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检索条件"任意字段=18th Image Processing: Algorithms and Systems Conference, IPAS 2020"

共 258 条记录，以下是241-250 订阅

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A Balanced Cost Fault-Tolerant Scheduling Algorithm in Heterogeneous Real-Time systems

A Balanced Cost Fault-Tolerant Scheduling Algorithm in Heter...

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IEEE International conference on High Performance Computing and Communications (HPCC)

作者： Jing Liu Yifu Zhang Yang Liu Pei Yang Cen Chen Ziqi Zhu Zeng Zeng College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Institute for Infocomm Research Singapore

ISBN: (纸本)9781728176499;9781728176505

High reliability is required for many safety-critical and real-time applications. It is difficult to avoid permanent faults during the executing process of tasks. Once such faults are not processed in time, real-time tasks cannot be completed within the specified time, which may lead to catastrophic consequences. In order to deal with the problem of permanent fault tolerance in heterogeneous real-time systems, in this paper, we propose a balanced cost fault-tolerant scheduling algorithm. First, the algorithm uses the task computation time and the average communication time of its successor nodes as the ranking factors when calculating task priority, so that the priority of the task with larger average communication time improved; second, a task cost table is established, and tasks are allocated to appropriate processors according to the values in the task cost table in processor allocation stage. Experimental results show that the proposed algorithm can effectively improve the system reliability and reduce the completion time.

关键词： Fault tolerance Program processors Scheduling algorithms High performance computing conferences Fault tolerant systems Real-time systems

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GPU-Accelerated Adaptive Dictionary Learning and Sparse Representations for Multispectral image Super-resolution

GPU-Accelerated Adaptive Dictionary Learning and Sparse Repr...

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IEEE India conference (INDICON)

作者： Trishna Barman Bhabesh Deka A.V.V. Prasad Tezpur University Tezpur India Natational Remote Sensing Center (NRSC) ISRO Hyderabad Telengana India

ISBN: (纸本)9781665441766

Recently, single image super-resolution (SISR) based on sparse representations has been gaining much attention from the research community in the field of remote sensing. In this paper, a fast SISR reconstruction framework is developed for multispectral remote sensing (MSRS) images based on adaptive dictionary learning and sparse representations. It consists of two major parts: first, a novel super-resolution approach is developed for MSRS using sparse coding and adaptive dictionary learning. High-frequency features present in the input low-resolution MS image are extracted by using Butterworth low-pass, difference of Gaussian (DoG), and Sobel filters in horizontal and vertical directions. the proposed feature extraction method reveals the edges and other detailed information present in the MS image effectively. Secondly, massively parallel algorithms are designed for adaptive dictionary learning and sparse reconstruction using the Compute Unified Device Architecture (CUDA)-enabled General Purpose-Graphics processing Unit (GP-GPU) programming model. the proposed method GP-GPU implementation not only gives better results in terms of visual quality and objective fidelity criteria, but also significantly reduces the computation time compared to its CPU counterparts to achieve near-real time operating speed.

关键词： Visualization image edge detection Superresolution Machine learning Programming Feature extraction Real-time systems

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A Novel Economical Design of Multistage Decimators for Low Latency Audio Applications

A Novel Economical Design of Multistage Decimators for Low L...

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IEEE International conference on High Performance Computing and Communications (HPCC)

作者： Wei Hu Chongwei Zheng Yonghao Wang Jing Liu Cen Chen Zeng Zeng College of Computer Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Digital Media Technology lab Birmingham City University Birmingham UK Institute for Infocomm Research Singapore

ISBN: (纸本)9781728176499;9781728176505

the oversampling sigma-delta analog-to-digital converter (ADC) is widely used in digital audio systems for its low hardware cost and high precision. the digital filter part of the ADC is the primary source of the silicon and power cost. In this work, we proposed an economical multistage decimation filter architecture to meet the requirements of commercial ADC specifications with a further novel multi-objective optimization strategy. We took group delay and computational cost as objective functions and used annealing algorithm to readjust the stopband attenuation, stopband edge and passband ripple of each filter. Our multistage architecture combines cascaded integrator-comb (CIC) filters, compensating filters and equiripple finite impulse response (FIR) filters at the different stages to balance the overall performance. the experimental results showed that our design approach worked with multi-objective optimization strategy could achieve low group delay with lower cost compared to traditional designs.

关键词： Sigma-delta modulation Finite impulse response filters Computer architecture Filtering algorithms Attenuation Linear programming Silicon

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Hand geometry and palmprint-based authentication using image processing

Hand geometry and palmprint-based authentication using image...

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International Symposium on Intelligent systems and Informatics (SISY)

作者： Laura Gulyás Oldal András Kovács John von Neumann Faculty of Informatics Óbuda University Budapest Hungary

ISBN: (数字)9781728173528

ISBN: (纸本)9781728173535

Nowadays, the importance of biometric authentication has increased, since biometric identifiers are unique to every individual, and are more reliable than knowledge or ownership factors. Palmprint and hand geometry features are suitable for high-confidence personal authentication. Contactless authentication is another topic which has been researched for capturing a palm image in more sanitary conditions. the combined use of multiple biometric identifiers results in a more reliable authentication system. this paper proposes a method for contactless hand geometry and palmprint-based authentication. the proposed method uses simple image processing algorithms and geometric relations for keypoint detection and palm image extraction. Morphological operations, averaging filters and different methods for noise filtering are used for palmprint principal line extraction. the corresponding points of palmprint images are detected by template-based matching which is followed by further filtering of matching points. With the use of hand geometry features and palmprint matching mechanism, the collected information of the hand and palm is suitable for authentication.

关键词： thumb Authentication Geometry Feature extraction image edge detection Wrist

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Low contrast sperm detection and tracking by Watershed algorithm and Particle filter

Low contrast sperm detection and tracking by Watershed algor...

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Iranian conference of Biomedical Engineering (ICBME)

作者： Mohammad R. Ravanfar Mohammad H. Moradi Biomedical Engineering Department Amirkabir University of Technology-á Tehran Iran

Sperm movement characteristic plays an important role in male fertility. Computer-aided Sperm Analysis (CASA) systems have tried to provide more accurate information about sperm motility and concentration. At the beginning of this goal, achieving correct sperm trajectory is necessary. On the other hand, sperm tracking is a challenging matter because, sperms have same size and shape, move fast also there is an uncertainty in their motions. therefore, most of designers have attempted to reduce tracking mistakes especially, when sperms collide with each other. In this paper we try to present a sperm tracking method based on Watershed segmentation and Particle filter estimation which focuses on the sperm collision problem. In contrast of most sperm tracking algorithms based on sperm head orientation, this method tries to track sperms independent of sperm head orientation. Moreover, sperm videos were taken by common camera in order to ease availability in every laboratory. Consequently image sequences have low contrast so, processing section bears more burdens in proportion to the imaging. In this paper, the method is evaluated by tracking 10 sperms which had at least one collision along their paths. the results showed the method is robust in the cluttered scenes.

关键词： Tracking Head image segmentation Particle filters Biomedical engineering Transforms

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MAMAP: Congestion Relieved Memetic Algorithm based Mapping Method for Mapping Large-Scale SNNs onto NoC-based Neuromorphic Hardware

MAMAP: Congestion Relieved Memetic Algorithm based Mapping M...

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IEEE International conference on High Performance Computing and Communications (HPCC)

作者： Limeng Zhang Shiming Li Lianhua Qu Ziyang Kang Shiying Wang Jihua Chen Lei Wang College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (纸本)9781728176505

Spiking neuron networks (SNNs) have been widely adopted to solve complex tasks such as pattern recognition, image classification, and natural language processing. Simulating SNNs on many-core neuromorphic hardware has been proved to be powerful and energy-efficient. Constrained by hardware resources, it is difficult to map a complete SNN on a single neuromorphic core. the SNNs need to be segmented and placed in different neuromorphic cores. the Network-on-Chip (NoC) connection strategy for interconnecting multiple neuromorphic cores has been widely used in modern neuromorphic hardware. However, the enormous inter-core communication from large-scale SNNs will lead to congestion of NoC and dramatically reduces performance. In this paper, we propose a congestion relieved memetic-algorithm-based mapping method (MAMAP) to find an efficient mapping solution that minimizes the latency and power consumption of neuromorphic hardware by maximizing bandwidth utilization. Besides, our proposed memetic algorithm combining the global search capability of the Particle Swarm Optimization (PSO) algorithm and the local search capability of the TABU, can find the best mapping solution fast. Compared with the state-of-art SpiNeMap, the experimental results show that MAMAP can reduce the average latency by 63% and average energy consumption by 69%.

关键词： Memetics Power demand Neuromorphics Neurons Network-on-chip Hardware Natural language processing

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A Novel Event Based image Sensor with spacial and temporal redundancy suppression

A Novel Event Based Image Sensor with spacial and temporal r...

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Annual IEEE Northeast Workshop on Circuits and systems (NEWCAS)

作者： Mohamed Akrarai Nils Margotat Gilles Sicard Laurent Fesquet CNRS TIMA Grenoble CEA LETI MINATEC Campus Grenoble France

ISBN: (数字)9781728170442

ISBN: (纸本)9781728170459

We present a new image sensor architecture that manages spacial and temporal redundancies. this frameless image sensor only generates few events over time in order to target an efficient power consumption compared to the commercial CMOS image sensors. Indeed, this image sensor does not generate anymore frames but events only when a change appears in the scene. Moreover, the event throughput depends on the luminance variations of the recorded scene. this means that more activity in the scene will generate more events and vice versa. Collecting events over a period of time will define an image. It is noticeable that, at each instant, the generated events characterize the area of interest (the active area) of the scene. Consequently, processing such images should requires less computing energy.

关键词： Voltage control Redundancy Kernel image sensors Photodiodes Standards throughput

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Calibration Data-based CNN Filter Pruning for Efficient Layer Fusion

Calibration Data-based CNN Filter Pruning for Efficient Laye...

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IEEE International conference on High Performance Computing and Communications (HPCC)

作者： Krishna Teja Chitty-Venkata Arun K. Somani Iowa state University Ames IA USA

ISBN: (纸本)9781728176505

Convolutional Neural Network (CNN) optimization is critical to reduce the inference latency on computing devices like CPU and GPU. the most important step in efficiently executing these algorithms involves combining multiple operations within a single convolutional layer through a process called Layer Fusion. We first form a correlation between Layer Fusion and data management on computing platforms like CPUs and GPUs, and analyze its significance for different networks under different model compression techniques, e.g., Pruning and Quantization. Weight Pruning removes redundant parameters in the network thereby shrinking the model size. this method, however, creates sparse matrices which can hamper the performance on CPU and GPU devices. Several node/filter pruning algorithms have been developed to resolve the bottlenecks of irregular pruning and reduce the inference time. Although symmetric pruning techniques reduce the forward path computation time, the weights and activations are still executed in floating point precision which can be further optimized by Quantization. this method reduces the bit-width of individual CNN parameter from high precision (Float32) to lower precision (Int8). Even though several model compression (pruning and quantization) techniques have been developed to improve performance, their integrated study with Layer Fusion has not been performed. In today's scenario, not all CPUs and GPUs explicitly support Int8 multiplication. Hence, we analyze the performance of optimized implementation of Int8 Quantization on such devices like Intel's Skylake and Nvidia's Tesla V100. We develop a novel Node Pruning algorithm to remove redundant filters which can aid in efficient implementation of Layer Fusion/Quantized Networks. We compare the execution time of our combined pruning and quantization implementation with a traditional node pruning algorithm which achieved a mean speedup of 3.5 times on Skylake CPU.

关键词： Performance evaluation Analytical models Quantization (signal) Computational modeling High performance computing Graphics processing units Filtering algorithms

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Development of an ATCA based data acquisition system for high speed, direct detection X-ray pixel sensors

Development of an ATCA based data acquisition system for hig...

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IEEE NPSS conference on Real Time

作者： John Joseph Devis Contarato Peter Denes Dionisio Doering Patrick McVittie John Weizeorick Lawrence Berkeley National Laboratory Berkeley CA USA Argonne National Laboratory Argonne IL USA

Large format X-ray pixel sensors operating at frame rates higher than 100 frames per second have driven the need to develop data acquisition systems capable of handling large volumes of acquired data using ultra-fast communication links operating at 10 Gigabit/s rates. the new generation 1 Megapixel X-Ray cameras currently being developed at LBNL, operating at readout speeds of up to 200 frames per second, are capable of producing greater than 400 Megabytes of image data per second. Because these sensors are used in continuous source applications with long acquisition periods (e.g., at synchrotron radiation light sources), the acquired data must be reliably processed and stored in real-time to minimize exposure dead time or data loss that could compromise the integrity of the data and thus limit the scientific reach of the experiment. this work describes the development and performance of an Advanced Telecommunications Computing Architecture (ATCA) based data acquisition system used for high speed, direct detection X-ray pixel sensors, focusing on the technical challenges and solutions of moving large data volumes through digital signal processing algorithms and to storage arrays in real-time.

关键词： Cameras Data acquisition Blades Sensors Real-time systems Charge coupled devices Head

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Collaborative Multi-Object Tracking as an Edge Service using Transfer Learning

Collaborative Multi-Object Tracking as an Edge Service using...

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IEEE International conference on High Performance Computing and Communications (HPCC)

作者： Han Sun Yu Chen Alexander Aved Erik Blasch Binghamton University SUNY Binghamton NY USA The U.S. Air Force Research Laboratory Rome NY USA

ISBN: (纸本)9781728176505

Video cameras have been pervasively deployed in Smart Cities and timely processing of the huge amount of video data brings up new challenges. Each individual camera is only able to cover a limited range due to the effective monitoring distances and the complicated layout of a building or an area. Once the object of interest moves away from the camera field of view, the system may lose object tracking. When the object appears and is captured by another camera, object detection, feature abstraction, and behavior classification require an association with other video data exploitation results. Hence, pragmatic approaches for object re-verification (or re-acquisition) are needed so as to not infer the object as a new track. In this paper, we propose a novel Collaborative Multi-Object Tracking system as an Edge service (CoMOTE) based on transfer learning technology. A transfer learning-based human detection and tracking method is introduced, which allocates most of the computing tasks on edge devices. An edge server is adopted to integrate information from edge cameras for decision-making to enable multi-view surveillance. the CoMOTE scheme not only reduces the dependence on a central server but also leads to a lower communication cost. Implemented on edge nodes (Raspberry Pi 4) and tested with WildTrack multi-view video database, extensive experimental results verify that the CoMOTE prototype achieved the design goal and outperforms multiple state-of-the-art methods.

关键词： Performance evaluation image edge detection Surveillance Transfer learning Collaboration Prototypes Object detection

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