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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Habelalmateen, Mohammed I. Archana Reddy, R. Reddy, Chetana Nagarjuna, N. Pradeep Kumar, S. Universiti Tun Hussein Onn Malaysia Parit Raja Faculty of Electrical and Electronic Engineering Johor Batu Pahat Malaysia The Islamic University College of Technical Engineering Department of Computer Technical Engineering Najaf Iraq Sr University School of Computer Science and Artificial Intelligence Telangana India Oxford College of Engineering Department of Electronics and Communication Bangalore India Cvr College of Engineering Department of Csit Hyderabad India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350317558

In the landscape of optimization for mitigating the Peak-to-Average Power Ratio (PAPR) in Multiple-Input Multiple-Output and Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems, prevalent techniques like the tone reservation scheme and l-norm Cyclic Algorithm (LNCA) algorithm grapple with inherent challenges linked to computational complexity and iterative algorithm derivation. While strides have been made with models such as Twin Symbol Hybrid Optimization by the premise of Partial Transmit Sequence (TSHO-PTS) and Advanced Switching Differential Evolution (ASDE), persistent concerns related to symbol overlaps and algorithmic limitations underscore the need for further refinement in computational efficiency within OFDM-based communication systems. In response to these challenges, the proposed Nonlinear Inertia Weight Factor within the Whale Optimization Algorithm (NIWF-WOA) emerges as a solution. Comparative analysis against existing methods like LCNA, Modified Dragonfly Optimization algorithm (MDOA), TSHO-PTS, and ASDE reveals the superior performance of NIWF-WOA. Numerical validation affirms its efficacy, with a recorded in Bit Error Rate (BER) of 10-2.5, SNR 10-3.4 and PAPR of.10-4.2, underscoring the substantial improvement achieved by NIWF-WOA in optimizing PAPR reduction for MIMO-OFDM systems. © 2024 IEEE.

关键词： Landscape of optimization MIMO-OFDM system Non-linear weight factor Peak to Average Power Ratio Whale optimization

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Non-Destructive pH Sensor using Honeycomb Resonator for Liquid-Mixture Characterization 4

Non-Destructive pH Sensor using Honeycomb Resonator for Liqu...

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4th Research, Invention, and Innovation Congress: Innovative Electricals and electronics, RI2C 2023

作者： Chudpooti, Sukanya Sangpet, Patchadaporn Robertson, Ian D. Akkaraekthalin, Prayoot Somjit, Nutapong Chudpooti, Nonchanutt Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand School of Electronic and Electrical Engineering University of Leeds Leeds United Kingdom

ISBN: (纸本)9798350330618

This paper presents a feasibility study of a pH sensor utilizing the microwave transmission technique within the CST Microwave Studio software. The resonator is designed based on the modified split ring resonator shape (SRR) by incorporating six segments in a circular shape. The proposed resonator shape resembles a honeycomb structure with a capacitive gap to enhance the electric field (E-field) distribution in its area. The operating frequency is set to the Industrial, Scientific, and Medical (ISM) band of 5.82 GHz. According to the simulation, the E-field demonstrates its maximum strength at the focused area, measuring 29,863 V/m. Consequently, this position is deemed suitable for selecting the sensing area for liquid-mixture detection. To investigate the performance of the proposed sensor, the industry model of a Teflon tube is imported and placed atop the focused honeycomb resonator. The performance of the proposed sensor is evaluated by modeling various liquid samples, adjusting electrical properties such as relative permittivity, Εr, and loss factor. The simulated results indicate that the proposed sensor provides a satisfactory transmission coefficient, S21, response for classifying relative permittivity, Εr, changes within the range of 1 to 81. However, for future development, the proposed sensor will undergo fabrication and further investigation to assess its performance in practical applications. ©2023 IEEE.

关键词： Microwave sensors

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Secure beamforming designs for maximizing secrecy sum rate in MISO-NOMA networks

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Digital Communications and Networks 2025年第1期11卷 83-91页

作者： Yanbo Zhang Zheng Yang Jingjing Cui Xianfu Lei Yi Wu Jun Zhang Chao Fang Zhiguo Ding College of Photonic and Electronic Engineering Fujian Normal UniversityFuzhou 350007China School of Electronics and Computer Science University of SouthamptonSouthampton SO171BJUK Institute of Mobile Communications Southwest Jiaotong UniversityChengdu 610031China Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and TelecommunicationsNanjing 210003China Faculty of Information Technology Beijing University of TechnologyBeijingChina School of Electrical and Electronics Engineering The University of ManchesterManchester M139PLUK

In this paper,the application of Non-Orthogonal Multiple Access(NOMA)is investigated in a multiple-input single-output network consisting of multiple legitimate users and a potential *** support secure transmissions from legitimate users,two NOMA Secrecy Sum Rate Transmit Beam Forming(NOMA-SSR-TBF)schemes are proposed to maximise the SSR of a Base Station(BS)with sufficient and insufficient transmit *** BS with sufficient transmit power,an artificial jamming beamforming design scheme is proposed to disrupt the potential eavesdropping without impacting the legitimate *** addition,for BS with insufficient transmit power,a modified successive interference cancellation decoding sequence is used to reduce the impact of artificial jamming on legitimate *** specifically,iterative algorithm for the successive convex approximation are provided to jointly optimise the vectors of transmit beamforming and artificial *** results demonstrate that the proposed NOMA-SSR-TBF schemes outperforms the existing works,such as the maximized artificial jamming power scheme,the maximized artificial jamming power scheme with artificial jamming beamforming design and maximized secrecy sum rate scheme without artificial jamming beamforming design.

关键词： NOMA Secure transmission Artificial jamming Beamforming design

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Performance evaluation of the non-local cost aggregation in stereo matching algorithm

Performance evaluation of the non-local cost aggregation in ...

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2022 engineering Technology International Conference, ETIC 2022

作者： Zahari, Madiha Hamzah, Rostam A. Manap, Nurulfajar A. Herman, Adi Irwan Faculty of Electronic and Computer Engineering Universiti Teknikal Malaysia Melaka Melaka Malaysia Faculty of Electrical and Electronic Engineering Technology Universiti Teknikal Malaysia Melaka Melaka Malaysia Product & Test Engineering Texas Instruments Melaka Batu Berendam Malaysia

ISBN: (纸本)9781839537820

This paper presents the evaluation of the efficiency and accuracy of the stereo matching algorithm using a census transform with the non-local aggregation technique. This technique is proposed to reduce the average error of the overall matching process in order to produce an accurate disparity map and to access the efficiency of the matching algorithm. Fundamentally, the stereo matching process highly depends on the quality of the image pair. The properties different between left and right images such as image brightness, exposure, occlusion and less texture area have a significant influence on the matching algorithm's accuracy. Thus, this paper analysed the performance of the stereo matching algorithm using Census Transform at matching cost computation, the non-local technique of cost aggregation and winner-take-all strategy at cost optimization. The initial disparity value obtained in cost optimization is used as the final disparity map. The disparity maps obtained from three different aggregation methods are evaluated in terms of accuracy and processing time. The experimental results using the Middlebury dataset show that the non-local method is more accurate compared with other aggregation methods and while maintaining the efficiency of the algorithm. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Efficiency

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Marine Predators Algorithm with Deep Learning-Based Leukemia Cancer Classification on Medical Images

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computer Modeling in engineering & Sciences 2024年第10期141卷 893-916页

作者： Sonali Das Saroja Kumar Rout Sujit Kumar Panda Pradyumna Kumar Mohapatra Abdulaziz S.Almazyad Muhammed Basheer Jasser Guojiang Xiong Ali Wagdy Mohamed Department of Computer Science&Engineering Gandhi Institute for TechnologyBPUTBhubaneswar752054India Department of Information Technology Vardhaman College of Engineering(Autonomous)Hyderabad501218TelanganaIndia Department of Electronic&Communication Engineering Vedang Institute of TechnologyBhubaneswar752010India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computing and Information Systems School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Research Centre for Human-Machine Collaboration(HUMAC) School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan

In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects *** depends on the type of leukemia and the extent to which cancer has established throughout the *** leukemia in the initial stage is vital to providing timely patient *** image-analysis-related approaches grant safer,quicker,and less costly solutions while ignoring the difficulties of these invasive *** can be simple to generalize computer vision(CV)-based and image-processing techniques and eradicate human *** researchers have implemented computer-aided diagnosticmethods andmachine learning(ML)for laboratory image analysis,hopefully overcoming the limitations of late leukemia detection and determining its *** study establishes a Marine Predators Algorithm with Deep Learning Leukemia Cancer Classification(MPADL-LCC)algorithm onMedical *** projectedMPADL-LCC system uses a bilateral filtering(BF)technique to pre-process medical *** MPADL-LCC system uses Faster SqueezeNet withMarine Predators Algorithm(MPA)as a hyperparameter optimizer for feature ***,the denoising autoencoder(DAE)methodology can be executed to accurately detect and classify leukemia *** hyperparameter tuning process using MPA helps enhance leukemia cancer classification *** results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches.

关键词： Leukemia cancer medical imaging image classification deep learning marine predators algorithm

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An Intelligent SCADA System for Power Distribution Network Cable Fault Detection with Real-Time Monitoring and Autonomous Maintenance

An Intelligent SCADA System for Power Distribution Network C...

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International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST)

作者： S M Nahidul Islam Shameem Ahmad Md. Mahbub-E-Elahi Prodhan Md. Shoaib Khan Chowdhury Sakib Bin Mizan Abir Ahmed Effat Jahan Md. Rifat Hazari Chowdhury Akram Hossain Department of Electrical and Electronic Engineering Faculty of Engineering American International University-Bangladesh Dhaka Bangladesh Department of Computer Engineering Faculty of Engineering American International University-Bangladesh Dhaka Bangladesh

ISBN: (数字)9798331530952

ISBN: (纸本)9798331530969

Supervisory Control and Data Acquisition (SCADA) systems are significant for boosting industrial operations, but established systems often struggle with fault management and real-time monitoring. To address these issues, this paper proposes an advanced SCADA system that integrates IoT technologies, multi-agent systems, and predictive maintenance for seamless data collection, analysis, and decision-making. Machine learning enhances fault detection and cybersecurity, while a modular hardware design allows scalability and flexibility to various industrial environments. The system is highly sensitive to detecting faults, including open circuit faults, line-to-ground faults, line-to-line faults, double line to ground faults and three-phase faults. It improves operational safety and efficiency by accurately locating faults and notifying professionals in real time. Extensive simulations and hardware tests were conducted to validate its performance in different fault scenarios. The results show that the proposed SCADA system has successfully detected different types of faults, offering a unified approach for fault management, maintenance, and real-time monitoring.

关键词： Power cables Fault detection Scalability SCADA systems Electrical fault detection Real-time systems Hardware Maintenance Circuit faults Predictive maintenance

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An Embedded Cellular Automata Algorithm for Infrared Object Detection 33

An Embedded Cellular Automata Algorithm for Infrared Object ...

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33rd International Scientific Conference electronics, ET 2024

作者： Rizanov, Stefan Yakimov, Peter Nikolov, Dimitar Boydens, Jeroen Technical University of Sofia Faculty of Electronic Engineering and Technologies 8 Kliment Ohridski blvd Sofia1000 Bulgaria KU Leuven Department of Computer Science Spoorwegstraat 12 Bruges8200 Belgium

ISBN: (纸本)9798350376449

This work proposes and presents a cellular automata algorithm for infrared object detection and tracking. The algorithm's application is aimed towards computationally limited edge computing sensory modules. Performed is a statistical evaluation of the algorithm's noise resilience through a 3D histogram method. Proposed is a methodology for the optimal choice of the algorithm's fine-tuning parameter values. The performance influence of applying low-pass Kernel filtering was evaluated and developed was a majority voting-based concurrent object mask-generating algorithm. © 2024 IEEE.

关键词： Cellular automata

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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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Short-Term Load Forecasting Using Deep Learning Algorithms with Hyperparameter Optimization

Short-Term Load Forecasting Using Deep Learning Algorithms w...

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International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST)

作者： Raihan Uddin Md. Rifat Hazari Shameem Ahmad Chowdhury Akram Hossain Md. Saniat Rahman Zishan Abir Ahmed Department of Electrical and Electronic Engineering Faculty of Engineering American International University-Bangladesh Dhaka Bangladesh Department of Computer Engineering Faculty of Engineering American International University-Bangladesh Dhaka Bangladesh

ISBN: (数字)9798331530952

ISBN: (纸本)9798331530969

The accurate forecasting of electrical load in short-term intervals is crucial for the efficient and secure operation of modern power systems. The dynamic nature of energy demand necessitates sophisticated forecasting techniques that can adapt to real-time changes, thereby optimizing power generation and distribution. This study proposes a Long Short-Term Memory (LSTM) model optimized using Hyperparameter Optimization (HPO) to address the inherent non-linearities in load data and enhance predictive performance. The LSTM with HPO achieved a Mean Absolute Percentage Error (MAPE) of 2.63%, substantially outperforming other models such as k-Nearest Neighbors (3.77%), Random Forest (3.46%), and Artificial Neural Networks (3.58%). It also recorded superior performance in Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), with values of 1.1423 and 1.0688, and an R2 of 0.9442. Additionally, experiments were conducted combining LSTM with other models to form hybrid architectures without applying HPO. These LSTM-based hybrids, such as RF-LSTM, consistently outperformed non-LSTM models, achieving a MAPE as low as 2.81%.

关键词： Deep learning Adaptation models Load forecasting Predictive models Hyperparameter optimization Real-time systems Forecasting Long short term memory Random forests Load modeling

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Image Acquisition and Deep Convolutional Neural Network Incorporated Cost-Effective Automated Nursery Disease Detection Method for the Tea Industry 10

Image Acquisition and Deep Convolutional Neural Network Inco...

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10th International Multidisciplinary engineering Research Conference on Moratuwa engineering Research, MERCon 2024

作者： Weerasekara, Dinithi Bhagya Kahatapitiya, Nipun Shantha Sanjeewa, Nuwan Wathudura, Vimukthi Madushan Prasanna, Nuwan Kalupahana, Deshan Wijenayake, Udaya Silva, Bhagya Nathali Wijethunge, Akila Wijesinghe, Ruchire Eranga University of Sri Jayewardenepura Faculty of Technology Department of Materials and Mechanical Technology Pitipana Sri Lanka University of Sri Jayewardenepura Faculty of Engineering Department of Computer Engineering Nugegoda Sri Lanka IOT Solutions Pvt Ltd Homagama Sri Lanka Sri Lanka Institute of Information Technology Faculty of Computing Department of Information Technology Malabe Sri Lanka Malabe Sri Lanka Sri Lanka Institute of Information Technology Faculty of Engineering Department of Electrical and Electronic Engineering Malabe Sri Lanka

ISBN: (纸本)9798331529048

Tea cultivation, a globally significant industry across over 60 countries, requires effective management of tea nurseries for robust plant growth and disease control. Implementing automated disease detection in tea nurseries using advanced technologies enhances efficiency, precision, and sustainability compared to manual inspection methods. An image acquisition system incorporating a Deep Convolutional Neural Network (DCNN) was developed as a cost-effective automated solution for nursery disease detection. First, images of Blister Blight (BB) disease in tea plants were collected primarily from various estates. These images were then preprocessed, labeled, and augmented to create a robust dataset. Two models based on the YOLOv5 architecture were trained, with the second model benefiting from dataset augmentation and enhanced labeling. The Improved Model achieved a mean Average Precision (mAP) of 80.6% at a 0.5 threshold, demonstrating its high accuracy in BB detection in tea nurseries. It was integrated with the image acquisition system to enable continuous monitoring of tea leaves in nursery plantations and facilitate the immediate detection of BB diseases. This system holds significant potential for further enhancement in disease management across the tea industry, promoting healthier crops, reducing costs, and minimizing environmental impact. © 2024 IEEE.

关键词： Convolutional neural networks

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