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SSRN 2023年

作者： Zhai, Jiahui Bi, Jing Yuan, Haitao Zhou, Mengchu School of Software Engineering Faculty of Information Technology Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States

High-dimensional expensive problems are often encountered in the design and optimization of complex robotic and automated systems and distributed computing systems, and they suffer from a time-consuming fitness evaluation process. It is extremely challenging and difficult to produce promising solutions in high-dimensional search space. This work proposes an evolutionary optimization framework with embedded autoencoders that effectively solve optimization problems with high-dimensional search spaces. Autoencoders provide strong dimension reduction and feature extraction abilities that compress a high-dimensional space to an informative low-dimensional one. Search operations are performed in a low-dimensional space, thereby guiding a whole population to converge to the optimal solution more efficiently. Multiple subpopulations coevolve iteratively in a distributed manner. One subpopulation is embedded by an autoencoder, and the other one is guided by a proposed Multi-swarm Gray wolf optimizer based on Genetic learning (MGG). Thus, the proposed multi-swarm framework is named Autoencoder-based MGG (AMGG). AMGG consists of three proposed strategies that well balance exploration and exploitation abilities, i.e., a Dynamic subgroup Number Strategy for reducing the number of subpopulations, a Subpopulation Reorganization Strategy for sharing useful information about each subpopulation, and a Purposeful Detection Strategy for jumping out of local optima and improving exploration ability. AMGG is compared with several widely used algorithms by using typical benchmark functions and a real-life optimization problem. Extensive experimental results prove that AMGG outperforms its peers in terms of search accuracy and convergence efficiency. © 2023, The Authors. All rights reserved.

关键词： Genetic algorithms

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Novel Size-Reduced Coaxial Cavity Filters For 5G Networks

Novel Size-Reduced Coaxial Cavity Filters For 5G Networks

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IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Tran Thi Thu Huong An Tri Bao Trong-Hieu Le Vu Van Yem Faculty of Electronics and Computer Engineering University of Economics - Technology for Industries Hanoi Vietnam 11 Physics 2 Hanoi - Amsterdam High School for The Gifted Hanoi Vietnam Faculty of Electronics and Telecommunications Electric Power University Hanoi Vietnam School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi Vietnam

ISBN: (数字)9798350351019

ISBN: (纸本)9798350351026

This paper presents a novel coaxial cavity filter using in 5G NR Base Stations. The resonant cylinder utilizes stepped impedance technology combined with folding edge bending and rectangular zigzag arm structure on the top to improve the electrical coupling mainline. Therefore, dimensions of cavity filter are reduced while maintaining the filter performance parameters. This proposed resonator is verified in the design of one filter integrated in C-Band 5G station with ten orders and four zero points. The dimensions of the filter are only 56.81 mm x 15.56 mm x 9.5 mm (length x width x height). The simulation results show the passband of 304MHz, the return loss of 15.6dB, the insertion loss of 0.1dB. These specifications are suitable for applying in Band C 5G network.

关键词： Couplings 5G mobile communication C-band Simulation Resonator filters Resonant frequency Insertion loss Cavity resonators Passband Impedance

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The graphene squeeze-film microphone

arXiv

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arXiv 2024年

作者： Abrahams, M.P. Martinez, J. Steeneken, P.G. Verbiest, G.J. Department of Precision and Microsystems Engineering Delft University of Technology Mekelweg 2 Delft2628 CD Netherlands Multimedia Computing Group Intelligent Systems Department Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 XE Netherlands

Most microphones operate by detecting the sound-pressure induced motion of a membrane. In contrast, here we introduce a microphone that operates by monitoring the sound-pressure-induced modulation of the compressibility of air. By driving a graphene membrane at its resonance frequency, the gas, that is trapped in a squeeze-film beneath it, is compressed at high frequency. Since the stiffness of the gas film depend on the air pressure, the resonance frequency of the graphene is modulated by variations in sound pressure. We demonstrate that this squeeze-film microphone principle can be used to detect sound and music by tracking the membrane's resonance frequency using a phase-locked loop (PLL). Since the sound detection principle is different from conventional devices, the squeeze-film microphone potentially offers advantages like increased dynamic range, and a lower susceptibility to pressure-induced failure and vibration-induced noise. Moreover, it might be made much smaller, as demonstrated by the microphone in this work that operates using a circular graphene membrane with an area that is more than a factor 1000 smaller than that of MEMS microphones. Copyright © 2024, The Authors. All rights reserved.

关键词： Graphene

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Enhanced obesity classification with wavelet packet decomposition and ANN–PSO: a biomedical signal processing approach

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The European Physical Journal Special Topics 2025年 1-12页

作者： Arslan, Rukiye Uzun Erkaymaz, Okan Yapici, İrem Senyer Perc, Matjaž Department of Electrical and Electronics Engineering Zonguldak Bulent Ecevit University Zonguldak Turkey Department of Computer Engineering Turkish Naval Academy National Defence University Istanbul Turkey Department of Computer Engineering Zonguldak Bulent Ecevit University Zonguldak Turkey Faculty of Natural Sciences and Mathematics University of Maribor Maribor Slovenia Community Healthcare Center Dr. Adolf Drolc Maribor Vošnjakova ulica 2 Maribor Slovenia Complexity Science Hub Vienna Vienna Austria

Obesity diagnosis using biomedical signals has received increasing attention in recent years and requires advanced signal processing techniques in order to accurately classify obesity. In this context, this study proposes an intelligent diagnostic system for obesity classification using flash electroretinogram (fERG) signals, with a specific focus on cone responses. A novel feature extraction method based on Wavelet Packet Decomposition (WPD) is employed to decompose the cone responses into high- and low-frequency components, enabling detailed time–frequency analysis with high resolution. Subsequently, statistical features, such as mean, standard deviation, skewness, and kurtosis, are extracted from the decomposed signals and refined to enhance the training of artificial neural networks (ANNs). To optimize model performance, Particle Swarm Optimization (PSO) is integrated with ANN, resulting in an ANN–PSO hybrid model. The experimental dataset, comprising fERG signals from 47 subjects across diverse obesity categories, was utilized to evaluate the proposed hybrid model. The ANN–PSO model demonstrated high classification performance, achieving average accuracies of $$95.74\%$$ and $$96.60\%$$ for right and left eye signals, respectively, outperforming traditional ANN models. These findings highlight the effectiveness of WPD in capturing intricate signal characteristics relevant to obesity levels and confirm the potential of the ANN–PSO model as a robust, efficient, and reliable diagnostic tool for clinical applications beyond conventional BMI assessments.

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Deep Reinforcement Learning for Robotics Motion Planning Combining Graph Neural Networks and Multi-Agent Systems

SSRN

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SSRN 2024年

作者： Rjoub, Gaith Bentahar, Jamal Elmekki, Hanae Pedrycz, Witold Drawel, Nagat Kassaymeh, Sofian Faculty of Information Technology Aqaba University of Technology Aqaba Jordan Montreal Canada Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Research Center of Performance and Productivity Analysis Istinye University Istanbul Turkey

In the dynamic realm of robotics, efficiently navigating complex and ever-changing environments remains a complex challenge, particularly in motion planning. Addressing this, we introduce a new approach to robotic motion planning that leverages a well-designed integration of Proximal Policy Optimization (PPO), Graph Neural Networks (GNNs), and Multi-Agent Systems (MASs) within a Centralized Training with Decentralized Execution (CTDE) framework. This method uniquely addresses the challenges in dynamic environments by enabling continuous information sharing among robots at each step, rather than merely at the conclusion of their paths as done in other benchmarks. This innovation not only facilitates real-time decision-making in complex scenarios but also demonstrates superior performance and robustness across various environments, including urban, rural, and industrial settings. Our rigorous comparative analysis establishes this methodology as a significant advancement in the field, markedly enhancing efficiency and adaptability in robotic motion planning. © 2024, The Authors. All rights reserved.

关键词： Multi agent systems

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Resistors Over Digital Analog Converter on PRBS Threat Generator with PLL Synchronizer: Comparison Between R2R & Weighted

Resistors Over Digital Analog Converter on PRBS Threat Gener...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Akram A. Almohammedi Mohammad Hafiz Mohd Yusof Saiyed Rasol bin Tuan Muda Vladimir Shepelev Mohammed Balfaqih Solahuddin Shamsuddin Electrical & Electronic Engineering Department Karabuk University Turkey AlSaeed Faculty for Engineering and Information Technology Taiz University Taiz Yemen Faculty of Computer and Mathematical Sciences Universiti Teknologi Mara UiTM Tapah Malaysia Automobile Transportation Department South Ural State University Chelyabinsk Russia Department of Computer and Network Engineering College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia

Intrusion Detection System (IDS) must be developed based on latest live traffic dataset. This will facilitate the study on new attributes of current threat. Once again, researchers are locked on the same vicious circle of inability to access live traffic dataset. This is coupled with public domain logged dataset may not be useful for a new threat research. Due to these standing hurdles, Pseudo Random Binary Sequence (PRBS) is used to generate synthetic dataset in IDS studies. One of its components is the Digital to Analog (D/A) converter. There are two types of D/A implementations; 1) through Weighted and 2) R2R resistors. This paper compares the advantages and the disadvantages of designing D/A converter by using the abovementioned resistors. It is advisable to opt for R2R version over Weighted Resistor since it is easier and practical to choose the multiple types of resistors during circuit design process. Furthermore, the shifted parallel output from the circuit can be synchronized by the proposed PLL circuit.

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Attention-Based Deep Neural Network for Coronary Heart Disease Risk Prediction 16th

Attention-Based Deep Neural Network for Coronary Heart Disea...

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16th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of Information Technology, Applications and Tools, FITAT 2020

作者： Amarbayasgalan, Tsatsral Park, Kwang Ho Munkhdalai, Lkhagvadorj Ryu, Keun Ho Database Laboratory School of Electrical and Computer Engineering Chungbuk National University Cheongju28644 Korea Republic of Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City700000 Viet Nam Department of Computer Science College of Electrical and Computer Engineering Chungbuk National University Cheongju28644 Korea Republic of

ISBN: (纸本)9789813367562

Coronary heart disease (CHD) is one of the top causes of global mortality. It does not give any symptoms at the early stage, and most patients are diagnosed during periods of CHD exacerbation. If CHD reaches a hard condition, it will require advanced and long-term treatments. Moreover, the early detection of CHD is not easy;doctors make a decision based on many kinds of clinical tests. In recent years, machine learning-based approaches are widely used in the healthcare domain to predict disease. In this study, we have proposed a deep neural network (DNN) model with an attention layer that gives importance weight to the input of the output layer for the CHD risk prediction model. The proposed method has been evaluated by predicting CHD risk in the Korean population and compared with typical machine learning algorithms. As a result, the proposed attention-based DNN improved the performance of DNN and outperformed regular machine learning classifiers. The performance measurements include accuracy, precision, recall, f1-score, and AUC score reached 82.7818%, 86.6499%, 77.2704%, 81.6631%, and 82.5802%, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Deep neural networks

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SugarcaneNet: An Optimized Ensemble of LASSO-Regularized Pre-trained Models for Accurate Disease Classification

arXiv

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arXiv 2024年

作者： Talukder, Md Simul Hasan Akter, Sharmin Nur, Abdullah Hafez Aljaidi, Mohammad Sulaiman, Rejwan Bin Alkoradees, Ali Fayez Department of Electrical and Electronic Engineering Dhaka University of Engineering & Technology Gazipur Bangladesh Department of Biomedical Engineering Jashore University of Science and Technology Jashore Bangladesh Department of Electronic and Telecommunication Engineering International Islamic University Chittagong Chittagong Bangladesh Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan School of Computer science and Technology Northumbria University United Kingdom Unit of Scientific Research Applied College Qassim University Saudi Arabia

Sugarcane, a key crop for the world's sugar industry, is prone to several diseases that have a substantial negative influence on both its yield and quality. To effectively manage and implement preventative initiatives, diseases must be detected promptly and accurately. In this study, we present a unique model called SugarcaneNet that outperforms previous methods for automatically and quickly detecting sugarcane disease through leaf image processing. Our proposed model consolidates an optimized weighted average ensemble of seven customized and LASSO-regularized pre-trained models, particularly InceptionV3, InceptionResNetV2, DenseNet201, DenseNet169, Xception, and ResNet152V2. Initially, we added three more dense layers with 0.0001 LASSO regularization, three 30% dropout layers, and three batch normalizations with "renorm" enabled at the bottom of these pre-trained models to improve the performance. The accuracy of sugarcane leaf disease classification was greatly increased by this addition. Following this, several comparative studies between the average ensemble and individual models were carried out, indicating that the ensemble technique performed better. The average ensemble of all modified pre-trained models produced outstanding outcomes: 100%, 99%, and 99.45% for f1 score, precision, recall, and accuracy, respectively. Performance was further enhanced by the implementation of an optimized weighted average ensemble technique incorporated with grid search. This optimized "sugarcaneNet" model performed the best for detecting sugarcane diseases, having achieved accuracy, precision, recall, and F1 score of 99.67%, 100%, 100%, and 100%, respectively. © 2024, CC BY.

关键词： Sugar industry

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SWIPT-Enabled MISO Ad Hoc Network Underlay RSMA-based Cellular Network with IRS

SWIPT-Enabled MISO Ad Hoc Network Underlay RSMA-based Cellul...

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IEEE Conference on Vehicular Technology (VTC)

作者： Nguyen Thi Thanh Van Nguyen Cong Luong Feng Shaohan Shimin Gong Dusit Niyato Dong In Kim Faculty of Computer Science PHENIKAA University Hanoi Vietnam School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou China School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou China College of Computing and Data Sciene Nanyang Technological University Singapore Department of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

In this paper, we propose a simultaneous wire-less information and power transfer (SWIPT)-enabled Ad hoc network underlay rate-splitting multiple access (RSMA)-based system with intelligent reflecting surface (IRS). Therein, a base station (BS) in a primary network uses RSMA to serve primary users (PUs), and secondary user (SU) pairs constitute an Ad hoc network sharing the spectrum with the primary network. The power splitting (PS)-based SWIPT protocol is used in the Ad hoc network that allow the SU receivers to decode the information and harvest energy simultaneously. An IRS is deployed to further enhance the system performance. We formulate optimization problems that optimize the common data rate allocation and beamformers associated with the common and private messages of RSMA, the beamformers and the PS factor in the Ad hoc network, and reflection coefficients of the IRS to maximize the minimum rate of the PUs while satisfying the requirements of harvested energy and data rate of the SU pairs. The optimization problems are non-convex and challenging to be solved. We propose a low complexity algorithm based on alternating descent techniques. Numerical results demonstrate the effectiveness and improvement of the proposed framework compared with the framework based on existing multiple access schemes, i.e., non-orthogonal multiple access (NOMA).

关键词： Cellular networks NOMA Vehicular and wireless technologies System performance Simulation Throughput Ad hoc networks

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Leveraging Adaptive Algorithms for Real-Time Data Analysis

Leveraging Adaptive Algorithms for Real-Time Data Analysis

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International Conference on Computing and Networking Technology (ICCNT)

作者： Girija Shankar Sahoo R Hannah Jessie Rani Manish Kumar Goyal Vazeer Ali Mohammed D Little Femilin Jana Namrata N. Wasatkar Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electrical and Electronics Engineering Faculty of Engineering and Technology Jain (Deemed to be University) Bangalore Karnataka India Department of Computer Science & Application Vivekananda Global University Jaipur India Department of Computer & Information System Lewis University Illinois USA Department of Science and Humanities Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune INDIA

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This paper proposes the use of adaptive algorithms for real-time data analysis. Adaptive algorithms are a set of practical methods used for acting information-driven optimization and studying. They have a wide variety of applications and may be implemented to a selection of facts analysis obligations. This paper offers an overview of existing adaptive algorithms and descriptions of several new programs in the area of actual-time facts analysis. In particular, the paper discusses the notion of adaptive thresholding, which allows for practical real-time evaluation of large datasets. The paper also provides a novel technique for making use of these algorithms in an online learning environment. Results from simulations illustrate the ability of the proposed method in real-international packages. Sooner or later, the paper presents numerous key lessons discovered and affords recommendations for future research and packages in this area.

关键词： Adaptation models Data analysis Real-time systems Optimization

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