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检索条件"任意字段=Conference on Parallel and Distributed Methods for Image Processing"

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An improved MCB localization algorithm based on received signal strength indicator 20

An improved MCB localization algorithm based on received sig...

引用

20th International conference on parallel and distributed Computing, Applications and Technologies, PDCAT 2019

作者： Qiao, Yan Chunyue, Zhou Baitong, Zhong Hui, Tian School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Laboratory of Communication Engineering Beijing Jiaotong University Beijing China Institute of Image Communication and Network Engineering Uniview Hangzhou Zhejiang Province China School of Information and Communication Technology Griffith University Australia

ISBN: (纸本)9781728126166

An improved Monte Carlo Localization Boxed (MCB) localization Algorithm based on received signal strength indicator (RSSI) for mobile wireless sensor networks node localization is proposed. Aiming at the characteristics of instantaneity, mobility and complexity of mobile node location, this algorithm combines RSSI ranging model with MCL algorithm which has high positioning accuracy, good performance and wide application. It establishes anchor node sampling box by using the actual distance of signal propagation obtained, and effectively reduces the sampling range. The simulation results show that this algorithm improves the sampling efficiency, shortens the sampling time, and increases the positioning accuracy compared to the MCL algorithm. It is a low-cost, low-power and low-complexity localization algorithm without additional hardware and communication overhead. © 2019 IEEE.

关键词： Monte Carlo methods

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Role of Mathematics in image processing

Role of Mathematics in Image Processing

引用

2019 International conference on Machine Learning, Big Data, Cloud and parallel Computing, COMITCon 2019

作者： Vashisht, Manisha Bhatia, Madhulika Manav Racha International Institute of Research and Studies Faridabad India

ISBN: (纸本)9781728102115

The various image processing technologies have revolutionized the way in which a particular image is captured, stored, or retrieved from a certain source. Most commonly used image processing tasks include;image Restoration, image Segmentation, image Enhancement, De-Blurring, De-noising, etc. Such images are well utilized in various ways by several of the Imaging techniques, like;Angiography, MRI (Magnetic Resonance Imaging), Arterial Spin Labeling (ASL), Computer Assisted Tomography (CT), Deep Brain Stimulation (DBS), Electroencephalography (EEG), etc. But, it is the, Mathematics that has played a crucial role in various image processing tasks mentioned above. Despite the innovation and rapid advances in the various Imaging Technology, one thing that has remained important throughout, is, the use of *** has been observed that image processing has got strong connection with Mathematics. Many of the image processing methods rely on the basic Mathematical Techniques of Histogram Equalization, Probability and Statistics, Discrete Cosine Transforms, Fourier Transforms, Differential Equations, Integration, Matrix and Algebra. For computational purposes, Matlab is one of the most commonly used tool by the researchers in the area of image processing. Other Tools which are commonly used include SciLab, GNU Octave, SageMath, *** paper critically reviews the role of Mathematics in various image processing Applications. This paper would also present the basic Mathematical tools and techniques, used in image processing, along with the literature review of the same. © 2019 IEEE.

关键词： Electrophysiology

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A Hybrid Multi-focus image Fusion Technique using SWT and PCA

A Hybrid Multi-focus Image Fusion Technique using SWT and PC...

引用

International conference on Confluence The Next Generation Information Technology Summit (Confluence)

作者： Tushar Tyagi Parth Gupta Prabhishek Singh Department of CSE Amity University Uttar Pradesh Noida India

ISBN: (数字)9781728127910

ISBN: (纸本)9781728127927

This paper presents a new hybrid and parallel processing image fusion technique for multi-focus images. Here, two different methods are used i.e. Stationary Wavelet Transform (SWT) and Principal Component Analysis (PCA) that are implemented on the input images in parallel. These two methods are applied on same input dataset. This method is although computationally bit slower than the compared method but still it shows better results. The fused images obtained from the SWT and PCA are later again fused using PCA method. This is a parallel processing technique. The result of proposed method is compared with other traditional and conventional methods like DWT, SWT and PCA. It is observed that the result of proposed method is better than the compared methods. The result of the proposed method is analyzed qualitatively (visual appearance) and quantitatively using CC (Correlation Coefficient), UIQI (Universal image Quality Index), and PSNR (Peak Signal-to-Noise Ratio). The proposed technique will have the capability to be implemented in real time applications of Visual Sensor Network (VSN).

关键词： conferences Cloud computing Data science Principal component analysis Wavelet analysis Wavelet transforms

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Hardware and Algorithm Co-Optimization for pointwise convolution and channel shuffle in ShuffleNet V2

Hardware and Algorithm Co-Optimization for pointwise convolu...

引用

IEEE International conference on Systems, Man and Cybernetics

作者： Zimeng Fan Wei Hu Hong Guo Fang Liu Dian Xu College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Wuhan City College Wuhan China School of Computer Science Wuhan University Wuhan China

ISBN: (纸本)9781665442084

Since the convolutional neural network (CNN) was proposed, it has achieved remarkable results in image recognition, and its accuracy rate surpasses other methods. In recent years, with the continuous improvement of computational complexity and the development of research on the deployment of neural networks on terminal devices, new neural network models suitable for terminal devices such as MobileNet, SqueezeNet and ShuffleNet have also appeared in CNN model. These network models have special structures or units to reduce the amount of *** lightweight features of the model cannot take advantage of due to the lack of customized design for them when GPU is used for acceleration. The characteristics of FPGA such as customization and parallelism just meet the needs of research. However, there is a little research on implementing and optimizing new lightweight networks on FPGAs. In this paper, we optimize each part of the model based on FPGA, achieve 8bit quantification, and redesign depthwise separable convolution operation and channel shuffle module, which make the module can be operated in a hardware-friendly way. Under the premise of maintaining the original model structure and performance, we make full use of the abundant LUT, FF and other resources on the FPGA. we use HLS (high-level synthesis) to analyze the structure of the ShuffleNet V2 model on xilinxzynqxc-7Z045. Compared with the performance of the original model in CPU, GPU and FPGA, our design achieves the acceleration performance of 8.7x, 1.3x and 3.3x respectively, and the accuracy is only reduced by 0.8%. Compared with other designs, it also achieved a result of 3.09x 60x in terms of latency.

关键词： image recognition Convolution Computational modeling Neural networks Graphics processing units parallel processing Hardware

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BAYESIAN RECONSTRUCTION OF UNDERSAMPLED MULTICOIL HARDI 26

BAYESIAN RECONSTRUCTION OF UNDERSAMPLED MULTICOIL HARDI

引用

26th IEEE International conference on image processing (ICIP)

作者： Gupta, Kratika Awate, Suyash P. Indian Inst Technol IIT Bombay Comp Sci & Engn CSE Dept Mumbai Maharashtra India

ISBN: (纸本)9781538662496

High-angular-resolution diffusion imaging (HARDI) relies on multicoil acquisitions for clinical applications. HARDI scan time can be reduced by undersampling the set of gradient directions. Typical methods for undersampled HARDI reconstruction use two-stage schemes that first take scanner-reconstructed magnitude images for the acquired directions, and then fit a model to the reconstructed undersampled diffusion signals, where they assume a Gaussian noise model that behaves poorly for high b values or high noise. In contrast, we propose a novel Bayesian framework for undersampled-HARDI reconstruction that directly fits to multicoil data. We use magnitude images per coil and a Rician noise model to bypass complicated phase-related artifacts and accurately reconstruct at large b values. We use a sparse dictionary prior on the diffusion signal across directions, and a multiscale wavelet regularity on each diffusion weighted image. Results on simulated and clinical HARDI data show that our method improves over the state of the art.

关键词： Reconstruction HARDI parallel imaging undersampling dictionary Rician noise Bayesian

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Application of distributed Machine Learning Model in Fault Diagnosis of Air Preheater 4

Application of Distributed Machine Learning Model in Fault D...

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4th Annual International conference on System Reliability and Safety (ICSRS)

作者： Lei, Haokun Liu, Jian Xian, Chun Univ Sci & Technol Beijing Sch Civil & Resource Engn Beijing Peoples R China Univ Sci & Technol Beijing Key Lab Minist Efficient Min & Safety Met Mines Beijing Peoples R China

ISBN: (纸本)9781728147819

Existing monitoring systems for the current operational status of power equipment and fault diagnosis detection systems mostly use serial computing methods, and less parallel distributed processing algorithms are used. With the development of intelligent work of power systems, more and more test data of power plant equipment is becoming more and more complex, which puts new demands on the implementation of data processing and the ability of data calculation. In this study, by using spark, two distributed machine learning models for state detection and fault diagnosis are established for the air preheater, and the confusion matrix is used for evaluation. The results show that the random forest model can effectively diagnose the faults of the air preheater.

关键词： distributed machine learning fault diagnosis spark air preheater

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Agile Approach on the Performance Prediction of ARM TrustZone-based Mandatory Access Control Security Enhancement

Agile Approach on the Performance Prediction of ARM TrustZon...

引用

IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Zhipeng Li Yan Ding Xiaofan Chen Pan Dong Chenlin Huang Liantao Song Peng Wang School of Computer Science National University of Defence Technology Changsha China

Mandatory Access Control (MAC) is one of the most important security mechanisms of Linux, but it may be threatened by vulnerabilities in kernel space. Currently, the ARM TrustZone-based MAC (TZ-MAC) security enhancement method has been proposed to protect the key security function of MAC with the hardware-based trusted execution environment. However, given that each call to the hook set of the TZ-MAC security modules will cause switching between the normal and secure worlds of ARM TrustZone, the specific design of the TZ-MAC framework will substantially affect system performance, and may even be considerably slow to prevent the security module from being applied. Therefore, when researchers design a security module, they need an agile method to predict the performance cost brought by the hook set realized, to assist the optimization of the design scheme and improve system performance. This study presents a performance prediction method of TZ-MAC based on the statistical analysis of the Linux Security Modules (LSM) hook calls. This method is universal for different TZ-MAC frameworks, security modules, and benchmarks. Moreover, the proposed method can predict only the performance based on the security module design of TZ-MAC, and the implementation of the module is not needed. An agile approach of performance prediction for TZ-MAC is conducted based on the performance prediction method. For two different TZ-MAC implementation ideas, we constructed prediction data sets based on the classic benchmarks, namely, LMbench and UnixBench, and verified the effectiveness of our method. The tests based on the security module SELinux show that over 50% of the overhead is caused by 3% of the LSM hook functions, which indicates the direction for future optimization.

关键词： Access control Costs Statistical analysis System performance Linux Prediction methods Switches

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Fingerprint image Enhancement processing Method Based on Weighted Median Filtering 20

Fingerprint Image Enhancement Processing Method Based on Wei...

引用

20th IEEE/ACIS International conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed Computing (SNPD 2019)

作者： Han, Ke Minist Publ Secur China Inst Forens Sci Dept Fingerprint Technol Beijing Peoples R China

ISBN: (纸本)9781728116518

When the traditional median filtering method is used to filter the impulse noise of the fingerprint image, the filtering window size is always fixed. The traditional median filtering method does not take into account the distance correlation between the surrounding pixel points and the central pixel point in the filtering window. To some extent, the traditional median filtering method is less effective in filtering impulse noise, especially in filtering high-density impulse noise. In order to solve these problems of the traditional median filtering method, a method based on weighted median filtering is proposed for fingerprint image enhancement processing in this paper. In the method, the filtering window is first initialized. The center pixel of the filtering window is detected according to its pixel value to determine whether it is impulse noise. If the center pixel is not impulse noise, the current center pixel value is taken as the filtered output value. If the central pixel is impulse noise, the pixels in the filtering window are detected to determine whether all these pixels are impulse noise. If not all these pixels are impulse noise, the non impulse noise pixel points in the window are regarded as valid pixel points, and the median value of the valid pixel points is taken as the filtered output value. If all the pixels in the window are impulse noise, the size of the filtering window is increased and it is detected whether the filtering window reaches the maximum value. At this time, whether all the pixels in the new filtering window are impulse noise is detected. If the filtering window size reaches the maximum value and all the pixels in the filtering window are impulse noise, the filtered pixel points in the filtering window are weighted to calculate the center pixel value of the filtering window. The proposed method is evaluated on fingerprint images contaminated by different density impulse noises, and is compared with the traditional median filtering m

关键词： image processing fingerprint enhancement median filtering impulse noise

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A Bytecode Service Composition Engine for Embedded Services

A Bytecode Service Composition Engine for Embedded Services

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Symposium of image, Signal processing, and Artificial Vision (STSIVA)

作者： Siao Wang Chenglie Du Jinchao Chen Zuo Zhao Ying Zhang Northwestern Polytechnical University China Science and Technology on Electro-iptic Control Laboratory China

ISBN: (纸本)9781665426565

With the development of embedded systems, people tend to abstract the capacity of an embedded equipment as a service in order to simplify the development, deployment, management and maintenance of the embedded software. By aggregating abilities of individual embedded devices via service composition, people can easily build a more reliable and efficient system. Although the service composition problem has been extensively studied in the field of Web, the adoptation of service composition technique to embedded devices stumps due to the resource limitation and platform heterogeneity of embedded systems. To address these problems, this paper builds a service composition engine for embedded systems that comprises three main works: First, this paper provides a uniform method to represent the embedded service composition problem. Second, this paper designs a compiling method based on topological sorting to convert the unified composition information into the service composition file that represents the way to implement the composite service. Third, this paper devises a bytecode virtual machine to execute the service composition file, and implements the composite service in a resource-friendly way. At last, a carefully devised experiment is conducted, and the result shows our devised engine provides a lightweight, reliable and well-performed way to realize the service composition technique on embedded devices.

关键词： Embedded systems Instruction sets Optimization methods parallel processing Maintenance engineering Virtual machining Software reliability

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The 16,384-node parallelism of 3D-CNN Training on An Arm CPU based Supercomputer

The 16,384-node Parallelism of 3D-CNN Training on An Arm CPU...

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International conference on High Performance Computing

作者： Akihiro Tabuchi Koichi Shirahata Masafumi Yamazaki Akihiko Kasagi Takumi Honda Kouji Kurihara Kentaro Kawakami Tsuguchika Tabaru Naoto Fukumoto Akiyoshi Kuroda Takaaki Fukai Kento Sato Fujitsu Limited Kawasaki Kanagawa Japan RIKEN Center for Computational Science Kobe Hyogo Japan

ISBN: (纸本)9781665410175

As the computational cost and datasets available for deep neural network training continue to increase, there is a significant demand for fast distributed training on supercomputers. However, porting and tuning applications for new advanced supercomputers requires tremendous amount of development efforts. Therefore, we present software tuning best practice for a 3D-CNN model training on a new Arm CPU based supercomputer, Fugaku. We (i) tune computation in DL by a JIT translator for aarch64, (ii) optimize collective communication such as Allreduce for 6D mesh/torus network topology, (iii) tune I/O by data staging with compression and data loader with caching, and (iv) parallelize training in data and model parallelism. We apply the proposed methods to a CosmoFlow 3D-CNN model, and achieve the training in 30 minutes using 16,384 nodes consisting of 4096 data- and 4 model-parallelism. This is the fastest result of any CPU-based systems in MLPerf HPC v0.7 in the world.

关键词： Training Computational modeling Neural networks parallel processing Supercomputers Software Computational efficiency

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