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Emerging Research in Computational Science (ICERCS), International conference on

作者： Lydia D Isaac V. Mohanraj Nisha Soms Department of Information Technology Sona College of Technology Salem India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

The swift progress of Internet of Things (IoT) devices and latency-sensitive submissions has driven the need for efficient data processing and task management in edge computing environments. Traditional edge computing frameworks often face challenges in optimizing resource utilization and handling dynamic workloads. The integration of adaptive intelligence in edge computing has arisen as a vital solution for augmenting data processing and task management efficiency in modern computing environments. This research introduces a novel algorithm, Edge Reinforcement Learning with Context-Aware Tasking (EDREL-CAT), designed to optimize task allocation and resource management at the edge. By utilizing reinforcement learning techniques coupled with context-aware algorithms, the proposed method aims to significantly improve task scheduling and processing efficiency while addressing the constraints inherent in edge computing. An all-inclusive simulation analysis is steered to evaluate the performance of EDREL-CAT against existing algorithms, including traditional scheduling methods and state-of-the-art approaches. The analysis employs various metrics, including task completion time, system throughput, task queue length and fairness index to provide a thorough comparison. The results demonstrate that EDREL-CAT outperforms existing solutions, confirming its potential as an effective tool for enhancing edge computing capabilities.

关键词： Performance evaluation Processor scheduling Simulation Reinforcement learning Data processing Throughput Real-time systems Resource management Internet of Things Edge computing

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Improving optical Character Recognition(OCR) Accuracy using Multi-Layer Perceptron(MLP)

Improving Optical Character Recognition(OCR) Accuracy using ...

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International conference on Trends in Electronics and Informatics (ICEI)

作者： M. Krishnamoorthi Karthi P Sri Ram M Sathyan T Vasanth Professor of the Department Dr.N.G.P Institute Of Technology Coimbatore India Department of Information Technology Dr.N.G.P Institute Of Technology Coimbatore India

optical Character Recognition (OCR) is a cutting-edge application that has been made possible due to advances in technology. optical Character Recognition involves using deep learning algorithms such as Multi-Layer Perceptron and Support Vector Machine to create a system that can recognize characters in an image. The optical Character Recognition system first segments each character individually as part of the pre-processing step, after which it post-processes the image to compare each character to the pre-processed data. In the modern world, optical Character Recognition is a crucial technology used in many industries to shorten turnaround times while increasing accuracy. However, one of the challenges faced with optical Character Recognition is that it takes longer to match exact results and produces results with lower precision and character misreadings. To address this challenge, the pre-processed data used by optical Character Recognition systems now includes practically all fonts. This means that the optical Character Recognition system has to match the characters with a much larger dataset, making the process more time-consuming and less accurate. To improve the accuracy and speed of optical Character Recognition systems, several methods have been employed. For instance, post-processing techniques such as error correction algorithms can be used to detect and correct errors in the optical Character Recognition output. Additionally, the use of deep learning techniques such as Conventional Neural Networks has been shown to improve the accuracy of optical Character Recognition systems. By employing these methods, optical Character Recognition can become an even more powerful tool that can shorten turnaround times while increasing accuracy.

关键词： Integrated optics Industries Deep learning Image recognition optical character recognition Neural networks optical imaging

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Research on network performance of D2D communication based on interference management 6

Research on network performance of D2D communication based o...

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6th International conference on Mechatronics and Intelligent Robotics, ICMIR 2022

作者： Song, Hao Peng, Yi Wang, Rui Zhao, Xiong Faculty of Computer Information Kunming Metallurgy College Kunming650033 China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Faculty of Information and Intelligent Engineering Yunnan University of Business Management Kunming650106 China

ISBN: (纸本)9781510656635

D2D(Device-to-device) improves communication quality by reusing cellular users' spectrum resources, becoming one of the key technologies for super-large data processing and massive devices accessing in the 5G. Due to load imbalance and co-frequency interference caused by distance change or increase in the number of users, this paper introduces interference management strategy in the 5G scenario where D2D communication and cellular communication coexist. By establishing up and down link resource model of D2D user reuse cellular communication to measure user throughput, and then control transmitting power and the distance between user and base station to reduce the influence of interference on the system. Simulation results show that compared with other traditional algorithms, the network performance of D2D communication based on interference management is significantly improved. © 2022 SPIE.

关键词： Data handling

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Simulation of Fast Transformation Procedures in Galois Fields to Estimate Efficiency of Perspective Signals Generation and processing devices

Simulation of Fast Transformation Procedures in Galois Field...

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2021 systems of Signals Generating and processing in the Field of on Board Communications, SOSG 2021

作者： Mikhaylov, V.Y. Mazepa, R.B. Moscow Aviation Institute National Research University Moscow Russia

ISBN: (纸本)9780738130897

The object of consideration is design automation and prototyping of complex signals and their generation and processing devices. The subject of the analysis is the substantiation of the structure, composition and implementation method of a modeling system aimed at modeling and prototyping promising devices for the generation and processing of complex signals based on fast transformations in Galois fields. The purpose of the analysis is a reasonable choice of methods and tools for computational, simulation and prototyping that achieve high display accuracy, performance, flexibility and ease of modeling. The solutions are based on the principles and mathematical models developed by the authors: methods and algorithms for fast transformations in Galois fields, an algebraic representation model and the structure of fast delay acquisition devices for a subclass of sequences. The article is devoted to the development of a software and hardware modeling systems that allows the most accurate mapping of fast algebraic transformations in Galois fields to algorithmic models of various levels of representation to ensure high performance, flexibility and ease of modeling, as well as prototyping of promising devices for the generation and processing of complex signals. The results of the work will be useful in the design of promising devices of various information and communication systems, scientific research, as well as in the educational process. © 2021 IEEE.

关键词： Algebra

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Editorial Introduction to JSTQE Special Issue on optical Computing

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IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS 2023年第2期29卷

作者： Shastri, Bhavin J. Buckley, Sonia Van Vaerenbergh, Thomas Lin, Xing Offrein, Bert Queens Univ Dept Phys Engn Phys & Astron Kingston K7L 3N6 ON Canada Natl Inst Stand & Technol Appl Phys Div Boulder CO 80305 USA HPE Belgium Hewlett Packard Labs B-1831 Diegem Belgium Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Zurich Res Lab IBM Res Europe CH-8803 Ruschlikon Switzerland

The twenty-eight papers included in this special section focuses on optical computing. There is a renaissance in the research of photonic computing driven by the demands of artificial intelligence and neuromorphic computing on current digital electronic hardware (which has a central processor and separated memory for sequential processing). optical physics could enable non-von Neumann computing and potentially enable new applications that require low latency, high bandwidth, and low energies. This special issue will cover the current status, prospects, and challenges of the field in using light for neuromorphic computing, machine learning, and quantum information processing. The purpose of this special section is to provide a glimpse of the current status and future trends, as well as provide original results and recent developments in the field of optical computing, from devices, integration technology, system architectures, and algorithms, to applications.

关键词： Special issues and sections optical computing Quantum computing Photonics Neuromorphic engineering Computer architecture systems architecture Semiconductor device manufacture

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Convergence Rate Maximization for Split Learning-based Control of EMG Prosthetic devices

Convergence Rate Maximization for Split Learning-based Contr...

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International conference on Intelligent Environments (IE)

作者： Matea Marinova Daniel Denkovski Hristijan Gjoreski Zoran Hadzi-Velkov Valentin Rakovic Faculty of Electrical Engineering and Information Technologies Ss. Cyril and Methodius University Skopje R. North Macedonia

ISBN: (数字)9798350386790

ISBN: (纸本)9798350386806

Split Learning (SL) is a promising Distributed Learning approach in electromyography (EMG) based prosthetic control, due to its applicability within resource-constrained environments. Other learning approaches, such as Deep Learning and Federated Learning (FL), provide suboptimal solutions, since prosthetic devices are extremely limited in terms of processing power and battery life. The viability of implementing SL in such scenarios is caused by its inherent model partitioning, with clients executing the smaller model segment. However, selecting an inadequate cut layer hinders the training process in SL systems. This paper presents an algorithm for optimal cut layer selection in terms of maximizing the convergence rate of the model. The performance evaluation demonstrates that the proposed algorithm substantially accelerates the convergence in an EMG pattern recognition task for improving prosthetic device control.

关键词： Deep learning Training Performance evaluation Electromyography Partitioning algorithms Pattern recognition Task analysis

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A Novel Approach to Depth Estimation and Disparity Map Generation in Stereoscopic Images Using Convolutional Neural Networks

A Novel Approach to Depth Estimation and Disparity Map Gener...

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Communication & Computing (WCONF), World conference on

作者： Vipin Solanki Rahul Gupta Preeti Naval Department of Applied Science Sanskriti University Mathura Uttar Pradesh India Department of Management School of Mangement - PG JAIN (Deemed to be University) Bangalore Karnataka India Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

This paper provides a singular approach to depth estimation and disparity map technology in stereoscopic snap shots using convolutional neural networks (CNNs). Stereoscopic pictures are pairs brand new two or more photographs taken from barely extraordinary points in space., commonly utilized in 3-D film manufacturing. A good way to extract depth information from the 2 photographs., a stereo-disparity map can be generated by way of comparing the similarities among the two. The undertaking latest estimating the disparity map traditionally calls for tedious and computational heavy feature matching algorithms. This paper introduces a CNN-based disparity estimation technique that is both light-weight and quicker than traditional feature-matching algorithms. In particular., a completely-convolutional neural community (FCN) model is employed to regress the disparity map from an input stereoscopic photograph pair. To train this community., a large-scale artificial dataset is generated using simulation software program., presenting the essential ground fact. An extensive list state-of-the-art experiments and comparisons with approaches were conducted to evaluate its performance. Outcomes display that the proposed approach is capable of estimating displacement maps in stereo snap shots speedy and as it should be.

关键词： Navigation Stereo image processing Software algorithms Estimation optical imaging Software Real-time systems Convolutional neural networks optical films Tuning

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Quantum Prototype Clustering: Advancing AI Enhanced Machine Learning

Quantum Prototype Clustering: Advancing AI Enhanced Machine ...

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International conference on Signal processing and Communication (ICSC)

作者： Ishan Shivansh Bangroo Ravi Kumar Department of Computer and Information Science and Engineering University of Florida Florida U.S.A. Department of Electronics and Communication Engineering Jaypee University of Engineering & Technology M.P. India

The quantization reconstruction of classical machine learning algorithms is an important research direction in quantum machine learning. Clustering, a widely applied algorithm in machine learning, also holds high research value when quantized. Currently, most quantum machine learning algorithms, including those employing $A I$ techniques, face challenges such as difficulty in replication and the lack of intuitive comparisons with classical algorithms. A quantum prototype clustering algorithm (QPC) is proposed to address these issues, which can be easily deployed on existing general-purpose quantum computing devices. This approach first utilizes single-qubit rotation properties to find the feature mapping with minimal information loss, creating single-qubit rotations from two-dimensional feature data. Then, based on the characteristics of multi-qubit entanglement and entanglement system collapse, a quantum circuit for generating specific quantum entanglement systems and measuring the collapse results of entanglement systems is designed. By establishing the relationship between controlled qubit rotation angles in the entanglement system and the collapse results of the entanglement system and combining the definition of Minkowski distance, a quantum distance is derived for evaluating the similarity of input samples. The quantum distance measurement module, with the same input-output format as the distance calculation module in classical computers, can directly replace the Minkowski distance calculation in prototype clustering without modification, thus reconstructing classical prototype clustering algorithms, with AI included, into QPC. Multiple repeated experiments on publicly available datasets from Kaggle and Scikitlearn demonstrate that with AI integration, QPC exhibits no significant differences in evaluation metrics, such as average sample centre distance, compared to classical prototype clustering algorithms.

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A High-Density Electromyography Sensor Based on MEMS E-Skin for Detecting Activity of Motor Units

A High-Density Electromyography Sensor Based on MEMS E-Skin ...

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IEEE International conference on Micro Electro Mechanical systems

作者： Yirong Wang Shuihan Shao Chunpeng Jiang Bin Yang Jingquan Liu National Key Laboratory of Advanced Micro and Nano Manufacture Technology Shanghai Jiao Tong University Shanghai China Department of Micro/Nano Electronics School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798331508890

ISBN: (纸本)9798331508906

This paper introduces a compact, ultrathin, and stretchable high-density surface electromyography (sEMG) electrode designed for targeted signal acquisition from 16 or more channels in localized muscle regions around the mouth and neck during swallowing. These signals are processed into motor unit potentials through advanced decomposition algorithms. The electrode features a serpentine structure approximately 6 micrometers thick, enabling conformal contact with the skin and accommodating cyclic stretching up to 160%, thereby minimizing motion artifacts and ensuring high-quality signal capture. Surface modifications with iridium oxide and platinum black are applied to reduce contact impedance. The high channel density and small form factor facilitate detailed analysis of deep motor unit activity in targeted muscles using high-density sEMG decomposition techniques.

关键词： Electrodes Micromechanical devices Surface impedance Electronic skin Signal processing algorithms Muscles Motors Electromyography Impedance Surface treatment

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An Improved EMD Denoising Framework for Underwater Distributed Acoustic Sensing Signals by Exploring Permutation Entropy

An Improved EMD Denoising Framework for Underwater Distribut...

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IEEE International conference on information Communication and Signal processing (ICICSP)

作者： Jiantong Chen Siyuan Cang Huayong Yang Chao Li School of Mechanical and Electrical Engineering Guangdong University of Technology Guangzhou China Southern Marine Science and Engineering Guangdong Laboratory(Guangzhou) Guangzhou China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

This paper presents a novel denoising framework designed to address the issue of unknown noise interference in Distributed Acoustic fiber Sensing (DAS) signals. The proposed method combines Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Permutation Entropy (PE) with Wavelet Threshold Denoising (WTD). The CEEMDAN algorithm is utilized to decompose the DAS signal into multiple Intrinsic Mode Functions (IMFs), followed by the calculation of the PE value for each IMF. IMFs with a PE value exceeding 0.5 undergo denoising through wavelet transform, and are subsequently reconstructed. Our approach exhibits strong performance in analyzing DAS signals with seismic events, highlighting its effectiveness and reliability through its high signal-to-noise ratio.

关键词： Wavelet transforms Noise reduction Signal processing algorithms Acoustics Entropy Real-time systems optical fiber communication Sensors Reliability Signal to noise ratio

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