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International Electrical engineering Congress (iEECON)

作者： Ian Grout Lenore Mullin Department of Electronic and Computer Engineering Faculty of Science and Engineering University of Limerick Limerick Ireland Department of Computer Science College of Nanotechnology Science and Engineering University at Albany SUNY Albany U.S.A.

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

Small size, hardware-only, custom data processing cores connected to a host processor are increasingly considered in digital systems design to provide the ability for accelerating data processing operations through hardware acceleration and parallel processing. These can be realized in different forms such as a custom core connected to a host processor on the same die as the processor, as a separate die (e.g., a chiplet) within the same package as the processor or as a separate Integrated Circuit. These require advanced design flows, low-geometry fabrication processes, and suitable test circuit insertion to implement a suitable test strategy. In this paper, the testability of custom hardware cores is considered through test circuit insertion based on the IJTAG standard. The core is to perform data processing operations based on the Mathematics of Arrays and serial communications is based on the Serial Peripheral Interface. This interface is used for both normal operating mode and the IJTAG based test mode. The system design is considered at the pre-synthesis design development stage and identifies architectural considerations for such system designs.

关键词： Fabrication Electrical engineering Memory management Parallel processing Data processing Mathematics Arrays System analysis and design Standards Hardware acceleration

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Performance Comparison of Interpolation Methods for Enriching WiFi Fingerprint Database 24

Performance Comparison of Interpolation Methods for Enrichin...

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9th International Conference on Cloud Computing and Internet of Things, CCIOT 2024

作者： Vu-Thanh, Trung He, Jing Duong-Bao, Ninh Thi, Luong Nguyen Lee, Seon-Woo Nguyen-Huu, Khanh College of Computer Science and Electronic Engineering Hunan University Changsha China Faculty of Mathematics and Informatics Dalat University Dalat Viet Nam Faculty of Information Technology Dalat University Dalat Viet Nam Division of Software Hallym University Chuncheon Korea Republic of Department of Electronics and Telecommunications Dalat University Dalat Viet Nam

ISBN: (纸本)9798400717161

Recently, WiFi Fingerprinting-based indoor positioning systems have gained significant attention due to the low cost of infrastructure deployment. In the data collection process, a higher density of reference points may lead to better positioning results. However, it makes this process time-consuming and laborious. To solve this problem, this study applies and compares different interpolation methods to enrich the fingerprint database without increasing the cost of the collection process. To evaluate the performance of the chosen methods, five interpolation methods are implemented in a multi-condition WiFi Fingerprinting dataset. Moreover, to check the robustness of the methods, different numbers of reference points are considered when creating the new interpolated fingerprint databases. According to the results, inverse distance weighting (IDW) is the most suitable interpolation method with low complexity and stable performance. The IDW-based database achieves an average interpolation error of less than 6 dBm compared to the original database. When applying this interpolated database to perform positioning tasks, it helps reduce the collection cost by 30% with slightly lower positioning results compared to the original database. This work demonstrates the feasibility of using interpolation to ease the data collection process of WiFi Fingerprinting, especially in complex and big areas such as airports or hospitals. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Wi-Fi

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A Comparative Study of Optimized-LSTM Models Using Tree-Structured Parzen Estimator for Traffic Flow Forecasting in Intelligent Transportation

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computers, Materials & Continua 2025年第5期83卷 3369-3388页

作者： Hamza Murad Khan Anwar Khan Santos Gracia Villar Luis Alonso DzulLopez Abdulaziz Almaleh Abdullah M.Al-Qahtani Department of Electronics University of PeshawarPeshawar25120KPKPakistan Department of Project Management(L.A.D.L) Higher Polytechnic School(S.G.V)Universidad Europea del AtlanticoSantander39011Spain Faculty of Engineering Universidad Internacional IberoamericanaCampeche24560Mexico Faculty of Engineering Universidade Internacional do CuanzaCuitoEN250BieAngola Department of Project Management Universidad Internacional IberoamericanaCampeche24560Mexico Department of Project Management Department of Management SciencesUniversid de La RomanaLa Romana22000Dominican Reublic Information Systems Department College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia Department of Electrical and Electronic Engineering College of Engineering&Computer ScienceJazan UniversityJazan45142Saudi Arabia

Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management *** algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational *** address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning *** Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad *** ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of *** also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction *** proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the *** traffic data is then input to the LSTM model with tuned parameters to perform the traffic *** three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM *** results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes.

关键词： Short-term traffic prediction sequential time series prediction TPE tree-structured parzen estimator LSTM hyperparameter tuning hybrid prediction model

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Hybrid Convtranslstm for Spatio-Temporal Classification: Identifying Early Parkinson's Disease from Gait Patterns

SSRN

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SSRN 2025年

作者： Mahali, Muhammad Izzuddin Avian, Cries Putro, Nur Achmad Sulistyo Prakosa, Setya Widyawan Leu, Jenq-Shiou Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taiwan Department of Electronics and Informatics Engineering Faculty of Engineering Universitas Negeri Yogyakarta Indonesia Department Electrical Engineering Faculty of Engineering Universitas Brawijaya Indonesia Department of Computer Science and Electronics Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada Indonesia

Parkinson's disease (PD) is a prevalent neurodegenerative disorder that affects motor function and impacts millions worldwide. Early diagnosis and precise staging of PD are critical for effective management and timely intervention. Traditional gait analysis focuses on lower limb movement to differentiate between moderate and advanced PD stages;however, integrating upper and lower limb dynamics offers a more comprehensive approach to distinguishing early-stage PD from *** study introduces a novel spatio-temporal model that integrates CNN, Transformer, and BiLSTM layers to classify PD based on the Hoehn and Yahr (H&Y) score. The architecture captures both spatial and temporal dependencies in gait patterns, with a CNN serving as the primary spatial feature extractor, utilizing a temporal-focused kernel, and augmented by a Transformer to enhance temporal feature learning. Additionally, a BiLSTM module is incorporated to effectively capture complex spatio-temporal gait dynamics across all modules. For data augmentation, sliding windows of 500, 800, and 1200 samples were employed, and the model's performance was rigorously assessed through 3-fold and 5-fold cross-subject *** included accuracy, F1-score, recall, precision, and a voting-based confidence score, while t-SNE visualization provided insights into spatio-temporal feature differentiation. The model demonstrated high performance, achieving optimal accuracy of 0.90 and an average confidence score of 0.97 with a 1200-sample window in 5-fold validation. These findings highlight the potential of a spatio-temporal approach incorporating upper and lower limb characteristics in gait analysis as a robust method for PD multi-class classification, supporting early and effective PD diagnosis from raw IMU signals. © 2025, The Authors. All rights reserved.

关键词： Neurodegenerative diseases

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Artificial sensory neurons and their applications

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Journal of Semiconductors 2025年第1期46卷 108-128页

作者： Jiale Shao Hongwei Ying Peihong Cheng Lingxiang Hu Xianhua Wei Zongxiao Li Huanming Lu Zhizhen Ye Fei Zhuge Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingbo 315201China School of Electronic and Information Engineering Ningbo University of TechnologyNingbo 315211China State Key Laboratory of Environment-friendly Energy Materials Southwest University of Science and TechnologyMianyang 621010China Institute of Wenzhou Zhejiang UniversityWenzhou 325006China State Key Laboratory of Silicon Materials School of Materials Science and EngineeringZhejiang UniversityHangzhou 310027China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesShanghai 200072China

With the rapid development of artificial intelligence(AI)technology,the demand for high-performance and energyefficient computing is increasingly *** limitations of the traditional von Neumann computing architecture have prompted researchers to explore neuromorphic computing as a *** computing mimics the working principles of the human brain,characterized by high efficiency,low energy consumption,and strong fault tolerance,providing a hardware foundation for the development of new generation AI *** neurons and synapses are the two core components of neuromorphic computing *** perception is a crucial aspect of neuromorphic computing,where artificial sensory neurons play an irreplaceable role thus becoming a frontier and hot topic of *** work reviews recent advances in artificial sensory neurons and their ***,biological sensory neurons are briefly ***,different types of artificial neurons,such as transistor neurons and memristive neurons,are discussed in detail,focusing on their device structures and working ***,the research progress of artificial sensory neurons and their applications in artificial perception systems is systematically elaborated,covering various sensory types,including vision,touch,hearing,taste,and ***,challenges faced by artificial sensory neurons at both device and system levels are summarized.

关键词： artificial sensory neurons artificial perception systems neuromorphic computing artificial intelligence

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AoI-Aware Sampling, Transmission, and Computation for Edge-Enabled Control System

AoI-Aware Sampling, Transmission, and Computation for Edge-E...

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IEEE Conference on Wireless Communications and Networking

作者： Mingan Luan Zheng Chang Jin Chi School of Computer Science and Engineering University of Electronic Science and Technology of China China Faculty of Information Technology University of Jyvaskyla Finland

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In this paper, an age of information (AoI)-aware joint design framework of sampling, transmission, computation, and control is considered for industrial cyber-physical systems. To enhance the control performance, we investigate an edge-enabled control scheme, which allows a physical entity to select its sampling adaptively and processing strategies based on de-mand. By analyzing the impact of sampling and short-packet decoding errors, and the coupling relationship between control accuracy and AoI, the AoI -aware control metric is established. Subsequently, we formulate a joint sampling time, computation offloading, and bandwidth allocation optimization problem to minimize the system's control and energy costs. To tackle the formulated NP-hard problem, we develop a BCD-based algorithm leveraging convex and game theories to obtain a joint optimization strategy in an iterative manner. Finally, the performance of the proposed edge-enabled control scheme is verified in the simulation results.

关键词： Measurement Costs NP-hard problem Simulation Scalability Process control Cyber-physical systems Channel allocation Control systems Optimization

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Improving Arabic Fake News Detection Across Context-Aware Attention Deep Model Based on Natural Language Processing 3rd

Improving Arabic Fake News Detection Across Context-Aware At...

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3rd International Conference on Language Processing and Knowledge Management, LPKM 2024

作者： Khudair, Enas Tariq Lazzez, Onsa Hamdani, Tarek M. Sadiq, Ahmed T. Alimi, Adel M. BP 1173 Sfax3038 Tunisia Avenue 5 August Rue Said Aboubaker Sfax3002 Tunisia Computer Science Department University of Monastir Monastir Tunisia Department of Electrical and Electronic Engineering Science University of Technology-Iraq Baghdad Iraq Faculty of Engineering and the Built Environment University of Johannesburg Johannesburg South Africa

ISBN: (纸本)9783031850660

With the enormous growth of social data in recent years, fake news detection has gained increasing research attention and has been widely explored in various languages. Arabic language nature imposes various challenges, such as the complicated morphological structure and the limited resources. Therefore, the current state-of-the-art methods for fake news detection remain to be enhanced, which inspired us to explore the application of the emerging deep-learning architecture to Arabic text classification. In this paper, we present an ensemble model that enhances the attention mechanism to detect fake news in Arabic sentences. An attention mechanism unit is incorporated to highlight the critical information from the contextual feature vectors. The context-related vectors generated by the attention mechanism layers are then concatenated and passed into a classifier to predict the final label. The experimental results show that the attention mechanism improves the model’s performance while yielding 99.8% in accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Arabic Fake News Detection Arabic Language Fake News Social Network

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BAT Optimized SMC for Continuous Stirred Tank Reactor

BAT Optimized SMC for Continuous Stirred Tank Reactor

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IEEE Information Technology and Mechatronics engineering Conference (ITOEC)

作者： Husam E. Elfadel Haroon Congzhi Huang School of Control and Computer Engineering North China Electric Power University Beijing China Department of Electrical and Electronic Engineering Faculty of Engineering Omdurman Islamic University Khartoum Sudan

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

This topic proposed a Sliding Mode Control (SMC) strategy utilizing the BAT Algorithm (BAT-SMC) for regulating concentration and temperature in a Continuous Stirred Tank Reactor (CSTR) system. The SMC is designed to maintain concentration (CA) and temperature (T) within desired setpoints by adapting sliding surfaces in the presence of disturbances and system uncertainties. The BAT Algorithm enhances the SMC by optimizing control parameters, reducing chattering effects, and improving tracking accuracy. Simulations validate the efficacy of BAT-SMC, showing improved dynamic performance for both concentration and temperature control compared to other controllers.

关键词： Uncertainty Mechatronics Heuristic algorithms Optimal control Coolants Temperature control Continuous-stirred tank reactor Feeds Information technology Sliding mode control

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A novel deep learning approach for investigating liquid fuel injection in combustion system

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Discover Artificial Intelligence 2025年第1期5卷 1-14页

作者： Inam, Syed Azeem Khan, Abdullah Ayub Ahmed, Noor Mazhar, Tehseen Shahzad, Tariq Khan, Sunawar Saeed, Mamoon M. Hamam, Habib Department of Artificial Intelligence and Mathematical Science Sindh Madressatul Islam University Sindh Karachi Pakistan Department of Computer Science and Information Technology Shaheed Benazir Bhutto University Lyari Karachi Pakistan Department of Computer Science Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology Karachi Pakistan Department of Computer Science School Education Department Government of Punjab Layyah31200 Pakistan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Software Engineering Islamia University of Bahawalpur Bahawalnagar62300 Pakistan Sana’a00967 Yemen Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Electrical and Electronic Engineering Sciences University of Johannesburg Johannesburg South Africa Av. Grandes Ecoles BP 1989 Libreville Gabon Bridges for Academic Excellence Spectrum Center-ville Tunis Tunisia

The intricacies and instability of introducing cryogenic propellants into the combustion system have piqued the curiosity of scientists studying the procedure. The latest innovation is utilizing data-driven machine learning and deep learning approaches to gain deeper insights into the related difficulties. However, the current work serves as a baseline for future research because relatively few studies have used data-driven methodologies to assess the temperature of liquid fuel injections in combustion systems. The performance of Linear Regression (LR), Random Forest (RF), Extra Trees Regressor (ETR), Polynomial Regression (PR), Support Vector Regressor (SVR), Decision Tree Regressor (DTR), Gradient Boost Regressor (GBR), XGB Regressor (XGBoost), AdaBoost Regressor (ABR), K-Neighbors Regressor (KNR), Long-Short Term Memory (LSTM), Bi-LSTM (Bi-directional Long-Short Term Memory) has all been investigated in this study. The study also suggested a Fully Connected Neural Network (FCNN) to examine its performance and paired it with an Extra Tree Regressor (ETR). The coupled FCNN and Extra Tree Regressor outperform the other algorithms with a Mean Square Error (MSE) of 0.0000005062, Root Mean Square Error (RMSE) of 0.00071148, Mean Absolute Error (MAE) of 0.00020672, and R-squared (R2) value of 0.99998689. Linear Regression, Polynomial Regression, and Support Vector Regressor are found to be the least-performing algorithms. The current work uses machine learning and deep learning methods to make data-driven decisions for liquid fuel injection in the combustion system. © The Author(s) 2025.

关键词： Deep learning

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Synthesis of Dynamic Virtual-Inertia for Microgrid System Applications

Synthesis of Dynamic Virtual-Inertia for Microgrid System Ap...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Justo, Jackson J. Demidova, Galina Lukichev, Dmitry Poliakov, Nikolai Makolo, Peter Bansal, Ramesh C. Itmo University Faculty of Control System and Robotics Saint-Petersburg Russia University of Dar es Salaam Department of Electrical Engineering P.O. Box 33335 Dar es Salaam Tanzania United Republic of University of Sharjah Depatment of Electrical Engineering Sharjah United Arab Emirates University of Pretoria Department of Electric Electronic and Computer Engineering Pretoria South Africa

ISBN: (纸本)9798331527495

During the last two decades, interest in grid integrated renewable energy sources (RERs) into the electrical power grids has significantly grown. The motive being a promising strategy to reduce greenhouse gas emissions generated by conventional power plants (CPPs) and hence environmental consequences, decrease losses in distribution lines to improve power system supply efficiency and reliability to consumers. As a result, several CPPs are being replaced by RERs such as solar PV system, wind energy, micro hydro-turbine, fuel cell, and small gas power plants. These are installed close to customers on low-voltage and medium-voltage sites to reduce losses in the network and improve power quality, overall efficiency and reliability. However, grid-connected RERs via inverter decreases system inertia due to decrease in CPPs with rotating mass serving as an immediate natural response to frequency deviations following network disturbances. Therefore, this paper presents the design of dynamic virtual-inertia control (DVIC) scheme. The developed DVIC incorporates the mechanism to adjust active and reactive demand in response to any frequency and voltage variations to ensure system stability. To validate the suitability of the presented technique, MATLAB/Simulink model is developed and simulated under various operating scenarios. Using the results obtained simulation study, it is evidently the proposed DVIC shows improved performance is achieved than when no inertia control system applied. © 2025 IEEE.

关键词： Solar fuels

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