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International Conference on Electrical Power and Energy systems (ICEPES)

作者： Franciskus Antonius Alijoyo Deepak A. Vidhate Chamandeep Kaur V Nivedan V. Kanpur Rani A. Balakumar School of Business and Information Technology STMIK LIKMI Bandung Indonesia Department of Information Technology Dr. Vithalrao Vikhe Patil College of Engineering Ahmednagar Maharashtra India Department of Computer Science Jazan University Jazan Saudi Arabia Department of Electronics and Communication System Sri Ramakrishna College of Arts & Science Coimbatore India Hindustan Institute of Technology and Science Chennai India Department of ECE K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798350390728

ISBN: (纸本)9798350390735

The incorporation of Internet of Things (IoT) cloud computing solutions with smart grids signifies a significant advancement in energy management. Traditional methods, though beneficial, often lack scalability and real-time capabilities, limiting their effectiveness in managing the complexity of modern energy systems. In this paper, a novel approach cloud-based IoT technologies to address these shortcomings and enhance smart grid functionalities. Existing energy management methods heavily rely on localized monitoring and control systems, constraining scalability and real-time responsiveness. Additionally, these methods struggle to handle the immense data generated by IoT devices dispersed throughout the grid. The proposed approach tackles these challenges by utilizing cloud computing to store, process, and analyze IoT sensor data in real-time. The novelty of this method lies in seamlessly integrating cloud-based platforms with IoT devices across the smart grid. Through utilising cloud resources, the limitations of traditional approaches are overcome, enabling scalable, real-time oversight and management of grid assets. The methodology involves deploying IoT sensors across the grid to compile information about energy usage, generation, and distribution, which is then transmitted to cloud servers for analysis and decision-making. Through simulations and real-world experiments, the effectiveness of this approach in optimizing grid performance, improving reliability, and facilitating demand-response programs is demonstrated. The results exhibit significant enhancements in grid efficiency and resilience, showcasing the potential of cloud-based IoT solutions to transform energy management in smart grids. Overall, this proposed method presents a promising pathway towards establishing more efficient, sustainable, and resilient energy infrastructure for the future.

关键词： Cloud computing Scalability Transforms Real-time systems Smart grids Internet of Things Servers

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Highly integrated reconfigurable radiofrequency filter based on microcomb and a silicon photonic processor

Highly integrated reconfigurable radiofrequency filter based...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Yuansheng Tao Lin Chang Bitao Shen Haowen Shu Weiqiang Xie Ming Jin Zihan Tao Bowen Bai Shaohua Yu Xingjun Wang John E. Bowers Department of Electronics State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Engineering and Computer Science Peking University Beijing China Department of Electrical and Computer Engineering University of California Santa Barbara USA Peng Cheng Laboratory Shenzhen China Frontiers Science Center for Nano-Optoelectronics Peking University Beijing China

We demonstrate a novel chip-scale reconfigurable radiofrequency filter by using AlGaAs-on-insulator microcomb and silicon photonics in combination, simultaneously bypassing the employment of bulky pulse shaper and lon... 详细信息

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

关键词： Radio frequency Employment Lasers and electrooptics Silicon photonics Electrooptical waveguides Optical fiber dispersion Fiber lasers

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CTMQ: Cyclic Training of Convolutional Neural Networks with Multiple Quantization Steps

arXiv

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arXiv 2022年

作者： Kim, HyunJin Shin, Jungwoo Del Barrio, Alberto A. School of Electronics and Electrical Engineering Dankook University 152 Jukjeon-ro Suji-gu Gyeonggi-do Yongin-si Korea Republic of Department of Computer Architecture and System Engineering Complutense University of Madrid Madrid Spain

This paper proposes a training method having multiple cyclic training for achieving enhanced performance in low-bit quantized convolutional neural networks (CNNs). Quantization is a popular method for obtaining lightweight CNNs, where the initialization with a pretrained model is widely used to overcome degraded performance in low-resolution quantization. However, large quantization errors between real values and their low-bit quantized ones cause difficulties in achieving acceptable performance for complex networks and large datasets. The proposed training method softly delivers the knowledge of pretrained models to low-bit quantized models in multiple quantization steps. In each quantization step, the trained weights of a model are used to initialize the weights of the next model with the quantization bit depth reduced by one. With small change of the quantization bit depth, the performance gap can be bridged, thus providing better weight initialization. In cyclic training, after training a low-bit quantized model, its trained weights are used in the initialization of its accurate model to be trained. By using better training ability of the accurate model in an iterative manner, the proposed method can produce enhanced trained weights for the low-bit quantized model in each cycle. Notably, the training method can advance Top-1 and Top-5 accuracies of the binarized ResNet-18 on the ImageNet dataset by 5.80% and 6.85%, respectively. © 2022, CC BY.

关键词： Large dataset

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Weighted Mask Recurrent-Convolutional Neural Network based Plant Disease Detection using Leaf Images

Weighted Mask Recurrent-Convolutional Neural Network based P...

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International Conference on ent Computing and Control systems (ICICCS)

作者： R. Sharmila R Kamalitta Moorthy Devesh Pratap Singh Amit Chauhan Purnendu Bikash Acharjee Department of MCA Dhanalakshmi Srinivasan Engineering College Perambalur Tamilnadu India Information Technology K.Ramakrishnan College of Engineering Trichy Tamilnadu India Electronics Engineer Production Dhaksha Unmanned System Pvt ltd Chennai Tamilnadu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttrakhand India Department of life Sciences CHRIST (Deemed to be University) Bengaluru Karnataka India Faculty Member Computer Science CHRIST University Pune Maharashtra India

Large losses in output, money, and quality/quantity of agricultural goods are incurred due to plant diseases. Seventy percent of India’s GDP is tied to the agricultural sector, thus protecting plants from diseases is crucial. For this reason, it is important to keep an eye on plants from the moment they sprout. The usual approach for this omission is naked eye inspection, which is more time-consuming, costly, and requires significant skill. Thus, automating the method for detecting diseases is necessary to speed up this process. It is imperative that image processing methods be used in the creation of the illness detection system. Disease detection involves a number of processes, including Weighted Mask R-CNN, GLCM feature extraction, Multi-thresholding image pre-processing, and K means image segmentation classification. The weighted Mask R-CNN outperforms the standard RNN, the Mask R-CNN, and the CNN in terms of accuracy and recall in analytical trials by a significant margin.

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Detection of S100 Calcium Binding Protein A6 by Silicon Nitride Photonic Sensor

Detection of S100 Calcium Binding Protein A6 by Silicon Nitr...

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2022 International Electrical engineering Congress, iEECON 2022

作者： Udomsom, Suruk Rattakorn, Punramon Mankong, Ukrit Umezawa, Toshimasa Matsumoto, Atsushi Kanno, Atsushi Yamamoto, Naokatsu Mendez-Astudillo, Manuel Atthi, Nithi Yamwong, Wittawat Intuyod, Kitti Pinlaor, Somchai Kawanishi, Tetsuya Imai, Kazuo Chiang Mai University Biomedical Engineering Institute Chiang Mai Thailand Chiang Mai University Faculty of Engineering Department of Electrical Engineering Chiang Mai Thailand National Institute of Information and Communications Network System Research Institute Tokyo Japan Ansys Inc. Tokyo Japan Thai Microelectronics Center National Electronics and Computer Technology Center Chachoengsao Thailand Khon Kaen University Faculty of Medicine Department of Pathology Khon Kaen Thailand Khon Kaen University Faculty of Medicine Department of Parasitology Khon Kaen Thailand Waseda University Department of Electronic and Physical Systems Tokyo Japan Imai Engineering Tokyo Japan

ISBN: (纸本)9781665402064

The detection of S100 Calcium Binding Protein A6, a useful cholangiocarcinoma marker, has been demonstrated for the first time using silicon nitride photonic resonator sensor. The sensor was designed and fabricated by low-cost 500 nm technology. Label-free sensing results based on the reactions between anti-S100A6 antibody and S100A6 antigen, showed good sensitivity and specificity. This sensing technique has potential in low-cost and low-level detection of the biomarker, deserving further study. © 2022 IEEE.

关键词： Silicon nitride

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LOG-LIO2: A LiDAR-Inertial Odometry with Efficient Uncertainty Analysis

arXiv

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arXiv 2024年

作者： Huang, Kai Zhao, Junqiao Lin, Jiaye Zhu, Zhongyang Song, Shuangfu Ye, Chen Feng, Tiantian The School of Surveying and Geo-Informatics Tongji University Shanghai China Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China The MOE Key Lab of Embedded System and Service Computing Tongji University Shanghai China Institute of Intelligent Vehicles Tongji University Shanghai China

Uncertainty in LiDAR measurements, stemming from factors such as range sensing, is crucial for LIO (LiDAR-Inertial Odometry) systems as it affects the accurate weighting in the loss function. While recent LIO systems address uncertainty related to range sensing, the impact of incident angle on uncertainty is often overlooked by the community. Moreover, the existing uncertainty propagation methods suffer from computational inefficiency. This paper proposes a comprehensive point uncertainty model that accounts for both the uncertainties from LiDAR measurements and surface characteristics, along with an efficient local uncertainty analytical method for LiDAR-based state estimation problem. We employ a projection operator that separates the uncertainty into the ray direction and its orthogonal plane. Then, we derive incremental Jacobian matrices of eigenvalues and eigenvectors w.r.t. points, which enables a fast approximation of uncertainty propagation. This approach eliminates the requirement for redundant traversal of points, significantly reducing the time complexity of uncertainty propagation from O(n) to O(1) when a new point is added. Simulations and experiments on public datasets are conducted to validate the accuracy and efficiency of our formulations. The proposed methods have been integrated into a LIO system, which is available at https://***/tiev-tongji/LOG-LIO2. Copyright © 2024, The Authors. All rights reserved.

关键词： Optical radar

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A Novel Ensemble Deep Learning Framework for Breast Cancer Prediction

A Novel Ensemble Deep Learning Framework for Breast Cancer P...

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Innovative Computing, Intelligent Communication and Smart Electrical systems (ICSES), International Conference on

作者： Pundru Chandra Shaker Reddy S Nithyapriya Nipun Sharma S Maheswari B Jayaram Tejaswini Katale School of Computer Science and Artificial Intelligence SR University Warangal Telangana India Artificial Intelligence and Data Science Bannari Amman Institute of Technology Anna University Chennai Tamil Nadu India Electronics and Communication Engineering Presidency University Bangalore Karnataka India Department of Mathematics Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Computer Science and Business System RMK Engineering College Kavaraipettai Tamil Nadu India Department of Computer Engineering Vishwakarma Institute of Technology Pune Maharashtra India

Because of its high incidence and high mortality rate, breast cancer requires reliable means of early detection and diagnosis to improve survival rates for its patients. With advantages including increased accuracy and efficiency, deep learning (DL) and machine learning (ML) methods are quickly becoming indispensable in the fight against breast cancer. However, there is a need for additional study because existing approaches have scalability and performance constraints. Utilizing a pre-trained ResNet50V2 and ensemble-based ML approaches, we offer a hybrid approach to reliable breast cancer diagnosis in this study. Learning and extracting latent trends from complex breast-cancer(BC) images is made possible by DL's incorporation, with ML strategies contributing interpretability and generalizability. We performed wide experiments utilizing a openly accessible Invasive-Ductal-Carcinoma(IDC) dataset based on breast histopathology images of varying sample sizes. The resilience and great performance of our hybrid model are supported by the results of our extensive experiments. When compared to state-of-the-art models, our 95% accuracy rate, 94.5 precision, 94.2 recall, and 94.6 F1 score are all improvements. We also found that the best ML model to use with the ResNet50V2 architecture is the Light Boosting Classifier (LGB). This study's findings provide substantial contributions to the field of breast cancer diagnosis thanks to its novel approach, in-depth performance analysis, and trustworthy evaluation. In addition, it may help doctors make better decisions, provide better care, and boost results for people with breast cancer.

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arXiv

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arXiv 2024年

作者： Ren, Yingying Li, Qiuli Guo, Yangyang Pedrycz, Witold Xing, Lining Liu, Anfeng Song, Yanjie School of Computer Electronics and Information Guangxi University Nanning China Guangxi Key Laboratory of Multimedia Communications and Network Technology China Key Laboratory of Parallel Distributed and Intelligent Computing Guangxi University China School of Systems Science Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer Istanbul Turkey School of Electronic Engineering Xidian University Xian China School of Electronic Engineering Central South University Changsha China National Engineering Research Center of Maritime Navigation System Dalian Maritime University Dalian China

With the rapid development of the satellite industry, the information transmission network based on communication satellites has gradually become a major and important part of the future satellite ground integration network. However, the low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system and needs to be solved urgently. Effectively planning the task of satellite ground networking by reasonably scheduling resources is crucial for the efficient transmission of task data. In this paper, we hope to provide a task execution scheme that maximizes the profit of the networking task for satellite ground network planning considering feeding mode (SGNPFM). To solve the SGNPFM problem, a mixed-integer planning model with the objective of maximizing the gain of the link-building task is constructed, which considers various constraints of the satellite in the feed-switching mode. Based on the problem characteristics, we propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks. To obtain more high-quality solutions, different similarity evaluation methods are designed to assist the algorithm in intelligently screening individuals. The DSGAalso uses an adaptive crossover strategy based on similarity mechanism, which guides the algorithm to achieve efficient population search. In addition, a task scheduling algorithm considering the feed-switching mode is designed for decoding the algorithm to generate a highquality scheme. The results of simulation experiments show that the DSGA can effectively solve the SGNPFM problem. Compared to other algorithms, the proposed algorithm not only obtains higher quality planning schemes but also has faster algorithm convergence speed. The proposed algorithm improves data trans

关键词： Profitability

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Correction: LPM-Net: A Data-Driven Resource-Efficient Predictive Motion Planner for Mobile Robots

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Neural Processing Letters 2025年第1期57卷 1-1页

作者： Amirhosseini, Fakhreddin Nilforoushan, Zahra Mirtaheri, Seyedeh Leili Electrical and Computer Engineering Department Kharazmi University Tehran Iran Department of Informatics Modeling Electronics and System Engineering (DIMES) University of Calabria Arcavacata Italy

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PAM-4 optical transmission beyond 224 Gbps based on an ultrahigh-bandwidth slow-light silicon modulator

PAM-4 optical transmission beyond 224 Gbps based on an ultra...

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CLEO: Applications and Technology in CLEO 2024, CLEO: A and T 2024 - Part of Conference on Lasers and Electro-Optics

作者： Han, Changhao Qin, Jun Yang, Qipeng Zheng, Zhao Shu, Haowen Zhang, Yunhao Wu, Yichen Sun, Yu Lu, Junde Zhou, Yan Ge, Zhangfeng Wang, Lei He, Zhixue Yu, Shaohua Hu, Weiwei Peng, Chao Bowers, John E. Wang, Xingjun State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Peking University Beijing100871 China Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing100192 China Frontiers Science Center for Nano-optoelectronics Peking University Beijing100871 China Peng Cheng Laboratory Shenzhen518055 China Peking University Yangtze Delta Institute of Optoelectronics Nantong226010 China

We experimentally demonstrate PAM-4 optical transmission beyond 224 Gbps based on an ultrahigh-bandwidth slow-light silicon modulator in C-band with the combination of the artificial neural network equalizers. © ... 详细信息

关键词： Light modulators

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