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5th IEEE International Conference on computer and Communications, ICCC 2019

作者： Zhang, Jianwei Yang, Rui Cao, Xuefeng Li, Jinghu Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian China Fujian Agriculture and Forestry University College of Computer and Information Sciences Fuzhou China

ISBN: (纸本)9781728147437

Although ternary content-addressable memory (TCAM) has a faster search speed, its power consumption and scalability is significantly poor compared to SRAM, so many SRAM-based TCAM structures have been proposed. Existing SRAM-based TCAMs reduce the power consumption but consume a lot of SRAM resources. In this paper, a resource-saving SRAM-based TCAM (RS-TCAM) structure is proposed, which can effectively reduce the amount of the SRAM by means of encoding and compressing the data in the coding region. Experimental results based on the real routing table data and Xilinx virtex-7 FPGA show that RS-TCAM uses less 10.94% SRAM than the UE-TCAM. © 2019 IEEE.

关键词： Field programmable gate arrays (FPGA)

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MetaExplainer: Revisit domain generalization of functional connectome analyses from the perspective of explainability

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Medical Image Analysis 2025年 105卷

作者： Xinmei Qiu Yongheng Sun Yilin Shi Xujun Duan Fan Wang Jianhua Ma School of Mathematics and Statistics Xi’an Jiaotong University Xi’an 710049 China Research Center for Intelligent Medical Equipment and Devices (IMED) Xi’an Jiaotong University Xi’an 710049 China School of Computer Science and Technology Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an 710049 China School of Life Science and Technology University of Electronic Science and Technology of China Chengdu 610000 China The Key Laboratory of Biomedical Information Engineering of Ministry of Education School of Life Science and Technology Xi’an Jiaotong University Xi’an 710049 China Pazhou Lab (Huangpu) Guangzhou 510000 China

Graph neural networks (GNNs) are at the forefront of learning-based functional connectome analyses and neuropsychiatric disorder diagnoses with fMRI data. The reliability of the deployed GNNs largely depends on their explainability and generalizability , due to the complexity of functional brain networks and heterogeneous fMRI data acquisitions. While developing explainable or generalizable GNNs has drawn great attention, the majority of the existing studies address these two critical topics independently. However, it is worth noting that explainability and generalizability are inherently associated from the application view, considering that stably capturing subtle alterations tied to the prodromal stage of a particular disease could be the precondition for cross-domain generalizable diagnosis. In this paper, we aim to bridge this gap by revisiting the domain-generalization (DG) of fMRI-based diagnoses from the perspective of explainability. Specifically, we propose MetaExplainer, a dedicated meta-learning framework powered by specific explanatory-generalizability regularizations, for the building of GNNs with fMRI data for accurate and explainable neuropsychiatric disorder diagnoses across varying clinical centers. By the dual-loop meta-learning process, MetaExplainer learns from fMRI BOLD signals task-oriented nonlinear functional networks that capture domain-agnostic, disease-related connectome alterations. Such explanation factors can be stably maintained across different centers to enhance domain-generalizable discriminative representation learning and disease diagnosis. Comprehensive experiments on two representative multi-center datasets (i.e., ABIDE and REST-meta-MDD) demonstrate that MetaExplainer achieves state-of-the-art performance (AUC: 76.32% for ASD, 65.31% for MDD) while revealing neurobiologically plausible biomarkers supported by existing studies (e.g., SMN dysfunction in MDD). We commit to releasing the complete source code and implementation det

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The Size And Weight Prediction For Intact Pineapples Using A Low Cost Vision System

The Size And Weight Prediction For Intact Pineapples Using A...

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Zooming Innovation in Consumer electronics International Conference (ZINC)

作者： Hasan Ali Gamal Al-kaf Kim Seng Chia Faiz Ridwan Bin Abdul mubin Department of Computer science University of Science and Technology Sana’a Yemen Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Parit Raja Malaysia

ISBN: (数字)9781728182599

ISBN: (纸本)9781728182605

The ever-changing technology has shaped people's way of life especially in the modern era of the business and agriculture sectors. Nowa days the price of pineapple depends on the size of the pineapple and also the type of pineapple that are related to the quality of pineapples. Different consumers have different desired size and weight of their purchased pineapples for their purposes. Consequently, traders and farmers need to sort the size of the pineapples to better understand the relationship among supplies and demands so that the values of pineapples can be maximized. Thus, this study proposes a system can automatically determine the size (i.e. length, diameter, and circumference) and the weight of pineapples to avoid laborious intensive and inconsistent manual sorting process that may deteriorate the consumers' interest due to wrong sorted fruits. Different positions and sizes of pineapples were investigated and studied. The performance of the proposed system was compared with manual measurement method. Results indicate that the proposed system is promising to be further developed to automate the size and weight sorting process that can improve the interest of consumers.

关键词： Mathematical model Manuals Sorting Cameras Neural networks Matlab Software

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Innovative exergaming program for antenatal exercise: Pregnant women's views and experiences

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Midwifery 2025年 148卷

作者： Gemechu Kumera Wirtu Rob Duffield Jaime Garcia Justine Salisbury Deborah Fox School of Public Health Faculty of Health University of Technology Sydney Sydney Address: 235 Jones St Ultimo NSW 2007 Australia. Electronic address: gemechu.k.wirtu@***. School of Sport Exercise and Rehabilitation Faculty of Health University of Technology Sydney Sydney Australia. Electronic address: rob.duffield@uts.edu.au. School of computer science Faculty of Engineering and IT University of Technology Sydney Sydney Australia. Electronic address: Jaime.garcia@uts.edu.au. Centre for Population Health NSW Ministry of Health Sydney Australia. Electronic address: justine.salisbury@health.nsw.gov.au. Collective for Midwifery Child and Family Health University of Technology Sydney Sydney Australia. Electronic address: deborah.fox@uts.edu.au.

BACKGROUND:Exergames are an emerging technology that employ interactive exercise-based video gaming with the aim to encourage physical activity. Exergames have been shown to encourage increased exercise participation in the non-pregnant population. However, the acceptability to pregnant women has not previously been explored. AIMS:The aim of this study was to explore pregnant women's views and experiences of using a home-based exergaming intervention designed specifically for pregnancy. METHODS:This study was a part of a multidisciplinary feasibility project investigating the feasibility of using an exergaming program to encourage physical activity in pregnancy. A qualitative study design was adopted using semi-structured interviews. Twelve pregnant women who completed a home-based exergaming program designed for pregnancy using the Nintendo Switch™, from 16 to 36 weeks of gestation, participated in interviews that were audio recorded and transcribed verbatim. Data were analysed using the thematic analysis methods. FINDINGS:The women participants found that the exergame programs were enjoyable, convenient, motivating and easy to use during pregnancy. The interactive nature of exergames was particularly appealing and they valued the ability to exercise at home. They suggested that future exergames designed and developed for pregnant women could incorporate more of a variety of exercise options that are specifically tailored for pregnancy. CONCLUSION:Exergame exercise programs were found to be enjoyable, and convenient for pregnant women participants. Our findings suggest exergames as a promising approach for encouraging regular exercise and addressing barriers to physical activity during pregnancy.

关键词： Antenatal care Exergame Nintendo switch Physical activity Pregnant women Qualitative research

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STAR-RIS Integrated Non-Orthogonal Multiple Access and Over-the-Air Federated Learning: Framework, Analysis, and Optimization

arXiv

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arXiv 2021年

作者： Ni, Wanli Liu, Yuanwei Eldar, Yonina C. Yang, Zhaohui Tian, Hui State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot7610001 Israel Zhejiang Lab Hangzhou31121 China College of Information Science and Electronic Engineering Zhejiang University Zhejiang Hangzhou310027 China Zhejiang Hangzhou310007 China

This paper integrates non-orthogonal multiple access (NOMA) and over-the-air federated learning (AirFL) into a unified framework using one simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). The STAR-RIS plays an important role in adjusting the decoding order of hybrid users for efficient interference mitigation and omni-directional coverage extension. To capture the impact of non-ideal wireless channels on AirFL, a closed-form expression for the optimality gap (a.k.a. convergence upper bound) between the actual loss and the optimal loss is derived. This analysis reveals that the learning performance is significantly affected by the active and passive beamforming schemes as well as wireless noise. Furthermore, when the learning rate diminishes as the training proceeds, the optimality gap is explicitly shown to converge with linear rate. To accelerate convergence while satisfying quality-of-service requirements, a mixed-integer non-linear programming (MINLP) problem is formulated by jointly designing the transmit power at users and the configuration mode of STAR-RIS. Next, a trust region-based successive convex approximation method and a penalty-based semidefinite relaxation approach are proposed to handle the decoupled non-convex subproblems iteratively. An alternating optimization algorithm is then developed to find a suboptimal solution for the original MINLP problem. Extensive simulation results show that i) the proposed framework can efficiently support NOMA and AirFL users via concurrent uplink communications, ii) our algorithms achieve faster convergence rate on IID and non-IID settings compared to existing baselines, and iii) both the spectrum efficiency and learning performance is significantly improved with the aid of the well-tuned STAR-RIS. Copyright © 2021, The Authors. All rights reserved.

关键词： Quality of service

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Hyperchaos on the dynamics of memristive Tabu learning neuron model under influence of electromagnetic radiation: Application in biomedical data privacy

Franklin Open

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Franklin Open 2025年 10卷

作者： Boya, Bertrand Frederick Boui A Kengne, Jacques Nanfak, Arnaud Muni, Sishu Shankar Nkapkop, Jean de Dieu Kenmoe, Germaine Djuidje Babenko, Lyudmila Klimentyevna Unité de Recherche d'Automatique et d'Informatique Appliquée (UR-AIA) IUT-FV Bandjoun University of Dschang Bandjoun P.O. Box 134 Cameroon Laboratory of Energy-Electric and Electronic Systems Department of Physics Faculty of Science University of Yaoundé I Yaoundé P.O. Box 812 Cameroon Department of Physics Faculty of Science University of Yaoundé 1 Yaoundé Cameroon Institute of Computer Technology and Information Security Southern Federal University P.O. Box 347922 Taganrog Russian Federation Laboratory for electronics electrical engineering automation and telecommunications National Higher Polytechnic School of Douala University of Douala Douala PO Box 2701 Cameroon School of Digital Sciences Digital University Kerala Technocity campus Kerala Mangalapuram India Indian Institute of Information Technology and Management Kerala Technopark road Kerala India Department of Electrical Engineering and Industrial Computing University Institute of Technology Douala P.O. Box 8698 Cameroon

This paper presents a novel biological neural networks based on memristive Tabu learning neuron (MTLN) model influenced by electromagnetic radiation. Despite the model having an unstable equilibrium plane, numerical investigation without the influence of electromagnetic radiation reveal that when increasing the memristor strength induces complex phenomena, including the coexistence of infinite bubbles of bifurcations, offset plane coexistence of various attractors. Furthermore, electromagnetic radiation can significantly impact the system's behavior, giving rise to hyperchaotic dynamics. A decent understanding is seen between the mathematical outcomes, approved through trial execution on an Arduino microcontroller-based. The insights gained from neural dynamics could pave the way for applications in neuromorphic computing, engineering, and brain-machine interfaces. Based on the complex dynamics of the model, a data privacy of biomedical data has been build and reveals good results, and enable to resist to robust attacks. © 2024 The Author(s)

关键词： Data privacy Electromagnetic radiation Hyperchaos Microcontroller design MTLN model Offset plane coexisting behaviors

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Automatic recognition of Slovak-English bilingual speech 19

Automatic recognition of Slovak-English bilingual speech

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19th Conference Information Technologies - Applications and Theory, ITAT 2019

作者： Pleva, Matus Liao, Yuan-Fu Hladek, Daniel Stas, Jan Lojka, Martin Juhar, Jozef Ondas, Stanislav Department of Electronics and Multimedia Communications Faculty of Electrical Engineering and Informatics Technical University of Košice Slovakia Department of Electronic Engineering College of Electrical Engineering and Computer Science National Taipei University of Technology Taiwan

This article describes the progress of a joint project on Multilingual Automatic Speech Recognition using Deep Neural Networks, in which the Technical University works together with National Taipei University of technology in Taiwan. During the last year, we managed to train multilingual models of combinations of Slovak - English and Slovak - English - Chinese/Mandarin languages. In this paper, we are presenting the results of the Slovak - English model based on deep learning with and without language detection. Furthermore we present new bilingual Slovak-English code-switching database for bilingual systems training and testing. The results indicate that the use of the language detection module can lower the error rate of the multilingual model to the result similar to monolingual models that are generally better for the monolingual tasks. Copyright © 2019 for this paper by its authors.

关键词： Deep neural networks

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Online handwriting recognition using encoder-decoder model

Online handwriting recognition using encoder-decoder model

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2019 International Conference on computer, Control, Electrical, and electronics engineering, ICCCEEE 2019

作者： Eisa, Abeer Abdalla, Lina Ahmed, Mohanad University of Khartoum Faculty of Engineering Department of Electrical and Electronic Engineering Algamaa Street Khartoum321 Sudan King Abdullah University of Science and Technology Division of Computer Electrical and Mathematical Sciences and Engineering Saudi Arabia

ISBN: (纸本)9781728110066

In this paper we propose a system that is capable of recognizing raw online handwritten data. The system consists of an advanced type of neural network known as LSTM (Long Short Term Memory) Encoder-decoder combined with a customized attention mechanism layer. The attention mechanism has greatly enhanced the system performance from a low character level accuracy of 53% to an excellent accuracy of 96%. Moreover, the system involves a segmentation algorithm designed to divide the sentences into segments of lines. For the training and testing we employ the IAM On-Line Handwriting database, the source can be found here [1]. The accuracy can be improved even further by integrating our system with a language model to spell check the outputs. © 2019 IEEE.

关键词： Long short-term memory

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Non-Technical Losses Detection in Smart Grids: An Ensemble Data-Driven Approach

Non-Technical Losses Detection in Smart Grids: An Ensemble D...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yufeng Xing Lei Guo Zongchao Xie Lei Cui Longxiang Gao Shui Yu School of Electronic Information Engineering Taiyuan University of Science and Technology China College of Information and Computer Taiyuan University of Technology China Faculty of Science University of Melbourne Australia School of Information Technology Deakin University Australia School of Computer Science University of Technology Sydney Australia

ISBN: (数字)9781728190747

ISBN: (纸本)9781728183824

Non technical losses (NTL) detection plays a crucial role in protecting the security of smart grids. Employing massive energy consumption data and advanced artificial intelligence (AI) techniques for NTL detection are helpful. However, there are concerns regarding the effectiveness of existing AI-based detectors against covert attack methods. In particular, the tampered metering data with normal consumption patterns may result in low detection rate. Motivated by this, we propose a hybrid data-driven detection framework. In particular, we introduce a wide & deep convolutional neural networks (CNN) model to capture the global and periodic features of consumption data. We also leverage the maximal information coefficient algorithm to analysis and detect those covert abnormal measurements. Our extensive experiments under different attack scenarios demonstrate the effectiveness of the proposed method.

关键词： Energy consumption Detectors Particle measurements Data models Smart grids Security Artificial intelligence

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Power Silicon Carbide Schottky Diodes as Current Mode $\gamma$ - Radiation Detectors

Power Silicon Carbide Schottky Diodes as Current Mode $\gamm...

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International Conference on Microelectronics, MIEL

作者： S. D. Ilić M. S. Andjelković M. A. Carvajal A. M. Lallena M. Krstić S. Stanković G. S. Ristić Center of Microelectronic Technologies Institute of Chemistry Technology and Metallurgy University of Belgrade Serbia Applied Physics Laboratory at Faculty of Electronic Engineering University of Niš Niš Serbia System Architectures Department IHP - Leibniz-Institut für innovative Mikroelektronik Frankfurt (Oder) Germany Department of Electronics and Computer Technology University of Granada Granada Spain Department of Radiation and Environmental Protection at the Vincča Institute of Nuclear Sciences Belgrade Serbia Department of Radiation and Environmental Protection at the Vinc?a Institute of Nuclear Sciences Belgrade Serbia Department of Microelectronics Faculty of Electronic Engineering University of Niš Niš Serbia

In this paper, the feasibility of using commercial power Silicon Carbide (SiC) Schottky diodes as a current mode γ-radiation detector have been examined. Diodes with almost identical electric characteristics are purchased from two different manufacturers, On Semiconductor and RoHM. They have been tested under gamma radiation exposure from a Co-60 source. The current response during irradiation has been measured for various dose rates with reversed diode bias. Investigated range of dose rates was from 0.258 Gy/h to 26.312 Gy/h, and reverse diode bias values were 10 V, 20 V, 50 V, 100 V and 200 V. Tested Schottky diodes produce stable current response for the investigated dose rates. Although the manufacturers are different, the results show that the dosimetric characteristics of these diodes have an excellent match. Sensitivity was proportional to the applied reverse bias voltage. A simple power-law can very well describe the dependence of measured radiation-induced current on dose rate.

关键词： Schottky diodes Semiconductor device measurement Radiation effects Voltage measurement Sensitivity Power measurement Silicon carbide

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