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2024 International Symposium on Intelligent Signal Processing and Communication systems, ISPACS 2024

作者： Bai, Yanbing Li, Siao Ju, Rui-Yang Yang, Zihao Yu, Jinze Chiang, Jen-Shiun Center for Applied Statistics School of Statistics Renmin University of China Beijing100872 China Graduate Institute of Networking and Multimedia National Taiwan University Taipei City106335 Taiwan Department of Mechanical Systems Engineering Tokyo University of Agriculture and Technology Tokyo183-8538 Japan Department of Electrical and Computer Engineering Tamkang University New Taipei City251301 Taiwan

ISBN: (纸本)9798350389210

Illegal, unreported, and unregulated (IUU) fishing activities seriously affect various aspects of human life. However, traditional methods for detecting and monitoring IUU fishing activities at sea have limitations. Although synthetic aperture radar (SAR) can complement existing vessel detection systems, extracting useful information from SAR images using traditional methods remains a challenge, especially in IUU fishing. This paper proposes a deep learning based fishing activity detection system, which is implemented on the xView3 dataset using six classical object detection models: SSD, RetinaNet, FSAF, FCOS, Faster R-CNN, and Cascade R-CNN. In addition, this work employs different enhancement techniques to improve the performance of the Faster R-CNN model. The experimental results demonstrate that training the Faster R-CNN model using the Online Hard Example Mining (OHEM) strategy increases the Avg-F1 value from 0.212 to 0.216. © 2024 IEEE.

关键词： Deep learning

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Evaluation of Leakage Currents in Memristor Crossbar Arrays

Evaluation of Leakage Currents in Memristor Crossbar Arrays

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IEEE National Conference on Aerospace and Electronics (NAECON)

作者： Muana Kasongo Trond Ytterdal John Lee Maher Rizkalla Mukesh Kumar Electrical and Computer Engineering Department IUPUI Indianapolis USA Electronic systems Department Norwegian Institute of Technology Trondheim Norway Electrical Engineering Department Indian Institute of Technology Indore Indore India

Memristors are novel electronic devices. Memristor-based crossbar arrays have gained a lot of attention for potential applications in nonvolatile memory, in-memory computing, logic design, neuromorphic computing systems, and neural networks. In this paper, we demonstrate the sneaky path current issue within a 3×3 crossbar array. 1T1M scheme has been used to reduce the leakage current. Integrating nMOS and pMOS devices in series with memristors in the crossbar array has optimized the leakage currents. The results show that nMOS devices reduce the leakage currents by 97.35%, whereas pMOS devices reduce the leakage currents by 92.20%. Thus, nMOS outperform pMOS devices in leakage currents optimization.

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Highly Accurate Crow Search Optimized CNN Classifier for Cyclone Prediction

Highly Accurate Crow Search Optimized CNN Classifier for Cyc...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： R. Ramya Kavin K. S R. Anuja A. T. R. Krishna Priya D. Karthikeyan Kiruba Thangam Raja Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Tamilnadu India Department of Electrical and Electronics Engineering Government College of Engineering Tirunelveli Tamilnadu India Department of Computer Science and Engineering Rohini College of Engineering and Technology Kanyakumari Tamilnadu India Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Chennai India Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems (SCORE) VIT University Vellore

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Accurate and timely prediction of cyclone events is essential for effective disaster management and risk mitigation. This study introduces a highly precise cyclone prediction model that combines Convolutional Neural Networks (CNNs) with the optimization capabilities of the Crow Search Algorithm (CSA). The resulting model, called Crow Search Optimized CNN (CSO-CNN), harnesses the feature extraction and classification strengths of CNNs, while the CSA is used to optimize the hyperparameters of the CNN architecture for optimal performance. The model is trained and validated on a comprehensive dataset that includes satellite imagery and meteorological data from a wide range of cyclone events. The CSO-CNN achieves remarkable classification accuracy, surpassing traditional CNN models and other leading techniques. Its ability to quickly and accurately assess the likelihood of cyclone formation provides crucial decision support for disaster management authorities, enabling timely and effective response strategies. By integrating the CSA optimization technique with the CNN framework, this study presents a novel approach to cyclone prediction. It highlights the potential of combining advanced Machine Learning (ML) algorithms to tackle complex environmental challenges and deepen our understanding of intricate atmospheric phenomena.

关键词： Accuracy Atmospheric modeling Disaster management Predictive models Prediction algorithms Feature extraction Cyclones Satellite images Convolutional neural networks Optimization

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Personalized Data-Driven State Models of the Circadian Dynamics in a Biometric Signal

Personalized Data-Driven State Models of the Circadian Dynam...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Chukwuemeka O. Ike Yunshi Wen John T. Wen Meeko M.K. Oishi Lee K. Brown A. Agung Julius Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY USA Lighting Enabled Systems and Applications (LESA) Engineering Research Center Troy NY USA Department of Electrical and Computer Engineering University of New Mexico Albuquerque NM USA Department of Internal Medicine University of New Mexico Albuquerque NM USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Circadian rhythms are endogenous 24-hour oscillations that are vital for maintaining our overall well-being. They are driven at a high level by a core circadian clock located in the brain, making their dynamics difficult to track. Various modeling approaches exist to predict the dynamics, but as the models are typically designed on population-level data, their performance is diminished on the individual level. This paper proposes a method for learning personalized latent state models, i.e., dynamical models that explicitly use latent state variables, that relate circadian input(s) to observable biometric signals. Our models combine an autoencoder with a recurrent neural network and use the pair to model the salient dynamics present in the data. We validate our method using experimental data, where the circadian input is light and the biometric data are actigraphy signals. We demonstrate that our method results in models with low-dimensional latent state that can accurately reconstruct and predict the observable biometric signals. Further, we show that the oscillation of the learned latent state agrees with the subjects’ circadian clock oscillation as estimated with melatonin *** relevance — This proposes a technique for personal-ized modeling of the circadian system with potential applications in feedback control and individualized circadian studies.

关键词： Biometrics Training Biological system modeling Transfer learning Predictive models Brain modeling Data models Circadian rhythm Oscillators Clocks

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Real-time Lightning 3D Imaging and Forecasting Project in Malaysia for Sustainable and Reliable Supply of Energy and Storm Disaster Early Warning 12

Real-time Lightning 3D Imaging and Forecasting Project in Ma...

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12th Asia-Pacific International Conference on Lightning, APL 2023

作者： Morimoto, Takeshi Ahmad, Mohd Riduan Baharuddin, Mohd Zafri Wang, Daohong Yamamoto, Kazuo Kudo, Takeshi Kahtani, Ammar Nasser Al Aziz, Mohamad Zoinol Abidin Abd Akita, Manabu Takayanagi, Yuji Kawasaki, Zen-Ichiro Torii, Tatsuo Kindai University Depertment of Electrical Electronic and Communication Engineering Osaka Japan Melaka Malaysia Universiti Tenaga Nasional Department of Electrical and Electronic Engineering Kajang Malaysia Gifu University Department of Electrical Electronic and Computer Engineering Gifu Japan Chubu University Deptment. of Electrical and Electronic Engineering Aichi Japan Fundamental and Advanced Research Group Otowa Electric Co. Ltd Hyogo Japan Universiti Tenaga Nasional Department of Electrical and Electronics Engineering Kajang Malaysia The University of Electro-Communications Department of Mechanical and Intelligent Systems Engineering Tokyo Japan Osaka University Osaka Japan Research Institute of Nuclear Engineering University of Fukui Fukui Japan

ISBN: (纸本)9781665456852

A joint research project 'real-time lightning 3D imaging and forecasting project for sustainable and reliable supply of energy and storm disaster early warning' has been selected for Science and Technology Research Partnership for Sustainable Development (SATREPS) program. Held in the coastal area of the Straits of Malacca in Malaysia, which suffers from serious lightning disasters with more than 200 lightning days per year, this research aims to contribute to disaster prevention by forecasting lightning strikes by constructing the world's highest lightning observation network. This research project is to conduct the field campaigns associated with artificial rocket-triggered lightning in the coastal area of the Straits of Malacca, where the thunderstorm activity is very high and there are many positive cloud-to-ground flashes that may damage electric, communication and other equipments. Measurements of radio waves in various frequency bands and high-energy radiations, and several Rogowski coils are set up surrounding the strike points. It is planned to realize an electromagnetic field observation network in VHF and LF bands to observe the entire progression of each lightning in detail in a wide area. 3D charge distribution in the thundercloud and the amounts of neutralized charges estimated from the information where the lightning discharge begins, how it progresses, and where it terminates. Then, it is examined by the lightning current waveform measured directly with the high structure and rocket-triggered lightning. By comprehensively grasping the charge behavior in the air related to lightning discharge, it is possible to estimate the charge distribution in the cloud and to predict lightning strike. In addition, promoting the control of electric power equipment using IoT, and the early warning system of severe weather disasters to be implemented in the society. This project further researches on active and adaptive lightning resistance and protection as well as

关键词： Imaging systems

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Robust Frequency Regulation for Dual-Area Interconnected Grids Using Hybrid Controller: A Comparative Study 3

Robust Frequency Regulation for Dual-Area Interconnected Gri...

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3rd IEEE International Conference on Smart Technologies for Power, Energy and Control, STPEC 2023

作者： Wazir, Awadh Ba Alhussainy, Abdullah A. Alobaidi, Abdulraheem H. Altaf, Aatif Kumar, Anil Kumar, Mahendra Alghamdi, Sultan King Abdulaziz University Faculty of Engineering Department of Electrical and Computer Engineering Jeddah Saudi Arabia King Abdulaziz University Center of Research Excellence in Renewable Energy and Power Systems Jeddah Saudi Arabia Dr. B R Ambedkar National Institute of Technology Jalandhar Instrumentation and Control Engineering Department Punjab Jalandhar India

ISBN: (纸本)9798350304732

This paper introduces a robust controller designed for load frequency control in a dual-area integrated power grid. Here, the suggested controller is a blend of a fuzzy logic controller with an integral controller. The parameters of the integral controller and frequency bias settings are optimized to enhance the dynamic response of the proposed controller. To attain this, three distinct optimization algorithms are employed, i.e., genetic algorithm, simulated annealing, and pattern search. A new objective function using settling time, settling-max, and settling-min with a suitable weight factor is used to improve the parameters estimation process. A comparative analysis is performed using the proposed approach and classical-I controller under two cases considering several load disturbance scenarios. Furthermore, the suggested hybrid controller is evaluated and compared with similar models in the literature. As expected, the proposed controller exhibits superior performance. © 2023 IEEE.

关键词： Fuzzy logic controller integral controller load frequency control optimization technique parameter estimation

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Encrypted Domain Secret Medical-Image Sharing with Secure Outsourcing Computation in IoT Environment

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IEEE Internet of Things Journal 2024年第2期11卷 2382-2393页

作者： Huang, Jingwang Cui, Qi Zhou, Zhili Yu, Keping Yang, Ching-Nung Choo, Kim-Kwang Raymond Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou510006 China Nanjing University Information Science and Technology Engineering Research Center of Digital Forensics Ministry of Education Nanjing210044 China Nanyang Technological University School of Electrical and Electronic Engineering Jurong West639798 Singapore Guangzhou University Institute of Artificial Intelligence and Blockchain Guangzhou510006 China Hosei University Graduate School of Science and Engineering Tokyo184-8584 Japan National Dong Hwa University Department of Computer Science and Information Engineering Hualien974301 Taiwan The University of Texas at San Antonio Department of Information Systems and Cyber Security San AntonioTX78249 United States

In existing secret medical-image sharing (SMIS) schemes, to protect and manage secret medical-images (SMIs), the sharing and recovery of each SMI are implemented by local servers of medical institutions. However, since a lot of SMIs are produced by personal smart terminal devices in Internet of Things (IoT) environment, directly implementing the sharing and recovery processes will cause excessive communication and computing burden for those local servers, which makes the existing SMIS schemes not suitable for IoT environment. To address the above issue, we propose an encrypted domain SMIS (Enc-SMIS) scheme with secure outsourcing computation for protecting and managing medical images in IoT environment. In the proposed scheme, the medical images are first encrypted using fully homomorphic encryption (FHE) and then outsourced to a cloud server for generating a set of image shares. Subsequently, these shares are stored separately in different local servers of medical institutions. Furthermore, the recovery process is also outsourced to the cloud server when doctors need to observe the patients' medical images. Compared with the existing SMIS schemes, the proposed Enc-SMIS scheme alleviates the computing and communication burden on local servers significantly with secure outsourcing computation in the semi-honest model, and thus supports the storage and management of medical images well in IoT environment. © 2014 IEEE.

关键词： Internet of things

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Synthesizing Robust Walking Gaits via Discrete-Time Barrier Functions with Application to Multi-Contact Exoskeleton Locomotion

Synthesizing Robust Walking Gaits via Discrete-Time Barrier ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Maegan Tucker Kejun Li Aaron D. Ames School of Electrical and Computer Engineering and the Woodruff School of Mechanical Engineering Georgia Institute of Technology Atlanta GA Department of Computation and Neural Systems California Institute of Technology Pasadena CA Department of Mechanical and Civil Engineering and the Department of Control and Dynamical Systems California Institute of Technology Pasadena CA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Successfully achieving bipedal locomotion remains challenging due to real-world factors such as model uncertainty, random disturbances, and imperfect state estimation. In this work, we propose a novel metric for locomotive robustness – the estimated size of the hybrid forward invariant set associated with the step-to-step dynamics. Here, the forward invariant set can be loosely interpreted as the region of attraction for the discrete-time dynamics. We illustrate the use of this metric towards synthesizing nominal walking gaits using a simulation-in-the-loop learning approach. Further, we leverage discrete-time barrier functions and a sampling-based approach to approximate sets that are maximally forward invariant. Lastly, we experimentally demonstrate that this approach results in successful locomotion for both flat-foot walking and multi-contact walking on the Atalante lower-body exoskeleton.

关键词： Legged locomotion Measurement Uncertainty Exoskeletons Robustness State estimation Robots

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Destination Scheduling for Secure Pinhole-Based Power-Line Communication

IEEE Open Journal of the Communications Society

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IEEE Open Journal of the Communications Society 2023年 4卷 2245-2260页

作者： Kundu, Chinmoy Dubey, Ankit Tonello, Andrea M. Nallanathan, Arumugam Flanagan, Mark F. University College Dublin School of Electrical and Electronic Engineering Dublin 4D04 V1W8 Ireland Indian Institute of Technology Jammu Department of Ee Jammu181221 India Institute of Networked and Embedded Systems University of Klagenfurt Klagenfurt9020 Austria Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom

We propose an optimal destination scheduling scheme to improve the physical layer security (PLS) of a power-line communication (PLC) based Internet-of-Things system in the presence of an eavesdropper. We consider a pinhole (PH) architecture for a multi-node PLC network to capture the keyhole effect in PLC. The transmitter-to-PH link is shared between the destinations and an eavesdropper which correlates all end-to-end links. The individual channel gains are assumed to follow independent log-normal statistics. Furthermore, the additive impulsive noise at each node is modeled by an independent Bernoulli-Gaussian process. Exact computable expressions for the average secrecy capacity (ASC) and the probability of intercept (POI) performance over many different networks are derived. Approximate closed-form expressions for the asymptotic ASC and POI are also provided. We find that the asymptotic ASC saturates to a constant level as transmit power increases. We observe that the PH has an adverse effect on the ASC. Although the shared link affects the ASC, it has no effect on the POI. We show that by artificially controlling the impulsive to background noise power ratio and its arrival rate at the receivers, the secrecy performance can be improved. © 2020 IEEE.

关键词： Impulse noise

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Towards Optimal Head-to-head Autonomous Racing with Curriculum Reinforcement Learning

arXiv

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arXiv 2023年

作者： Kalaria, Dvij Lin, Qin Dolan, John M. The Robotics Institute Carnegie Mellon University United States The Electrical Engineering and Computer Science Department Cleveland State University United States

Head-to-head autonomous racing is a challenging problem, as the vehicle needs to operate at the friction or handling limits in order to achieve minimum lap times while also actively looking for strategies to overtake/stay ahead of the opponent. In this work we propose a head-to-head racing environment for reinforcement learning which accurately models vehicle dynamics. Some previous works have tried learning a policy directly in the complex vehicle dynamics environment but have failed to learn an optimal policy. In this work, we propose a curriculum learning-based framework by transitioning from a simpler vehicle model to a more complex real environment to teach the reinforcement learning agent a policy closer to the optimal policy. We also propose a control barrier function-based safe reinforcement learning algorithm to enforce the safety of the agent in a more effective way while not compromising on optimality. © 2023, CC BY.

关键词： Reinforcement learning

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