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2024 OES China Ocean Acoustics, COA 2024

作者： Sibo, Li rui, Guo dianlun, Zhang Yujuan, Li Xiaoqian, Hou Shengfeng, Wang Science and Technology on Underwater Acoustic Laboratory/ Technology R&d Center Harbin Engineering University Haiying Enterprise Group Co. Ltd Wuxi Harbin China Technology R&d Center Haiying Enterprise Group Co. Ltd Wuxi China Science and Technology on Underwater Acoustic Laboratory Harbin Engineering University Harbin China

ISBN: (纸本)9798350340297

In active low-frequency and high-power sonar systems, target detection must overcome strong reverberation. Active sonar reverberation interference strongly fluctuates with distance and exhibits significant temporal and spatial variations. Active sonar reverberation is a typical nonstationary background. Complex changes in reverberation directly affect the target detection performance of active sonar systems. The major difficulty associated with current detection algorithms lies in autonomous detection, particularly in terms of the adaptability of detection algorithms to complex background interference. Accurately performing dynamic background estimation, suppressing interference, controlling false alarms, and highlighting weak targets are the focus of object detection. This paper proposes an active sonar target detection method driven by a combination of data and models. The proposed method can achieve target detection under strong reverberation interference with low sidelobe false alarms and high robustness. © 2024 IEEE.

关键词： reverberation

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SEr Analysis of GFdM System οver α – μ Fading Channel for 5G Wireless Applications

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Journal of engineering science and Technology review 2024年第4期17卷 205-209页

作者： Jada, Lingaiah Shiyamala, S. School of Electrical and Communication Engineering ECE Vel Tech Rangarajan Sagunthala R&D Institute of Science and Technology Avadi Tamilnadu Chennai600 062 India

A flexible radio waveform, generalised frequency division multiplexing (GFdM) allows for significant degrees of freedom in adjusting the number of time slots, subcarriers, and pulse shaping filters. The GFdM is one of the multi-carrier techniques that has been proposed to achieve 5G requirements. Initially, this study discusses about the derivation of novel analytical expression of symbol error rate under α − μ fading channel using the GFdM system. The proposed derivation includes rayleigh, Nakagami-m, and Nakagami-q fading channels as special cases for different α and μ values. Further, a simulation test-bed has been developed in MATLAB to access the simulation results and they are in good agreement with the derived theoretical results for various simulation parameters such as different roll-off factors, modulation orders and various fading parameters. © 2024 School of science, dUTH. All rights reserved.

关键词： MATLAB

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Federated Learning Approaches for decentralized data Processing in Edge Computing 5

Federated Learning Approaches for Decentralized Data Process...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Janaki, G. Umanandhini, d. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Computer Science & Engineering Chennai India

ISBN: (纸本)9798331504403

Federated Learning (FL) has evolved into a decentralized Machine Learning (ML) paradigm with a high potential for enabling model training across various Edge devices (Ed) to conserve data privacy. This survey explores the challenges and future directions of implementing FL in edge computing environments. This Comprehensive review addresses key technical challenges, including the heterogeneity of edge devices, resource constraints, security and privacy concerns, and communication overhead. To guide future research, this study highlights several promising research directions, including advanced model aggregation techniques, edge device optimization, cross-domain federated learning applications, robust security and privacy frameworks, and regulatory compliance. Additionally, This study reviews various Federated Learning methods-such as Federated Averaging (FedAvg), Federated Learning with Secure Aggregation (FedSecAgg), Hierarchical Federated Learning, and Peer-to-Peer Federated Learning-discussing their strengths, weaknesses, and suitable use cases. Finally, this review paper presents benchmarking results using standard datasets and performance metrics, comparing these approaches' efficiency, accuracy, scalability, and privacy preservation. This systematic review renders a broad perspective on the current trends and developments in federated Learning for edge computing, this analysis enlightenments researchers and Professionals with practical experience. © 2024 IEEE.

关键词： Federated learning

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An Automated deep Learning-based VAr for Enhancing referee decision-Making in Football 3

An Automated Deep Learning-based VAR for Enhancing Referee D...

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3rd International Conference on Intelligent data Communication Technologies and Internet of Things, IdCIoT 2025

作者： Zaken, Hamza Abu Prasanth, A. Jahjah, Mohamad Ali Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9798331527549

While football is the most popular sport the world has ever seen, the question of referee decision-making has always been a shadow that hampers the spirit and justice of the game. Video Assistant referee (VAr) was introduced to solve officiating errors through video reviews. Nonetheless, this technology has drawn heavy criticism for causing huge delays and not completely eradicating referee bias. In this research, a Tube Convolutional Neural Network (T-CNN) model is incorporated into a new VAr system as an advanced solution. By allowing for the input of spatial and temporal features for football footage, the T-CNN model processes features which allows for rapid and accurate analysis of complex match events such as offsides, fouls, and goal-line incidents. The proposed system makes gameplay more real-time while ensuring fairness by minimizing human involvement and the consequent delays in the decision-making process. Additionally, its unbiased AI-driven decisions also eliminate the challenge of home team bias. Comparative analyses with traditional VAr methods established that the T-CNN system achieves greater accuracy rate, real-time responsiveness, and fairness, ultimately transforming football officiating. © 2025 IEEE.

关键词： Convolutional neural networks

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Control Study of Enhanced STATCOM for Supercapacitor Applying Virtual Synchronous Generator Control

Control Study of Enhanced STATCOM for Supercapacitor Applyin...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Nada, Kaho Fujii, Toshiyuki Sano, Kenichiro Mitsubishi Electric Corporation Advanced Technology R&d Center Hyogo Japan Institute of Science Tokyo Department of Electrical and Electronic Engineering Tokyo Japan

ISBN: (纸本)9798350376067

The enhanced static synchronous compensator (E-STATCOM), which is equipped with a supercapacitor and can output active power, is attracting attention as a compensator that can both suppress grid voltage fluctuations and maintain short-term frequency during transients. The authors propose a virtual inertia control based on current control-type converters. The method can suppress overcurrent and manage the dC voltage of the converter, which are issues with the typical grid forming control employing voltage control-type converters. The proposed control configuration is based on an established current-controlled STATCOM. However, a virtual synchronous generator control can be achieved simply by adding to the dC voltage reference of the conventional STATCOM control. The simulation results show that it is possible to achieve continuous operation for the three-phase line to ground fault, and STATCOM's active and reactive power outputs are possible to suppress system oscillations after the fault is cleared. © 2024 IEEE.

关键词： Synchronous generators

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A Novel Method for Extracting Asymmetric Source and drain resistance in IGZO Vertical Channel Transistors

A Novel Method for Extracting Asymmetric Source and Drain Re...

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2024 IEEE Symposium on VLSI Technology and Circuits, VLSI Technology and Circuits 2024

作者： Yoo, S.W. Lee, Y. Jung, W.J. Kim, H. Byeon, S. Kim, M. Lee, J. Lee, T. Hong, M.J. Song, Y.G. Lee, S. Terai, M. Yoo, K.J. Sung, C. Lee, W. Cho, M.H. Kim, d. Ha, d. Ahn, S. Song, J.H. Semiconductor R&d Center Korea Republic of Computational Science & Engineering Group Samsung Electronics Co. Ltd. Hwaseong Korea Republic of

ISBN: (纸本)9798350361469

Unlike conventional resistance extraction method, a novel method enabling to simply extract the source/drain resistance including contact resistance is, for the first time, hereby proposed in the IGZO vertical channel transistor (VCT). IGZO VCT has uneven source and drain structure inducing considerable asymmetricity in source and drain resistances, thus the extraction of them is very important. This method is based on the fact that the amount of current varies significantly depending on the direction of electron injection in the structure forming the schottky contacts. Experimental results show that this method is adequate to evaluate improvements in the contact resistance and thus applicable for source and drain resistance analysis in oxide semiconductor structures. © 2024 IEEE.

关键词： asymmetric source/drain IGZO source/drain resistance vertical channel transistor (VCT)

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Chiral inorganic nanomaterials:Harnessing chirality-dependent interactions with living entities for biomedical applications

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Nano research 2023年第8期16卷 11107-11124页

作者： Qi Gao Lili Tan Zhihao Wen daidi Fan Junfeng Hui Peng-peng Wang Shaanxi Key Laboratory of Degradable Biomedical Materials Shaanxi R&D Center of Biomaterials and Fermentation EngineeringSchool of Chemical EngineeringNorthwest UniversityXi’an 710069China State Key Laboratory for Mechanical Behavior of Materials Shaanxi International Research Center for Soft MatterSchool of Materials Science and EngineeringXi’an Jiaotong UniversityXi’an 710049China

Chirality is an intriguing and fundamental property of natural matter,which is especially crucial in supporting the processes of living *** selective interactions between natural chiral compounds are widespread at all levels in living entities and play a vital role in biochemical *** cutting-edge advancements in synthetic chiral inorganic nanostructures have led to significant progress in their applications within biological *** developments have unraveled chirality-dependent interactions at the nanoscale and molecular scale,providing a better understanding of intricate process of chiral selection in biological systems and demonstrating the potential of chiral inorganic nanostructures for life science ***,we summarize recent progress in understanding the chirality origin of inorganic chiral nanoparticles and the development of wet-chemical *** also discuss the captivating interaction between chiral inorganic nanostructures and biological entities at various ***,we discuss the challenges and potential of functional chiral nanomaterials for future biomedical and bioengineering applications,offering design ideas and a forecast for their future impact.

关键词： chiral inorganic nanomaterials living system chirality-dependent interaction bioengineering application

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The Impact of Wall Structure with Photovoltaic Function on Prefabricated Buildings

Renewable Energy and Power Quality Journal

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renewable Energy and Power Quality Journal 2024年第4期22卷 76-84页

作者： Zhu, Jiahui Hu, Jiaying College of Civil Engineering Lanzhou University of Technolog Gansu Lanzhou China R&D Center Chengdu Zhilan Landscape Design Co. Ltd Sichuan Chengdu China

The wall structure is the main load-bearing component of the prefabricated building, which plays an important role in the stability and aesthetics of the building. With the deepening of the application of new energy in the construction field, the application of photovoltaic wall with photovoltaic function is becoming more and more extensive. However, there is still controversy about whether the photovoltaic function can meet the requirements of the building. On this basis, this paper discusses the wall structure with photovoltaic function in depth, and analyzes its energy consumption, heat preservation, ventilation and cost indicators. The results show that the wall structure of photovoltaic function can improve the aesthetics of prefabricated buildings, meet customer demand rate of more than 75%, save energy consumption by 25%~30%, increase indoor temperature by 1~3 degrees, and ventilate at 1.2 levels. It can be inferred that the load-bearing wall of photovoltaic function is suitable for prefabricated buildings and has the potential and role of vigorous development. © 2024, European Association for the development of renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.

关键词： Walls (structural partitions)

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diversified Critical-Scenario-Search for defect detection of Autonomous driving System

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-15页

作者： Xie, Yizhou Zhang, Yong Yin, Chengliang Huang, Hongcheng dai, Kunpeng National Engineering Laboratory for Automotive Electronic Control Technology Shanghai Jiao Tong University Shanghai China Shanghai Intelligent and Connected Vehicle R&D Center Shanghai China

Scenario-based test is recognized as an effective way to evaluate and verify the Safety of the Intended Functionality (SOTIF) of an autonomous driving system, and a critical-scenario is beneficial to foreground the system defect more obviously. However, for the state of the art, time consumption of critical-scenario-search in a real-time test is still high, especially for searching critical-scenarios with dissimilarities, which assists designers in discovering more failure modes of autonomous in limited time. To address above challenges, in this paper, we improve our previous scenario-searching approach considering dissimilarity assessments in iterations, we present a searching framework which combines control and exploration approaches, and implement the efficient evolution of scenarios with a backward target-tree search. We carry out a Vehicle-in-the-Loop (VIL) test with a real vehicle to verify our new approach, and the results show that the approach is able to search for scenarios directly against a black-box system in a real-time test, and achieve scenarios with both high criticality and dissimilarity, including unknown-unsafe cases. In our time-limited test, compared with different methods, our approach can achieve 1.9 times the amount of diversified scenarios with the almost same (slightly higher) criticality, either achieve 1.5 times the amount attached with 75% improvement in criticality. IEEE

关键词： real time systems

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Segmentation of the Corpus Callosum from Brain Magnetic resonance Images Using dual deep Learning Classifiers and Optimized U-Shaped Neural Networks

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SN Computer science 2024年第1期5卷 1-18页

作者： Sarma, A. Padmanabha Saranya, G. Department of Biomedical Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

Segmentation of the corpus callosum (CC) from Mr images is an important step in neuroimaging analysis for various applications, such as brain morphometry, tractography, and connectivity analysis. This research proposes a deep learning-based model for segmenting CC in T1-weighted midsagittal brain MrIs. The model consists of five major phases, namely, image augmentation, pre-processing, segmentation, feature extraction, and feature selection. The collected raw MrI images are augmented using synthetic minority oversampling technique (SMOTE) and pre-processed using improved Gabor filtering and contrast stretch for contrast enhancement. The region of Interest (rOI) has been identified through optimized U-NETS using a new hybrid optimization model. Shape, texture, and colour features are extracted from the segmented image, from which optimal features are selected through proposed hybrid optimization model. The CC segmentation phase will employ a dual deep-learning classifiers approach, with bidirectional LSTM and convolutional block attention module (CBAM) trained using optimally selected features, and a modified convolutional neural network (M-CNN) to provide final detected outcomes. The proposed model aims to improve the accuracy of CC segmentation in brain MrIs. © The Author(s),.

关键词： Corpus callosum Improved Gabor filtering Modified convolutional neural network (M-CNN) Optimized U-NETS

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