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Effective recognition of pollen grains based on parametric adaptation of the image identification model

Effective recognition of pollen grains based on parametric a...

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International conference on Information Science and Communications Technologies (ICISCT)

作者： Jumanov Isroil Ibragimovich Djumanov Olimjon Isroilovich Xolmonov Sunatillo Maxmudovich Samarkand State University) Samarkand Uzbekistan Samarkand State University Samarkand Uzbekistan

ISBN: (数字)9781728199696

ISBN: (纸本)9781728199702

Constructive approaches, principles and methods of image processing in conditions of a priori insufficiency, parametric uncertainty, low accuracy of information have been developed on the basis of statistical, dynamic, neural network models for identifying micro-objects. methods of point and nonlinear verification of the correspondence of the contours of the input and reference objects based on the selection, segmentation, interpolation of specific characteristics of images of pollen grains and regulation of raster parameters are proposed. Modified computational schemes for learning neural networks with forward and backward propagation of errors, regulation of interneural connections in layers, neuron weights, variable activation functions, network architecture, training samples, superposition of continuous input-output dependencies. A computer complex for identification in the C++ language in the “CUDA” parallel computing environment has been implemented for the recognition, classification and systematization of pollen grains [1].

关键词： Analytical models image segmentation Computational modeling Feature extraction Data models Object recognition Biological neural networks

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Coherence Parameters Estimation Algorithm for distributed Coherent Aperture Radar in Low SNR Environment

Coherence Parameters Estimation Algorithm for Distributed Co...

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image processing, Electronics and Computers (IPEC), Asia-Pacific conference on

作者： Xueqi Wang Gangyi Tu Shaopeng Wu Human Resources Department No.724 Research Institute of China Shipbuilding Industry Corporation Nanjing China General department of Detection System No.724 Research Institute of China Shipbuilding Industry Corporation Nanjing China Development Demonstration Center No.724 Research Institute of China Shipbuilding Industry Corporation Nanjing China

ISBN: (数字)9781728160672

ISBN: (纸本)9781728160689

A method of reducing parameters estimation error by multi-pulse accumulation is proposed to deal with the problem of large error and low coherence performance of distributed coherent aperture radar in low SNR(signal-to-noise ratio) environment. This method uses different coherent accumulation methods for low-speed and high-speed moving targets respectively. For low-speed targets without range migration, discrete Fourier transform is used to achieve multi-pulse coherent accumulation. For high-speed targets, the keystone transform is used to correct the range migration firstly, and then the coherent accumulation is realized by discrete Fourier transform. Then, the peak picking method is used to estimate the coherence parameters. Finally, the coherence parameters estimation process in low SNR environment is given in combination with two cases. The simulation results show that, compared with the method of monopulse signal, this method can obtain higher estimation performance of coherence parameters in low SNR environment. And with the increase of accumulated pulses, the estimation performance of coherence parameters also improves.

关键词： low SNR distributed coherence parameters estimation multi-pulse accumulation keystone transform low SNR Parameter estimation coherence low speed

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A High Throughput parallel Hash Table Accelerator on HBM-enabled FPGAs

A High Throughput Parallel Hash Table Accelerator on HBM-ena...

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IEEE International conference on Field-Programmable Technology (FPT)

作者： Yang Yang Sanmukh R. Kuppannagari Viktor K. Prasanna University of Southern California

Hash table is a key component in a number of AI algorithms such as Graph Convolutional Neural Networks, Approximate Nearest Neighbor Search, Bag-of-Words based Text Mining algorithms, etc. Efficient implementation of hash tables is needed for a wide range of AI applications. High bandwidth memory (HBM), which provides significantly higher memory bandwidth than traditional DDR, has recently gained popularity. In this work, we propose a high throughput parallel hash table targeting HBM-enabled FPGAs. Our design is tailored for HBM architecture, allowing flexible and balanced mapping between processing engines and HBM channels at design time given query distribution and hash table properties (key/value length, collision handling, and hash table size). We further develop a novel data organization and query flow which enable our accelerator to scale up to 16 processing engines (PEs). The proposed design supports parallel search, insert, and delete queries. Experimental results demonstrate that our hash table accelerator can achieve up to 3575 million operations per second (MOPS) for search-only queries and up to 1470 MOPS for 50%/50% distributed search/update queries on HBM-enabled FPGAs. It achieves better throughput than the state-of-the-art GPU and FPGA designs by up to 3.5× and 3.2× respectively.

关键词： Text mining Graphics processing units Organizations Bandwidth Machine learning Nearest neighbor methods Throughput

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Epilepsy Seizure Detection by using Bayesian Optimize Bi-LSTM Model

Epilepsy Seizure Detection by using Bayesian Optimize Bi-LST...

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International conference on parallel distributed and Grid Computing (PDGC)

作者： Vidhi Sood Deepak Kumar Vijay Anant Athavale Suresh Chand Gupta Panipat Institute of Engineering and Technology Kurukshetra University Panipat India

ISBN: (数字)9781728171326

ISBN: (纸本)9781728171333

In medical Science field, the EEG signal classification is present with a plethora of applications. The health monitoring is depending on modern technology like EEG and ECG signal recording. The EEG signals are analyzed to identify the abnormal condition of the human brains. The unusual activity of the brain is known as the seizure The electrical signal generated in the braincauses epilepsy. In this proposed work, a deep learning model Bi-LSTM is projected for the epilepsy signal classification. The Bonn university EEG dataset is used for the testing purpose. The EEG signal classification has three significant steps data pre-processing, features extraction, and classification. Data pre-processing is done by DCT and filter conversion. The Hurst exponent and ARMA feature sets are extracted from the pre-process EEG signal. A Bayesian optimization tuned Bi-LSTM model is suggested for the EEG signal classification task. The epileptic EEG signals are recognized by the proposed method. The hyperparameters of the Bi- LSTM model is tuned by the Bayesian optimization rule. Three different class ictal, pre-ictal, and inter-ictal are classified from the EEG signal data. A comparative study is also provided for the epilepsy signal classification task. The classification accuracy of for ictal is 94%, pre-ictal is 92%, and inter-ictal is 91%, which more significant than the LSTM and SVM based classifier model.

关键词： Electroencephalography Optimization Bayes methods Brain modeling Epilepsy Feature extraction Computational modeling

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An energy-saving routing algorithm for opportunity networks based on sleeping mode 20

An energy-saving routing algorithm for opportunity networks ...

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

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

ISBN: (纸本)9781728126166

Opportunistic network is a type of tolerant delay network (DTN). Establishing routing path from the source node to the destination node may not be possible. So the traditional routing algorithms are not suitable for this network except the Epidemic. But due to its replication characteristics, the energy of node will be used up quickly. Many researchers have achieved lots of results on how to forwarding data strategically in Epidemic, but pay little attention to the energy consumption of nodes. This paper proposes an energy-saving algorithm named ERSE which combines the asynchronous sleep mechanism. The ERSE is based on the node communication model, making the node enter the sleeping state when it's not active. It also controls the sleep duration dynamically to ensure that the node does not miss the communication opportunities. ERSE makes the low-energy state nodes enter a forced sleeping state at the same time to avoid them going to use up their energy quickly. The simulation results show that ERSE routing algorithm can greatly save the energy consumption of nodes. © 2019 IEEE.

关键词： Energy conservation

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An efficient high-precision data detection for massive MU-MIMO systems

An efficient high-precision data detection for massive MU-MI...

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2019 International conference on Electronic Engineering and Informatics, EEI 2019

作者： Qiu, Shikai Wang, Yu Chen, Lirui Xing, Zuocheng National Laboratory for Parallel and Distributed Processing National University of Defense Technology Changsha Hunan China

ISBN: (纸本)9781728140766

Data detection is among the most crucial process task for massive multi-user (MU) multiple-input multiple-output (MIMO) wireless systems. In this letter, we propose a novel efficient high precision soft-output data detection algorithm, which iteratively generates a signal vector and reduces its complexity meanwhile. This algorithm guarantees its significant error-rate performance and reduced complexity by combing an optimization method, ADMM, and modified neighborhood search algorithm. Simulation results demonstrate that the proposed detection achieves superior performance over the existing methods at low computational complexity. © 2019 IEEE.

关键词： Iterative methods

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Efficient image Watercolorization Based on Smart Phones

Efficient Image Watercolorization Based on Smart Phones

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High Performance Big Data and Intelligent Systems (HPBD&IS)

作者： Huijuan Zhang Xuezhong Xiao School of Computer Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9781728165127

ISBN: (纸本)9781728158976

image watercolorization is a hot research topic in the field of non-photorealistic rendering. There are many methods proposed to simulate the various features of watercolor painting including edge darkening, pigment dispersion, turbulence, and paper texture. However, almost all of them are implemented for desktop systems and cannot be directly transplanted to mobile devices. From the view of resultant effects and computing efficiency, an image watercolorization framework is proposed for smart phones, which employs several existing image processing algorithms to simulate the visual features of watercolor painting. Finally, the method is optimized in parallel to improve the efficiency. The experiments show the presented method has good performance.

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Research on CNN parallel Computing and Learning Architecture Based on Real-Time Streaming Architecture 15th

Research on CNN Parallel Computing and Learning Architecture...

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15th International conference on distributed Computing and Internet Technology (ICDCIT)

作者： Zhu, Yuting Qian, Liang Wang, Chuyan Ding, Lianghui Yang, Feng Wang, Hao Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China Air Force Mil Representat Off Shanghai Nanjing Nanjing Peoples R China

ISBN: (纸本)9783030053666;9783030053659

Convolutional neural network (CNN) is a deep feed-forward artificial neural network, which is widely used in image recognition. However, this mode highlights the problems that the training time is too long and memory is insufficient. Traditional acceleration methods are mainly limited to optimizing for an algorithm. In this paper, we propose a method, namely CNN-S, to improve training efficiency and cost based on Storm and is suitable for every algorithm. This model divides data into several sub sets and processes data on several machine in parallel flexibly. The experimental results show that in the case of achieving a recognition accuracy rate of 95%, the training time of single serial model is around 913 s, and in CNN-S model only needs 248 s. The acceleration ratio can reach 3.681. This shows that the CNN-S parallel model has better performance than single serial mode on training efficiency and cost of system resource.

关键词： CNN parallel computing Apache storm Real time

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GPU-accelerated Calculation of Acoustic Echo Characteristics of Underwater Targets

GPU-accelerated Calculation of Acoustic Echo Characteristics...

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image processing, Electronics and Computers (IPEC), Asia-Pacific conference on

作者： Xianyun Wu Lixue Su Keyan Wang Yunsong Li State Key Laboratory of Integrated Service Networks Xidian University Xi’an China

ISBN: (数字)9781728160672

ISBN: (纸本)9781728160689

In this paper, we study the echo characteristics of submerged targets at far field. Based on the thought of shooting and bouncing ray (SBR), which uses geometrical optics (GO) principle and physical optics (PO) integration in combination, we propose a more efficient GPU-accelerated prediction method based on multiresolution grid algorithm in the SBR to calculate the target strength (TS) of concave complex targets. According to the simulation and experiment results on rigid sphere, cylinder and submarine model, the accuracy of the proposed approach is proved. Furthermore, the GPU-based SBR is more than 750x faster than CPU via GPU parallel computing architecture. And the proposed GPU-accelerated multiresolution SBR improves runtime performance at least 2. 4x over GPU-based SBR.

关键词： Echo characteristics acoustic target strength (TS) shooting and bouncing ray (SBR) multiresolution GPU implementation SBR Underwater targets runtime performance Echoes Underwater Geometrical optics Prediction methods shooting central processing units

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Automatic Exploration of Reduced Floating-Point Representations in Iterative methods 25th

Automatic Exploration of Reduced Floating-Point Representati...

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25th International conference on parallel and distributed Computing (Euro-Par)

作者： Chatelain, Yohan Petit, Eric Castro, Pablo de Oliveira Lartigue, Ghislain Defour, David Univ Versailles St Quentin En Yvelines Li PaRAD Versailles France Univ Perpignan Via Domitia LAMPS Perpignan France Normandie Univ CORIA CNRS Caen France Exascale Comp Res Paris France Intel Corp Paris France

ISBN: (纸本)9783030294007;9783030293994

With the ever-increasing need for computation of scientific applications, new application domains, and major energy constraints, the landscape of floating-point computation is changing. New floatingpoint representation formats are emerging and there is a need for tools to simulate their impact in legacy codes. In this paper, we propose an automatic tool to evaluate the effect of adapting the floating point precision for each operation over time, which is particularly useful in iterative schemes. We present a backend to emulate any IEEE-754 floating-point operation in lower precision. We tested the numerical errors resilience of our solutions thanks to Monte Carlo Arithmetic and demonstrated the effectiveness of this methodology on YALES2, a large Combustion-CFD HPC code, by achieving 28% to 67% reduction in communication volume by lowering precision.

关键词： Iterative methods

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