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检索条件"任意字段=IEEE International Conference on Robotics, Intelligent Systems and Signal Processing"

共 7158 条记录，以下是511-520 订阅

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Vision-based Distributed Multi-UAV Collision Avoidance via Deep Reinforcement Learning for Navigation

Vision-based Distributed Multi-UAV Collision Avoidance via D...

引用

ieee/RSJ international conference on intelligent Robots and systems (IROS)

作者： Huang, Huaxing Zhu, Guijie Fan, Zhun Zhai, Hao Cai, Yuwei Shi, Ze Dong, Zhaohui Hao, Zhifeng Shantou Univ Coll Engn Key Lab Digital Signal & Image Proc Guangdong Pro Shantou Peoples R China

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

Online path planning for multiple unmanned aerial vehicle (multi-UAV) systems is considered a challenging task. It needs to ensure collision-free path planning in real-time, especially when the multi-UAV systems can become very crowded on certain occasions. In this paper, we presented a vision-based decentralized collision-avoidance policy learning method for multi-UAV systems. The policy takes depth images and inertial measurements as sensory inputs and outputs UAV's steering commands, and it is trained together with the latent representation of depth images using a policy gradient-based reinforcement learning algorithm and autoencoder in the multi-UAV three-dimensional workspaces. Each UAV follows the same trained policy and acts independently to reach the goal without colliding or communicating with other UAVs. We validate our method in various simulated scenarios. The experimental results show that our learned policy can guarantee fully autonomous collision-free navigation for multi-UAV in three-dimensional workspaces, and its navigation performance will not be greatly affected by the increase in the number of UAVs.

关键词： Learning systems Training Navigation Reinforcement learning Autonomous aerial vehicles Path planning Robustness

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Native Multi-Band Audio Coding Within Hyper-Autoencoded Reconstruction Propagation Networks 48

Native Multi-Band Audio Coding Within Hyper-Autoencoded Reco...

引用

48th ieee international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Petermann, Darius Jang, Inseon Kim, Minje Indiana University Department of Intelligent Systems Engineering BloomingtonIN47408 United States Electronics and Telecommunications Research Institute Daejeon34129 Korea Republic of

ISBN: (纸本)9781728163277

Spectral sub-bands do not portray the same perceptual relevance. In audio coding, it is therefore desirable to have independent control over each of the constituent bands so that bitrate assignment and signal reconstruction can be achieved efficiently. In this work, we present a novel neural audio coding network that natively supports a multi-band coding paradigm. Our model extends the idea of compressed skip connections in the U-Net-based codec, allowing for independent control over both core and high band-specific reconstructions and bit allocation. Our system reconstructs the full-band signal mainly from the condensed core-band code, therefore exploiting and showcasing its bandwidth extension capabilities to its fullest. Meanwhile, the low-bitrate high-band code helps the high-band reconstruction similarly to MPEG audio codecs' spectral bandwidth replication. MUSHRA tests show that the proposed model not only improves the quality of the core band by explicitly assigning more bits to it but retains a good quality in the high-band as well. © 2023 ieee.

关键词： audio coding autoencoders deep learning multi-band spectral bandwidth replication U-Net

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Data processing model for equipment monitoring and diagnostics systems 5

Data processing model for equipment monitoring and diagnosti...

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5th international Youth conference on Radio Electronics, Electrical and Power Engineering, REEPE 2023

作者： Sultanov, Mahsud M. Boldyrev, Ilya A. Menshikov, Pavel D. National Research University MPEI Digital Technology Research Laboratory Volzhsky Russia

ISBN: (纸本)9798350399523

In this paper, considers the application of equipment monitoring and diagnostics system on the example of turbine condenser. The analysis of the absolute error of the measuring channel after the whole sequence of signal conversion is performed. The measuring channel was simulated using SimInTech, the results were processed using the Office software package. To analyze the obtained data on the system states it is necessary to apply the methods of signal processing. It should be taken into account that the data are not complete and have a number of errors associated with the method of their acquisition, conversion, averaging, and recording in a database and, if necessary, recovery from this averaged sample to analyze the original signal. © 2023 ieee.

关键词： signal processing

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Exploring Domain Shift on Radar-Based 3D Object Detection Amidst Diverse Environmental Conditions 27

Exploring Domain Shift on Radar-Based 3D Object Detection Am...

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27th ieee international conference on intelligent Transportation systems, ITSC 2024

作者： Zhang, Miao Abdulatif, Sherif Loesch, Benedikt Altmann, Marco Schwarz, Marius Yang, Bin Robert Bosch GmbH Corporate Research 71272 Germany University of Stuttgart Institute of Signal Processing and System Theory 70569 Germany

ISBN: (纸本)9798331505929

The rapid evolution of deep learning and its integration with autonomous driving systems have led to substantial advancements in 3D perception using multimodal sensors. Notably, radar sensors show greater robustness compared to cameras and lidar under adverse weather and varying illumination conditions. This study delves into the often-overlooked yet crucial issue of domain shift in 4D radar-based object detection, examining how varying environmental conditions, such as different weather patterns and road types, impact 3D object detection performance. Our findings highlight distinct domain shifts across various weather scenarios, revealing unique dataset sensitivities that underscore the critical role of radar point cloud generation. Additionally, we demonstrate that transitioning between different road types, especially from highways to urban settings, introduces notable domain shifts, emphasizing the necessity for diverse data collection across varied road environments. To the best of our knowledge, this is the first comprehensive analysis of domain shift effects on 4D radar-based object detection. We believe this empirical study contributes to understanding the complex nature of domain shifts in radar data and suggests paths forward for data collection strategy in the face of environmental variability. © 2024 ieee.

关键词： Network security

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Codebook-Based User Tracking in IRS-Assisted mmWave Communication Networks 48

Codebook-Based User Tracking in IRS-Assisted mmWave Communic...

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48th ieee international conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Garkisch, Moritz Jamali, Vahid Schober, Robert

ISBN: (纸本)9781728163277

In this paper, we present a novel mobile user tracking (UT) scheme for codebook-based intelligent reflecting surface (IRS)-aided millimeter wave (mmWave) systems. The proposed UT scheme exploits the temporal correlation of the direction from the IRS to the mobile user for selecting IRS phase shifts that provide reflection towards the user. To this end, the user's direction is periodically estimated based on a generalized likelihood ratio test (GLRT) and the user's movement trajectory is extrapolated from several past direction estimates. The efficiency of the proposed UT scheme is evaluated in terms of the average effective rate, which accounts for both the required signaling overhead and the achieved signal-to-noise ratio (SNR). Our results show that for medium-to-high SNR, the proposed codebook-based UT scheme achieves a higher effective rate than two reference approaches based on full codebook search and optimization of the individual IRS unit cells, respectively. © 2023 ieee.

关键词： signal to noise ratio

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A New Island Genetic Algorithm for Path Planning Problems *

A New Island Genetic Algorithm for Path Planning Problems *

引用

2023 ieee international conference on Real-Time Computing and robotics, RCAR 2023

作者： Zhang, Jinghao Chen, Zhe Li, Ning Ministry of Education of China Key Laboratory of System Control and Information Processing Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China Tsinghua University Department of Automation Beijing100084 China

ISBN: (纸本)9798350327182

Genetic algorithm (GA) is an effective method for path planning problems. As a powerful variant of GA, island genetic algorithm (IGA) has considerable improvement in performance. In this paper, a new island model of GA is proposed to avoid the premature phenomenon and achieve better efficiency. First, a new method of creating subpopulations is presented based on K-means to expand the searching area for the optima. Meanwhile, recombining subpopulations is proposed as a new strategy to improve the diversity of populations and save computational time. Moreover, a method is designed based on Monte Carlo sampling to handle the uncertainty of maps. Comparative experiments are presented to verify the efficiency of the proposed algorithm. Then, a proper number of samples is found by simulation to balance the accuracy and the time cost of Monte Carlo sampling. © 2023 ieee.

关键词： Genetic algorithms

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Performance Analysis of Dual-Hop Relaying for THz/RF systems 7

Performance Analysis of Dual-Hop Relaying for THz/RF Systems

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7th international conference on intelligent Computing and signal processing, ICSP 2022

作者： Shi, Jianxin Chen, Ziliang Zhang, Tingze Chongqing Jiaotong University Chongqing China Huazhong University of Science and Technology Wuhan China Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (纸本)9781665478571

In this paper, we study the performance of a dualhop hybrid terahertz and radio frequency communication (THz/RF) transmission system based on a decode-and-forward (DF) relay. Considered that the THz channel follows α-μ fading with pointing errors and the RF link is the recon-figurable intelligent surface (RIS) assisted communication system and undergoes Gamma fading. Based on this model, analytical results for its outage probability (OP), average bit error rate (BER), and average channel capacity (ACC) are derived. Aim to get more information, we also give the asymptotic analysis of OP and average BER. In addition, the diversity order of the system and the influence of different system parameters on the system performance are further studied. Finally, Monte-Carlo simulations verify the accuracy of our numerical results. © 2022 ieee.

关键词： intelligent systems

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intelligent Model Management: Using Reinforcement Learning to Detect Data Drift and Retrain Industrial Machine Learning systems 26

Intelligent Model Management: Using Reinforcement Learning t...

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26th international conference on Digital signal processing and its Applications, DSPA 2024

作者： Loufakis, Michail Manis, Othonas Kioroglou, Christos Kolokas, Nikolaos Ioannidis, Dimosthenis Tzovaras, Dimitrios Stankovski, Mile Center for Research & Technology Hellas Information Technologies Institute Thessaloniki57001 Greece Titan Cement Group Greece Cyril and Methodius University Faculty of Electrical Engineering and Information Technologies Skopje Macedonia

ISBN: (纸本)9798350361728

In the evolving landscape of industrial processes, maintaining the accuracy of machine learning (ML) models is a formidable challenge due to the phenomenon of data drift. This paper introduces an innovative reinforcement learning (RL) framework designed to monitor and adaptively retrain ML models in response to data drift, ensuring sustained model performance in industrial settings. We apply our methodology to a real-world scenario within the cement milling industry, demonstrating that our RL agent can effectively identify optimal retraining intervals and maintain high model accuracy over time. Our results indicate that ML models managed by the RL agent surpass the performance of static models, highlighting the potential of RL in continuous model management. The task-agnostic nature of our approach also suggests its applicability across various domains, paving the way for future research in adaptive systems and realtime data processing. © 2024 ieee.

关键词： Reinforcement learning

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3D Visualization Indoor Positioning System with Sequence Feature Enhancement Transformer Network 2

3D Visualization Indoor Positioning System with Sequence Fea...

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2nd international conference on Artificial Intelligence, Human-Computer Interaction and robotics, AIHCIR 2023

作者： Yu, Chenyu Cai, Hao Du, Chenghu Wuhan Textile University Department of Computer and Artificial Intelligent Wuhan China

ISBN: (纸本)9798350360363

Currently, Indoor Positioning systems (IPS) deliver two-dimensional positioning information, which falls short in complex situations necessitating a three-dimensional (3D) visualization of positioning results. To address this issue, we propose an advanced 3D visualization indoor positioning system with Sequential Feature Enhancement Transformer Network (SFETNet). This system utilizes an intelligent helmet as the user's terminal collection device to gather the user's positioning information and motion information, transmitting them to the 3D visualization platform. The 3D visualization platform showcases the user's real-time position and movement status in a three-dimensional manner, providing immediate feedback. In addition, we propose the SFETNet to optimize the classification of sequential signals. SFETNet addresses the issue of limited network learning ability caused by single feature simplicity in sequential signal series, demonstrating superior accuracy and performance compared to other mainstream classification networks. This system can display the user's position and motion status in realtime with 3D visualization in complex indoor scenes. It has tremendous application potential in smart industry, healthcare, and other fields. © 2023 ieee.

关键词： Indoor positioning systems

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Unsupervised Learning-Based Joint Precoding and Phase Shift Design for RIS-Assisted mmWave Communication systems 14

Unsupervised Learning-Based Joint Precoding and Phase Shift ...

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14th ieee international conference on Wireless Communications and signal processing (WCSP)

作者： Jiao, Yang Han, Yu Li, Xiao Jin, Shi Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China

ISBN: (纸本)9781665450850

This paper investigates a reconfigurable intelligent surface (RIS) aided downlink millimeter-wave multiple-input-multiple-output (MIMO) system. We aim to maximize the system spectral efficiency by jointly design the hybrid precoder at the base station (BS), the RIS phase shifts and the digital combiner at the user equipment (UE). For practical application, we adopt discrete Fourier transform (DFT) codebook-based hybrid precoding, and discrete RIS phase shifts. In order to achieve high performance with small computational delay, we propose a novel two-stage unsupervised learning-based approach to jointly design the codebook-based hybrid precoder and the discrete RIS phase shifts. Corresponding to the two-stage network architecture, we also develop a efficient phased training strategy. Simulation results demonstrate that the proposed approach can achieve satisfactory performance and the computational delay is small.

关键词： Reconfigurable intelligent surface hybrid precoding unsupervised learning mmWave

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