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2023 IEEE Renewable Energy and Sustainable E-Mobility Conference, RESEM 2023

作者： Yakubu, Sufyan Samikannu, Ravi Lydia, J. Vimalraj, Leones Sherwin Karpagam, R. Yahya, Abid Pan African University Institute of Water and Energy Sciences University of Tlemcen Tlemcen Algeria Saveetha Institute of Medical and Technical Science Saveetha School of Engineering Chennai India Easwari Engineering College Department of Electrical and Electronics Engineering Chennai India Panimalar Engineering College Department of Electronics and Communication Engineering Chennai India Botswana International University of Science and Technology Deparment of Electrical Computer Telecommunications Engieering Palapye Botswana

ISBN: (纸本)9798350311327

This study examines the development of distributed Photo Voltaic (PV) systems for rural communities in Ghana. This study reviews the present situation of distributed PV systems in Ghana, highlighting the benefits and challenges associated with their implementation. The review draws on several key studies that analyze the policy landscape, potential for co-benefits, barriers to development, and economic feasibility of distributed PV systems. The findings indicate that while distributed PV systems offer numerous benefits, including increased access to electricity and reduced carbon emissions, several significant barriers to their development include financial constraints, limited private sector involvement, and unfavorable policies. The review concludes by suggesting that further research is needed to explore innovative financing mechanisms, effective policy frameworks, and improved public-private partnerships, which could accelerate the deployment of distributed PV systems and improve energy access for rural communities in Ghana. © 2023 IEEE.

关键词： Rural areas

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A Novel Electronic Wheel Chair Design using Artificial Intelligence Assisted Smart Sensors and Controller

A Novel Electronic Wheel Chair Design using Artificial Intel...

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Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： Abhay Kumar Manmohan Singh Megha Singh Ramprabu J Alok Dubey Sheetal Mujoo Department of Computer Science and Engineering IES College of Technology Bhopal Department of Computer Science and Engineering Oriental university Indore Deparment Of EEE Kumaraguru College of Technology Coimbatore Department of Preventive Dental Sciences College of Dentistry Jazan University Jazan Saudi Arabia Denartment of Maxillofacial Surgery and Diagnostic Sciences College of Dentistry Jazan University Jazan Saudi Arabia

This research work introduces a design paradigm for an electronic wheelchair, incorporating Artificial Intelligence (AI) assisted smart sensors and controllers to enhance overall functionality and user experience. Core components include advanced sensors for environmental and health data, an intelligent controller driven by AI algorithms, and motorized wheels for achieving seamless mobility. The smart sensors, spanning temperature, ECG, oxygen, and heart rate sensors, contribute to real-time health monitoring and environmental awareness. The AI-assisted controller optimizes wheelchair navigation, providing a responsive and adaptive system. Comparative performance metrics reveal the proposed design's superiority over traditional control methods, demonstrating heightened precision, sensitivity, specificity, and an impressive overall accuracy. User feedback corroborates the success of this innovative design, attaining high ratings for ease of use, health monitoring capabilities, overall comfort, and higher accuracy. This work outlines a transformative approach to electronic wheelchair design, emphasizing cutting-edge technologies to address user needs, enhancing performance standards, and redefine the landscape of mobility assistance and proposed DNN model achieved an accuracy of 0.97.

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Progression of Disease Forecasting in Recurrence Cervical Cancer Using Logistic Regression

Progression of Disease Forecasting in Recurrence Cervical Ca...

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International conference of Electronics, Communication and Aerospace technology (ICECA)

作者： S. Geeitha P. Renuka S. Ramya K. Sinduja Department of Information Technology M.Kumarasamy College of engineering Karur India Deparment of Computer Science and Engineering Paavai engineering college Nammakal India Department of Information Technology Sri Krishna College of engineering and technology Coimbatore India

Cervical cancer is the fourth most prevalent disease in women globally as well as main cause of cancer- related mortality among women. The overall survival rates of cervical carcinoma patients are significantly impacted by the detection of recurrence. The disease is entirely treatable, though, if it is found in its early, precancerous levels. According to current figures, the death percentage a few decades ago was excessive. Women who have previously had treatment with CIN3 are most at riskof developing cervical cancer once more. The dataset includes information on the effects of various treatments on post- recurrence survival. The progression of a disease is detected in this research. Of the 4913 patients who were diagnosed withcervical cancer overall, 260 women with the disease suffered recurrences. The Data has been roughly categorized into 27 characteristics that are important for illness staging and therapy. Thevery important attributes selected are FIGO 2009, chemotherapy,radiotherapy, recurrence confirmed by a pathologist, etc. Five different models are trained to predict the progression. Logistic regression is finally predicted with a high level of accuracy of 96.3%. Two hundred and sixty patients with 27 attributes consistof a number of missing values. Research is implemented through the Orange platform. The missing values are balanced through Average and Most frequent data imputation method. Important attributes are selected through the ReliefF technique, and then disease progression is predicted by 5 different algorithms Logistic regression, Support vector machine, Naive Bayes, Stochastic gradient decent and neural network. Finally logistic regression predict the progression of a disease with highest accuracy of 96.3%.

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An Incorporated 3D-Printed Lens with Square Microstrip Patch Antenna for NaCl Solution Discrimination

An Incorporated 3D-Printed Lens with Square Microstrip Patch...

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2022 Research, Invention, and Innovation Congress: Innovative Electricals and Electronics, RI2C 2022

作者： Chudpooti, Nonchanutt Sangpet, Patchadaporn Chudpooti, Sukanya Akkaraekthalin, Prayoot Robertson, Ian D. Somjit, Nutapong King Mongkut's University of Technology North Bangkok Facutly of Appied Science Department of Industrial Physics and Medical Instrumentation Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Deparment of Electrical and Computer Engineering Bangkok Thailand University of Leeds School of Electronic and Electrical Engineering Leeds United Kingdom

ISBN: (数字)9781665473248

ISBN: (纸本)9781665473248

This paper presents a 3D-printed hemispherical lens incorporated with a square patch microstrip antenna for liquid-mixture characterization. The proposed hemispherical lens antenna is designed, fabricated, and integrated with the microstrip patch planar antenna. An Acrylonitrile Butadiene Styrene (ABS) is selected to design the 3D-printed lens antenna by using the fused deposition modeling (FDM) method, due to available 3D-printed material in the laboratory. The optimum dimensions and shape of the hemispherical lens antenna are obtained by using the 3D EM Simulation CST Studio, which is used to investigate the characteristic of the antenna, e.g., gain, radiation pattern, and reflection coefficient. To characterize the liquid content in NaCl solution, the level of the transmission coefficient (S21) is detected. The proposed sensor system offers various preferable features, e.g., non-destructive method and non-contact measurement. Five liquid solutions under test (LUT), e.g., 5%, 10%, 15%, 20%, and 25% NaCl in the NaCl-aqueous solutions, are measured and performed to generate the extraction model. The proposed sensor also offers other advantages, e.g., real-time monitoring and no life-cycle limitation. © 2022 IEEE.

关键词： Microstrip antennas

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Advanced Tracking Algorithm for Multiple Dynamic Vehicles using Attention Mechanisms and Kinematic Models

Advanced Tracking Algorithm for Multiple Dynamic Vehicles us...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Kishore Ravikumar Kamal Sutaria Ganesh Pandit Pathak Shailesh Rastogi Department of Mechanical Engineering Chennai Institute of Technology Kundrathur India Deparment of Computer Science and Engineering Faculty of Engineering and Technology Parul University Vadodara India Balaji Institute of Modern Management Pune India Symbiosis Institute of Business Management Nagpur Symbiosis International (Deemed University) Pune India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Efficient vehicle tracking in urban environments is crucial for intelligent transportation systems but faces challenges such as nonlinear motion, dynamic backgrounds, and frequent occlusions. To address these complexities, we propose the AM-Vehicle-Track system, designed under the tracking-by-detection paradigm. This system integrates the Lightweight Convolutional Block Attention Module (LCBAM), which enhances detection accuracy by refining feature maps through channel-wise and spatial attention, and the Noise-Adaptive Extended Kalman Filter (NSA-EKF), which dynamically adjusts observation noise based on detection confidence to handle nonlinear vehicle motion. Additionally, the Byte data association technique is employed to mitigate issues with low-confidence detections and reduce identity switches. Tested on the challenging VisDrone-MOT dataset, AM-Vehicle-Track achieves a mean object tracking accuracy (MOTA) of 42.2%, an identity F1 score (IDF1) of 51.2%, and processes at 72 frames per second (FPS), outperforming existing state-of-the-art systems. These results highlight its robustness and efficiency in real-time scenarios, making it suitable for intelligent transportation applications. By significantly improving detection accuracy, tracking precision, and scalability, AM-Vehicle-Track establishes a new benchmark in multi-object tracking technologies and demonstrates its potential to enhance the reliability and efficiency of vehicle tracking in complex urban environments.

关键词： Accuracy Attention mechanisms Tracking Heuristic algorithms Urban areas Refining Real-time systems Kalman filters Vehicle dynamics Integrated circuit modeling

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Data Center Traffic Characteristics in the Era of Cloud Application

Data Center Traffic Characteristics in the Era of Cloud Appl...

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OptoElectronics and Communications Conference, OECC

作者： Yanxian Bi Fulong Yan Shengjie Guo Zhihao Chen Xiangliang Ma Jie Zhang CETC Academy of Electronics and Information Technology Group Co. Ltd China Academy of Electronic and Information Technology Deparment of Electrical Engineering Beijing University of Posts and Telecommunications Beijing China Beijing Institute of Computer Technology and ApplicationsChina Aerospace Science and Technology Corporation Beijing China School of Integrated Circuits Beijing University of Posts and Telecommunications Beijing China

The traffic pattern of a cloud data center is presented with intra-Pod traffic ratio of ≥50%. Besides, the quantitative investigation of queues demonstrates that the switch link utilization ratio could approach 0.4 w... 详细信息

ISBN: (数字)9798350308396

ISBN: (纸本)9798350308402

关键词： Data centers Packet loss Optimization methods Switches Queueing analysis

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Modelling of FinFET based ON bipolar cell of silicon retina

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Biosensors and Bioelectronics: X 2023年 15卷

作者： Shah, Payal Sonkusare, Reena Rathod, Surendra S. Department of Electronics Engineering Sardar Patel Institute of Technology Maharashtra Mumbai India Department of Electronics and Telecommunication Engineering Sardar Patel Institute of Technology Maharashtra Mumbai India Deparment of Electronics and Computer Science Engineering Fr. Conceicao Rodrigues College of Engineering Maharashtra Mumbai India

—In this paper, we proposed a SOI FinFET based ON bipolar cell of silicon retina which plays an important role in neuromorphic applications like differential motion detection. Low power consumption along with biological plausibility is one of the crucial parameters of any neuromorphic retinal circuit as in the brain all biological systems consisting of billions of neurons consume power in terms of a few μwatts. First, the output of linkage cell between inner and outer plexiform layer i.e. bipolar cell is reconstructed and verified using developed ionic current based mathematical model which is established considering Hodgkin-Huxley model as the base model, and then biological behavior of the ON bipolar cell is mimicked through circuit using 180 nm, 90 nm and 45 nm Metal Oxide Semiconductor Field Effect Transistor (MOSFET). The power consumption in each case is obtained to be approximately 18.5 nW, 10.5 nW and 1 nW which is much larger than the power consumed by biological bipolar cell. Moreover, the same circuit is implemented with 32 nm Silicon on Insulator Fin Field Effect Transistor (SOI FinFET) device. It is observed that SOI FinFET based ON bipolar cell consumes 17.5 pW power which is less in comparison to MOSFET based bipolar cell. The output of bipolar cell is matched with biological nature of the waveform in each case. © 2023 The Author(s)

关键词： FinFET

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A Random Forest Stock Prediction Model Based on Bayesian Optimization

A Random Forest Stock Prediction Model Based on Bayesian Opt...

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International Conference on Artificial Intelligence and Big Data (ICAIBD)

作者： Yajuan Zhang Xiuyan Zheng Sihan Yang Shangyu Meng Z. Y. Yang Xinghui Fei College of Information Engineering Hainan Vocational University of Science and Technology Haikou Hainan China School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu China Network engineer department Guiyang Shengteng Network Technology Co. LTD Guiyang China Electrical and Computer Engineer Deparment Colorado State University Fort Collins U.S.

ISBN: (数字)9798350385106

ISBN: (纸本)9798350385113

Random forest models often rely on human-determined parameters, which may introduce subjectivity and affect the degree of optimization of the model. To address this issue, we use a random forest approach based on Bayesian optimization to reduce the influence of human factors and improve the fitting. Bayesian optimization prevents the model from falling into the dilemma of local optimum. In order to show the effect of the optimization, we compare the support vector machine, the original random forest and the LGBM model, and perform Bayesian optimization on the latter two to achieve better results. Experiments show that the optimized model performs well in stock return prediction with a reduction of 0.01 in RMSE and MAE, a reduction of 0.4 in MAPE, and an improvement of 0.02 in R2 value compared to the other models. The Bayesian optimization based Random Forest model achieves the lowest prediction error on different datasets and has wide applicability.

关键词： Support vector machines Accuracy Time series analysis Human factors Predictive models Data models Bayes methods

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Next-Generation Computational Models for Enhanced Interpretation of Biospeckle Images Leveraging Deep Learning and Fourier Transform Techniques in Biomedical Applications

Next-Generation Computational Models for Enhanced Interpreta...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： R Gowtham Raj S Sreekanth Ginni Nijhawan Shubhi Gupta Saurabh Sharma T Mounika Deparment of Mechanical Engineering New Horizon College of Engineering Bangalore India Computer Science and Engineering(Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad India Plot No. 11 Knowledge Park II Greater Lloyd Law College Noida Uttar Pradesh India Lovely Professional University Phagwara India Amity School of Engineering & Technology Amity University Greater Noida India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad Telangana India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

This study looks at advanced computational techniques that enhance the understanding of biospeckle images in biological contexts. Our models use Fourier transform and deep learning techniques to achieve improved specificity, recall, accuracy, and precision. The Fourier transform may provide a more accurate description of frequency domain data as biological processes evolve over time. Deep learning algorithms recognise patterns in biospeckle images, facilitating analysis, memory, and response. The analysis research delineates the several components that influence the outcome and provides evidence of their respective roles. This research shows how helpful the models are and offers suggestions on how to make them better. Non-invasive biomedical testing uses these computer models to illustrate the temporal evolution of biological cells. Our techniques improve the interpretation of biospeckle images, potentially revolutionising non-invasive biomedical imaging. Future researchers may evaluate these models using different datasets and testing conditions. This guarantees their adaptability and power in biological contexts.

关键词： Deep learning Adaptation models Fourier transforms Accuracy Evolution (biology) Computational modeling Biological system modeling Frequency-domain analysis Biomedical imaging Testing

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Mathematical analysis of the gradients in deep learning

arXiv

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arXiv 2025年

作者： Dereich, Steffen Do, Thang Jentzen, Arnulf Weber, Frederic Institute for Mathematical Stochastics Faculty of Mathematics and Computer Science University of Münster Germany School of Data Science The Chinese University of Hong Kong Shenzhen [CUHK-Shenzhen China Deparment of Probability and Statistic Institute of Mathematics Vietnam Academy of Science and Technology Viet Nam School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen [CUHK-Shenzhen China Applied Mathematics: Institute for Analysis and Numerics Faculty of Mathematics and Computer Science University of Münster Germany

Deep learning algorithms – typically consisting of a class of deep artificial neural networks (ANNs) trained by a stochastic gradient descent (SGD) optimization method – are nowadays an integral part in many areas of science, industry, and also our day to day life. Roughly speaking, in their most basic form, ANNs can be regarded as functions that consist of a series of compositions of affine-linear functions with multidimensional versions of so-called activation functions. One of the most popular of such activation functions is the rectified linear unit (ReLU) function R ∋ x 7→ max{x, 0} ∈ R. The ReLU function is, however, not differentiable and, typically, this lack of regularity transfers to the cost function of the supervised learning problem under consideration. Regardless of this lack of differentiability issue, deep learning practioners apply SGD methods based on suitably generalized gradients in standard deep learning libraries like TensorFlow or Pytorch. In this work we reveal an accurate and concise mathematical description of such generalized gradients in the training of deep fully-connected feedforward ANNs and we also study the resulting generalized gradient function analytically. Specifically, we provide an appropriate approximation procedure that uniquely describes the generalized gradient function, we prove that the generalized gradients are limiting Fréchet subgradients of the cost functional, and we conclude that the generalized gradients must coincide with the standard gradient of the cost functional on every open sets on which the cost functional is continuously *** Codes 68T07 Copyright © 2025, The Authors. All rights reserved.

关键词： Cost functions

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