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"abstract": "In this work, we propose a novel approach of volume visualization without explicit traditional rendering pipeline. In our proposed method, volumetric images can be interactively `reversed' given the volumetric data and a static volume rendered image under the desired rendering effect. Our pipeline enables 3D-navigation on it for exploring the given volumetric data without explicit transfer function. In our approach, deep neural networks, combined usage of Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNN) are employed to synthesize high-resolution and perceptually authentic images directly, inheriting the desired transfer function and viewing parameter implicitly given by the input images respectively.",
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"title": "2015 IEEE 28th International Symposium on Computer-Based Medical Systems (CBMS)",
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"title": "Multiscale Tetrahedral Meshes for FEM Simulations of Esophageal Injury",
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"abstract": "The radiofrequency cardiac ablation is a minimal invasive surgical procedure used for treating tachycardia, atrial fibrillation and atrial flutter. A possible complication is esophageal injury: the union of tissues from left atrium and esophagus, through necrosis. While this operation is being made, it is necessary to monitor the tissues temperatures accurately. The tests needed are complex and imply in death risks of the patient. Researchers are directed to simulate, with the finite element method, the behavior of the tissues under the influence of different levels of temperature: the objective is improving the cardiac ablation. Computational strategies were described in this work in order to obtain integrated meshes from thoracic and abdominal structures which are relevant to the study of this procedure. The methodology was based on strategies to represent structures in different scale and integrate software packages to generate meshes. The results were integrated, multiscale, and highly refined tetrahedral meshes from thoracic structures. The models were evaluated from dihedral angles histograms, indicating values between 5 and 170 degrees.",
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"content": "The radiofrequency cardiac ablation is a minimal invasive surgical procedure used for treating tachycardia, atrial fibrillation and atrial flutter. A possible complication is esophageal injury: the union of tissues from left atrium and esophagus, through necrosis. While this operation is being made, it is necessary to monitor the tissues temperatures accurately. The tests needed are complex and imply in death risks of the patient. Researchers are directed to simulate, with the finite element method, the behavior of the tissues under the influence of different levels of temperature: the objective is improving the cardiac ablation. Computational strategies were described in this work in order to obtain integrated meshes from thoracic and abdominal structures which are relevant to the study of this procedure. The methodology was based on strategies to represent structures in different scale and integrate software packages to generate meshes. The results were integrated, multiscale, and highly refined tetrahedral meshes from thoracic structures. The models were evaluated from dihedral angles histograms, indicating values between 5 and 170 degrees.",
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"Biothermics",
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"Diseases",
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"Radiofrequency Heating",
"Surgery",
"Multiscale Tetrahedral Meshes",
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"abstract": "How can we find useful patterns and anomalies in large scale real-world data with multiple attributes? For example, network intrusion logs, with (source-ip, target-ip, port-number, timestamp)? Tensors are suitable for modeling these multi-dimensional data, and widely used for the analysis of social networks, web data, network traffic, and in many other settings. However, current tensor decomposition methods do not scale for tensors with millions and billions of rows, columns and ‘fibers’, that often appear in real datasets. In this paper, we propose HaTen2, a scalable distributed suite of tensor decomposition algorithms running on the MapReduce platform. By carefully reordering the operations, and exploiting the sparsity of real world tensors, HaTen2 dramatically reduces the intermediate data, and the number of jobs. As a result, using HaTen2, we analyze big real-world tensors that can not be handled by the current state of the art, and discover hidden concepts.",
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"abstract": "We investigate an efficient parallelization of the most common iterative sparse tensor decomposition algorithms on distributed memory systems. A key operation in each iteration of these algorithms is the matricized tensor times Khatri-Rao product (MTTKRP). This operation amounts to element-wise vector multiplication and reduction depending on the sparsity of the tensor. We investigate a fine and a coarse-grain task definition for this operation, and propose hypergraph partitioning-based methods for these task definitions to achieve the load balance as well as reduce the communication requirements. We also design a distributed memory sparse tensor library, HyperTensor, which implements a well-known algorithm for the CANDECOMP-/PARAFAC (CP) tensor decomposition using the task definitions and the associated partitioning methods. We use this library to test the proposed implementation of MTTKRP in CP decomposition context, and report scalability results up to 1024 MPI ranks. We observed up to 194 fold speedups using 512 MPI processes on a well-known real world data, and significantly better performance results with respect to a state of the art implementation.",
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"content": "We investigate an efficient parallelization of the most common iterative sparse tensor decomposition algorithms on distributed memory systems. A key operation in each iteration of these algorithms is the matricized tensor times Khatri-Rao product (MTTKRP). This operation amounts to element-wise vector multiplication and reduction depending on the sparsity of the tensor. We investigate a fine and a coarse-grain task definition for this operation, and propose hypergraph partitioning-based methods for these task definitions to achieve the load balance as well as reduce the communication requirements. We also design a distributed memory sparse tensor library, HyperTensor, which implements a well-known algorithm for the CANDECOMP-/PARAFAC (CP) tensor decomposition using the task definitions and the associated partitioning methods. We use this library to test the proposed implementation of MTTKRP in CP decomposition context, and report scalability results up to 1024 MPI ranks. We observed up to 194 fold speedups using 512 MPI processes on a well-known real world data, and significantly better performance results with respect to a state of the art implementation.",
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"title": "A Traffic Congestion Forecasting Model using CMTF and Machine Learning",
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"abstract": "Traffic congestion severely affects many cities around the world causing various problems like fuel wastage, increased stress levels, delayed deliveries and monetary losses. Therefore, it is urgent to make an accurate prediction of traffic jams to minimize these losses. But forecasting is a real challenge to obtain promising results for vibrant and ambiguous traffic flows in urban networks. This paper proposes a new traffic jam prediction model using pre-calculated density from node information table based on previous traffic data. In this model, we predicted traffic congestion of an intersection according to its adjacent road's node information table, where node information table contains the traffic density of all incoming lanes of an intersection (node). Besides, for this model, we consider all intersections of a city as individual nodes, and we prepare node information table for each node. Our work can be divided into two parts: (1) we perform time series analysis on previous data of a node and its adjacent nodes, and (2) then apply those calculated values to this model and make the prediction based on it. The forecasted value will always be between 0 and 1. Where 0 means no traffic congestion, close to 0 means low traffic congestion and 1 means heavy traffic or close to 1 means congested traffic lane, accordingly.",
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"content": "Traffic congestion severely affects many cities around the world causing various problems like fuel wastage, increased stress levels, delayed deliveries and monetary losses. Therefore, it is urgent to make an accurate prediction of traffic jams to minimize these losses. But forecasting is a real challenge to obtain promising results for vibrant and ambiguous traffic flows in urban networks. This paper proposes a new traffic jam prediction model using pre-calculated density from node information table based on previous traffic data. In this model, we predicted traffic congestion of an intersection according to its adjacent road's node information table, where node information table contains the traffic density of all incoming lanes of an intersection (node). Besides, for this model, we consider all intersections of a city as individual nodes, and we prepare node information table for each node. Our work can be divided into two parts: (1) we perform time series analysis on previous data of a node and its adjacent nodes, and (2) then apply those calculated values to this model and make the prediction based on it. The forecasted value will always be between 0 and 1. Where 0 means no traffic congestion, close to 0 means low traffic congestion and 1 means heavy traffic or close to 1 means congested traffic lane, accordingly.",
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"abstract": "Due to a growth in the number of vehicles and people, traffic flow on highways has expanded dramatically during the last few decades. Congestion is caused by fixed road infrastructure and excessive traffic on traffic lanes, especially in developing international cities. In major cities, traffic bottlenecks are common, resulting in increased travel time, increased fuel use, and increased pollution. This paper proposes a Road Steering system with several metrics(RSSSM) that analyses traffic congestion circumstances using many metrics and suggests efficient best routes to vehicles based on those conditions. The suggested mechanism is simulated in the smart vehicular network using SUMO and a python script. The suggested mechanism, RSSSM, beats existing systems, according to the data whether efficiency of traffic, duration of journey, consumption of fuel or level of pollutions are concerned.",
"abstracts": [
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"content": "Due to a growth in the number of vehicles and people, traffic flow on highways has expanded dramatically during the last few decades. Congestion is caused by fixed road infrastructure and excessive traffic on traffic lanes, especially in developing international cities. In major cities, traffic bottlenecks are common, resulting in increased travel time, increased fuel use, and increased pollution. This paper proposes a Road Steering system with several metrics(RSSSM) that analyses traffic congestion circumstances using many metrics and suggests efficient best routes to vehicles based on those conditions. The suggested mechanism is simulated in the smart vehicular network using SUMO and a python script. The suggested mechanism, RSSSM, beats existing systems, according to the data whether efficiency of traffic, duration of journey, consumption of fuel or level of pollutions are concerned.",
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"authors": [
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"affiliation": "School of Electronics and Electrical Engineering, Lovely Professional University,Punjab",
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"affiliation": "School of Electronics and Electrical Engineering, Lovely Professional University,Punjab",
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"title": "Analyzing the Cascading Effect of Traffic Congestion Using LSTM Networks",
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"abstract": "This paper presents a data-driven approach for predicting the propagation of traffic congestion at road segments as a function of the congestion in their neighboring segments. In the past, this problem has mostly been addressed by modelling the traffic congestion over some standard physical phenomenon through which it is difficult to capture all the modalities of such a dynamic and complex system. While other recent works have focused on applying a generalized data-driven technique on the whole network at once, they often ignore intersection characteristics. On the contrary, we propose a city-wide ensemble of intersection level connected LSTM models and propose mechanisms for identifying congestion events using the predictions from the networks. To reduce the search space of likely congestion sinks we use the likelihood of congestion propagation in neighboring road segments of a congestion source that we learn from the past historical data. We validated our congestion forecasting framework on the real world traffic data of Nashville, USA and identified the onset of congestion in each of the neighboring segments of any congestion source with an average precision of 0.9269 and an average recall of 0.9118 tested over ten congestion events.",
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"affiliation": "Vanderbilt University,Electrical Engineering and Computer Science Department,USA",
"fullName": "Sanchita Basak",
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"abstract": "The phenomenon of urban traffic congestion has largely restricted the overall efficiency of the traffic planning control network. Traditional cities' road planning and traffic control facilities seem to be stretched in the face of the problem of high-traffic vehicle traffic. How to effectively alleviate and manage urban traffic congestion will affect the healthy and sustainable development of the domestic economy. Through the investigation and research of the existing intelligent technology, this paper mainly discusses how to apply the mature intelligent technology to the urban traffic planning and control system to alleviate the urban traffic congestion.",
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"affiliation": "Economics and Management Beijing Jiaotong University,Beijing,China",
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"abstract": "Traffic congestion is a major problem in many cities. It happens due to the demand-supply imbalance in the transportation network and poor management. Traffic flow slows down when the number of vehicles that travels on the road increases or the roadway capacity decreases due to various reasons. In order to solve this problem, different solutions are proposed to provide reliable, real-time transport management services in an Intelligent Transportation System (ITS). In this paper, we propose a novel real-time video analytics using low-cost IoT devices and LoRaWAN networks to realize new services and applications that include traffic management through IoT edge computing. The use of LoRaWAN for such application is our main contribution. We retrain YOLO v3 object detection machine learning model (transfer learning) for vehicle detection and counting, to make it lightweight and fast enough to be able to run on a Raspberry Pi, a single-board computer with limited RAM. The edge node, with low-cost smart camera and connectivity through LoRaWAN networks counts the number of vehicles using real-time video analytic and report only traffic count to the server. This experimental work provides insight into the applicability of a low-cost IoT system to traffic management with a resource-constrained environment. Real-world video analysis of vehicle detection and counting show the effectiveness of the designed solution. The results demonstrate the effectiveness of the proposed approach.",
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"content": "Traffic congestion is a major problem in many cities. It happens due to the demand-supply imbalance in the transportation network and poor management. Traffic flow slows down when the number of vehicles that travels on the road increases or the roadway capacity decreases due to various reasons. In order to solve this problem, different solutions are proposed to provide reliable, real-time transport management services in an Intelligent Transportation System (ITS). In this paper, we propose a novel real-time video analytics using low-cost IoT devices and LoRaWAN networks to realize new services and applications that include traffic management through IoT edge computing. The use of LoRaWAN for such application is our main contribution. We retrain YOLO v3 object detection machine learning model (transfer learning) for vehicle detection and counting, to make it lightweight and fast enough to be able to run on a Raspberry Pi, a single-board computer with limited RAM. The edge node, with low-cost smart camera and connectivity through LoRaWAN networks counts the number of vehicles using real-time video analytic and report only traffic count to the server. This experimental work provides insight into the applicability of a low-cost IoT system to traffic management with a resource-constrained environment. Real-world video analysis of vehicle detection and counting show the effectiveness of the designed solution. The results demonstrate the effectiveness of the proposed approach.",
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"affiliation": "Addis Ababa University,Addis Ababa,Ethiopia",
"fullName": "Salahadin Seid",
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"abstract": "This paper deals with the analysis of the modeling deficiencies of the traditional 3D well bore trajectory and puts forward a new method of slice-based modeling for the tubular well bore trajectory. The method implements the 3D tubular well bore trajectory seamless connection efficiently and conveniently without interpolation smoothing treatment of the well bore trajectory's bender. In order to directly observe, analyze and effectively control the drilling trajectory for the drill staff, the paper implements the 3D tubular well bore trajectory based on virtual reality technology and provides a flexible human-computer interaction function. It also details in the well bore trajectory modeling and 3D visualization followed by an application example.",
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"abstract": "Video stabilization is highly desirable when videos undergo severe jittering artifacts. The difficulty of obtaining sufficient training data obstructs the development of video stabilization. In this work, we address this issue by presenting a Sim2RealVS benchmark with more than 1,300 pairs of shaky and stable videos. Our benchmark is curated by an in-game simulator with diverse scenes and various jittering effects. Moreover, we propose a simple yet strong baseline approach, named Motion-Trajectory Smoothing Network (MTSNet), by fully exploiting our Sim2RealVS data. Our MTSNet consists of three main steps: motion estimation, global trajectory smoothing and frame warping. In motion estimation, we design a Motion Correction and Completion (MCC) module to rectify the optical flow with low confidence, such as in textureless regions, thus providing more consistent motion estimation for next steps. Benefiting from our synthetic data, we can explicitly learn a Trajectory Smoothing Transformer (TST) with ground-truth supervision to smooth global trajectories. In training TST, we propose two fully-supervised losses, i.e., a motion magnitude similarity loss and a motion tendency similarity loss. After training, our TST is able to produce smooth motion trajectories for the shaky input videos. Extensive qualitative and quantitative results demonstrate that our MTSNet achieves superior performance on both synthetic and real-world data.",
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"abstract": "Summary form only given: We study compressed representations of web graphs. Among previous work, the solution by Hernandez and Navarro [1] supports more queries than alternative approaches, including in-neighbour queries, out-neighbour queries and a set of mining queries. Their main strategy is to extract dense subgraphs from the given graph, and encode them using succinct data structures such as wavelet trees. Previous experimental studies on wavelet trees, however, test performance using textual data, and more engineering work is needed for the data generated from web graphs.Our strategy is to use different implementations to encode bit vectors at different levels of the wavelet trees constructed for dense subgraphs, based on the observation that bit vectors at top levels are more compressible than the rest. These implementations are considered: RRR, practical implementations [2] of the structure by Raman et al. [3]; RLEG, a bit vector structure based on run-length and Elias gamma codes [4]; and Plain, an uncompressed representation with low overheads [5]. Two specific approaches are used to combine them: The first approach encodes bit vectors using RRR starting from the root of a wavelet tree, until a level for which Plain uses less space is reached. Then, starting from this level downwards, Plain is used to encode bit vectors. The second approach uses RLEG, RRR and Plain in a similar top-down fashion, and different tradeoffs can be achieved by using different block sizes for RLEG.We implemented these approaches with code from [1, 4] and the compact structures library libcds (http://recoded.cl/), to encode data sets from the WebGraph Framework project (http://webgraph.di.unimi.it/). We obtained a rich set of time/space tradeoffs that can not be achieved using a single bit vector structure for all levels. The following three tradeoffs are particularly interesting: A new encoding scheme that decreases the space cost of Hernandez and Navarro's structure by 9% to 19% (more than 13% for all but one graph), while only doubling query time; a new scheme that decreases the space cost by 4% to 12% (10% or more for most graphs), with roughly the same query time; and a new scheme that decreases the space cost and the query time by about 2% and 1%-9% (5% or more for most graphs), respectively.",
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"abstract": "The Internet of things (IoT) technologies are witnessing an ever increasing interest in supply chain management. Where many communications in the IoT based suppy chain network are based on point-to-multipoint communication paradigm. So, the supply chain can be modeled in multi branch tree form for different companies. As security is a key aspect to pave the way to IoT deployment, this architecture will be based on Merkle Tree that has proved its robustness and efficiency for key management. In this paper, we propose a supply chain design to efficiently manage a group key in our tree based architecture for supply chain managgement. In our dynamic IoT based supply chain, we will try to reduce the computational overhead and network traffic due to supply chain changes caused by items joins and leaves.",
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"content": "The Internet of things (IoT) technologies are witnessing an ever increasing interest in supply chain management. Where many communications in the IoT based suppy chain network are based on point-to-multipoint communication paradigm. So, the supply chain can be modeled in multi branch tree form for different companies. As security is a key aspect to pave the way to IoT deployment, this architecture will be based on Merkle Tree that has proved its robustness and efficiency for key management. In this paper, we propose a supply chain design to efficiently manage a group key in our tree based architecture for supply chain managgement. In our dynamic IoT based supply chain, we will try to reduce the computational overhead and network traffic due to supply chain changes caused by items joins and leaves.",
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"abstract": "With the development of digital technologies and urbanization, there are plenty of visionary technologies coming around. One of the most important products of our era is the smartphone. Smartphones have already become peoples daily necessaries as one kind of the way we work and play. Extending the field of smartphones' application has become a hot topic. Among them, utilizing embedded sensors to develop application is one of the research and industrial trend. However, the sensors inside modern smartphones present a range of problem, the biggest problem is inaccuracy. e.g., GPS and digital compass sensors data may not exactly match the reality. Therefore, due to the inaccuracy of the low-cost embedded sensors, it is important but still challenging to model the error distribution of these sensors. In this manuscript, we develop a hybrid 3-dimensional model to measure the sensor error distribution by using laser ranger and the sensor meta-data themselves, including positioning sensor and digital compass. Experimental results show that the model we have achieved is promising.",
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"content": "With the development of digital technologies and urbanization, there are plenty of visionary technologies coming around. One of the most important products of our era is the smartphone. Smartphones have already become peoples daily necessaries as one kind of the way we work and play. Extending the field of smartphones' application has become a hot topic. Among them, utilizing embedded sensors to develop application is one of the research and industrial trend. However, the sensors inside modern smartphones present a range of problem, the biggest problem is inaccuracy. e.g., GPS and digital compass sensors data may not exactly match the reality. Therefore, due to the inaccuracy of the low-cost embedded sensors, it is important but still challenging to model the error distribution of these sensors. In this manuscript, we develop a hybrid 3-dimensional model to measure the sensor error distribution by using laser ranger and the sensor meta-data themselves, including positioning sensor and digital compass. Experimental results show that the model we have achieved is promising.",
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"affiliation": "Sino-Dutch Biomedical and Information Engineering school, Northeastern University, No.195 Chuangxin Road, Hunnan District, Shenyang, Liaoning Province, China, 110169",
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"affiliation": "Sino-Dutch Biomedical and Information Engineering school, Northeastern University, No.195 Chuangxin Road, Hunnan District, Shenyang, Liaoning Province, China, 110169",
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"affiliation": "Sino-Dutch Biomedical and Information Engineering school, Northeastern University, No.195 Chuangxin Road, Hunnan District, Shenyang, Liaoning Province, China, 110169",
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"title": "2018 Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR)",
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"abstract": "In the last decade, several robotic wheelchairs possessing user-friendly interfaces and/or autonomous functions for reaching a goal have been proposed to meet the needs in aging societies. Moreover, it is vital to consider for the researchers, how to reduce the companion's load and support their activities like bus boarding and disembarkation. In this paper, we propose an autonomous bus boarding wheelchair system that can give freedom of movement to wheelchair users by following its companion side by side or in front-behind positions and simultaneously scan the environment to move smoothly in outside terrain. Additionally, our bus boarding wheelchair is able to detect buses and bus doors with precise measurements of the doorstep's height and width to board and disembark the bus. Our experiments show the effectiveness and applicability of our system in moving around urban areas in using community bus services.",
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{
"abstractType": "Regular",
"content": "In the last decade, several robotic wheelchairs possessing user-friendly interfaces and/or autonomous functions for reaching a goal have been proposed to meet the needs in aging societies. Moreover, it is vital to consider for the researchers, how to reduce the companion's load and support their activities like bus boarding and disembarkation. In this paper, we propose an autonomous bus boarding wheelchair system that can give freedom of movement to wheelchair users by following its companion side by side or in front-behind positions and simultaneously scan the environment to move smoothly in outside terrain. Additionally, our bus boarding wheelchair is able to detect buses and bus doors with precise measurements of the doorstep's height and width to board and disembark the bus. Our experiments show the effectiveness and applicability of our system in moving around urban areas in using community bus services.",
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{
"affiliation": "Graduate School of Science and Engineering, Saitama University, Saitama, 338-8570, Japan",
"fullName": "Shamim Al Mamun",
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"affiliation": "Graduate School of Science and Engineering, Saitama University, Saitama, 338-8570, Japan",
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"affiliation": "Graduate School of Science and Engineering, Saitama University, Saitama, 338-8570, Japan",
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"affiliation": "Graduate School of Science and Engineering, Saitama University, Saitama, 338-8570, Japan",
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"affiliation": "Graduate School of Science and Engineering, Saitama University, Saitama, 338-8570, Japan",
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"abstract": "As a kind of automated loading and unloading, handling equipment and high-precision transportation platform, the AGV mobile platform has a range of applications in the fields of smart factories, smart warehousing and smart logistics. It is important for the research of AGV automatic navigation and positioning. Aiming at the application scenarios of AGV motion millimeter-level spatial positioning accuracy, this paper studies an angle measurement method of orthogonal line laser scanning mechanism and photoelectric sensor network perception, and proposes a spatial positioning method based on a virtual camera projection model. The system parameter calibration method that is based on the beam adjustment method is studied to realize the high-precision 3D positioning of the AGV mobile platform.",
"abstracts": [
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"content": "As a kind of automated loading and unloading, handling equipment and high-precision transportation platform, the AGV mobile platform has a range of applications in the fields of smart factories, smart warehousing and smart logistics. It is important for the research of AGV automatic navigation and positioning. Aiming at the application scenarios of AGV motion millimeter-level spatial positioning accuracy, this paper studies an angle measurement method of orthogonal line laser scanning mechanism and photoelectric sensor network perception, and proposes a spatial positioning method based on a virtual camera projection model. The system parameter calibration method that is based on the beam adjustment method is studied to realize the high-precision 3D positioning of the AGV mobile platform.",
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"normalizedAbstract": "As a kind of automated loading and unloading, handling equipment and high-precision transportation platform, the AGV mobile platform has a range of applications in the fields of smart factories, smart warehousing and smart logistics. It is important for the research of AGV automatic navigation and positioning. Aiming at the application scenarios of AGV motion millimeter-level spatial positioning accuracy, this paper studies an angle measurement method of orthogonal line laser scanning mechanism and photoelectric sensor network perception, and proposes a spatial positioning method based on a virtual camera projection model. The system parameter calibration method that is based on the beam adjustment method is studied to realize the high-precision 3D positioning of the AGV mobile platform.",
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"affiliation": "Beijing Spacecrafts,Beijing,China,100080",
"fullName": "Jingyu Liu",
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"affiliation": "Beijing Spacecrafts,Beijing,China,100080",
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"abstract": "It is of great significance to detect the weld surface to ensure welding quality. Aiming at the problem of weld surface defect detection and 3D reconstruction algorithm, a method of defect detection and three - dimensional reconstruction based on laser scanning 3D point cloud has been proposed. The method of BP neural network is used to filter the point cloud data obtained by laser displacement sensor scanning, and the result is qualitatively and quantitatively analyzed. The three-dimensional reconstruction model of pit defects on weld surface is established. The reconstructed error is within the allowable range. The experimental results show that applying BP neural network to weld defect detection can effectively reduce the complexity of 3D reconstruction and improve the accuracy of 3D reconstruction of weld surface.",
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"content": "It is of great significance to detect the weld surface to ensure welding quality. Aiming at the problem of weld surface defect detection and 3D reconstruction algorithm, a method of defect detection and three - dimensional reconstruction based on laser scanning 3D point cloud has been proposed. The method of BP neural network is used to filter the point cloud data obtained by laser displacement sensor scanning, and the result is qualitatively and quantitatively analyzed. The three-dimensional reconstruction model of pit defects on weld surface is established. The reconstructed error is within the allowable range. The experimental results show that applying BP neural network to weld defect detection can effectively reduce the complexity of 3D reconstruction and improve the accuracy of 3D reconstruction of weld surface.",
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"normalizedAbstract": "It is of great significance to detect the weld surface to ensure welding quality. Aiming at the problem of weld surface defect detection and 3D reconstruction algorithm, a method of defect detection and three - dimensional reconstruction based on laser scanning 3D point cloud has been proposed. The method of BP neural network is used to filter the point cloud data obtained by laser displacement sensor scanning, and the result is qualitatively and quantitatively analyzed. The three-dimensional reconstruction model of pit defects on weld surface is established. The reconstructed error is within the allowable range. The experimental results show that applying BP neural network to weld defect detection can effectively reduce the complexity of 3D reconstruction and improve the accuracy of 3D reconstruction of weld surface.",
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"affiliation": "Guangdong Provincial Welding Engineering Technology Research Center, Guangdong University of Technology,Guangzhou,China,510006",
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"abstract": "Little is known about the affective expressivity of multisensory stimuli in wearable devices. While the theory of emotion has referenced single stimulus and multisensory experiments, it does not go further to explain the potential effects of sensorial stimuli when utilized in combination. In this paper, we present an analysis of the combinations of two sensory modalities — haptic (more specifically, vibrotactile) stimuli and auditory stimuli. We present the design of a wrist-worn wearable prototype and empirical data from a controlled experiment (N=40) and analyze emotional responses from a dimensional (arousal + valence) perspective. Differences are exposed between “matching” the emotions expressed through each modality, versus \"mixing\" auditory and haptic stimuli each expressing different emotions. We compare the effects of each condition to determine, for example, if the matching of two negative stimuli emotions will render a higher negative effect than the mixing of two mismatching emotions. The main research question that we study is: When haptic and auditory stimuli are combined, is there an interaction effect between the emotional type and the modality of the stimuli? We present quantitative and qualitative data to support our hypotheses, and complement it with a usability study to investigate the potential uses of the different modes. We conclude by discussing the implications for the design of affective interactions for wearable devices.",
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"content": "A precise domain-independent computational model for diagnostic problem-solving at multiple levels of abstraction is presented. The knowledge representation framework allows causal knowledge to be represented in a precise, yet natural, way that reflects a human diagnostician's experience in guiding diagnostic reasoning at multiple levels of abstraction. The inference mechanism clearly defines how to form plausible diagnostic hypotheses guided by explicit causal links and the principle of parsimonious covering. It permits the efficient formation of high-level diagnostic hypotheses, while at the same time ensuring that all plausible diagnostic alternatives will be considered if one wishes to reason at sufficiently detailed levels. A prototype implementation has been constructed to demonstrate that the proposed inference model is natural for capturing a small but representative fragment of medical causal knowledge and that with the addition of reasonable heuristics, the inference mechanism exhibits desirable problem-solving behavior.<>",
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"abstract": "Characterizes the problem of multiple levels of abstraction in simulation modeling and develops an approach that addresses the problem. In this paper, we describe: (i) the notion of abstraction and the technical problems associated with multiple levels of abstraction, (ii) how abstractions affect different activities during the simulation modeling process, (iii) a preliminary approach for addressing the problems associated with multiple levels of abstraction, (iv) the conceptual architecture of a simulation modeling environment that implements the proposed approach, and (v) a summary of the research on questions of abstraction in simulation.",
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"abstract": "Recently, deep learning-based image denoising methods have achieved significant improvements over traditional methods. Due to the hardware limitation, most deep learning-based image denoising methods utilize cropped small patches to train a convolutional neural network to infer the clean images. However, the real noisy images in practical are mostly of high resolution rather than the cropped small patches and the vanilla training strategies ignore the cross-patch contextual dependency in the whole image. In this paper, we propose Cross-Patch Net (CPNet), which is the first deep-learning-based real image denoising method for HR (high resolution) input. Furthermore, we design a novel loss guided by the noise level map to obtain better performance. Compared with the vanilla patch-based training strategies, our approach effectively exploits the cross-patch contextual dependency. Besides, owing to the difficulty in capturing real noisy and noise-free image paired training data, we propose an effective method to generate realistic sRGB noisy images from their corresponding clean sRGB images for denoiser training. Denoising experiments on real-world sRGB images show the effectiveness of the proposed method. More importantly, our method achieves state-of-the-art performance on practical sRGB noisy image denoising.",
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"content": "Recently, deep learning-based image denoising methods have achieved significant improvements over traditional methods. Due to the hardware limitation, most deep learning-based image denoising methods utilize cropped small patches to train a convolutional neural network to infer the clean images. However, the real noisy images in practical are mostly of high resolution rather than the cropped small patches and the vanilla training strategies ignore the cross-patch contextual dependency in the whole image. In this paper, we propose Cross-Patch Net (CPNet), which is the first deep-learning-based real image denoising method for HR (high resolution) input. Furthermore, we design a novel loss guided by the noise level map to obtain better performance. Compared with the vanilla patch-based training strategies, our approach effectively exploits the cross-patch contextual dependency. Besides, owing to the difficulty in capturing real noisy and noise-free image paired training data, we propose an effective method to generate realistic sRGB noisy images from their corresponding clean sRGB images for denoiser training. Denoising experiments on real-world sRGB images show the effectiveness of the proposed method. More importantly, our method achieves state-of-the-art performance on practical sRGB noisy image denoising.",
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"abstract": "The amount of interest in Virtual Reality (VR) research has significantly increased over the past few years, both in academia and industry. The release of commercial VR Head-Mounted Displays (HMDs) has been a major contributing factor. However, there is still much to be learned, especially how views and input techniques, as well as their interaction, affect the VR experience. There is little work done on First-Person Shooter (FPS) games in VR, and those few studies have focused on a single aspect of VR FPS. They either focused on the view, e.g., comparing VR to a typical 2D display or on the controller types. To the best of our knowledge, there are no studies investigating variations of 2D/3D views in HMDs, controller types, and their interactions. As such, it is challenging to distinguish findings related to the controller type from those related to the view. If a study does not control for the input method and finds that 2D displays lead to higher performance than VR, we cannot generalize the results because of the confounding variables. To understand their interaction, we propose to analyze in more depth, whether it is the view (2D vs. 3D) or the way it is controlled that gives the platforms their respective advantages. To study the effects of the 2D/3D views, we created a 2D visual technique, PlaneFrame, that was applied inside the VR headset. Our results show that the controller type can have a significant positive impact on performance, immersion, and simulator sickness when associated with a 2D view. They further our understanding of the interactions that controllers and views have and demonstrate that comparisons are highly dependent on how both factors go together. Further, through a series of three experiments, we developed a technique that can lead to a substantial performance, a good level of immersion, and can minimize the level of simulator sickness.",
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"content": "The amount of interest in Virtual Reality (VR) research has significantly increased over the past few years, both in academia and industry. The release of commercial VR Head-Mounted Displays (HMDs) has been a major contributing factor. However, there is still much to be learned, especially how views and input techniques, as well as their interaction, affect the VR experience. There is little work done on First-Person Shooter (FPS) games in VR, and those few studies have focused on a single aspect of VR FPS. They either focused on the view, e.g., comparing VR to a typical 2D display or on the controller types. To the best of our knowledge, there are no studies investigating variations of 2D/3D views in HMDs, controller types, and their interactions. As such, it is challenging to distinguish findings related to the controller type from those related to the view. If a study does not control for the input method and finds that 2D displays lead to higher performance than VR, we cannot generalize the results because of the confounding variables. To understand their interaction, we propose to analyze in more depth, whether it is the view (2D vs. 3D) or the way it is controlled that gives the platforms their respective advantages. To study the effects of the 2D/3D views, we created a 2D visual technique, PlaneFrame, that was applied inside the VR headset. Our results show that the controller type can have a significant positive impact on performance, immersion, and simulator sickness when associated with a 2D view. They further our understanding of the interactions that controllers and views have and demonstrate that comparisons are highly dependent on how both factors go together. Further, through a series of three experiments, we developed a technique that can lead to a substantial performance, a good level of immersion, and can minimize the level of simulator sickness.",
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"abstract": "As bioinformatics has evolved from a reductionistic approach to a complementary multi-scale integrative approach, new challenges in ultra-scale visualization have arisen. Even though visualization is a critical component to large-scale biological data analysis, the ultra-scale nature of systems biology has given rise to novel problems in visualization that are not addressed by existing methods. Visualization is a rich and actively researched domain, and there are many open research questions pertaining to the increasing demands of visualization in bioinformatics. In this paper, we present several broadly important ultra-scale visualization challenges and discuss specific examples of ultra-scale applications in systems biology.",
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