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Efficient Joint Deployment of Multi-UAVs for Target Tracking 19th

Efficient Joint Deployment of Multi-UAVs for Target Tracki...

19th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness, QShine 2023

作者： Wang, Jiashuai Sun, Lu Wan, Liangtian Zheng, Jibin Wang, Xianpeng Institute of Information Science Technology Dalian Maritime University Dalian China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Dalian China National Laboratory of Radar Signal Processing Xidian University Xian China State Key Laboratory of Marine Resource Utilization in South China Sea and College of Information Science and Technology Hainan University Haikou China

ISBN: (纸本)9783031651229

Target tracking plays an important role in many real-world applications, such as in vegetation protection, disaster rescue, wildlife observation, etc. Multiple unmanned aerial vehicles (multi-UAVs) can provide effective services for target tracking through efficient joint deployment satisfying the system constraints. The constraints include the energy and no-fly zone constraint of each UAV, the communication distance and safe distance among multi-UAVs, the number of UAVs. As there exists no ready-made model that considers all the above constraints, meanwhile, the energy and no-fly zone restrict the deployment of multi-UAVs for target tracking. In this paper, we propose a model which considers no-fly zone and the energy constraint simultaneously for the joint deployment of multi-UAVs. The objective function is defined as minimizing the distance of each UAV and the threats of target so that it can deploy multi-UAVs reasonably. Second, we propose an improved Nondominated Sort Genetic Algorithm II (NSGAII) with new encoding and decoding mechanisms and the no-fly zone avoidance strategy which can improve the searching performance of joint deployment of multi-UAVs. Finally, we design a set of experiments and the experimental results demonstrate that our proposed algorithm can deploy multi-UAVs for target tracking efficiently compared with the state-of-the-arts methods. © ICST institute for Computer sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Unmanned aerial vehicles (UAV)

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Segmentation of Hemorrhagic Stroke Using Graph Convolutional Network

Segmentation of Hemorrhagic Stroke Using Graph Convolutional...

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IEEE International Symposium on Biomedical Imaging

作者： Adailton Dos Santos Holanda Roger Moura Sarmento Pedro P. Reboças Filho Laboratory of Image Processing Signals and Applied Computing (LAPISCO) Federal Institute of Education Science and Technology Ceará (IFCE) Fortaleza Brazil

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Stroke is a serious global public health problem, and several studies on tools to assist its diagnosis have been conducted, many of them involving image processing techniques and deep learning. However, the use of graph convolutional networks (GCN) for stroke classification and segmentation has yet to be explored. This work uses GCN to implement a hemorrhagic stroke segmentation system in Computed Tomography (CT) scans. The results show that the proposed methodology performs well in segmentation, reaching 95.98% in the Dice coefficient and 92.54% in the Jaccard coefficient.

关键词： Measurement Deep learning image segmentation Graph convolutional networks Computed tomography image edge detection Sensitivity and specificity Reliability Hemorrhaging Public healthcare

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Acoustic User Authentication with Smartphones

Acoustic User Authentication with Smartphones

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IEEE International Conference on Microwaves, Communications, Antennas and Electronic Systems, COMCAS

作者： Neta Gevirtzer Ofir Ben Yosef Alon Eilam Signal and Image Processing Laboratory (SIPL) Andrew and Erna Viterbi Faculty of Electrical & Computer Engineering Technion - Israel Institute of Technology Technion City Haifa Israel

ISBN: (数字)9798350348187

ISBN: (纸本)9798350348194

This paper describes a work on an acoustic user authentication system using smartphones. The system implements two-factor authentication for Windows workstations, where the authentication procedure, including locking and unlocking the workstation is transparent to the user. Since workstations and smartphones have built-in microphones and speakers, the system does not require additional hardware. The uniqueness of the solution is being based on acoustic signals. These signals are transmitted by the user's smartphone and received by the workstation microphone. The system is “pure play acoustic” since no wiring or radio transmission is used. The system configuration supports multiple users in the same area. Eavesdropping prevention is provided by sequentially generated random one-time keys. Acoustic communication can be applied either in the audible range or beyond the human hearing range depending on the sampling rate of the smartphone and the workstation.

关键词： Microwave antennas Wiring Prevention and mitigation Authentication Auditory system Microwave communication Acoustics

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Spatiotemporal distance embedded hybrid ant colony algorithm for a kind of vehicle routing problem with constraints

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Frontiers of Information technology & Electronic Engineering 2023年第7期24卷 1062-1079页

作者： Zhenhui FENG Renbin XIAO School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China Institute of Artificial Intelligence Huazhong University of Science and TechnologyWuhan 430074China Key Laboratory of Image Information Processing and Intelligent Control Ministry of EducationWuhan 430074China

We investigate a kind of vehicle routing problem with constraints(VRPC)in the car-sharing mobility environment,where the problem is based on user orders,and each order has a reservation time limit and two location point transitions,origin and *** is a typical extended vehicle routing problem(VRP)with both time and space *** consider the VRPC problem characteristics and establish a vehicle scheduling model to minimize operating costs and maximize user(or passenger)*** solve the scheduling model more accurately,a spatiotemporal distance representation function is defined based on the temporal and spatial properties of the customer,and a spatiotemporal distance embedded hybrid ant colony algorithm(HACA-ST)is *** algorithm can be divided into two ***,through spatiotemporal clustering,the spatiotemporal distance between users is the main measure used to classify customers in categories,which helps provide heuristic information for problem ***,an improved ant colony algorithm(ACO)is proposed to optimize the solution by combining a labor division strategy and the spatiotemporal distance function to obtain the final scheduling *** analysis is carried out based on existing data sets and simulated urban *** with other heuristic algorithms,HACA-ST reduces the length of the shortest route by 2%–14%in benchmark *** VRPC testing instances,concerning the combined cost,HACA-ST has competitive cost compared to existing VRP-related ***,we provide two actual urban scenarios to further verify the effectiveness of the proposed algorithm.

关键词： Vehicle routing problem with constraints(VRPC) Spatiotemporal distance function Labor division strategy Ant colony algorithm(ACO)

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SADDLE: Spacecraft Anomaly Detection using Deep Learning

SADDLE: Spacecraft Anomaly Detection using Deep Learning

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Space, Aerospace and Defence Conference (SPACE), IEEE

作者： Ankit Srivastava Neeraj Badal BS Manoj Department of Avionics Indian Institute of Space Science and Technology Thiruvananthapuram India Signal and Image Processing Area (SIPA) Space Applications Centre (SAC) Ahmedabad India

ISBN: (数字)9798350367386

ISBN: (纸本)9798350367393

Efficiently detecting anomalies in spacecraft data poses a significant challenge in modern space missions. We introduce Spacecraft Anomaly Detection using Deep Learning (SADDLE), a novel transformer network-based model tailored for spacecraft anomaly detection. SADDLE leverages an attention-based encoder to analyze telemetry data, capturing broader temporal trends critical for anomaly identification. It utilizes self-conditioning for robust feature extraction across multiple telemetry modalities. Additionally, SADDLE leverages Meta Gradient Descent to adapt faster to spacecraft data characteristics, simultaneously enabling effective training with limited anomaly examples. Extensive evaluations conducted on spacecraft datasets demonstrate that SADDLE outperforms all existing methods while significantly reducing training time.

关键词： Space vehicles Training Deep learning Adaptation models Space missions Transformers Market research

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Spatially Adaptive Super-Resolution Via Deep Flow Estimation And Wiener Filtering For Satellite imagery

Spatially Adaptive Super-Resolution Via Deep Flow Estimation...

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Remote Sensing Symposium (InGARSS), 2020 IEEE India Geoscience and

作者： Jatin Dhall Ashutosh Gupta School of Computer Science and Engineering Vellore Institute of Technology Vellore Vellore India Signal And Image Processing Area Space Applications Centre ISRO Ahmedabad India

ISBN: (数字)9798350390346

ISBN: (纸本)9798350390353

Applications based on satellite imagery depend heavily on the resolution of the images captured by Earth observation satellites. The higher the resolution of the captured images, the better the precision in differentiating fine details in the acquired image, which is a key requirement for critical downstream applications. Though hardware-based methods for improving resolution exist, physical and sensor-based limitations such as optical and motion blur, and noise are difficult to avoid and result in low-resolution images. Super Resolution (SR) is one of the most promising fields in Remote Sensing (RS), which aims to computationally improve the resolution of an image, using one or many input Low Resolution (LR) images. This paper presents a spatially adaptive multiimage super-resolution approach using deep flow estimation and wiener filtering. Our method uses the best of both deep learning and classical approaches. The deep-flow estimation improves the registration performance whereas the spatially adaptive wiener filtering efficiently interpolates the information from low-resolution images to form the super-resolved output. Our method can process an arbitrary number of input LR frames and outputs a super-resolved image. We demonstrate the ability of the method to reconstruct high-frequency details from multiple low-resolution frames using Skysat imagery.

关键词： Optical filters Deep learning Wiener filters Satellites Superresolution Estimation Satellite images Optical sensors Spatial resolution Remote sensing

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Dolphin Vocal Sound Generation via Deep WaveGAN

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Journal of Electronic science and technology 2022年第3期20卷 282-293页

作者： Lue Zhang Hai-Ning Huang Li Yin Bao-Qi Li Di Wu Hao-Ran Liu Xi-Feng Li Yong-Le Xie the Institute of Acoustics Chinese Academy of SciencesBeijing100190China University of Chinese Academy of Sciences Beijing100049China the Key Laboratory of Science and Technology on Advanced Underwater Acoustic Signal Processing Chinese Academy of SciencesBeijing100190China the School of Automation Engineering University of Electronic Science and Technology of ChinaChengdu611731SichuanChina

The marine biological sonar system evolved in the struggle of nature is far superior to the current artificial sonar. Therefore, the development of bionic underwater concealed detection is of great strategic significance to the military and economy. In this paper, a generative adversarial network(GAN) is trained based on the dolphin vocal sound dataset we constructed, which can achieve unsupervised generation of dolphin vocal sounds with global consistency. Through the analysis of the generated audio samples and the real audio samples in the time domain and the frequency domain, it can be proven that the generated audio samples are close to the real audio samples,which meets the requirements of bionic underwater concealed detection.

关键词： Bionic underwater concealed detection dolphin generative adversarial network(GAN) wave generative adversarial network(WaveGAN)

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Relative-Posture-Fixed Model Predictive Human-Following Control with Visibility Constraints in Obstacle Environments 8

Relative-Posture-Fixed Model Predictive Human-Following Cont...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Zhang, Yikun Huang, Jian Yu, Jinqi Zhu, Yaonan Hasegawa, Yasuhisa School of Artificial Intelligence and Automation Huazhong University of Science and Technology Key Laboratory for Image Processing and Intelligent Control Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute ShenZhen518060 China Nagoya University Department of Micronano Mechanical Science and Engineering Nagoya464-8603 Japan

ISBN: (纸本)9798350300178

The relative-posture-fixed human following is critical for the human-robot interaction and cooperation in daily scenes such as domestic service and healthcare. However, few of previous researches have discussed the difficulties faced by mobile robots in the real world, including the limitation of sensing range and hindrance from obstacles. For practicality, in this paper we investigate the relative-posture-fixed following control with collision avoidance and visibility constraints. The control task is formulated as a receding-horizon optimization problem and solved under the Nonlinear Model Predictive Control (NMPC) framework, while an Extended Kalman Filter (EKF) based human trajectory predict algorithm is integrated into the robot controller. The effectiveness of the proposed algorithm is verified in a simulation, and the results demonstrate a promising performance of our controller for the human-following task in complicated environments. © 2023 IEEE.

关键词： Human robot interaction

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Enhanced fault diagnosis of wind energy conversion systems using ensemble learning based on sine cosine algorithm

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Journal of Engineering and Applied science 2023年第1期70卷 56页

作者： Attouri, Khadija Dhibi, Khaled Mansouri, Majdi Hajji, Mansour Bouzrara, Kais Nounou, Hazem Higher Institute of Applied Sciences and Technology of Kasserine Kairouan University Kasserine1200 Tunisia Chemical Engineering Program Texas A&M University at Qatar 23874 Doha Qatar Electrical and Computer Engineering Program Texas A&M University at Qatar 23874 Doha Qatar Laboratory of Automatic Signal and Image Processing National Engineering School of Monastir University of Monastir Monastir5019 Tunisia

This paper investigates the problem of incipient fault detection and diagnosis (FDD) in wind energy conversion systems (WECS) using an innovative and effective approach called the ensemble learning-sine cosine optimization algorithm (EL-SCOA). The evolved strategy involves two primary steps: first, a sine-cosine algorithm is used to extract and optimize features in order to only select the most descriptive ones. Second, to further improve the capability, thereby providing the highest accuracy performance, the newly gathered dataset is introduced as input to an ensemble learning paradigm, which merges the benefits of boosting and bagging techniques with an artificial neural network classifier. The essential goal of the developed proposal is to discriminate between the diverse operating conditions (one healthy and six faulty conditions). Three potential and frequent types of faults that can affect the system behaviors including short-circuit, open-circuit, and wear-out are considered and thereby injected at diverse locations and sides (grid and generator sides) in order to evaluate the availability and performance of the proposed technique when compared to the conventional FDD methods. The diagnosis performance is analyzed in terms of accuracy, recall, precision, and computation time. The acquired outcomes demonstrate the efficiency of the suggested diagnostic paradigm compared to conventional FDD techniques (accuracy rate has been successfully achieved 98.35%). © 2023, This is a U.S. Government work and not under copyright protection in the US;foreign copyright protection may apply.

关键词： Failure analysis

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ANTI-JAMMING PERFORMANCE ANALYSIS OF MULTIOAM-MODE VORTEX RADAR BASED ON WAVEFORM DIVERSITY

ANTI-JAMMING PERFORMANCE ANALYSIS OF MULTIOAM-MODE VORTEX RA...

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2022 International Conference on Radar Systems, RADAR 2022

作者： Hu, Dunfa Liu, Lin Duan, Yaohui Jiang, Xinrui Lv, Kun Ma, Hui National Laboratory of Radar Signal Processing Xidian University Xi’an China Beijing Institute of Remote-sensing Equipment Science and Technology on Millimeter-wave Laboratory Beijing China Guangzhou Institute of Technology Xidian University Guangzhou China

ISBN: (纸本)9781839537776

This paper exploits the electromagnetic vortex (EMV) radar system in anti-jamming applications of digital radio frequency memory (DRFM), considering the orthogonal-waveform-based uniform circular array (UCA) system specifically. Compared to conventional multiple-input-multiple-output (MIMO) system, the EMV radar has a novel dimension of EMV mode proportional to the orbital angular momentum (OAM), providing a new dimension for the orthogonal decomposition of the transmitted waveform, as well as a new dimension for distinguish the DRFM and a real target. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Vortex flow

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