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Meta-Heuristic Approaches to parallel processing in Signal Representation and Real-Time Analysis

Meta-Heuristic Approaches to Parallel Processing in Signal R...

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Automation and Computation (AUTOCOM), international conference on

作者： Harisha Naik N T Sudhanshu Dev Aniket Shivam Neelesh Singh Shreenidhi H S Awakash Mishra Department of Computer Applications (DCA) Presidency College Bengaluru India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Engineering and Technology Computer Science and Engineering JAIN (Deemed to be University) Bangalore Karnataka Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Meta-heuristic techniques have been popular in solving highly complex optimization problems. parallel processing is very important in signal representation and real-time analysis, as it helps lower the time required for the data process so that algorithms can perform faster. The biggest challenge with the traditional parallel processing techniques is load balancing and higher communication overhead, which may need to scale up to a larger extent. Meta-heuristic techniques reduce the complexity of problems that are NP-complete or NP-hard by providing satisfactory solutions and approximate answers to difficult ones. This has helped in parallel processing for signal representation and real-time analysis. parallel processing has also been useful in using meta-heuristic algorithms like genetic algorithms, ant colony optimization, and particle swarm optimization. Meta-heuristic algorithms produce flexible, adaptive mechanisms for solving complex problems.

关键词： Heuristic algorithms Metaheuristics Signal processing algorithms parallel processing Signal processing Approximation algorithms Load management Real-time systems Signal representation Particle swarm optimization

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MACSQ: Massively Accelerated DeepQ Learning on GPUs Using On-the-fly State Construction 22nd

MACSQ: Massively Accelerated DeepQ Learning on GPUs Using On...

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22nd international conference on parallel and distributed Computing, applications and Technologies (PDCAT 2021)

作者： Koester, Marcel Gross, Julian Krueger, Antonio German Res Ctr Artificial Intelligence DFKI Saarland Informat CampusCampus D3-2 D-66123 Saarbrucken Germany

ISBN: (纸本)9783030967727;9783030967710

The current trend of using artificial neural networks to solve computationally intensive problems is omnipresent. In this scope, DeepQ learning is a common choice for agent-based problems. DeepQ combines the concept of Q-Learning with (deep) neural networks to learn different Q-values/matrices based on environmental conditions. Unfortunately, DeepQ learning requires hundreds of thousands of iterations/Q-samples that must be generated and learned for large-scale problems. Gathering data sets for such challenging tasks is extremely time consuming and requires large data-storage containers. Consequently, a common solution is the automatic generation of input samples for agent-based DeepQ networks. However, a usual workflow is to create the samples separately from the training process in either a (set of) pre-processing step(s) or interleaved with the training process. This requires the input Q-samples to be materialized in order to be fed into the training step of the attached neural network. In this paper, we propose a new GPU-focussed method for on-the-fly generation of training samples tightly coupled with the training process itself. This allows us to skip the materialization process of all samples (e.g. avoid dumping them disk), as they are (re)constructed when needed. Our method significantly outperforms usual workflows that generate the input samples on the CPU in terms of runtime performance and memory/storage consumption.

关键词： Massively-parallel processing Neural networks Q-learning Graphics processing units GPUs State construction

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distributed ICT solutions for scoliosis management

Distributed ICT solutions for scoliosis management

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Lorella Bottino Marzia Settino Mario Cannataro Dep. of Medical and Surgical Sciences Magna Graecia University Catanzaro Italy

Scoliosis is a abnormal curvature of the spine often found in adolescents. Commonly the management of patients with scoliosis is done through manual methods. The use of smartphone applications with integrated sensors allows the remote distributed management of scoliosis. Patients and doctors can communicate and collaborate each other through two different methods: web-based methods and app-based methods. Scoliosis management moves from a centralized system to a decentralized system with obvious benefits for both the doctor and the patient. In particular, the applications allow the doctor to monitor the patients scoliosis curve remotely, saving time and money. Furthermore, they allow the patient to easily check the progress of the scoliotic curvature at home and receive immediate feedback about the correctness and effectiveness of the physical exercises prescribed. We report a brief survey of main apps for scoliosis management and depicts a possible distributed software architecture for scoliosis management.

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Fall Direction Detection Using parallel CNN Based on Millimeter-Wave Radar Multi-Dimensional Feature Fusion

Fall Direction Detection Using Parallel CNN Based on Millime...

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2024 conference on Spectral Technology and applications, CSTA 2024

作者： Ren, Shukun Chen, Hanbo Liao, Kefei Deng, Honggao Liao, Guisheng Fu, Tao Li, Jingjing School of Information and Communication Guilin University of Electronic Technology Guangxi Guilin541004 China State and Local Joint Engineering Research Center for Satellite Navigation and Location Service Guilin University of Electronic Technology Guangxi Guilin541004 China Engineering Research Center of Trustworthy AI Ministry of Education Jinan University Guangzhou China

ISBN: (纸本)9781510683082

Falls pose a serious threat to the safety of the elderly. Accurately detecting falls and their direction is crucial for medical personnel to assess injury locations and promptly formulate treatment plans. Traditional methods primarily integrate distance and Doppler features of fall behavior, neglecting a significant amount of spatial features. Additionally, the determination of fall direction is easily affected by environmental noise and limb-induced Doppler interference. To this end, this paper proposes a parallel convolutional neural network fall direction detection method based on millimeter-wave radar multidimensional feature fusion, which introduces the fall angle as spatial information while effectively attenuating the interference of environmental noise and limb micro-Doppler frequency to improve the accuracy of fall direction detection. Specifically, this method utilizes techniques such as pulse compression, MTI, Range Fast Fourier Transform (RFFT), STFT, and Capon beamforming to obtain the range-time, Doppler-time, and angle-time features from the human motion state to the falling state. Then, the Doppler-time-map (DTM) and angle-time-map (ATM) after the accumulation of multiple frames are subjected to column threshold feature extraction and column normalization feature processing, respectively, to achieve the effect of scrambling frequency removal and feature enhancement. Finally, the processed feature spectrograms are input into the parallel convolutional neural network for independent extraction of each dimension's features and multi-dimensional fusion, enabling the detection of the fall direction. The experimental results show that the proposed method achieves an average recognition accuracy of 97.52% for different fall directions, compared to an improvement of 2.31% over the traditional feature fusion method. © 2024 SPIE.

关键词： Fast Fourier transforms

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A Review on Satellite Image Segmentation Using Metaheuristic Optimization techniques 1

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5th international conference on Advances in distributed Computing and Machine Learning, ICADCML 2024

作者： Bandikolla, Lakshmi Md Khairuzzaman, Abdul Kayom School of Electronics Engineering VIT-AP University Amaravati Andhra Pradesh Vijayawada522237 India

ISBN: (数字)9789819718412

ISBN: (纸本)9789819718405

Image segmentation is essential in digital image processing applications. Multilevel thresholding is a popular technique for image segmentation. An image can be divide into multiple classes. In this paper, the image quality metrics such as peak signal-to-noise ratio (PSNR), mean squared error (MSE), mean structural similarity (SSIM), feature similarity (FSIM) are compared using metaheuristic algorithms. The CPU time of different metaheuristics is also compared to determine the time complexity of the techniques. The Otsu’s method, Kapur’s entropy, Tsallis entropy, and Masi entropy are used as objective functions. Some of the metaheuristic algorithms, adaptive Cuckoo search algorithm (ACS), chaotic coronavirus optimization algorithm (COVIDOA), dynamic quantum-inspired genetic algorithm (DQGA), a modified artificial bee colony algorithm (MABC), dynamic Harris hawks with mutation mechanism (DHHO/M), and particle swarm optimization algorithms (PSO) are compared. As per the comparison of the quality metrics, DHHO/M with Otsu was more efficient in average PSNR. CS with Tsallis provides best value in average MSE, ACS with Otsu gives best results in average SSIM, ACS with Tsallis is more efficient in average FSIM, and MABC with Kapur’s combination was more efficient in average CPU time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image segmentation

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A Generic Efficient Scientific Workflow Engine for the Optimizations of Run-time Execution

A Generic Efficient Scientific Workflow Engine for the Optim...

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1st IEEE international conference on Software Services Engineering (IEEE SSE)

作者： Bai, Changxin Liu, Junwen Tahabilder, Anik Imran, M. M. Lu, Shiyong Che, Dunren Wayne State Univ Dept Comp Sci Detroit MI 48202 USA Southern Illinois Univ Dept Comp Sci Carbondale IL USA

ISBN: (纸本)9798350340754

Workflow has proven to be a highly effective computing model for a variety of scientific applications, offering flexible data types and unstructured parallelism that surpasses simple parallel execution models such as MapReduce. However, current workflow management systems in cloud computing environments experience unnecessary delays in task execution due to the separation of task execution and data transfer processes, which causes a child task to wait until all its predecessor tasks complete, rather than waiting only for necessary input data becoming ready. The goal of this paper is to eliminate the unnecessary delay of child tasks in a workflow, which is achieved through a new workflow engine architecture that separates workflow planner from workflow executor in the general framework of the DATAVIEW scientific workflow management system. This new engine architecture can be generalized and applied to other workflow systems. Our design integrates a new task release mechanism based on a data dependency model with the workflow executor of DATAVIEW. This approach enables prompt task launching once input data becomes available, instead of waiting for all predecessor tasks to finish. The architecture employs distributed algorithms for implementing the workflow executor and the task executors, performing various optimization on data movement, task movement, and communication among different subsystems. The experiments show that our new architecture based on the new task release model can significantly reduce overall execution time of a workflow in DATAVIEW.

关键词： Workflow Engine Workflow Execution parallel processing Task Releasing Optimization

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distributed Work Stealing in a Task-Based Dataflow Runtime 14th

Distributed Work Stealing in a Task-Based Dataflow Runtime

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14th international conference on parallel processing and Applied Mathematics, PPAM 2022

作者： John, Joseph Milthorpe, Josh Strazdins, Peter Australian National University Canberra Australia Oak Ridge National Laboratory Oak RidgeTN United States

ISBN: (纸本)9783031304415

The task-based dataflow programming model has emerged as an alternative to the process-centric programming model for extreme-scale applications. However, load balancing is still a challenge in task-based dataflow runtimes. In this paper, we present extensions to the PaRSEC runtime to demonstrate that distributed work stealing is an effective load-balancing method for task-based dataflow runtimes. In contrast to shared-memory work stealing, we find that each process should consider future tasks and the expected waiting time for execution when determining whether to steal. We demonstrate the effectiveness of the proposed work-stealing policies for a sparse Cholesky factorization, which shows a speedup of up to 35% compared to a static division of work. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Data flow analysis

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Halide Code Generation Framework in Phylanx 28th

Halide Code Generation Framework in Phylanx

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28th international European conference on parallel and distributed Computing , Euro-Par 2022

作者： Tohid, R. Shirzad, Shahrzad Taylor, Christopher Sakin, Sayef Azad Isaacs, Katherine E. Kaiser, Hartmut Center for Computation and Technology Louisiana State University Baton Rouge United States Tactical Computing Labs Muenster United States SCI Institute University of Utah Salt Lake City United States

ISBN: (纸本)9783031312083

Separating algorithms from their computation schedule has become a de facto solution to tackle the challenges of developing high performance code on modern heterogeneous architectures. Common approaches include Domain-specific languages (DSLs) which provide familiar APIs to domain experts, code generation frameworks that automate the generation of fast and portable code, and runtime systems that manage threads for concurrency and parallelism. In this paper, we present the Halide code generation framework for Phylanx distributed array processing platform. This extension enables compile-time optimization of Phylanx primitives for target architectures. To accomplish this, (1) we implemented new Phylanx primitives using Halide, and (2) partially exported Halide’s thread pool API to carry out parallelism on HPX (Phylanx’s runtime) threads. (3) showcased HPX performance analysis tools made available to Halide applications. The evaluation of the work has been done in two steps. First, we compare the performance of Halide applications running on its native runtime with that of the new HPX backend to verify there is no cost associated with using HPX threads. Next, we compare performances of a number of original implementations of Phylanx primitives against the new ones in Halide to verify performance and portability benefits of Halide in the context of Phylanx. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Array processing

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Biometric Security using EEG Signal processing - Acquisition, Representation and Classification Approaches

Biometric Security using EEG Signal Processing - Acquisition...

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2022 IEEE international conference on distributed Computing and Electrical Circuits and Electronics, ICDCECE 2022

作者： Biradar, Shashank D. Nalbalwar, Sanjay L. Deosarkar, Shankar B. Dbatu Dept. of E&tc Engineering 402104 India Vpkbiet 413133 India

ISBN: (纸本)9781665483162

Authentication and security issues are rapidly increasing with the development of new coding techniques and network interfacing. Biometric security has shown a promising performance for security mechanism compared to key based security mechanisms. Biometric security system operates on physiological and neurological inputs and their behavioral characteristic. In Biometric security system, forging and synthetic false inputs limits its application and reduces the reliability. Inputs such as face, iris, voice etc., are highly vulnerable to such attack. Biological signaling observes a higher reliability in providing security. Electroencephalogram (EEG) has gained a large interest in recent past for biometric security provisioning. This paper outlines a brief review on the recent development in the area of provisioning security using EEG signal for biometric security. Various approaches of EEG interface for biometric security, classification and its applications were reviewed. © 2022 IEEE.

关键词： Electroencephalography

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A New distributed-STBC Scheme for Cooperative Relaying in Wireless Networks 7

A New Distributed-STBC Scheme for Cooperative Relaying in Wi...

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7th international conference on Image and Signal processing and their applications, ISPA 2022

作者： Tayakout, Hakim Zohra Bouchibane, Fatma Boutellaa, Elhocine Centre de Développement des Technologies Avancées Division Télécommunication Algiers Algeria

ISBN: (纸本)9781665480420

Space-time encoded communications aim at improving the quality and reliability of a wireless high-data rate link by exploiting both the temporal and spatial signal dimensions. However, these techniques are impeded by typically low profile requirement of the terminals, making impractical the deployment of multiple antennas, more particularly at the relay side. In this paper, we propose a new and simplified distributed space-time block Coding (D-STBC) scheme in which the STBC code is artificially generated by relying on three single-antenna nodes in the cooperative network and using less time slots than the conventional counterpart. The proposed scheme is then generalized to encompass both multiple cooperative relays and multiple receive antennas cases. Furthermore, our proposal is investigated with both decode- and-forward (DaF) and amplify-and-forward (AaF) relaying protocols. At the destination, zero-forcing detector is adopted for the D-STBC decoding, and maximum ratio combining (MRC) of the relays signals is performed when retaining multiple relays configuration. It is shown that, with one relay, our proposed scheme exhibits similar performance in terms of data reliability with the conventional STBC alternative, with the advantage of resorting to only one antenna per transmitting/retransmitting node. Moreover, the viable capabilities of the proposed scheme are fully pointed out with an increased number of relays. © 2022 IEEE.

关键词： Cooperative communication

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