } |
| }, |
| "CO3037-chapter-8-slide-259-0000": { |
| "id": "CO3037-chapter-8-slide-259-0000", |
| "text": "Assignment Project 20% and +2 maximum Management Database Software Hierarchical Data storage Data retrieval databases Network databases Data security System Features Types Relational databases Data integrity Object-Orientec DBMS databases Data management Database Management System System Size of data Complexity of data Factors Software Types of data Data Technology Performance reguirements", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_8/slide_027.png", |
| "page_index": 259, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:49:26+07:00" |
| } |
| }, |
| "CO3037-chapter-8-slide-260-0000": { |
| "id": "CO3037-chapter-8-slide-260-0000", |
| "text": "General Information Pick one of the following topics: Al, DA, Blockchain or Software Engineering 12 16 8 15 20 7 10 .14 6 19 .18 sbudent company group meeting teamworkmedia table 5 together homework executive is Computer iniormabion study a technology cooperabio professiona 17.139 projectbrau research book dealing assignment strategy businessman management eracur people students school corporate education 2 knowledge indoor finance software working_O concept paper lapbop 0 DA (World Cloud) AI (MediaPipe 28", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_8/slide_028.png", |
| "page_index": 260, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:49:33+07:00" |
| } |
| }, |
| "CO3037-chapter-8-slide-261-0000": { |
| "id": "CO3037-chapter-8-slide-261-0000", |
| "text": "Example 1: Gesture Detection 29", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_8/slide_029.png", |
| "page_index": 261, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:49:34+07:00" |
| } |
| }, |
| "CO3037-chapter-8-slide-262-0000": { |
| "id": "CO3037-chapter-8-slide-262-0000", |
| "text": "Example 2: World Cloud Generation Software 30", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_8/slide_030.png", |
| "page_index": 262, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:49:36+07:00" |
| } |
| }, |
| "CO3037-chapter-8-slide-263-0000": { |
| "id": "CO3037-chapter-8-slide-263-0000", |
| "text": "Project Presentation Introduction to the project Use-Case diagram Sequence Diagram, Flowchart or Algorithm of one or two typical use-cases Implementation Results and Demo 31", |
| "metadata": { |
| "doc_type": "slide", |
| "course_id": "CO3037", |
| "source_file": "/workspace/data/converted/CO3037_Internet_of_Things_Application_Development/Chapter_8/slide_031.png", |
| "page_index": 263, |
| "language": "en", |
| "ocr_engine": "PaddleOCR 3.2", |
| "extractor_version": "1.0.0", |
| "timestamp": "2025-10-31T18:49:39+07:00" |
| } |
| } |
| } |
