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12,400 | Please write an abstract with title: Inspections and historical data in teaching software engineering project course, and key words: Inspection, Education, Software engineering, Statistics, Personnel, Software systems, Project management, Information technology, Minutes, Management training. Abstract: Software engineering project course document inspection data have been collected from meeting minutes. The derived statistics are used in inspections, in project management lectures, and also in software engineering training outside university. The use of historical project data, such as statistics on working hours, and comments from participants in previous courses, have been considered very valuable in teaching, by both the students and course personnel. These are the two specialities that have been used for five years at the Institute of Software Systems. This paper gives some hints to project course personnel, for what kind of useful data could be collected easily. |
12,401 | Please write an abstract with title: Identification of underwater mines from electro-optical imagery using an operated-assisted reinforcement on-line learning, and key words: Filters, Object detection, Image sensors, Image segmentation, Optical imaging, Laser radar, Signal to noise ratio, Optical devices, Learning, Voting. Abstract: This paper presents a new approach for using an operated-assisted reinforcement on-line learning for mine identification from electro-optical images. The images acquired from Streak Tube Imaging Lidar (STIL) that constitute contrast and range maps are used. A reduced set of features using the Zernike moments is extracted from each preprocessed and detected/segmented object. This set is fed to a flexible network which uses a new on-line reinforcement learning based on expert operator's votes. An important feature of this system is that it allows for the incorporation of new objects learning without deleting or modifying the previously learnt cases. The performance of this preliminary in-situ learning system will be demonstrated in this paper on several STIL images and the confusion matrix of the overall system will be presented. |
12,402 | Please write an abstract with title: A comparison of two buffer insertion ring architectures with fairness algorithms, and key words: Throughput, Bandwidth, Traffic control, Optical buffering, Feedback control, Computer architecture, Optical fiber networks, Laboratories, Computer networks, Optical computing. Abstract: Buffer insertion rings (BIR) are known to provide higher throughputs than other competing ring technologies. With the introduction of spatial reuse, MAN's and LAN's are at a greater advantage of maximizing bandwidth efficiency. Spatial reuse introduces the concept of congestion and fairness algorithms are needed to police the fair access of the low priority traffic on the ring. Two architectures are studied in this paper, mono transit buffer (MTB) and the dual transit buffer (DTB). Different from earlier BIR architectures, the congestion control mechanisms studied in this paper are rate based and traffic streams are regulated using leaky buckets. It has been shown through simulations that both architectures exhibit oscillatory behavior under certain congestion conditions. MTB oscillates due to the buffer threshold settings. We show that by correctly setting parameters, oscillations can be dampened to achieve fair throughputs for all nodes contributing to the congestion. |
12,403 | Please write an abstract with title: Matrix microsensor information scanning, and key words: Microsensors, Electrodes, Tactile sensors, Biomembranes, Image resolution, Signal resolution, Information processing, Ceramics, Rubber, Acoustic pulses. Abstract: The problem with tactile sensing in the modern computer systems isn't decided yet. Concrete tactile sensor versions are developed for a specific application. In some papers, we represented the research results for the suggested ferropiezoelectric tactile matrix microsensor. It is applicable for 2D image obtaining. The basic advantage is the high resolution at small number of the sensor pins and direct digital coding of the information. The problems via the increased resolution (Todorova and Mladenov, 2002; Todorova and Mladenov, 2004) impose using of new type electronic interface (Todorova and Mladenov, 2004). In this paper the operating principle and the electronic circuit structure of the suggested interface are considered. |
12,404 | Please write an abstract with title: A high density, low on-resistance p-channel trench lateral power MOSFET for high side switches, and key words: MOSFET circuits, Power MOSFET, Silicon, Switches, Voltage, Wire, Switching converters, Logic devices, Fabrication, Surface resistance. Abstract: A trench lateral power MOSFET (TLPM) has a unique structure. The gate is formed on the trench sidewall, and the drain is connected with metal wire at the bottom of the trench. This structure enables high device density compared to conventional lateral DMOSs. A p-channel TLPM for high side switches has been first integrated. A specific on-resistance of 28 m/spl Omega/ - mm/sup 2/ with a breakdown voltage of 35 V has been realized. This is the best specific on-resistance in this voltage class for a p-channel lateral power MOSFET embedded with logic devices. P-channel, n-channel TLPM devices and a 0.6 /spl mu/m bi-CMOS are fabricated on the same silicon wafer, thus a synchronous switching converter can be implemented on a single chip. |
12,405 | Please write an abstract with title: Efficient Subset Predicate Encryption for Internet of Things, and key words: Access control, Conferences, Encryption, Internet of Things. Abstract: With the rapid development of Internet technologies, emerging network environments have been discussed, such as Internet of Things. In this manuscript, we proposed a novel subset predicate encryption for the access control in Internet of Things. Compared with the existing subset predicate encryption schemes, the proposed scheme enjoy the better efficiency due to the short private key and the efficient decryption procedure. |
12,406 | Please write an abstract with title: Land Suitability Analysis for PV Investments using Geographical Information Systems (GIS): Case of Vjosa Watershed, and key words: Renewable energy sources, Wind energy, Hydroelectric power generation, Europe, Transportation, Solar energy, Rivers. Abstract: The remaining wild watercourses in Europe are threatened by the hydropower plants (HP) as a consequence of the increasing energy demand. In developing countries like the Western Balkans, HP are considered the most comfortable energy investment that have been extensively implemented in the past. However, the social- ecological adverse consequences of these mega-projects are becoming clearer leading to calls for alternative solutions. Current literature shows that innovative technologies for renewable energy harvesting are useful alternatives especially in regions of high social-ecological sensitivity. This is a vital approach that safeguards the endangered pristine watersheds. As an example, Vjosa River is acknowledged among the few remaining unique wild rivers in Europe, and its conservation is highlighted as a priority by international scholars and community. In this context, the aim of this study is to investigate the areas suitable for photovoltaic (PV) investment as an alternative to hydropower projects in the Vjosa River basin in Albania. First, critical socio-ecological characteristics are assigned to specific buffers that define the restricted areas where PV projects must be prohibited. The results show that about 20% of the catchment area (782 km<sup>2</sup>) is available for PV projects. These areas are prioritized based on various criteria of social, geophysical, and environmental aspects. Solar radiation, altitude, slope, orientation, proximity to the transportation network and to the existing electricity grid are the criteria considered in the suitability analysis. The relative influence factor of each criterion is calculated using the Analytical Hierarchy Processing (AHP) pairwise comparison method. The results show that there are significant areas within the basin that are suitable for PV investment. This study can serve as a stimulus for future work investigating other low-impact renewables, including wind energy, as alternatives to hydropower installations, to minimize social-ecological impacts on the remaining wild rivers in Europe and beyond. |
12,407 | Please write an abstract with title: Efficient Computation of SHAP Values for Piecewise-Linear Decision Trees, and key words: Machine learning algorithms, Additives, Computational modeling, Neural networks, Predictive models, Boosting, Prediction algorithms. Abstract: The interpretability of machine learning models is important in many applied data analysis problems. In recent years, a universal SHAP interpretation paradigm based on Shapley values has become popular. However, to interpret the values using SHAP directly, it is necessary to calculate the model’s prediction the number of times that is exponential to the size of the feature space, which leads to the prohibitive complexity of this calculation method for complex models over complex feature spaces, for example, for neural networks or gradient boosting ensembles. It was shown that there are efficient algorithms for calculating SHAP values for decision trees and additive ensembles based on them, using structure of trees for an optimized calculation. One of interesting new classes of machine learning models is piecewise linear decision trees and gradient boosting ensembles based on them. In this paper, an efficient algorithm for computing SHAP values for piecewise linear decision trees and additive ensembles based on them was proposed. |
12,408 | Please write an abstract with title: Low Power Consumption and Intelligent Design of Power Line IoT Edge Nodes, and key words: Schedules, Power demand, Power transmission lines, Processor scheduling, Wires, Solar energy, Software. Abstract: Most of the sensors and edge nodes in the Internet of Things are deployed on the transmission poles and towers or transmission wires. Instead of direct power supply, the equipment is mostly powered by batteries and the solar energy or other energy acquisition methods. As a result, the edge intelligent equipment with large power consumption must take low-power technology as the premise. This paper analyzes the power consumption of transmission line IoT equipment and designs the power consumption model of sensor and edge node equipment. It proposes the power consumption optimization algorithm and operation mode control method, and solves the problem of edge calculation and edge coordination under the constraint of low power consumption by intelligently adjusting and controlling the power consumption. |
12,409 | Please write an abstract with title: Gentle Normalization and Translation in Graph Neural Network for Few-shot Learning, and key words: Learning systems, Graph neural networks, Image restoration, Image classification. Abstract: Few-shot learning methods based on graph neural networks (GNNs) have shown powerful capabilities. However, GNNs have a very intractable problem called oversmoothing. The oversmoothing problem refers to that as the number of GNN layers increases, the node information will converge to a similar value, which is difficult to be distinguished, thus reducing the classification performance. In this paper, a Gentle Normalization and Translation (GNT) model is proposed to solve the above problem. On the basis of the original Normalization method, a Gentle Normalization is presented to solve the oversmoothing problem and reduce the model variance by reducing the scaling range. Further, a Translation operation is developed to deal with the oversmoothing problem caused by the ReLU layer. In addition, the Initial Residual is added which can also solve the oversmoothing problem to a certain extent. The experiments on public datasets show that the classification performance has been improved considerably. |
12,410 | Please write an abstract with title: Identification and Analysis of Phishing Website based on Machine Learning Methods, and key words: Industrial electronics, Machine learning algorithms, Phishing, Web pages, Feature extraction, Resource description framework, Decision trees. Abstract: People are increasingly sharing their details online as internet usage grows. Therefore, fraudsters have access to a massive amount of information and financial activities. The attackers create web pages that seem like reputable sites and transmit the malevolent content to victims to get them to provide subtle information. Prevailing phishing security measures are inadequate for detecting new phishing assaults. To accomplish this aim, objective to meet for this research is to analyses and compare phishing website and legitimate by analyzing the data collected from open-source platforms through a survey. Another objective for this research is to propose a method to detect fake sites using Decision Tree and Random Forest approaches. Microsoft Form has been utilized to carry out the survey with 30 participants. Majority of the participants have poor awareness and phishing attack and does not obverse the features of interface before accessing the search browser. With the data collection, this survey supports the purpose of identifying the best phishing website detection where Decision Tree and Random Forest were trained and tested. In achieving high number of feature importance detection and accuracy rate, the result demonstrates that Random Forest has the best performance in phishing website detection compared to Decision Tree. |
12,411 | Please write an abstract with title: A Survey of Deep Learning based Online Transactions Fraud Detection Systems, and key words: Machine learning, Credit cards, Biological neural networks, Convolutional neural networks, Online banking, Recurrent neural networks. Abstract: With the advancement of technology, today most of the modern commerce is relying upon the online banking and cashless payments. Due to adaption of online payment among businesses, the fraud cases are also increasing which cause financial losses to them. Fraudsters are inventing new techniques to perform fraudulent transaction which seem legitimate. Hence, there is an urgent need to develop fraud detection measures which can deal with these fraudsters on real time basis. Deep learning techniques have the capability to detect these fraudulent transactions efficiently and has a huge scope in fraud detection. However, there are many challenges faced by the researchers in online transactions fraud detection because the datasets are not publicly available due to privacy issue of the financial institutions as customers data is sensitive and it can be misused and the datasets which are available are imbalanced. This paper presents a review of deep learning techniques used for online transactions fraud detection. It also provides the information about datasets used by the researchers and the results achieved by them in their research work. |
12,412 | Please write an abstract with title: Binary vulnerability mining technology based on neural network feature fusion, and key words: Computers, Neural networks, Semantics, Feature extraction, Software, Data models, Data mining. Abstract: The high complexity of software and the diversity of security vulnerabilities have brought severe challenges to the research of software security vulnerabilities Traditional vulnerability mining methods are inefficient and have problems such as high false positives and high false negatives, which can not meet the growing needs of software security. To solve the above problems, this paper proposes a binary vulnerability mining technology based on neural network feature fusion. Firstly, this method constructs binary vulnerability data sets containing multiple vulnerability types, then decompile them to the pcode intermediate language level, and then extracts relevant feature vectors from binary vulnerability data sets according to Bert fine tuning model and bilstm model respectively. In order to fully obtain the semantic information of vulnerabilities, this method standardized the two, fused them, and carried out relevant experiments. The experimental results show that the accuracy of vulnerability detection on SARD data set is 96.92%, which is higher than other binary vulnerability detection methods based on neural network. |
12,413 | Please write an abstract with title: CNN for Breast Cancer Metastases Classification, and key words: Measurement, Image segmentation, Hospitals, Semantics, Neural networks, Computer architecture, Data models. Abstract: We present a Deep Convolutional Neural Network based on VGG16 for the classification of patch images from the benchmark of PatchCamelyon 2016. This dataset consists of 327680 color images from histopathologic scans of lymph node sections. The images were originally acquired at 2 different hospital centers with a 40x objective.The main way to optimize the construction of our model was using a train, validation and test set, which is the standard approach to do it. We made changes to the architecture and its parameters one by one, observing its performance in the validation set in terms of accuracy and average loss.Finally, we could obtain a optimized CNN architecture that performed better than the VGG16 model by itself. Also, the AUĆs obtained in the valid and test set were 0.91 and 0.90 respectively, which shows the neural network was trained in a optimal way so that it did not perform overfitting. The final accuracy for valid was 89% and 86% for test set. This shows that our model is between those in the state of the art in classification and can be used for further harder problems such as semantic segmentation in the actual Camelyon dataset. |
12,414 | Please write an abstract with title: Accurate Semidefinite Relaxation Method for 3-D Rigid Body Localization Using AOA, and key words: Location awareness, Simulation, Signal processing, Minimization, Relaxation methods, Speech processing, Noise level. Abstract: This paper addresses the rigid body localization problem using angle-of-arrival measurements. We formulate the problem as a constrained weighted least squares (CWLS) minimization problem with the rotation matrix and position vector as variables, which is a challenging non-convex problem. To approximately solve this problem, we first relax it as a convex semidefinite program (SDP), and then tighten the relaxed problem by adding some reasonable second-order cone constraints. Simulations show that the tightened SDP problem is able to reach the performance of the original CWLS problem, making its solution achieve the Cramer-Rao lower bound accuracy, when the noise level is not too high. |
12,415 | Please write an abstract with title: Sub-dermal battery-less wireless sensor for the automatic monitoring of cattle fever, and key words: Antennas, Animals, Radiofrequency identification, Cows, Temperature measurement, Antenna measurements, Temperature sensors. Abstract: Fever detection in cattle is today a time-consuming manual procedure. Isolating infected animals to avoid the spread of the diseases is therefore not straightforward with the consequence of massive dose of antibiotics delivered to the whole cattle. This work proposes a subdermal implantable flexible battery-less wireless sensor for un-cooperative monitoring of core temperature in pigs. The sensor consists in a shaped loop antenna sewed into a textile scaffold, as anti-migration support, for a natural integration with the animal fatty tissue. By resorting to the UHF-RFID communication protocol, the device can be interrogated from a distance of up to 1.7 m that is compatible with the automatic temperature reading when the animal approaches the cattle feeder. |
12,416 | Please write an abstract with title: Development of a low-energy proton accelerator system for the Proton Engineering Frontier Project (PEFP), and key words: Proton accelerators, Particle beams, Linear particle accelerator, Radio frequency, Structural beams, Electromagnetic waveguides, Assembly systems, Magnets, Design engineering, Power engineering and energy. Abstract: The development of a low-energy proton accelerator has started as the first phase of the Proton Engineering Frontier Project (PEFP). The low-energy proton accelerator system consists of a 50 keV proton injector, low-energy beam transport (LEBT), 350 MHz, 3 MeV radiofrequency quadrupole (RFQ), 350 MHz, 20 MeV drift-tube linac (DTL), and RF system. The proton injector is under operation, RFQ is testing RF power, and a design of DTL has finished. |
12,417 | Please write an abstract with title: Study on Characteristic Model based Linear Active Disturbance Rejection Controller for a Class of Nonlinear Systems, and key words: Adaptation models, Real-time systems, Complexity theory, Nonlinear systems, Adaptive control, Tuning. Abstract: The control problem of high-order nonlinear systems has always been a research hotspot in the field of control science. Linear active disturbance rejection controller (LADRC) regards the part which is different from the canonical form as the total disturbance, and estimates and compensates in real time, which has good control performance for nonlinear systems. However, for nonlinear systems with high-order modes, it is difficult to tune the parameters of high-order LADRC. It is just that characteristic model can compress the high-order information into the time-varying parameters of the low-order model without loss of high-order information, and LADRC based on low-order characteristic model is more convenient in implementation and parameter tuning. Therefore, in this paper, a first-order characteristic model is established first for a class of nonlinear high-order systems, then forgetting factor recursive least-squares (FFRLS) algorithm is used to obtain the parameters. Finally, based on the identified parameters and the first-order characteristic model, a first-order LADRC is designed. In order to verify the performance of the proposed method, comparative simulation has been done for a class of nonlinear system. The result shows that proposed method has faster tracking ability and stronger disturbance rejection ability than PID and golden section adaptive control (GSAC). |
12,418 | Please write an abstract with title: On Routing Scalability in Flat SDN Architectures, and key words: Tactile Internet, Scalability, Quality of service, Network architecture, Routing, Rigidity, Network operating systems. Abstract: The rigidity of traditional network architectures, with tightly coupled control and data planes, impair their ability to adapt to the dynamic requirements of future application domains, such as the Tactile Internet or Holographic-Type Communications. Software-Defined Networking (SDN) architectures, which provide programmability to configure the network, have the potential to provide the required dynamism. However, given its centralized essence, SDN suffers from scalability issues. Therefore, efforts have been made to propose alternative decentralized solutions, such as the flat distributed SDN architecture. Despite its potential, the real applicability and scalability of decentralized SDN solutions are still open research questions. This paper presents a comparative analysis of the effects of different routing approaches on the scalability of flat distributed SDN architectures. Using the Open Network Operating System (ONOS) as our evaluation architecture, we have studied the tradeoff between routing overhead in the control data plane and inter-controller communications for different degrees of decentralization. We have found that routing applications, which only require control-data plane communication for setting the path, benefit more from decentralization than the ones which utilize inter-controller communications and ensure Quality of Service (QoS). Our findings highlight the need for efficient routing mechanisms to deal with inter-controller overhead while lowering the amount of control-data plane communication. |
12,419 | Please write an abstract with title: Point Proposal Network: Accelerating Point Source Detection Through Deep Learning, and key words: Scalability, Pipelines, Fitting, Detectors, Telescopes, Gaussian distribution, Extraterrestrial measurements. Abstract: Point source detection techniques are used to identify and localise point sources in radio astronomical surveys. With the development of the Square Kilometre Array (SKA) telescope, survey images will see a massive increase in size from Gigapixels to Terapixels. Point source detection has already proven to be a challenge in recent surveys performed by SKA pathfinder telescopes. This paper proposes the Point Proposal Network (PPN): a point source detector that utilises deep convolutional neural networks for fast source detection. Results measured on simulated MeerKAT images show that, although less precise when compared to leading alternative approaches, PPN performs source detection faster and is able to scale to large images, unlike the alternative approaches. |
12,420 | Please write an abstract with title: Offloading Media Traffic to Programmable Data Plane Switches, and key words: Servers, Relays, Media, IP networks, Protocols, Ice, Quality of service. Abstract: According to estimations, approximately 80% of Internet traffic represents media traffic. Much of it is generated by end users communicating with each other (e.g., voice, video sessions). A key element that permits the communication of users that may be behind Network Address Translation (NAT) is the relay server. This paper presents a scheme for offloading media traffic from relay servers to programmable switches. The proposed scheme relies on the capability of a P4 switch with a customized parser to de-encapsulate and process packets carrying media traffic. The switch then applies multiple switch actions over the packets. As these actions are simple and collectively emulate a relay server, the scheme is capable of moving relay functionality to the data plane operating at terabits per second. Performance evaluations show that the proposed scheme not only produces optimal results regarding Quality of Service (QoS) parameters (no packet loss, minimum delay, negligible delay variation, high Mean Opinion Score) but also scales much better than current solutions. Evaluations conducted with up to 35Gbps of media traffic or its equivalent of 400,000 simultaneous G.711 media sessions (limited only by the traffic generator rather than by the switch) show an ideal operation of the switch-based solution (using$\sim \text{l}$% of the switching capacity). In contrast, a relay server with a modern CPU model used for evaluations can process up to 900 simultaneous G.711 media sessions per core. |
12,421 | Please write an abstract with title: Low voltage and low power aspects of data converter design, and key words: Low voltage, CMOS technology, Integrated circuit technology, CMOS analog integrated circuits, Analog integrated circuits, Transistors, Threshold voltage, Laboratories, Feedback circuits, Circuit stability. Abstract: Low voltage design is becoming an important issue for analogue circuits expected to operate at around 1 V supply voltage in sub-100 nm CMOS technologies. This contribution discusses the impact of low voltage on circuit architecture, opamp configuration to maintain speed and DC gain, common-mode feedback and its stability, switching speed, as well as trends in achievable signal to noise ratio and speed for a given power consumption. |
12,422 | Please write an abstract with title: A Joint Framework of Denoising Autoencoder and Generative Vocoder for Monaural Speech Enhancement, and key words: Speech enhancement, Vocoders, Noise measurement, Noise reduction, Spectrogram, Couplings. Abstract: Conventional monaural speech enhancement methods usually enhance the magnitude spectrum of noisy speech and leave the phase unchanged. Recent studies suggest that phase is also important for both speech intelligibility and perceptual quality. Although deep learning exhibits great potential on enhancing the magnitude and phase spectra in complex spectrogram domain and waveform domain, complex spectrogram and waveform are always more difficult to predict than the magnitude spectrum due to lack of clear structure in them. In this study, a Mel-domain denoising autoencoder and a deep generative vocoder are stacked to form a joint framework for monaural speech enhancement, in which the clean speech waveform is reconstructed without using the phase. Specifically, a convolutional recurrent network (CRN) is employed as the denoising autoencoder to enhance the Mel power spectrum of noisy speech. Then, the enhanced Mel power spectrum is fed to a deep generative vocoder to synthesize the speech waveform. Furthermore, the denoising autoencoder and generative vocoder are jointly fine-tuned. Experimental results show that the proposed method significantly improves speech intelligibility and perceptual quality. More importantly, our method achieves much better generalization ability for untrained noises than previous methods. |
12,423 | Please write an abstract with title: Fundamental Fault Detection Limitations in Linear Non-Gaussian Systems, and key words: Fault detection, Maximum likelihood estimation, Filtering, Colored noise, Testing, Electrical fault detection, Automatic control, Performance analysis, Vectors, Maximum likelihood detection. Abstract: Sophisticated fault detection (FD) algorithms often include nonlinear mappings of observed data to fault decisions, and simulation studies are used to support the methods. Objective statistically supported performance analysis of FD algorithms is only possible for some special cases, including linear Gaussian models. The goal here is to derive general statistical performance bounds for any FD algorithm, given a non-linear non-Gaussian model of the system. Recent advances in numerical algorithms for nonlinear filtering indicate that such bounds in many practical cases are attainable. This paper focuses on linear non-Gaussian models. A couple of different fault detection setups based on parity space and Kalman filter approaches are considered, where the fault enters a computable residual linearly. For this class of systems, fault detection can be based on the best linear unbiased estimate (BLUE) of the fault vector. Alternatively, a nonlinear filter can potentially compute the maximum likelihood (ML) state estimate, whose performance is bounded by the Cramér-Rao lower bound (CRLB). The contribution in this paper is general expressions for the CRLB for this class of systems, interpreted in terms of fault detectability. The analysis is exemplified for a case with measurements affected by outliers. |
12,424 | Please write an abstract with title: Optical Camera Communications with Convolutional Neural Network for Vehicle-toVehicle Links, and key words: Training, Cameras, Optical receivers, Optical transmitters, Optical signal processing, Signal detection, MIMO communication. Abstract: This paper describes a vehicle-to-vehicle (V2V) communication system, employing optical camera communications (OCC). The system comprises the light emitting diode (LED)-based taillights and a raspberry camera used as the transmitter (Tx) and the receiver (Rx), respectively. The sectorized taillights (i.e., Tx) are intensity modulated at different frequencies, and a convolutional neural network (CNN) at the Rx is used for scene analysis, the region of interest (RoI) selection, and symbol detection. Results show that, the system data rates are constrained by the camera frame rate and symbol duration. The link performance is dependent on the CNN training set and we show that, the use of CNN allows a robust implementation, able to provide response under multiple situations: taillight obstruction, variable link distances, and misaligned Tx-Rx. Furthermore, CNN enables multiple input multiple output (MIMO) signal detection without the need for dedicated training. |
12,425 | Please write an abstract with title: Individually addressed high-power diode-laser-bars for material processing, and key words: Diodes, Materials processing, Printing, Geometry, Electronics industry, Soldering, Electronic components, Rapid thermal processing, Crosstalk, Prototypes. Abstract: In order to widen the applications of high-power diode-laser-bars in the fields of micro-system technology and printing industry (e.g. soldering electronic components or rapid-prototyping) the intensity profile must be adjusted to the work piece's respective geometry. In this presentation an estimation of the thermal and electrical crosstalk for different geometries of high-power diode-laser-bars is discussed. Different methods to increase the fill factor are shown. At the conclusion three prototypes with 19 up to 48 individually addressable high-power diode-laser-bars used in the field of micro-system technology and printing industry are presented. |
12,426 | Please write an abstract with title: Local-Global-Aware Convolutional Transformer for Hyperspectral Image Classification, and key words: Adaptation models, Convolution, Smart cities, Computational modeling, Transformers, Feature extraction, Natural language processing. Abstract: In recent years, more and more end-to-end deep learning methods have been applied to remote sensing image classification. Convolutional neural networks (CNN) is the most representative one. However, the convolution kernel that makes CNN successful also limits its ability of global perception due to its fixed size. Transformer, as an emerging model, has achieved great success in both computer vision and natural language processing (NLP). Without any convolution module, Transformer is almost entirely composed of Attention mechanism that brings it context-aware ability and makes computational parallelization easier. Therefore, this paper proposes a two-branch convolutional Transformer network, which combines the local feature extraction capabilities of CNN and context-aware capabilities of Transformer to get a better feature representation while modeling dual-path long-range dependence. In addition, we flexibly combine 1D convolutional with linear projection to realize feature embedding while fusing convolutional features. The comparative experiments results on the public dataset confirm the effectiveness of our method, and the ablation experiment proves the necessity of our module and network design. |
12,427 | Please write an abstract with title: Average current-mode control for a boost converter using an 8-bit microcontroller, and key words: Microcontrollers, DC-DC power converters, Inductors, Voltage control, Helium, Control systems, Switches, Digital signal processing, Student members, USA Councils. Abstract: This paper presents hybrid average current-mode control (ACMC) for a boost converter using an 8-bit microcontroller. In order to demonstrate the feasibility of a hybrid ACMC DC-DC converter, an ACMC boost converter operating in the continuous conduction mode has been designed using a PIC16C782 microcontroller. System modeling, main design procedures, as well as some hardware and software implementation issues are discussed. Experimental results are presented, and encouragingly demonstrate the performance that a hybrid ACMC DC-DC converter can achieve. A pure analog controller using a UC3K86 analog control chip from Texas Instruments has also been implemented for comparison. |
12,428 | Please write an abstract with title: Fourth-Order Buck-Boost Converters Exhibiting Minimum Phase by Reverse Inductor Coupling, and key words: Couplings, Inductors, Topology, Transfer functions, Steady-state, Simulation, Transistors. Abstract: Three fourth-order buck-boost converters with improved dynamic response are proposed. The improved dynamic response is achieved starting from the uncoupled inductors topologies and performing reverse coupling. This leads to the shift of the right half plane (RHP) zeros to the left half plane (LHP). The state-space small signal model is developed and the control-to-output transfer functions are derived. A comparison is made to the uncoupled and direct coupled topologies revealing the RHP zeros elimination. The simulations confirm that also with reverse coupling the input and output currents still maintain their smooth nature. |
12,429 | Please write an abstract with title: Feature Selection Using Batch-Wise Attenuation and Feature Mask Normalization, and key words: Deep learning, Data analysis, Neural networks, Memory management, Feature extraction, Attenuation, Complexity theory. Abstract: Feature selection is generally used as one of the most important preprocessing techniques in machine learning, as it helps to reduce the dimensionality of data and assists researchers and practitioners in understanding data. Thereby, by utilizing feature selection, better performance and reduced computational consumption, memory complexity and even data amount can be expected. Although there exist approaches leveraging the power of deep neural networks to carry out feature selection, many of them often suffer from sensitive hyperparameters. This paper proposes a feature mask module (FM-module) for feature selection based on a novel batch-wise attenuation and feature mask normalization. The proposed method is almost free from hyperparameters and can be easily integrated into common neural networks as an embedded feature selection method. Experiments on popular image, text and speech datasets have shown that our approach is easy to use and has superior performance in comparison with other state-of-the-art deep-learning-based feature selection methods. |
12,430 | Please write an abstract with title: Performance Evaluation of Isolated Three-Phase Differential Flyback Inverter with Ripple-Free Input Current for Grid-tied Applications, and key words: Performance evaluation, Switching frequency, Capacitors, Switches, Harmonic analysis, Inverters, Reliability. Abstract: DC-AC inverters with voltage boosting capability are widely utilized in many applications for power conversion requirements especially for low/medium PV applications. In this paper, performance evaluation and parameters design of the isolated three-phase differential flyback inverter (TDFI) is presented. The proposed TDFI draws a ripple-free input DC current considering a small input film capacitor, which mitigate the requirement for bulky electrolytic capacitor at the input DC side and enhances its reliability and lifetime. In addition, the TDFI offers number of merits such as; reduced passive and switching components, compact size, voltage boosting-bucking property, and enhanced footprint. Moreover, cascaded low-pass filter (LPF) is used for second-order harmonic elimination (SOHE). The proposed SOHE strategy improves the grid currents THD to meet the harmonic standard limits. In addition, SOHE eliminates the third order harmonic component from the input current, which allows a ripple-free input DC current for grid integrated solar PV applications. The single carrier based control scheme of the proposed TDFI has been experimentally verified at 1.6 kW, 200 W, and 50 kHz switching frequency. |
12,431 | Please write an abstract with title: The not-so-private party: As the public web marks its 30th anniversary, has our data now become too public? Can lives be private in the face of a massively expanded web of devices? capital photos / philips elect, and key words: Companies, Solids, Intelligent sensors, Databases, Web sites, Videos, TV. Abstract: THOUGH we are meant to be celebrating 30 years since the birth of the World Wide Web, the kind of web we have today still has a couple of years to go before its thirtieth birthday. The version proposed by Tim Berners-Lee at the end of the 1980s focused mainly on text organised through hyperlinks. The web's inventor came up with the idea of the far more general universal resource identifier (URI) in June 1994, just at the point that the public was gaining access to the internet. |
12,432 | Please write an abstract with title: Expedited Amplitude and Phase Tolerance Analysis of Reflector Antenna Systems With Vector Spherical Waves, and key words: Antenna radiation patterns, Antenna feeds, Reflector antennas, Method of moments, Geometry, Finite element analysis, Correlation. Abstract: This work presents the relevant advantages of analyzing a complete reflector antenna system based on: 1) the expansion of the feed radiated field in terms of vector spherical waves (VSWs) and 2) the characterization of the reflector domain with VSWs. It requires that the reflector be initially analyzed under the illumination of each single VSW. The output of each of these analyses is the radiated field of the reflector for each VSW excitation. Then, the accurate response of the complete antenna system, both in amplitude and phase, can be directly obtained by just linear combination of these individual-VSW-excited radiated fields, weighted by the feed transmission vector that relates the coefficients of the VSW expansions for the feeder with its input excitation mode. Thanks to the orthogonality of the VSWs, and the applicability of the far-field approximation, it turns into a very efficient approach for the whole end-to-end analysis, adding useful capabilities and flexibility for the expedited assessment of tolerances. |
12,433 | Please write an abstract with title: An Efficient Secure Coded Edge Computing Scheme Using Orthogonal Vector, and key words: Encoding, Edge computing, Decoding, Complexity theory, Computer science, Distributed databases, Computational modeling. Abstract: In recent years, Edge Computing (EC) has attracted increasing attention for its advantages in handling latencysensitive and compute-intensive applications. It is becoming a widespread solution to solve the last mile problem of cloud computing. However, in actual EC deployments, data confidentiality becomes an unignorable issue because edge devices may be untrusted. In this paper, a secure and efficient edge computing scheme based on linear coding is proposed. Generally, linear coding can be utilized to achieve data confidentiality by encoding random blocks with original data blocks before they are distributed to unreliable edge nodes. However, the addition of a large amount of irrelevant random blocks also brings great communication overhead and high decoding complexities. In this paper, we focus on the design of secure coded edge computing using orthogonal vector to protect the information theoretic security of the data matrix stored on edge nodes and the input matrix uploaded by the user device, while to further reduce the communication overhead and decoding complexities. In recent years, Edge Computing (EC) has attracted increasing attention for its advantages in handling latencysensitive and compute-intensive applications. It is becoming a widespread solution to solve the last mile problem of cloud computing. However, in actual EC deployments, data confidentiality becomes an unignorable issue because edge devices may be untrusted. In this paper, a secure and efficient edge computing scheme based on linear coding is proposed. Generally, linear coding can be utilized to achieve data confidentiality by encoding random blocks with original data blocks before they are distributed to unreliable edge nodes. However, the addition of a large amount of irrelevant random blocks also brings great communication overhead and high decoding complexities. In this paper, we focus on the design of secure coded edge computing using orthogonal vector to protect the information theoretic security of the data matrix stored on edge nodes and the input matrix uploaded by the user device, while to further reduce the communication overhead and decoding complexities. |
12,434 | Please write an abstract with title: Broad-band internally and externally matched lumped element symmetrical 5-ports, and key words: Bandwidth, Admittance, Coupling circuits, Transformers, Equations, Inductors, Capacitors, Lead, Shunt (electrical), Circuits and systems. Abstract: Some new broad-band internally matched lumped element 5-port networks are proposed. In one case different lumped element circuits connect adjacent and nonadjacent ports. In another version, with a 20/1 bandwidth, ideal transformers are required. It is shown how the bandwidth of these lumped element circuits may be further increased by including external matching. |
12,435 | Please write an abstract with title: Minimizing congestion in general networks, and key words: Intelligent networks, Routing, Network topology, Multiprocessor interconnection networks, Bandwidth, Computer science, Mathematics, Application software, Circuit topology, Workstations. Abstract: A principle task in parallel and distributed systems is to reduce the communication load in the interconnection network, as this is usually the major bottleneck for the performance of distributed applications. We introduce a framework for solving online problems that aim to minimize the congestion (i.e. the maximum load of a network link) in general topology networks. We apply this framework to the problem of online routing of virtual circuits and to a dynamic data management problem. For both scenarios we achieve a competitive ratio of O(log/sup 3/ n) with respect to the congestion of the network links. Our online algorithm for the routing problem has the remarkable property that it is oblivious, i.e., the path chosen for a virtual circuit is independent of the current network load. Oblivious routing strategies can easily be implemented in distributed environments and have therefore been intensively studied for certain network topologies as e.g. meshes, tori and hypercubic networks. This is the first oblivious path selection algorithm that achieves a polylogarithmic competitive ratio in general networks. |
12,436 | Please write an abstract with title: Application of Community Detection Algorithms on Social Internet-of-things Networks, and key words: Detection algorithms, Sensors, Complexity theory, Internet, Smart phones, Quality of service, Quality of experience. Abstract: The Internet-of-things (IoT) networks are witnessing a drastic increase over the years. Twenty billion devices connected to the Internet are expected in 2022. The need for identifying communities within such networks can serve as a strong complexity reduction mean for many discovery and identification services. The idea of communities in IoT networks is also motivated by the emerging concept of socializing IoT devices. In this paper, we investigate the application of two community detection algorithms, namely Louvain and Bron-Kerbosch algorithms, on IoT networks usually represented by large-scale graphs. The objective is to convert the complex IoT network into multiple overlapping and non-overlapping communities where its elements share common characteristics. Starting from a real-world IoT networks, we use its dataset to extract community-structured IoT network based on different types of relationships such as co-location, owner social relationships, and autonomously build object relationships among objects. Our analysis showcases how community detection algorithms structure the IoT network into communities based on the different relationships established between objects. |
12,437 | Please write an abstract with title: Widely Tunable O-band Lithium Niobite/III-V Hybrid Laser, and key words: Reflectivity, Couplings, Power lasers, Lithium, Laser excitation, Transceivers, Reflection. Abstract: We demonstrate an electrically pumped widely tunable lithium Niobite/III-V hybrid laser offering over 40 nm tuning range in the O-band, and a maximum output power of 5.2 mW. |
12,438 | Please write an abstract with title: Comparing methods of calculating voltage drop in low voltage feeders, and key words: Load modeling, Circuit breakers, Diversity methods, Monte Carlo methods, Indexes, Conductors, Low voltage. Abstract: A generalised model of loads and networks is used to assess the accuracy of several different algorithms for calculation of voltage drop in low voltage feeders. The load models include single parameter, Normal and Beta distributions. Benchmark networks include simple and branched feeders with balanced and unbalanced three phase loading, as well as a single phase feeder. The results of the different algorithms are compared with a base case Monte Carlo simulation. The Herman Beta algorithm is found to be the most reliable. The other algorithms tested give large errors of estimate of the voltage drop. |
12,439 | Please write an abstract with title: Stylized-Colorization for Line Arts, and key words: Art, Image color analysis, Face recognition, Training data, Generators, Data models, Task analysis. Abstract: We address a novel problem of stylized-colorization which colorizes a given line art using a given coloring style in text. This problem can be stated as multi-domain image translation and is more challenging than the current colorization problem because it requires not only capturing the illustration distribution but also satisfying the required coloring styles specific to anime such as lightness, shading, or saturation. We propose a GAN-based end-to-end model for stylized-colorization where the model has one generator and two discriminators. Our generator is based on the U-Net architecture and receives a pair of a line art and a coloring style in text as its input to produce a stylized-colorization image of the line art. Two discriminators, on the other hand, share weights at early layers to judge the stylized-colorization image in two different aspects: one for color and one for style. One generator and two discriminators are jointly trained in an adversarial and end-to-end manner. Extensive experiments demonstrate the effectiveness of our proposed model. |
12,440 | Please write an abstract with title: A Fast Jamming Waveform Design Method for Deployment in Precision Electronic Warfare Scenario, and key words: Technological innovation, Design methodology, Electromagnetic scattering, Jamming, Covariance matrices, Electromagnetics, Distributed algorithms. Abstract: Precision electronic warfare (PREW) is a focused energy delivery application that has been recently developed to address a number of issues associated with present electronic warfare approaches. However, current efforts to design jamming waveforms appropriate to the ultra-sparse and distributed arrays employed in PREW scenarios have had limited success. This paper addresses the issue by proposing a cyclic algorithm for the first time to facilitate the rapid design of constant-modulus jamming waveforms suitable for deployment in PREW scenarios. The jamming effects of the waveforms generated by the proposed algorithm are analyzed via numerical computations under a standard PREW scenario. The proposed algorithm is demonstrated to provide reasonable constant-modulus jamming waveforms suitable for deployment in PREW scenarios in a relatively short time. |
12,441 | Please write an abstract with title: A Physical-Based Algorithm for Retrieving Land Surface Temperature From Moon-Based Earth Observation, and key words: Microwave radiometry, Microwave theory and techniques, Microwave imaging, Earth, Land surface temperature, Brightness temperature, Electromagnetic heating. Abstract: Land surface temperature (LST) is a key parameter and plays an important role in hydrology, ecology, environment, and biogeochemistry. It is difficult for the existing satellites to acquire global LST with spatial and temporal consistency. The Moon-based Earth observation platform with a long life, large coverage can observe continuously the Earth, and obtain the global-scale LST. At present, various approaches for retrieving LST from passive microwave remote sensing data have been developed for the satellite remote sensing data with small and constant viewing zenith angle, however, the Moon-based Earth observation platform located outside the Earth's ionosphere has the viewing zenith angle of 0-90°. In this study, a modified physical-based method of LST retrieval from passive microwave data was developed for wide viewing zenith angles, and the LST and emissivity can be simultaneously estimated through the analysis of various atmospheric and ionosphere parameters. Three types of data, including the FengYun-3B satellite microwave radiation imager data, the multichannel Advanced Microwave Scanning Radiometer data, and Moon-based microwave radiation simulation data with the viewing zenith angles of 52-53°, 55°, and 0-90°, respectively, were used to retrieve LST. Results show that the estimation accuracy of LST decreases with the increase of viewing zenith angle. The 23.8 and 36.5 GHz brightness temperature is optimum for the LST estimation under a large-scale viewing zenith angle, and the root mean square errors of the LST are 5.18, 5.44, and 4.79 K, respectively. |
12,442 | Please write an abstract with title: Ultrastructural features of poorly differentiated hepatocellular carcinoma, and key words: Pathology, Sensitivity, Transmission electron microscopy, Microscopy, Conferences, Genomics, Mice. Abstract: Hepatocellular carcinoma (HCC) is a highly heterogeneous malignant neoplasm, which is characterized by morphological heterogeneity. Different subclones within the tumor can exhibit unique clonal phenotypes and show various sensitivity to the chemotherapy. This study aimed to examine the ultrastructure of poor differentiated HCC and identify morphological HCC heterogeneity on subcellular level. Intracellular components and tumor microvasculature were assessed in HCC-bearing mice. Rough endoplasmic reticulum area, mitochondria, autophagy-related structures and nuclearcytoplasmic ratio were measured by transmission electron microscopy. Our results suggest the heterogeneity of the basic cellular structures of poorly-differentiated HCC at the ultrastructural level. Furthermore, the variable nuclearcytoplasmic ratio of HCC cells and abundant autophagy-related structures were revealed. The observed phenotypic changes in the HCC microvasculature included presence of atypical "mother" blood vessels, sinusoidal capillarization and patterned microcirculation. We presented the ultrastructural basis for the formation of significant pathological events in the poorly differentiated HCC. The data confirmed the presence of vasculogenic mimicry in a poorly differentiated type of HCC. |
12,443 | Please write an abstract with title: FerroCoin: Ferroelectric Tunnel Junction-Based True Random Number Generator, and key words: Switches, Voltage, Entropy, Junctions, Generators, Power demand, Nickel. Abstract: In this paper, we propose a Ferroelectric Tunnel Junction (FTJ)-based true random number generator (TRNG) that utilizes the stochastic domain switching phenomenon in ferroelectric materials. Ferroelectrics are promising for extracting randomness owing to their innate switching entropy in the multi-domain state. The random numbers generated by the proposed TRNG are shown to pass all the NIST SP 800-22 tests. The robustness of the proposed TRNG is also validated at various temperature and process corners. Important metrics such as power, bit rate, and energy/bit are calculated. This is the first comprehensive work demonstrating a ferroelectric-based TRNG with all these metrics. Compared to state-of-the-art TRNGs using other emerging technologies, we can achieve a higher bit rate with lower power consumption. We also perform material-level optimization with different ferroelectric materials, and showcase the trade-off between the bit rate and the power consumption. The proposed TRNG shows high robustness and reliability, and thus has the potential for implementing a low power on-chip solution. |
12,444 | Please write an abstract with title: REPFINDER: Finding Replacements for Missing APIs in Library Update, and key words: Software maintenance, Codes, Libraries, Task analysis, Software engineering. Abstract: Libraries are widely adopted in developing software projects. Library APIs are often missing during library evolution as library developers may deprecate, remove or refactor APIs. As a result, client developers have to manually find replacement APIs for missing APIs when updating library versions in their projects, which is a difficult and expensive software maintenance task. One of the key limitations of the existing automated approaches is that they usually consider the library itself as the single source to find replacement APIs, which heavily limits their accuracy.In this paper, we first present an empirical study to understand characteristics about missing APIs and their replacements. Specifically, we quantify the prevalence of missing APIs, and summarize the knowledge sources where the replacements are found, and the code change and mapping cardinality between missing APIs and their replacements. Then, inspired by the insights from our study, we propose a heuristic-based approach, REPFINDER, to automatically find replacements for missing APIs in library update. We design and combine a set of heuristics to hierarchically search three sources (deprecation message, own library, and external library) for finding replacements. Our evaluation has demonstrated that REPFINDER can find replacement APIs effectively and efficiently, and significantly outperform the state-of-the-art approaches. |
12,445 | Please write an abstract with title: Pricing-Based Distributed Control of Fast EV Charging Stations Operating Under Cold Weather, and key words: Batteries, Meteorology, Electric vehicle charging, Pricing, Charging stations, Vehicles, Safety. Abstract: As the electric vehicle (EV) adoption rates increase, there is a pressing need for designing charging stations that account for heterogeneity (e.g., charging rates and duration) in EV demand. Battery temperature is a major factor determining the maximum charging rates as the EV charging power is limited to ensure the safety of batteries. As a consequence, each vehicle receives a different charging rate, and the station can be modeled as a multiclass queuing facility. As the coverage of such networks grows, the power network elements become more congested, and controlling the charging demand is needed to avoid overloading. This article models a network of fast chargers as a multidimensional loss system and proposes an EV load control framework that leverages pricing dynamics to keep the aggregate demand below station capacity with minimal loss of load (outage) events. The global problem is formulated to maximize the social welfare of all users, and optimal arrival rates are calculated in a distributed manner. The mathematical analysis further shows how to induce a socially optimal charging behavior through the computation of congestion prices. The results show that class-specific prices provide fairness to EVs with colder batteries, as they receive slower service. |
12,446 | Please write an abstract with title: Encrypted Cloud-Based Set-Theoretic Model Predictive Control, and key words: Cryptography, Cloud computing, Actuators, Privacy, Networked control systems, Predictive control, Homomorphic encryption. Abstract: We propose an encrypted set-theoretic model predictive control (ST-MPC) strategy for cloud-based networked control systems. In particular, we consider a scenario where the plant is subject to state and input constraints, and a curious but honest cloud provider is available to implement the control logic remotely. We address the inherent privacy issue by jointly using an additive homomorphic cryptosystem and a modified version of the ST-MPC algorithm, which is tailored to run on encrypted data. We show that, by leveraging a family of zonotopic inner approximations of robust one-step controllable sets and a half-space projection algorithm, we can design the unavoidable computational load on the smart actuator’s side to be real-time affordable by the available hardware compared to other existing solutions. A simulation experiment, considering a two-tank water system, is presented to verify the effectiveness of the proposed approach. |
12,447 | Please write an abstract with title: Application of distance learning in professional development, and key words: Computer aided instruction, Information science, Adaptive systems, Knowledge acquisition, Mathematical models, Communications technology, Planning. Abstract: The article examines the essence of distance learning, its state at the present stage, both in Uzbekistan and abroad. The advantages and disadvantages of this knowledge acquisition system are discussed, and the results of its application are analyzed. |
12,448 | Please write an abstract with title: A Few Milliseconds-Fast SRS-Induced Loss and Tilt Compensation Algorithm for Dynamic C+L-band Networks, and key words: Optical losses, Stimulated emission, Heuristic algorithms, Sociology, Raman scattering, Europe, Optical fiber networks. Abstract: We demostrate a ~20 milliseconds fast algorithm implemented in amplifiers for compensation of Stimulated Raman Scattering (SRS) induced loss and tilt in dynamic C+L-band networks. Simulation and lab results matche closely and thus, verify the algorithm. |
12,449 | Please write an abstract with title: Efficient collection of sensor data in remote fields using mobile collectors, and key words: Delay, Base stations, Postal services, Routing, Buffer storage, Mobile computing, Automotive engineering, Wireless communication, Vehicles, Volcanoes. Abstract: This paper proposes using a mobile collector, such as an airplane or a vehicle, to collect sensor data from remote fields. We present three different schedules for the collector: round-robin, rate-based, and min movement. The data are not immediately transmitted to the base station after being sensed but buffered at a cluster head; hence, it is important to ensure the latency is within an acceptable range. We compare the latency and the energy expended of the three schedules. We use the ns-2 network simulator to study the scenarios and illustrate conditions under which rate-based outperforms round-robin in latency, and vice-versa. The benefit of min movement is in minimizing the energy expended. |
12,450 | Please write an abstract with title: Efficient Traffic Routing Method at Busy Intersections, and key words: Trajectory, Delays, Scheduling algorithms, Measurement, Time complexity, System recovery, Routing. Abstract: Autonomous traffic control is always a challenging task due to improper resource management. This paper presents the theoretical aspects of a reservation based scheduling algorithm for smart traffic management. The proposed approach can be applied in any semi-autonomous environment for its collision avoidance, delay minimizing, congestion reduction, starvation free, deadlock preventing and time efficient nature. The method involves reservation based scheduling algorithm of traffic streams which decides it's next traffic movement based on the optimization of delay and chances of a collision. Experimental results show that the maximum value of the sum of estimated heuristic delay and vehicle-vehicle collision probability are decreased by 62.5% and 70% respectively, compared to other approaches. |
12,451 | Please write an abstract with title: Model Compression for Communication Efficient Federated Learning, and key words: Training, Servers, Data models, Computational modeling, Artificial neural networks, Collaborative work, Standards. Abstract: Despite the many advantages of using deep neural networks over shallow networks in various machine learning tasks, their effectiveness is compromised in a federated learning setting due to large storage sizes and high computational resource requirements for training. A large model size can potentially require infeasible amounts of data to be transmitted between the server and clients for training. To address these issues, we investigate the traditional and novel compression techniques to construct sparse models from dense networks whose storage and bandwidth requirements are significantly lower. We do this by separately considering compression techniques for the server model to address downstream communication and the client models to address upstream communication. Both of these play a crucial role in developing and maintaining sparsity across communication cycles. We empirically demonstrate the efficacy of the proposed schemes by testing their performance on standard datasets and verify that they outperform various state-of-the-art baseline schemes in terms of accuracy and communication volume. |
12,452 | Please write an abstract with title: P1857.12/D2, Jul 2022 - IEEE Draft Standard for Smart Media Transport, and key words: IEEE Standards, Data models, Media, Data compression. Abstract: An IP-based media protocol over both broadcasting networks and broadband networks, including data modeling, data transmission methods, signaling messages, and media presentation mechanisms is defined in this standard. |
12,453 | Please write an abstract with title: Adversarial Malware Examples for Terminal Cyberspace Attack Analysis in Cyber-Physical Power Systems, and key words: Codes, Semantics, Cyberspace, Detectors, Malware, Libraries, Robustness. Abstract: With the introduction of advanced information technology of Cyber-physical Power System (CPPS), the information exchange between CPPS and the outside is increasingly inevitable and frequent while attackers have rising motivation to attack CPPS terminals especially through malware. The existing detectors against malware mainly detect files at the static level, of which the key lies in the malware signature library. The detectors can efficiently identify the known viruses whose signatures have been included in the library, while for new or mutated malware, such technologies often do not play a good role. Meanwhile, the weak anti-disturbance and robustness make the detectors easy to suffer adversarial attacks. Therefore, an effective adversarial example generation technology capable of improving the detection ability of CPPS terminals is necessary. This paper proposes two code obfuscation approaches as well as their combination, which are able to obfuscate the codes of malware while keeping the semantics consistent. The approaches aim to extend the obfuscation coverage and alter the static characteristic of malware, finally to generate adversarial examples which can mislead malware detectors so as to finally upgrade detection technologies. We evaluated our generated adversarial examples using four different types of commercial detectors. The results confirm that generated adversarial examples are capable of bypassing the commercial malware detectors at the static level, especially for the detectors based on feature matching and heuristic detection. |
12,454 | Please write an abstract with title: Adaptive Traffic Engineering Based on Active Network Measurement Towards Software Defined Internet of Vehicles, and key words: Network architecture, Reliability, Computer architecture, Routing, Monitoring, Adaptive systems. Abstract: With the rapid development of urbanization, enormous amounts of vehicular services have been emerging and challenge both the architectures and protocols of the Internet of Vehicles. The high-speed mobility features of nodes in the vehicular networks changes the network topology frequently, resulting in low routing efficiency, and higher packet loss. In this article, we utilize software-defined networking (SDN) technology to decouple the network control plane from the data forwarding plane, and divide the vehicular networks into three functional layers: data, control, application layers. Based on the proposed network architecture, we propose an adaptive traffic engineering (TE) mechanism to guarantee the V2V continuous traffic in vehicular networks with high-speed mobile vehicles or dynamic network topology. In particular, the proposed TE is based on a proposed active network measurement mechanism under the assistance of the centralized management ability of the SDN technique. The proposed active network measurement approach is a greedy approach where the next hop determination for the measurement packet takes multiple link reliability factors (e.g., the delay, the length, the packet error rate, the neighbors, etc.) into account. Then, we utilize the artificial bee colony (ABC) algorithm to optimize the TE mechanism that can be deployed and executed in the SDN controller. By the proposed TE mechanism, multiple candidate end-to-end paths can be concurrently measured, and the optimal data forwarding path can be adaptively switched. Simulation results demonstrate that our approach performs better than some recent research outcomes, especially in the aspect of performing reliable data forwarding (almost 5% better than the compared objects). |
12,455 | Please write an abstract with title: On the Design of Optimal and Robust Supervisors for Deterministic Finite State Automata, and key words: Robustness, Automata, Robust control, Optimal control, Automatic control, Algorithm design and analysis, Frequency estimation, Frequency measurement, Uncertainty, Sufficient conditions. Abstract: In this paper, the problem of optimal and robust controller design for finite state automata is addressed. The approach presented is based on the language measure introduced in Wang and Ray. However, it differs from previous approaches to optimal controller design by using a new definition of the performance of the supervised automaton. This new definition is, in our opinion, more appropriate in cases where the performance weights are related to the relative frequency of the events. |
12,456 | Please write an abstract with title: Asymptotic robust Neyman-Pearson hypothesis testing based on moment classes, and key words: Robustness, Testing, Uncertainty, Probability distribution, Constraint optimization. Abstract: A robust hypothesis testing framework is introduced in which candidate hypotheses are characterized by moment classes. It is shown that there exists a test sequence that is asymptotically optimal in the min-max sense, and that it is expressed as a comparison of a log-linear combination of the constraint functions to a predetermined threshold. |
12,457 | Please write an abstract with title: A Fault Detection and Reconfigurable Control Architecture for Unmanned Aerial Vehicles, and key words: Fault detection, Unmanned aerial vehicles, Aerospace control, Aircraft, Vehicle detection, Actuators, Aerospace simulation, Fault tolerance, Fault diagnosis, Adaptive systems. Abstract: The past decade has seen the development of several reconfigurable flight control strategies for unmanned aerial vehicles. Although the majority of the research is dedicated to fixed wing vehicles, simulation results do support the application of reconfigurable flight control to unmanned rotorcraft. This paper develops a fault tolerant control architecture that couples techniques for fault detection and identification with reconfigurable flight control to augment the reliability and autonomy of an unmanned aerial vehicle. The architecture is applicable to fixed and rotary wing aircraft. An adaptive neural network feedback linearization technique is employed to stabilize the vehicle after the detection of a fault. Actual flight test results support the validity of the approach on an unmanned helicopter. The fault tolerant control architecture recovers aircraft performance after the occurrence of four different faults in the flight control system: three swash-plate actuator faults and a collective actuator fault. All of these faults are catastrophic under nominal conditions. |
12,458 | Please write an abstract with title: Performance Limits for Fingerprinting-Based Indoor Optical Communication Positioning Systems Exploiting Multipath Reflections, and key words: Optical transmitters, Visible light communication, Optical receivers, Optical pulses, Optical signal processing, Fingerprint recognition. Abstract: Multipath reflection degrades the performance of visible light communications (VLC) based localization systems, where it is often considered as a strong random noise. However, due to the inherent transmission features of light, the optical wireless indoor channel is static; therefore, multipath components can be modeled as deterministic functions of the transceiver location, furnishings, and room geometry. In this paper, we investigate the performance limits of fingerprinting-based localization with multipath reflection as a source of information, i.e., a fingerprinting map. Limits on the localization accuracy are determined using the Cramer-Rao lower bound (CRLB) for different numbers of photodetectors deployed in the system and received signal features captured. The tightness of the analytical CRLB is tested by comparing it to the performance of a fingerprint-based positioning algorithm that uses the nearest neighbor method. Simulation results show an achievable root mean squared positioning accuracy of 45 cm and 5 cm (for one and four photodetectors, respectively), for an empty room. We then investigate the practical limitations on localization accuracy caused by a narrow transceiver bandwidth. Numerical results show that the localization system can still achieve decimeter accuracy for system bandwidths of 200 MHz, which makes fingerprinting schemes practical for off-the-shelf infrared devices. |
12,459 | Please write an abstract with title: A computer-aided probing strategy for workpiece localization, and key words: Sampling methods, Strategic planning, Computer errors, Algorithm design and analysis, Computer aided manufacturing, Coordinate measuring machines, Probes, Computational modeling, Computer simulation, Virtual manufacturing. Abstract: This paper presents an optimal planning problem for workpiece measurement. Two sequential optimization algorithms are introduced to find maximum determinant solutions. Then, based on a reliability analysis of workpiece localization and the sequential optimization algorithms, a computer-aided probing strategy is proposed. With this strategy, given the desired translation and orientation error bounds and desired confidence limit, we can experimentally find the least number of points needed to measure. Simulation results show the efficiency of the computer-aided probing strategy. |
12,460 | Please write an abstract with title: GeoSim: Realistic Video Simulation via Geometry-Aware Composition for Self-Driving, and key words: Geometry, Image segmentation, Solid modeling, Visualization, Three-dimensional displays, Semantics, Manuals. Abstract: Scalable sensor simulation is an important yet challenging open problem for safety-critical domains such as self-driving. Current works in image simulation either fail to be photorealistic or do not model the 3D environment and the dynamic objects within, losing high-level control and physical realism. In this paper, we present GeoSim, a geometry-aware image composition process which synthesizes novel urban driving scenarios by augmenting existing images with dynamic objects extracted from other scenes and rendered at novel poses. Towards this goal, we first build a diverse bank of 3D objects with both realistic geometry and appearance from sensor data. During simulation, we perform a novel geometry-aware simulation-by-composition procedure which 1) proposes plausible and realistic object placements into a given scene, 2) renders novel views of dynamic objects from the asset bank, and 3) composes and blends the rendered image segments. The resulting synthetic images are realistic, traffic-aware, and geometrically consistent, allowing our approach to scale to complex use cases. We demonstrate two such important applications: long-range realistic video simulation across multiple camera sensors, and synthetic data generation for data augmentation on downstream segmentation tasks. Please check https://tmux.top/publication/geosim/ for high-resolution video results. |
12,461 | Please write an abstract with title: Cross-Domain Fault Diagnosis with One-Dimensional Convolutional Neural Network, and key words: Conferences, Automation, Computer aided software engineering. Abstract: Intelligent fault diagnosis methods based on deep learning have been widely used in intelligent manufacturing. Most of these methods focus on the diagnosis of fault data with the same distribution in a single domain, but pay poor attention to the diagnosis of cross-domain fault data with different distributions. To address this problem, this paper firstly integrates the fault datasets from eight universities into a cross-domain dataset. A new model named one-dimensional improved LeNet-5 (ID ILeNet-5) is proposed for cross-domain fault diagnosis. One-dimensional convolutional operation is used for feature extraction and batch normalization technique is introduced to accelerate the network convergence in this model. The effectiveness and generalization performance of this method are verified using the aforementioned cross-domain dataset. The results demonstrate that our method outperforms the state-of-the-art transfer learning model with fewer parameters and shorter training time. |
12,462 | Please write an abstract with title: Low complexity lossless video compression, and key words: Video compression, Delay, Displays, Bandwidth, Costs, Propagation losses, Computer architecture, Video sequences, Entropy coding, Hardware. Abstract: We present a line-based adaptive lossless video compression (LALVC) algorithm for interactive multimedia applications that demand low complexity and low latency. Communications between high-resolution display and storage devices require high bandwidth for exchanging raw data. To reduce cost of video transmission without losing data accuracy, lossless video compression is necessary. Considering low complexity and low delay, LALVC adopts a simple and efficient architecture that adopts one-pass, raster-scan, transform-free coding process and a simple predictor. For low latency, zero-motion prediction and one-frame buffer are used to reduce temporal redundancy. In addition, to maximize the coding efficiency for both natural and computer-generated video sequences, LALVC adaptively selects the best coding mode for each line of a frame. The entropy coding of each line is based on Golomb code that can enhance coding efficiency with less computation load and is easy for hardware realization. The simulation results show that temporal preprocessing and line-based mode decision of LALVC can increase compression ratio with properly increased complexity as compared to that of JPEG-LS. |
12,463 | Please write an abstract with title: Design and implementation of a cache-conscious index manager for the Tachyon, a main memory DBMS, and key words: Memory management, Telecommunication computing, Transaction databases, Concurrency control, Binary search trees, Conference management, Computer applications, Communication system software, Application software, Indexes. Abstract: The main memory DBMS (MMDBMS) efficiently supports various database applications that require high performance since it employs main memory rather than disk as a primary storage. In this paper, we discuss the cache-conscious index manager of the Tachyon, a next generation MMDBMS. The index manager is an essential sub-component of a DBMS used to speed up the retrieval of objects from a large volume of a database in response to a certain search condition. Recently, the gap between the CPU processing and main memory access times is becoming much wider due to rapid advance of CPU technology. By devising data structures and algorithms that utilize the behavior of the cache in CPU, we are able to enhance the overall performance of MMDBMSs considerably. In this paper, we address the practical implementation issues and our solutions for them obtained in developing the cache-conscious index manager of the Tachyon. The main issues touched are (1) consideration of the cache behavior, (2) compact representation of an index entry, (3) support of variable-length keys, (4) support of multiple-attribute keys, (5) support of duplicated keys, and (6) definition of the system catalog for indexes. We also show the effectiveness of our approach through extensive experiments. |
12,464 | Please write an abstract with title: Joint Power Allocation Scheme for Distributed Secure Spatial Modulation in High-Speed Railway, and key words: Modulation, Wireless communication, Transmitting antennas, Bit error rate, Rail transportation, Security. Abstract: In this article, a distributed secure spatial modulation (DSSM) is proposed to improve the secrecy performance of transmission system in high-speed railway (HSR) scenario. N-remote antenna units are connected with central processing unit to build a cooperative DSSM system. Considering the influences of correlated shadow fading, spatial-temporal correlated fading and Doppler shift, the expressions of secrecy capacity (SC) and bit error rate (BER) are derived. Based on the design of artificial noise, two power allocation algorithms are jointly proposed as security schemes in HSR scenario. At first, a joint optimal secrecy capacity algorithm (JOSCA) is proposed to maximize SC. Another Lagrange constraint extreme algorithm (LCEA) is proposed to maximize inhibition of eavesdropper's BER performance. The numerical results show that compared to conventional spatial modulation, the proposed DSSM with JOSCA brings higher SC and keeps a high value of cooperative SC as the advance of train. Moreover, the proposed LCEA brings better BER performance of legitimate receiver while greatly limiting that of eavesdropper. In terms of the tradeoff between SC and BER, the DSSM gets better balance at the viaduct of high-speed railway scenario. |
12,465 | Please write an abstract with title: Classification of Covid-19 Infection Status in Japan, and key words: COVID-19, Visualization, Sociology, Statistics. Abstract: The purpose of this study was to visualize and classify the COVID-19 infection status in Japan by prefecture. Three methods were used for classification, taking into account the difference in the number of tests by day of the week and the difference in the scale of infection by population. |
12,466 | Please write an abstract with title: A Joint Clustering and Routing Algorithm based on GA for Multi Objective Optimization in WSN, and key words: Routing, Genetic algorithms, Biological cells, Wireless sensor networks, Statistics, Sociology, Optimization. Abstract: Reducing energy consumption is the most serious issue of the Wireless Sensors Networks (WSN) topology design due to the resources constraint. Genetic Algorithm (GA) is used as a suitable solution to better manage energy consumption and increase the whole network lifetime. In such context, a new joint Clustering and Routing algorithm based on GA for Multi Objective Optimization (CRMOGA) is proposed for finding the optimal network configuration. Simulation result proofs that the proposed CRMOGA approach performs better than LEACH and the recently proposed Two-Level Clustering (TLC) scheme and enhances the network lifetime. |
12,467 | Please write an abstract with title: Invertibility of observable multivariable nonlinear systems, and key words: Nonlinear systems, Steady-state, Sufficient conditions, Vibration control, Shape control, Kinetic theory, Equations, Linear approximation, Laser stability, Laser theory. Abstract: Based on the algorithm of Hirschorn [1] for obtaining inverses, sufficient conditions for invertibility of observable multivariable nonlinear systems are derived. For observable and invertible systems, a static left-inverse system is constructed. For certain noninvertible systems, the sufficient condition yields a characterization of input space on which the input-output map is injective. |
12,468 | Please write an abstract with title: Execution Analysis of Machine Learning Technique Based Detection and Classification of Brain Tumor from MRI images, and key words: Support vector machines, Shape, Magnetic resonance imaging, Capacitance-voltage characteristics, Cerebrum, Streaming media, Particle measurements. Abstract: The precise detection of brain tumors through magnetic resonance imaging (MRI) at an early stage in clinical imaging applications is a difficult task for scientists these days. The death rate from mental disease-related deaths is reduced when the increase is detected early. Because of its low ionization and radiation, MRI is a popular clinical imaging modality, although manual assessment takes a long time. In this paper, we describe a Machine-Learning-Technique (MLT) that uses the cerebrum MRI dataset to discriminate and categorize tumorous and non-tumorous regions. Then, using the chan-vese (C-V) technique, the dynamic growth is portioned by selecting a precise starting point. In the extremely subsequent stage, the elements of the cancer region are extricated utilizing the gray level co-event network (GLCM), and afterward, significant measurable highlights were picked. At long last, a two-class classifier is carried out utilizing the support vector machine (SVM) and its presentation is then approved with k nearest neighbor (KNN). The presentation of the proposed stream work was assessed as far as exactness, affectability, particularity, and accuracy by performing on the BRATS 2017 benchmark dataset. The recreation results uncover that the proposed framework performs better compared to the current strategies with high exactness. |
12,469 | Please write an abstract with title: Optimal Self-Tuning Control for Data-Centers’ 48V-12V ZCS-STC, and key words: Data acquisition, Switches, Control systems, Delays, Transistors, Zero current switching, Resonators. Abstract: This paper presents a zero-current-switching (ZCS) control method with self-tuning capabilities employed on a 4-to-1 switched-tank converter (STC) for Google’s and associated OCP standard data-centers. The control scheme ensures ZCS operation of each of the STC’s resonant tanks individually, regardless of components mismatch, and adjusts the switching period upon variations of the components’ values. The switching-state of the STC’s resonators is evaluated at the turn-off instance by dedicated zero-current-detection indicator. Two approaches have been developed for the sensor’s data acquisition, accounting for the inherent delay between the digital-controller gating command and the actual turn-off of the switches. The operation of the control has been validated experimentally on 650W 4-to-1 STC with PCB area of 5cmX2cmX0.6cm, demonstrating exceptional self-tuning capabilities over the entire load range and for various component mismatch scenarios. Peak efficiency of 98.6% is achieved at 200W. |
12,470 | Please write an abstract with title: Real-time RFID-based item tracking using IoT & efficient inventory management using Machine Learning, and key words: Radiofrequency identification, Real-time systems, Machine learning, Support vector machines, Inventory management, Classification algorithms, Databases. Abstract: Internet of Things (IoT) and Machine Learning (ML) based connected intelligence framework has become a technology enabler in various segments of supply chain like inventory management. In this paper, we have proposed an IoT-cloud architecture for passive RFID tag based real-time Stock Keeping Units (SKUs) tracking and ML algorithms for predictive stock analysis. The SKUs are fitted with RFID tags, those are scanned at entry and exit of the warehouse and this real-time data is sent to cloud server over internet. The acquired data is then processed using ML based software engine using techniques like classification, training and testing. We have considered, ABC Inventory Classification for the SKUs in warehouse and three ML algorithms as: Support Vector Machine (SVM), K-nearest neighbors (KNN) and Bayes for predictive analysis of SKUs in inventory. The result indicates SVM is outperforming with 84.8% accuracy. The accuracy of KNN is 83.6% and Bayes at 74%. |
12,471 | Please write an abstract with title: A Priori Quantification of Transfer Learning Performance on Time Series Classification for Cyber-Physical Health Systems, and key words: Measurement, Deep learning, Training, Machine learning algorithms, Wearable computers, Transfer learning, Time series analysis. Abstract: Cyber-Physical Systems fully encompass the intelligent system from signal acquisition through to physical computing and computation - it requires consideration of the deep entanglement between computational and physical elements. Human health and performance is increasingly being measured and analyzed using machine learning to identify complex relationships using wearable and pervasive computing. This combination defines the focused area of Cyber-Physical Health Systems. Modern deep learning algorithms, such as one-dimensional convolutional neural networks, have demonstrated excellent performance in classifying time series data because of the ability to identify time-invariant features. A primary challenge of deep learning for time series classification is the large amount of data required for training and many application domains, such as medicine, have challenges obtaining sufficient data. Transfer learning is a deep learning method used to apply feature knowledge from one deep learning model to another; this is a powerful tool when both training datasets are similar and offers smaller datasets the power of more robust larger datasets. This makes it vital that the best source dataset is selected when performing transfer learning and presently there is no a priori metric defined for this purpose. Analyzing time-series data from public human-activity-recognition datasets a neural network autoencoder was used to first transform the source and target datasets into a time-independent feature space. To quantify the suitability of transfer learning datasets the average embedded signal from each dataset was used to calculate the distance between each dataset centroid. Our metric was then applied to predict the success of transfer learning from one dataset to another for the purpose of general time series classification. |
12,472 | Please write an abstract with title: An applying aspect-oriented concept to sequential logic design, and key words: Logic design, Hardware, Process design, Programming, Design engineering, Sequential circuits, Clocks, Java, Feedback circuits, Software design. Abstract: Because of rapid hardware design's evolution, hardware circuits are more complex. Hardware designer would not spend too much time to produce the circuits. One of hardware designers' problems is redesigning of circuit occurring when specifications were changed. The existing research proves that object-oriented concept can be applied efficiently in hardware design covering both combinational and sequential logic circuits. Aspect-oriented is a new paradigm in software development. It can solve some problems such as crosscutting concerns where object oriented concept cannot. This paper proposes the idea that aspect-oriented concept can be implemented to the sequential logic circuit resulting in reducing time in hardware design process. |
12,473 | Please write an abstract with title: Block Thermal Model for High Power Lidded Packages, and key words: Heating systems, Conferences, Electronic components, Predictive models, Electronic packaging thermal management, Data models, Junctions. Abstract: This paper proposes a new approach, block thermal model, by characterizing detailed thermal model in the end-use application environment, including thermal solution interface with package lid. The resulting block thermal model would accurately capture the interface heat transfer between package lid and external thermal solution, containing no proprietary information regarding die or package. The procedure of constructing a block thermal model from detailed thermal model is described.As part of this study, several block thermal models were validated in end-use applications against detailed thermal models, with varying TIM2 and Heatsink assumptions for high power lidded packages. Results showed that the junction temperatures predictions from block thermal models were in very good agreement with those from detailed thermal model simulations, demonstrating that block thermal model can accurately represent detailed thermal model in terms of junction temperature for end-use applications. Data from such verification simulations is discussed in detail. |
12,474 | Please write an abstract with title: A Novel Feature Extraction Approach for Mechanical Fault Diagnosis Based on ESAX and BoW Model, and key words: Feature extraction, Fault diagnosis, Time series analysis, Symbols, Aggregates, Time-frequency analysis, Dimensionality reduction. Abstract: Condition monitoring and fault diagnosis are of great significance to the development of modern industry, for they enable enterprises to avoid unexpected interruptions or severe accidents, and extracting the fault-related features from vibration signals is a critical step to achieve accurate diagnosis. Among diverse of feature extraction approaches, symbolic aggregate approximation (SAX) is a promising one that has been introduced into fault diagnosis recently. Nevertheless, when dealing with the sampled vibration signals, the SAX ignores the change of signal frequency characteristics, which eventually leads to information aliasing and cannot ensure the information validity. In this work, the information aliasing is analyzed from the perspective of signal processing, and the extremum symbolic aggregate approximation (ESAX) is developed as a substitution on the premise of maintaining the validity of the information. Subsequently, to convert the symbol strings generated by the ESAX into usable digital feature vectors, the bag-of-words (BoW) model in natural language processing (NLP) is employed to perform the counting statistics of the fault-related words, and the Laplacian score (LS) algorithm is then utilized to rerank the statistical results, thereby realizing the extraction of mechanical fault feature. The superiority of the developed method is verified by experiments. |
12,475 | Please write an abstract with title: Research on Question Answering System of Character Relationship Based on Literary Works, and key words: Education, Tagging, Knowledge discovery, Natural language processing, Information management, Data mining, Character recognition. Abstract: In view of the huge chapter system and complicated character relationships of existing literary works, users obtain accurate character relationships difficulty, the paper proposes a research on the character relationship question answering system based on literary works and conducts an example analysis. Natural language processing technologies such as part-of-speech tagging and entity recognition are used to study the automatic extraction method of character relationships in literary works. The paper realizes the function of quickly giving answers to related relationships based on the names of characters entered by the users. Typical cases verify the effectiveness of the question answering system. |
12,476 | Please write an abstract with title: AI-FML Agent with Patch Learning Mechanism for Robotic Game of Go Application, and key words: Learning systems, Computational modeling, Training data, Games, Data models, Robots, Testing. Abstract: In this paper, we propose an AI-FML agent with a patch learning (PL) mechanism for the robotic game of Go applications. The proposed AI-FML agent contains three kinds of intelligence, including a perception intelligence, a cognition intelligence, and computational intelligence, for the robotic application. Additionally, we embed the PL mechanism into the AI-FML agent. The method for running PL involves three steps. It first trains an initial global model, then trains a patch model for each identified patch, and finally updates the global model using the training data that do not fall into any patch. This paper adopts the Google DeepMind Master 60 games to be the training data and testing data set. The experimental results show the AI-FML agent with the patch learning mechanism can improve the performance of regression for the robotic game of Go applications. |
12,477 | Please write an abstract with title: Optimizing the memory bandwidth with loop morphing, and key words: Bandwidth, Processor scheduling, Strips, Embedded system, Memory architecture, Performance loss, Delay, Runtime, Costs, Optimizing compilers. Abstract: The memory bandwidth largely determines the performance of embedded systems. However, very often compilers ignore the actual behavior of the memory architecture, causing large performance loss. To better utilize the memory bandwidth, several researchers have introduced instruction scheduling/data assignment techniques. Because they only optimize the bandwidth inside each basic block, they often fail to use all available bandwidth. Loop fusion is an interesting alternative to more globally optimize the memory access schedule. By fusing loops we increase the number of independent memory operations inside each basic block. The compiler can then better exploit the available bandwidth and increase the system's performance. However, existing fusion techniques can only combine loops with a conformable header. To overcome this limitation we present loop morphing; we combine fusion with strip mining and loop splitting. We also introduce a technique to steer loop morphing such that we find a compact memory access schedule. Experimental results show that with our approach we can decrease the execution time up to 88%. |
12,478 | Please write an abstract with title: Synchronization in Multiple Neural Networks With Delay and Disconnected Switching Topology via Event-Triggered Impulsive Control Strategy, and key words: Synchronization, Switches, Topology, Network topology, Delays, Artificial neural networks. Abstract: In this article, an event-triggering impulsive control strategy is proposed, in which the impulsive time sequence can be different from the event-triggered time sequence. The synchronization problems of a multiple neural network with delay (MDNN) and the directed disconnected switching topology are discussed by using this strategy. The considered switching topology is assumed to be directed and sequentially or jointly connected. The combined measurement method is adopted in the event-triggering strategy, so that each delayed neural network only updates the control rules at the moment of its event triggering. First, we prove that the designed event-triggering rules can avoid Zeno behavior. Then, the sufficient conditions of event-based quasi-synchronization (synchronization) for the MDNN can be obtained by using the iterative method. In addition, as an extension, the case of a jointly connected topology and the case of a pure impulsive control protocol are considered, too. Finally, a numerical example is provided to test the results in theory analysis. |
12,479 | Please write an abstract with title: Comparison between read and spontaneous speech assessment of L2 Korean, and key words: Standards, Correlation, Distance measurement, Grammar, Phonetics, Task analysis. Abstract: This paper describes two experiments aimed at exploring the relationship between linguistic aspects and perceived proficiency in read and spontaneous speech. 5,000 utterances of read speech by 50 non-native speakers of Korean in Experiment 1, and of 6,000 spontaneous speech utterances in Experiment 2 were scored for proficiency by native human raters and were analyzed by factors known to be related to perceived proficiency. The results show that the factors investigated in this study can be employed to predict proficiency ratings, and the predictive power of fluency and pitch and accent accuracy is strong for both read and spontaneous speech. We also observe that while proficiency ratings of read speech are mainly related to segmental accuracy, those of spontaneous speech appear to be more related to pitch and accent accuracy. Moreover, proficiency in read speech does not always equate to the proficiency in spontaneous speech, and vice versa, with Pearson???s per-speaker correlation score of 0.535. |
12,480 | Please write an abstract with title: A Rhesus Monkey Model and WBA SAR, and key words: Legged locomotion, Animals, Reverberation chambers, Asia, Electromagnetic fields. Abstract: The surface model of a rhesus monkey in the Visible Monkey project of Korea had been implemented. However, the posture of the monkey model is very different from that of a live monkey because the original images were obtained in a supine position. Therefore, the monkey models in walking and sitting postures, close to those of a live monkey were realized. This paper presents the SAR results calculated for the monkey model in a walking posture exposed to electromagnetic fields in a reverberation chamber. |
12,481 | Please write an abstract with title: Characterizing and Improving the Probability of Correct Phase Ambiguity Resolution for Uniform Circular Array Phase Interferometers, and key words: Phased arrays, Phase measurement, Costs, Direction-of-arrival estimation, Statistical analysis, Wavelength measurement, Simulation. Abstract: Correct phase ambiguity resolution (CPAR) for a uniform circular array (UCA) phase interferometer is formally defined and some theoretic results for CPAR are derived. The probability of CPAR is numerically investigated considering the impact of baseline formulation, number of elements, radius wavelength ratio and phase measuring mechanism. Three methods for improving the probability of CPAR are proposed based on the findings of the characteristics of UCA phase interferometers. An extensive numerical investigation is conducted to validate the effectiveness of the proposed methods. The investigation results validate the effectiveness of the proposed methods and suggest choosing among the three proposed methods by considering the specific needs and engineering limitations. |
12,482 | Please write an abstract with title: A review of development of hybrid excitation synchronous machine, and key words: Synchronous machines, Magnetic flux, Air gaps, Stator cores, Iron, Magnetic cores, Permanent magnet machines, Magnetic fields, Permanent magnets, Hybrid power systems. Abstract: Hybrid excitation synchronous machine (HESM) profit the advantages of permanent machine and electric excitation machine in quality, so it has a widely used prospect. The paper introduces its operation principle, classification, characteristics and the developing situation. And, the present application situation of HESM is summarized. |
12,483 | Please write an abstract with title: Exchange anisotropy in PtMn/Ni/sub 80/Fe/sub 20/(110) quad-crystal films, and key words: Anisotropic magnetoresistance, Iron, Substrates, Molecular beam epitaxial growth, Temperature, Physics, X-ray diffraction, Displays, Crystallization, Capacitive sensors. Abstract: Exchange anisotropy for an ordered tetragonal PtMn/Ni/sub 80/Fe/sub 20/(110) quad-crystal film grown on an Mo(001) step surface was studied. The PtMn/Ni/sub 80/Fe/sub 20/ bilayers grown at 300/spl deg/C show a unidirectional exchange anisotropy with the easy axis parallel to the underlying Mo step-edge direction. The azimuthal distribution of the coercive fields displays a fourfold symmetry. The azimuthal distribution of the coercive fields can be explained by the fourfold crystalline-induced interfacial strain between Ni/sub 80/Fe/sub 20/ and Mo. The azimuthal distribution of the exchange fields was mainly attributed to the step-induced unidirectional anisotropy and the biquadratic coupling between antiferromagnetic-ferromagnetic interface. |
12,484 | Please write an abstract with title: Communication Efficient Tensor Factorization for Decentralized Healthcare Networks, and key words: Privacy, Tensors, Frequency modulation, Hospitals, Servers, History, Uplink. Abstract: Tensor factorization has been proved as an efficient unsupervised learning approach for health data analysis, especially for computational phenotyping, where the high-dimensional Electronic Health Records (EHRs) with patients history of medical procedures, medications, diagnosis, lab tests, etc., are converted to meaningful and interpretable medical concepts. Federated tensor factorization distributes the tensor computation to multiple workers under the coordination of a central server, which enables jointly learning the phenotypes across multiple hospitals while preserving the privacy of the patient information. However, existing federated tensor factorization algorithms encounter the single-point-failure issue with the involvement of the central server, which is not only easily exposed to external attacks, but also limits the number of clients sharing information with the server under restricted uplink bandwidth. In this paper, we propose CiderTF, a communication-efficient decentralized generalized tensor factorization, which reduces the uplink communication cost by leveraging a four-level communication reduction strategy designed for a generalized tensor factorization, which has the flexibility of modeling different tensor distribution with multiple kinds of loss functions. Experiments on two real-world EHR datasets demonstrate that CiderTF achieves comparable convergence with the communication reduction up to 99.99%. |
12,485 | Please write an abstract with title: Investigation of the Plasma of a DC hybrid-switch model at beginning of contact separation, and key words: Resistance, Semiconductor device modeling, Spectroscopy, Contacts, Optical switches, Commutation, Voltage. Abstract: Electrical and optical methods permitted to characterize the plasma of a hybrid-switched DC-current discharge at the beginning of contact separation. The experimental setup consists of a model switch, which employs a linear motor as mechanical drive. A semiconductor connected in parallel to the contacts allows commutating the current after a given arcing time and therefore a hybrid switching. Firstly, the current flow remains initially through the contact path due to lower resistance of the closed contacts. During contact opening, the resistance rises due to reduced contact force, finally ending up with a single metal bridge that will be heated until explosion, leading thus to a metal-dominated discharge plasma. Through the commutation of the current to the low resistive semiconductor path, the arc is extinguish. An also parallel-connected MOV takes over the residual energy after current zero, preventing a higher voltage between the electrodes, which could re-ignite the arc. To capture the radiation of the arc plasma were combined high-speed imaging and time resolved spectroscopy. First results of the arc plasma characteristics during the short length and short duration arc were obtained. |
12,486 | Please write an abstract with title: A New Framework for Automatic Modulation Classification using Deep Belief Networks, and key words: Feature extraction, Modulation, Signal to noise ratio, Machine learning, Computational complexity, Manganese, Real-time systems. Abstract: Automatic Modulation Classification (AMC) is the process of determining the modulation scheme of an intercepted signal with no a priori information about its characteristics. AMC's main advantage is that no communication overhead needs to be allocated for control information to inform the receiver about changes in a transmitted signal's modulation scheme. Proposed approaches for AMC traditionally suffer from inherent computational complexity that prevents real-time AMC applications. Several extensions for lowering the computational complexity have been developed, including Feature-based AMC with machine learning classifiers. For the contribution of this research, we propose a new classifier called Deep Belief Network (DBN) for AMC applications, which is an algorithm derived from the Restricted Boltzmann Machine (RBM). DBN is capable of learning the probability distribution over its set of inputs and is comprised of layers of RBM stacked together. Additionally, this composition of layers in DBN leads to a faster learning procedure compared to conventional algorithms, and can help this classifier operate in real-time. Essentially, High-Order Statistics-based (HoS) features are utilized in the feature extraction stage for which the authors also investigate the bias issue of the estimator. The standard RadioML dataset is utilized to assess the performance of the studied AMC platform. This research shows the comparison of high order modulation schemes results in lower-bound performance of the AMC classifier, which are classified with notable higher probability of correct classification compared to conventional deep learning classifiers especially for lower signal-to-noise (SNR) ratio scenarios. |
12,487 | Please write an abstract with title: Temperature effect on ultra thin SiO/sub 2/ time-dependent-dielectric-breakdown, and key words: Temperature, Electric breakdown, Stress, Kinetic theory, Equations, Tunneling, Voltage, Charge carrier processes, Current density, Electrons. Abstract: The unusually high temperature-acceleration factor and the non-Arrhenius behavior of ultra thin oxide can be explained by kinetic analysis using the physics-based kinetic model of oxide defect generation during electrical stress. The semi-quantitative treatment using known experimental value range is in good agreement with the reported data in the literature, lending strong support for the kinetic model. The estimated activation energy difference between thick and thin oxide relies on the fact that for thick oxide the hole current to electron current ratio is approximately constant. The resulting agreement with reported data lend support to the anode-hole-injection model (under high-field stress) unless the hydrogen release per injected electron happens to be of a similar order of magnitude. |
12,488 | Please write an abstract with title: Acoustic MEMS Sensor Array for Quench Detection of CICC Superconducting Cables, and key words: Micromechanical devices, Superconducting magnets, Superconducting cables, High-temperature superconductors, Sensor arrays, Heating systems, Coolants. Abstract: A novel quench detection method using micro-electro-mechanical system (MEMS) sensor technology has been investigated in use for high temperature superconducting (HTS) conductors such REBCO tape cables. The sensor array along a superconducting cable, such as a cable-in-conduit-conductor (CICC), is installed in a cooling channel. It will allow sensitive and quick detection for a local quench of a superconducting cable. This work has confirmed that a quench of a single REBCO tape can be detected in liquid nitrogen by a MEMS piezoelectric microphone sensor. The quench detection design utilizing a MEMS sensor array method is discussed for the case of a toroidal field (TF) magnets of a fusion Tokamak device. |
12,489 | Please write an abstract with title: Multi-Purpose Lidar System Covering Wide Altitude Range Over Equatorial Region, and key words: Laser radar, Temperature, Terrestrial atmosphere, Clouds, Aerosols, Optical pulses, Telescopes, Resonance, Raman scattering, Internet. Abstract: The multi-lidar system which measures vertical profiles of temperature, aerosol, cloud, water vapor and metallic species in the atmosphere over Kototabang (100.3E, 0.2S), Indonesia is presented. The most parts of this lidar system are remotely controlled via the Internet from Tokyo Metropolitan University (TMU) in Japan. |
12,490 | Please write an abstract with title: Relay Hybrid Precoding in UAV-Assisted Wideband Millimeter-Wave Massive MIMO System, and key words: Precoding, Relays, Matching pursuit algorithms, Autonomous aerial vehicles, Optimization, Massive MIMO, Complexity theory. Abstract: Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) offers a promising technique to fulfil the high data demand and connectivity of the Internet-of-Things (IoT) and 5G communications because it owns valuable and unknown spectrum resources. Using massive antennas with recently introduced drone-enabled aerial computing platforms, named unmanned aerial vehicles (UAVs), can cast high energy consumption if fully-digital precoding is employed at the UAVs. Using hybrid precoding at a UAV can reduce hardware complexity and energy consumption but is challenging with a need for joint optimization of three precoding matrices at the UAV (sixth-order polynomial objective function). In this paper, we propose to decompose the original UAV hybrid precoding challenge into three subproblems and develop a coordinated descent optimization (CDO) algorithm to solve the three problems recursively. In addition, the convergence and complexity of this new technique are analyzed. Numerical studies indicate the improved effectiveness of the proposed solution over existing solutions. |
12,491 | Please write an abstract with title: Hardware Deployment of HBONext using NXP Bluebox 2.0, and key words: Deep learning, Embedded systems, Computational modeling, Memory management, Object detection, Tools, Hardware. Abstract: Deep learning models require a lot of computation and memory, so they can only be run on high-performance computing platforms such as CPUs or GPUs. However, due to resource, energy, and real-time constraints, they often fail to meet portable requirements. As a result, there is an increasing interest in real-time object recognition solutions based on CNNs, which are typically implemented on embedded systems with limited resources and energy consumption. Recently, hardware accelerators have been developed to provide the computing power needed by AI and machine learning tools. These edge accelerators deliver high-performance hardware while maintaining the needed accuracy for the task at hand. This paper takes a step forward by suggesting a design approach for porting CNNs to low-resource embedded systems, bridging the gap between deep learning models and embedded edge systems. To complete our task, we employ closer computing approaches to minimize the computational load and memory consumption of the computer while maintaining impressive deployment performance. HBONext is one of those models that was designed to be easily deployable on embedded and mobile devices. We demonstrate how to use NXP BlueBox 2.0 to introduce a real-time HBONext image classifier in this work. Incorporating this concept into this hardware has been a huge success due to its limited architectural scale of 3 MB. This model was trained and validated using the CIFAR10 data set, which performed exceptionally well due to its smaller size and higher accuracy. |
12,492 | Please write an abstract with title: Background Compensation of Frequency Interleaved DAC for Optical Transceivers, and key words: Adaptation models, Temperature distribution, Optical distortion, Transceivers, Optical fiber communication, Optical transmitters, Optical noise. Abstract: This work proposes a novel adaptive background compensation scheme for frequency interleaved digital-to-analog converters (FI-DACs). The technique is applicable to high speed transceivers such as those used in coherent optical communications. Adaptive background techniques for FI-DAC have not been reported so far. They can compensate errors caused by process, voltage, and temperature variations in the technology (e.g., CMOS, SiGe) implementation of the data converters, and therefore ensure high manufacturing yield. Background compensation is important because it does not need to interrupt the normal operation of the transceiver. The key ingredients of the proposed technique are a multiple-input multiple-output (MIMO) equalizer and the backpropagation algorithm used to adapt the coefficients of the latter. Simulations show more than 20dB of signal to noise and distortion ratio (SNDR) improvement because of the accurate compensation. Analog mismatch impact elimination is achieved, resulting in a high performance transmitter system. |
12,493 | Please write an abstract with title: Closed-loop Simulation Method of Stability Control System Based on Virtual Component Technology, and key words: stability control system, virtualization technology, component, simulation test. Abstract: The stability control system is an important means to ensure the safe and stable operation of the power system. In view of the shortcomings of the existing offline/real-time simulation test methods, a closed-loop simulation scheme based on virtual component technology is proposed. The virtual component technology of the stability control device is researched, and the virtualization method of the external hardware interface of the device is designed. On this basis, a closed-loop simulation framework of the stability control system under the offline/real-time simulation test environment is proposed. The functional logic of each component under the framework are analyzed. The calculation and feedback process of the entire closed-loop simulation system are analyzed in depth, which effectively solves the difficulty of simulation analysis and test verification of large-scale stability control system, providing a new method for the research and verification of stability control system strategy. |
12,494 | Please write an abstract with title: Skin Cancer Classification Based On Convolutional Neural Network, and key words: Training, Analytical models, Transfer learning, Feature extraction, Data models, Skin, Convolutional neural networks. Abstract: Skin cancer is a kind of cancer that is usually diagnosed by images from dermoscopy. In recent years, researchers have attempted to utilize deep learning technology, especially convolution neural networks (CNN), in the recognition of skin cancer images. Many CNN models have already performed great applicability, like DenseNet, Inception, and Xception. This paper carried a comparative experiment on ISIC 2019 challenge dataset which includes 25,331 skin cancer images of 8 different kinds. On the classification task on the ISIC 2019, it introduced 6 models, VGGNet19, ResNet50, ResNet152, DenseNet201, Inception-v3, and Xception, then conducted a comparative analysis of their performance involving 2 methods (data enhance and transfer learning) and 2 optimizers (Adam and SGD), aiming to explore the impact of different methods and structures on the accuracy, in order to find traits for potential models of higher accuracy. In the 24 groups of results, Xception with data enhance, transfer learning (pretraining) and Adam optimizer had the highest accuracy of 83.8%, while VGGNet19 without transfer learning had the lowest of 66.67%. The influence of transfer leaning is positive on all models, both on accuracy and training time; similar to Adam optimizer, except for a noticeable enhancement effect on Inception-v3 and Xception. The data enhance method applied in this paper had a weak, non-directed impact. Possible reasons for this phenomenon are discussed in depth in the study. |
12,495 | Please write an abstract with title: Impact of clustering on the BER performance in ad hoc wireless networks, and key words: Bit error rate, Intelligent networks, Wireless networks, Peer to peer computing, Spread spectrum communication, Radio communication, Degradation, Power control, Routing, Media Access Protocol. Abstract: Ad hoc wireless networks are characterized by multihop radio communication. The spatial distribution of the nodes is seldom perfectly regular. In a realistic ad hoc wireless network communication scenario, the nodes are likely to be clustered, i.e., the nodes are likely to configure themselves in subgroups such that the nodes inside each subgroup are relatively close to each other with respect to the distance between different subgroups. In this paper, we consider a very simple clustering scenario, defined as uniformly clustered, in order to derive a parameterized analytical description. The proposed clustering model, although simple and idealistic, allows one to gain insights valid also in a more general case with nonregular clustering. In particular, the obtained results highlight the fact that a single long hop can significantly degrade the network communication performance. The benefits of topology-dependent power control are discussed and evaluated. |
12,496 | Please write an abstract with title: Auction-based Peer-to-peer Energy Transaction Model with Prosumer-side Energy Scheduling, and key words: Production, Electricity supply industry, Peer-to-peer computing, Distributed power generation, Wind turbines, Power systems, Security. Abstract: The increasing penetration of small- or medium-sized distributed energy resources, such as PV panels, wind turbines, and batteries, is facilitating the emergence of a more consumer-centric electricity market. Meanwhile, with the increase of such distributed energy resources, traditional market design suffers more and more from the security and low-efficiency issues. Thus, toward the consumer-centric electricity market, it is important to design more flexible energy transaction mechanisms to meet the demand of the more consumer-centric energy distribution. In this paper, we design such an electricity market framework to enable all peers of the energy network to carry out energy transactions directly with others in a decentralized manner. Firstly, for the energy prosumer side, the ensemble learning algorithm is applied to forecast future energy production and consumption. Based on the forecasting result, a power flow optimization is designed to determine the optimal power scheduling of the power system including the P2P trading strategy. For the energy transaction, we apply the discrete double auction adapting McAfee's mechanism to achieve its peer-to-peer manner. We simulate a number of test cases with various renewable resources penetration levels to validate its viability using real-world data and compare our P2P market with the traditional centralized market. |
12,497 | Please write an abstract with title: Segmentation of spectroscopic images of the low solar atmosphere by the self-organizing map technique, and key words: methods: data analysis, Sun: chromosphere, Sun: photosphere, sunspots. Abstract: We describe the application of semantic segmentation by using the self-organizing map technique to an high spatial and spectral resolution data set acquired along the H α line at 656.28 nm by the Interferometric Bi-dimensional Spectrometer installed at the focus plane of the Dunn solar telescope. This machine learning approach allowed us to identify several features corresponding to the main structures of the solar photosphere and chromosphere. The obtained results show the capability and flexibility of this method to identifying and analysing the fine structures which characterize the solar activity in the low atmosphere. This is a first successful application of the SOM technique to astrophysical data sets. |
12,498 | Please write an abstract with title: High-Speed and High-Resolution Optical Fiber Sensor Interrogation Based on Optical Injection in Semiconductor Laser and Microwave Filtering, and key words: Optical fiber sensors, Optical filters, Optical fibers, Microwave filters, Optical pulses, Wavelength measurement, High-speed optical techniques. Abstract: Real-time and high-speed interrogation of optical fiber sensors normally requires sophisticated detection systems. Here a novel microwave photonic approach for interrogation of high-speed and high-resolution optical fiber sensors based on optical injection in a semiconductor laser and simple passive microwave frequency filtering is proposed and experimentally demonstrated. An intensity-modulated master laser is injected into a semiconductor laser to produce a wavelength scanning optical sideband. A fiber Bragg grating (FBG) sensor is embedded into a fiber ring laser. Beating of the scanning optical sideband and the fiber ring laser wavelength at a photodetector generates a linearly frequency-chirped microwave signal. The real-time wavelength shift of the FBG sensor is converted into the change of the microwave center frequency. After a simple passive microwave bandpass filter, two electrical pulses are obtained corresponding to positive and negative frequency sweeping. The FBG wavelength can be retrieved from the time interval of the two pulses. A proof-of-concept experiment to measure an FBG strain sensor has been carried out. A high interrogation speed of 1 MHz and measurement sensitivity of 17.3 ns/μϵ have been achieved. |
12,499 | Please write an abstract with title: Individual Convolution of Ankle, Hip, and Wrist Data for Activities-of-Daily-Living Classification, and key words: Wrist, Accelerometers, Legged locomotion, Privacy, Magnetic heads, Task analysis, Older adults. Abstract: The Activities of Daily Living (ADL) include activities such as brushing teeth, sweeping, and walking that are critical to on-going health, especially in older adults. Activities may be determined using recorded video and 2D-CNNs, however video recordings present privacy and coverage challenges in personal spaces. Smartphones and newer wristworn devices that record motion data can also be used for activity recognition tasks. Ankle or shoe-based devices such as the retired Nike+ sensor are less common, however ear-based devices which may record head movement are gaining popularity. In this work we use accelerometer data from a recently released dataset using devices placed on the ankle, hip, and wrist. First, we evaluate a simple 1D-CNNs ability to classify the 17 included activities in subject-dependent and subject-independent analysis. Then we process the accelerometer data from the three sensors individually to evaluate each location’s ability to predict activities. Finally, we develop a functional model which independently executes a 1D-CNN for each sensor’s data and combines the results using Global Average Pooling. The functional model achieves a subject-independent accuracy of 70.7%. |
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