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2024 Asia Communications and Photonics Conference, ACP 2024 and International Conference on Information Photonics and Optical Communications, IPOC 2024

作者： Zhou, Lingjun Fang, Xiansong Zhu, Yixiao Zhu, Junbo Li, Yunchen Nie, Xiaomin Zhou, Xian Yang, Chuanchuan Wang, Lei Hu, Weisheng Li, Ke He, Zhixue Zhang, Fan School of Electronic and Computer Engineering Peking University Shenzhen China Peng Cheng Laboratory Shenzhen China State Key Laboratory of Advanced Optical Communication System and Networks School of Electronics Peking University Beijing China State Key Laboratory of Advanced Optical Communication System and Networks Shanghai Jiao Tong University Shanghai China School of Computer & Communication Engineering University of Science and Technology Beijing Beijing China

We demonstrate the record 320-GBd single-polarization QPSK and 300-GBd dual-polarization QPSK signal generation and intradyne coherent detection. This result is achieved with a novel broadband segmented TFLN I/Q modul... 详细信息

ISBN: (纸本)9798350379266

关键词： Quadrature phase shift keying

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IntML-KNN: A Few-Shot Radio Frequency Fingerprint Identification Scheme for Lora Devices

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IEEE Signal Processing Letters 2025年 32卷 2259-2263页

作者： Cao, Xin Tan, Weijie Gao, Qiangqiang Hu, Zhilong Li, Chunguo Guizhou University College of Computer Science and Technology Guiyang550025 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610025 China Ministry of Industry and Information Technology Laboratory of Cryptograph Application Technology Innovation and Testing Verification Beijing100015 China Southeast University School of Information Science and Engineering Nanjing210096 China

Deep learning (DL) is widely used in radio frequency fingerprint identification (RFFI). However, in few-shot case, traditional DL-based RFFI need to construct auxiliary dataset to realize radio frequency fingerprint identification. To address this issue, we propose a few-shot RFFI (FS-RFFI) method based on interpolating metric learning and KNN (IntML-KNN). Specifically, the method first extends the dataset with data augmentation, and CutMix interpolation. Secondly, combining with metric learning to enhance the generalization capacity of the model. Finally, KNN algorithm is designed to realize device classification and detection. The proposed IntML-KNN method is verified on the commercial available LoRa dataset. The experimental results indicate that the proposed scheme exhibits strong classification and generalization performance in FS-RFFI. Meanwhile, the recognition rate of the proposed IntML-KNN reaches 97.00% with only 10% samples. © 1994-2012 IEEE.

关键词： Deep learning

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Advances in Noble Metal-Decorated Metal Oxide Nanomaterials for Chemiresistive Gas Sensors:Overview

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Nano-Micro Letters 2023年第6期15卷 353-427页

作者： Li‑Yuan Zhu Lang‑Xi Ou Li‑Wen Mao Xue‑Yan Wu Yi‑Ping Liu Hong‑Liang Lu State Key Laboratory of ASIC and System Shanghai Institute of Intelligent Electronics and SystemsSchool of MicroelectronicsFudan UniversityShanghai 200433People’s Republic of China School of Opto‑Electronic Information and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093People’s Republic of China State Key Laboratory of Metal Matrix Composites School of Material Science and EngineeringShanghai Jiao Tong UniversityShanghai 200240People’s Republic of China

Highly sensitive gas sensors with remarkably low detection limits are attractive for diverse practical application fields including real-time environmental monitoring,exhaled breath diagnosis,and food freshness *** various chemiresistive sensing materials,noble metal-decorated semiconducting metal oxides(SMOs)have currently aroused extensive attention by virtue of the unique electronic and catalytic properties of noble *** review highlights the research progress on the designs and applications of different noble metal-decorated SMOs with diverse nanostructures(e.g.,nanoparticles,nanowires,nanorods,nanosheets,nanoflowers,and microspheres)for high-performance gas sensors with higher response,faster response/recovery speed,lower operating temperature,and ultra-low detection *** key topics include Pt,Pd,Au,other noble metals(e.g.,Ag,Ru,and Rh.),and bimetals-decorated SMOs containing ZnO,SnO_(2),WO_(3),other SMOs(e.g.,In_(2)O_(3),Fe_(2)O_(3),and CuO),and heterostructured *** addition to conventional devices,the innovative applications like photo-assisted room temperature gas sensors and mechanically flexible smart wearable devices are also ***,the relevant mechanisms for the sensing performance improvement caused by noble metal decoration,including the electronic sensitization effect and the chemical sensitization effect,have also been summarized in ***,major challenges and future perspectives towards noble metal-decorated SMOs-based chemiresistive gas sensors are proposed.

关键词： Noble metal Bimetal Semiconducting metal oxide Chemiresistive gas sensor Electronic sensitization Chemical sensitization

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Development of High-Power Bidirectional DC Solid-State Power Controller for Aircraft Applications

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IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS 2022年第5期10卷 5498-5508页

作者： Dong, Zhou Ren, Ren Wang, Fei Department of Electrical Engineering and Computer Science The University of Tennessee Knoxville TN USA Monolithic Power System Inc. San Jose CA USA Oak Ridge National Laboratory Oak Ridge TN USA

The high-power solid-state power controller (SSPC) will be a critical component for the future electrified aircraft propulsion system. This article presents the development of a 1 kV 500 A bidirectional dc SSPC using SiC power modules and transient voltage suppression (TVS) diodes. The design procedure and implementation of the SSPC are presented in detail and the system level influence is discussed. Due to the limited power of a single switch, parallel and/or series connections of switches are necessary for high-power SSPCs, and the corresponding challenges are investigated with possible solutions. In addition, the high peak-to-average clamping voltage ratio caused by Ldi/dt of the turn-off current commutation and foldback characteristics of high-power TVS diodes is addressed. Finally, the testing setups for the high-power SSPC are presented, and all key functions are tested for the SSPC. The power rating and the specific power of the developed SSPC are superior to state-of-the-art counterparts to show the advantage of the developed one.

关键词： Switches Diodes Voltage Transient analysis Absorption Multichip modules Cable TV Electrified aircraft SiC power module solid-state circuit breaker solid-state power controller TVS diode

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From Laboratory to Field:Unsupervised Domain Adaptation for Plant Disease Recognition in the Wild

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Plant Phenomics 2023年第2期5卷 195-208页

作者： Xinlu Wu Xijian Fan Peng Luo Sruti Das Choudhury Tardi Tjahjadi Chunhua Hu College of Information Science and Technology Nanjing Forestry UniversityNanjing 210037China Institute of Forest Resource Information Techniques Chinese Academy of ForestryBeijing 100091China Key Laboratory of Forestry Remote Sensing and Information System National Forestry and Grassland AdministrationBeijing 100091China Department of Computer Science and Engineering University of Nebraska-LincolnLincolnNE 68588USA School of Engineering University of WarwickCoventry CV47ALUK

Plant disease recognition is of vital importance to monitor plant development and predicting crop ***,due to data degradation caused by different conditions of image acquisition,e.g.,laboratory *** environment,machine learning-based recognition models generated within a specific dataset(source domain)tend to lose their validity when generalized to a novel dataset(target domain).To this end,domain adaptation methods can be leveraged for the recognition by learning invariant representations across *** this paper,we aim at addressing the issues of domain shift existing in plant disease recognition and propose a novel unsupervised domain adaptation method via uncertainty regularization,namely,Multi-Representation Subdomain Adaptation Network with Uncertainty Regularization for Cross-Species Plant Disease Classification(MSUN).Our simple but effective MSUN makes a breakthrough in plant disease recognition in the wild by using a large amount of unlabeled data and via nonadversarial ***,MSUN comprises multirepresentation,subdomain adaptation modules and auxiliary uncertainty *** multirepresentation module enables MSUN to learn the overall structure of features and also focus on capturing more details by using the multiple representations of the source *** effectively alleviates the problem of large interdomain *** adaptation is used to capture discriminative properties by addressing the issue of higher interclass similarity and lower intraclass ***,the auxiliary uncertainty regularization effectively suppresses the uncertainty problem due to domain *** was experimentally validated to achieve optimal results on the PlantDoc,Plant-Pathology,Corn-Leaf-Diseases,and Tomato-Leaf-Diseases datasets,with accuracies of 56.06%,72.31%,96.78%,and 50.58%,respectively,surpassing other state-of-the-art domain adaptation techniques considerably.

关键词： Plant breakthrough details

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Self-aligned dual-beam superresolution laser direct writing with a polarization-engineered depletion beam

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Photonics Research 2024年第6期12卷 1194-1200页

作者： GUOLIANG CHEN DEWEI MO JIAN CHEN QIWEN ZHAN School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Shanghai Key Laboratory of Modern Optical System University of Shanghai for Science and TechnologyShanghai 200093China Zhangjiang Laboratory Shanghai 201204China

A fiber-based,self-aligned dual-beam laser direct writing system with a polarization-engineered depletion beam is designed,constructed,and *** system employs a vortex fiber to generate a donut-shaped,cylindrically polarized depletion beam while simultaneously allowing the fundamental mode excitation beam to pass *** results in a co-axially self-aligned dual-beam source,enhancing stability and mitigating assembly *** size of the central dark spot of the focused cylindrical vector depletion beam can be easily adjusted using a simple polarization rotation *** a depletion wavelength of 532 nm and an excitation wavelength of800 nm,the dual-beam laser direct writing system has demonstrated a single linewidth of 63 nm and a minimum line spacing of 173 *** optimization of this system may pave the way for practical superresolution photolithography that surpasses the diffraction limit.

关键词： polarization beam depletion

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Enhanced Heart Functionality Prediction Using Acoustic Signals and Deep Learning Algorithms

Enhanced Heart Functionality Prediction Using Acoustic Signa...

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Data science and Business systems (ICDSBS), International Conference on

作者： R. Indumathi R. Jayaraj Department of Computer Science and Engineering Manakula Vinayagar Institute of Technology' Department of Data Science and Business System SRM Institute of Science and Technology

ISBN: (数字)9798331585600

ISBN: (纸本)9798331585617

Cardiovascular diseases primarily present as acute events as the result of block or blocks in the arteries created by a build up in fatty material that slows or cuts off blood supply to the heart or brain [10]. The classic approach to diagnosing heart disease and predicting heart attacks or strokes is by analysis of heart sounds via auscultation with a stethoscope. The proposed hybrid approach utilizes superior methods, namely, Convolutional Neural Networks and Gated Recurrent Units for advanced analysis in heart sound. CNNs particularly perform well by extracting spatial pattern from data, and GRU handles temporal dependency efficiently, therefore this combination could be more favorable for sequential analysis of heart sound. With the usage of both models, it will predict cardiovascular events by patterns and anomalies in heart sounds that most traditional algorithms fail to identify, such as Random Forest and Decision Trees. Utility of the possibility of working with heart sound data for detection of changes; those changes that are associated with the effects caused by conditions like pericardial effusion due to cancer metastasis on cardiac structure. This system, therefore, will prove comparatively how the system might predict much more accurately than others, hence making it a stronger diagnostic tool for early detection of cardiovascular diseases that will benefit more patients and cut down the heart-related health issues..

关键词： Heart Deep learning Machine learning algorithms Transforms Stroke (medical condition) Prediction algorithms Convolutional neural networks Decision trees Random forests Testing

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An Adaptive Traffic Signal Management system Incorporating Reinforcement Learning

An Adaptive Traffic Signal Management System Incorporating R...

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2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent systems, AICERA/ICIS 2023

作者： Masfequier Rahman Swapno, S.M. Chhabra, Gunjan Kaushik, Keshav Nobel, S.M. Nuruzzaman Islam, Md Babul Shahiduzzaman, Md. Bangladesh University of Business and Technology Dept of Computer Science and Engineering Dhaka Bangladesh Graphic Era Hill University Dehradun India University of Petroleum and Energy Studies Dehradun India Unical Dept. of Computer Modeling Electronic and System Engineering Rende Italy

ISBN: (纸本)9798350303452

Although there has been a lot of study done on traffic monitoring, control, modeling, and the use of artificial intelligence to handle these problems, there are still a lot of critical problems that need to be solved. In existing traffic signal research, a major problem is longer delays and queues, causing significant traffic congestion. Our research aims to minimize these issues, successfully reducing delays and queues. This has led to improved traffic flow and a significant decrease in congestion. Reinforcement learning for traffic signal control uses RL techniques to create intelligent agents that make real-time decisions about changing traffic signals at intersections. Its goal is to enhance traffic flow, reduce congestion, and improve overall transportation efficiency by optimizing signal phases dynamically. Reinforcement property state contain the current circumstances at an intersection, actions correspond to the choices made by the traffic signal controller, and rewards reflect the controller's performance and are in line with traffic objectives like minimizing delays, reducing congestion, cutting down on travel times, and improving safety. We scrutinize Deep Q learning by reason of Deep Q Learning gives Q value, By estimating and updating Q-values, the agent can learn which actions are more beneficial in different traffic conditions. We are able to cut the queue length by 9.7% to shorten wait times and relieve traffic. Get a happy outcome of traffic signal control and improve rewards by 9.7%. This research study showcases the promising capabilities of reinforcement learning in enhancing the synchronization of traffic light controllers, effectively mitigating the adverse consequences of traffic congestion within urban environments. The adoption of this innovative approach holds the promise of fostering a more sustainable and streamlined transportation system for the future. © 2023 IEEE.

关键词： Reinforcement learning

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A Comprehensive Study on Drones and Big Data for Supply Chain Optimization Using a Novel Approach 1

A Comprehensive Study on Drones and Big Data for Supply Chai...

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1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Dankan Gowda, V. Palanikkumar, D. Suganthi, N. Malleswari, A.S. Thapa, Sanjog Tanguturi, Rama Chaithanya Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India Dr Ngp Institute of Technology Department of Computer Science and Engineering Tamilnadu Coimbatore India Dayananda Sagar College of Engineering Department of Electrical and Electronics Engineering Karnataka Bangalore India Aditya College of Engineering and Technology Department of Computer Science & Engineering Andhra Pradesh Surampalem India Lamar University Department of Information System and Analysis BeaumontTX United States Woxsen University School of Business Hyderabad India

ISBN: (纸本)9798350367720

This research paper aims to provide qualitative and quantitative insights regarding the application of drones and big data techniques in the SCM field through a new scientific method. The proposed method exploits the opportunities of drones for real-time data capturing and big data technologies to enrich decision making in supply chain. The study is pertinent to modern tendencies in supply chain systems and proves the efficiency of the new method through the precise calculations and contrastive analysis. The following are some of the findings that suggest efficiency, cost decrease, and outstanding performance of the supply chain. The studies being offered in this research will prove beneficial for practitioners and researchers who endeavor to advance the concepts and practice of logistics and supply chain management. © 2024 IEEE.

关键词： Big Data Cost Reduction Data Analytics Decision-making Drones Efficiency Improvement Logistics Novel Approach Real-time Data Collection Supply Chain Optimization

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Analysis of Sleep apnea Considering Electrocardiogram Data Using Deep learning Algorithms 8

Analysis of Sleep apnea Considering Electrocardiogram Data U...

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8th International Virtual Conference on Biosignals, Images, and Instrumentation, ICBSII 2022

作者： Rajabrundha, A. Lakshmisangeetha, A. Balajiganesh, A. Electronic System Design Lab Velammal Engineering College India Department of Computer Science Engineering Velammal Engineering College India Velammal Engineering College Chennai India

Sleep is a vital component of every human being. Adequate restful and restorative sleep reenergizes the body, enhances overall health and psychological well-being. Sleep hygiene, chaotic lifestyles, disorder breathing, stress, and anxiety contribute to poor sleep quality. Obstructive sleep apnea (OSA) sleep respiratory disorder causes temporary lapses of breathing results in gasping, choking, snoring sounds during sleep. The individual does not consciously wake up, but the brain has to start breathing again which disrupts the sleep quality. Polysomnography (PSG) sleep study is employed to diagnose sleep disorders by using either in-home or laboratory-based comprehensive tests. The untreated OSA leads to deterioration in health, performance consequences with severity including daytime sleepiness, motor vehicle accidents, workplace errors, cardiovascular morbidity, and mortality. The pre-processed, interpolated and segmented ECG signal is considered for the examination of OSA. This paper focuses on three types of deep learning classifiers-based prediction models for detection of apnea from the ECG signal. The accuracy value of Long Short Term Memory model (LSTM) is 85 percent and classifier's ability to distinguish between normal and apnea events is *** Gated Recurrent Unit (GRU) classifier and Convolution Neural Network (CNN) model have an f1- score value of 0.80. The proposed LSTM model provides the optimal performance in comparison to other deep learning models used for classification with respect to area under the curve (AUC) and accuracy metrics. © Published under licence by IOP Publishing Ltd.

关键词： Electrocardiograms

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