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5th IEEE Advanced Information technology, Electronic and Automation Control Conference, IAEAC 2021

作者： Guo, Shuting Luo, Liyan Wang, Mei Zhou, Zou Deng, Xiaofang Bai, Lu Zhao, Dancheng Provincial Ministry of Education Key Laboratory of Cognitive Radio and Signal Processing Guilin University of Electronic Technology Guilin Guangxi541004 China Guilin University of Electronic Technology Guangxi Key Laboratory of Wireless Broadband Communication and Signal Processing Guilin541004 China College of Information Science and Engineering Guilin University of Technology Guilin Guangxi541007 China

The traditional denoising algorithm can reduce the noise from environment, yet an evident downside of this is the sharp decrease of efficacy under non-stationary noise with low signal to noise ratio (SNR). In this case, this paper presents a denoising algorithm for environmental sound based on non-negative matrix factorization (NMF) and optimally-modified log-spectral amplitude (OMLSA). First of all, prior information is obtained from the noise and environmental sound dataset to separate the noise from noisy sound using supervised NMF. Then, decision-direction approach (DD) is used to calculate a priori SNR estimation according to the power spectral density (PSD) of the separated noise. Finally, under the log minimum mean square error criterion, the OMLSA gain function is obtained to filter out the noise in the environment. In this paper, the performance of the proposed algorithm is tested with white noise, F16 and babble under -5dB, 0dB and 5dB SNR conditions respectively. The experimental results show that this algorithm has strong ability of filtering out the noise superimposed in the environmental sound and improving the SNR gains. Moreover, comparing with other noise suppression algorithms, the proposed algorithm preforms better in preserving weak environmental sound, significantly reducing environmental sound distortion and avoiding the introduction of musical noise. © 2021 IEEE.

关键词： Non-negative matrix factorization

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Quality of Service-based Multi-Task Scheduling for Cooperative Multi-radar Systems

Quality of Service-based Multi-Task Scheduling for Cooperati...

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International Conference on Control, Automation and Information sciences (ICCAIS)

作者： Zishi Zhang Ye Yuan Wei Yi Junkun Yan School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China National Key Laboratory of Radar Signal Processing Xidian University Xi’an China

In this paper, an algorithm based on quality of service (QoS) framework is proposed to address the multitask scheduling problem in complex collaborative multi-radar scenarios. The task scheduling problem is described as an NP-hard mixed integer optimization problem in terms of task quality and task efficiency. To efficiently tackle the characteristic of the optimization problem, we proposed a two-step decoupling algorithm based on convex relaxation focusing on optimizing overall performance. Then, two comparison algorithms are proposed in this paper to benchmark against the proposed task scheduling algorithm. By contrasting these algorithms with the proposed approach, the study assesses the effectiveness and superiority of the new method in optimizing task utility and addressing the challenges of multi-task scheduling in multi-radar cooperative surveillance scenarios.

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Network completion via deep metric learning

Network completion via deep metric learning

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2020 International Conference on Big Data Mining and Information processing, BDMIP 2020

作者： Wei, Qiang School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China National Key Laboratory of Science and Technology on Blind Signal Processing Chengdu610041 China

Completing networks is often a necessary step when dealing with problems arising from applications in incomplete network data mining. This paper investigates the network completion problem with node attributes. We proposed a new method called DeepMetricNC by exploiting the correlation between node attributes and the underlying network structure. In DeepMetricNC, the correlation is modeled as a nonlinear mapping from node attributes to the probability of edge existence. To obtain the mapping, deep metric learning is applied with batch training and random negative sampling. DeepMetricNC has linear training time complexity and can adapt to large-scale network completion tasks. Experiments of real networks show that DeepMetricNC completes network structures better than other methods, and is more suitable when the portion of the observed part is small. © Published under licence by IOP Publishing Ltd.

关键词： Mapping

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Energy-Efficient Content Caching Strategy in Cell-Free Massive MIMO Networks with Reinforcement Learning

Energy-Efficient Content Caching Strategy in Cell-Free Massi...

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IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Yuan Peng Fangqing Tan Qiang Liu Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin University of Electronic Technology Guilin China College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China

To reduce the burden of transmission delay on the fronthaul link, this paper investigates an energy-efficient content caching, access point (AP) clustering and digital-to-analog converter resolution selection strategy in cell-free massive multiple-input multiple-output (CF-mMIMO) network with cache enabled are investigated. Specifically, we first give the signaling transmission model, cache model and power model. Furthermore, we formulated an optimization problem for maximizing the energy-efficiency of the considered system by optimizing the content caching, AP clustering and ADC/DAC resolution. Finally, a reinforcement learning method-a deep deterministic policy gradient algorithm is adopted to acquire the optimal solution.

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High-Resolution Radar Image Fast Imaging Formula and Simulation

High-Resolution Radar Image Fast Imaging Formula and Simulat...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Zhixin Wu Yuexin Gao Jixiang Fu Min Xue Mengdao Xing National Key Laboratory of Radar Signal Processing Xidian University Xi’an China School of Aerospace Science and Technology Xidian University Xi’an China Academy of Advanced Interdisciplinary Research Xidian University Xi’an China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

The Fast-Imaging Formula (FIF) enables rapid radar image simulation through One-Shot ray-tracing. However, the classical Imaging Formula is approximate results under the conditions of small angle and bandwidth. The application of FIF to wideband high-resolution radar images is subject to discussion. This study constructs simulations by employing the '6+6' Degrees of Freedom (DoF) motion model, determining the imaging plane. Utilizing the Discrete Fourier Transform (DFT), the range profile formula for large bandwidth is obtained, and the result is frequency dependent. According to the principle of spectrum correction, the integration is modified to obtain the image primitive suitable for high-resolution image simulation. The simulation results of the ship target model and the range profile analysis of the ray tube verify the effectiveness of the proposed method.

关键词： Radar remote sensing Azimuth Simulation Discrete Fourier transforms Refining Imaging Radar

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Azimuth Direction of Arrival Estimation Using Near-Surface Tilted Arrays in Deep-Sea Bottom-Bounce Area

Azimuth Direction of Arrival Estimation Using Near-Surface T...

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OCEANS

作者： Bo Zhang Yu Li Cheng Chi Haining Huang Guanqun Wang Institute of Acoustics Chinese Academy of Sciences Beijing Key Laboratory of Science and Technology on Advanced Underwater Acoustic Signal Processing Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350362077

ISBN: (纸本)9798350362084

The bottom bounce mode plays a crucial role in underwater acoustic detection in the shadow zone of deep water. When both the source and hydrophone are situated at depths of 300 meters or shallower, the main contributions to the sound field come from four multipath rays that reflected by the bottom only once. The sound field in the shadow zone mostly consists of larger- angle rays. Under such conditions, when the bottom bounce sound is received by a vertical line array, the lack of azimuthal resolution prevents obtaining source azimuth information. When the bottom bounce sound is received by a horizontal line array, an azimuth deviation appears because of the coupling effect between elevation angle and azimuth angle. This paper explains the reasons for the deviation. Then based on the trigonometric relation between the elevation angle and the azimuth angle, the deviation could be modified by the tilted array. The method is demonstrated with the simulated result.

关键词： Meters Couplings Direction-of-arrival estimation Accuracy Azimuth Echo sounders Oceans

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Joint Colour Casts Correction and Zero-Shot Learning for Real-World Sand-Dust Image Enhancement

SSRN

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SSRN 2024年

作者： Shi, Fei Ren, Dayong Jia, Zhenhong Zhou, Yanyun School of Information Science and Engineering Xinjiang University Urumqi830046 China Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi830046 China National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China

Low illumination and various colour casts are present in images taken in sand-dust weather. A deep learning-based or prior-based desanding approach may not produce the best results. This work proposes an efficient joint colour cast correction and zero-shot learning framework to formulate the image desanding problem as the task of improving image visibility and contrast. In the colour cast correction stage, by using the compensation between each colour channel, Based on the statistical properties of sand dust images, an adaptive colour cast correction is carried out. At the contrast enhancement stage, we present an end-to-end illumination map estimate method based on zero-shot learning. First, we propose a novel method for generating a degraded image by controlling the perturbation of the Retinex model. Then, the input and degraded images are used to train the model, which is then used to make the luminance map. Finally, combining all configuration constraints gives a good visual quality enhancement. Evaluations on a large number of different sand dust images, especially those with significant colour casts, low illumination, and uneven illumination. The outcomes show that our approach outperforms state-of-the-art approaches. © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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Estimating Target Depth for Deep-Sea Active Sonars Through Channel-Impulse-Response Matching

Estimating Target Depth for Deep-Sea Active Sonars Through C...

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OCEANS

作者： Chunhui Chen Haining Ruang Cheng Chi Yu Li Shenglong Jin Institute of Acoustics Chinese Academy of Sciences Beijing Key Laboratory of Science and Technology on Advanced Underwater Acoustic Signal Processing Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350362077

ISBN: (纸本)9798350362084

Target depth estimation in deep sea is an important topic in underwater acoustic signal processing. In recent years, researchers [1]–[3] proposed some methods of estimating depth of underwater sources for passive acoustic systems with vertical line arrays. These vertical arrays are located at great depth at the point where the sound speed equals the maximum speed near the surface, which makes use of the deep-sea propagation characteristics of reliable acoustic path. However, using active sonars located at the great depth to estimate near-surface target depth has rarely been investigated. This paper focuses on target depth estimation of active sonars with a single hydrophone or horizontal line array at the great depth.

关键词： Sea surface Array signal processing Sonar Estimation Acoustic arrays Reflection Reliability

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Generalized Tensor-Aided Channel Estimation for Hardware Impaired Device Identification

arXiv

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arXiv 2025年

作者： Wu, Qi Sui, Zeping Ngo, Hien Quoc Wan, Qun Matthaiou, Michail School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China National Key Laboratory on Blind Signal Processing Chengdu610041 China Queen’s University Belfast BelfastBT3 9DT United Kingdom Department of Electronic Engineering Kyung Hee University Gyeonggi-do Yongin-si17104 Korea Republic of

In this paper, we investigate the joint generalized channel estimation and device identification problem in Internet of Things (IoT) networks under multipath propagation. To fully utilize the received signal, we decompose the generalized channel into three components: transmitter hardware characteristics, path gains, and angles of arrival. By modeling the received signals as parallel factor (PARAFAC) tensors, we develop alternating least squares (ALS)-based algorithms to simultaneously estimate the generalized channels and identify the transmitters. Simulation results show that the proposed scheme outperforms both Khatri-Rao Factorization (KRF) and the conventional least squares (LS) method in terms of channel estimation accuracy and achieves performance close to the derived Cramér–Rao lower bound. © 2025, CC BY.

关键词： Tensors

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Mobile Edge Computing Tasking Offloading Strategy in Cell-Free Massive MIMO with Graph Neural Network

Mobile Edge Computing Tasking Offloading Strategy in Cell-Fr...

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IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Shiwei Li Fangqing Tan Qiang Liu Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin University of Electronic Technology Guilin China College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China

Combining cell-free massive multiple-input multiple-output (CF-mMIMO) and mobile edge computing (MEC) facilitates the processing of compute-intensive and latency-sensitive tasks in the distributed IoT. For MEC-enabled CF-mMIMO system, this paper designs a task offloading strategy for local computing and multi-access points (APs) collaboration. Under energy constraints, we aim to minimize the latency of computing offloading. According to the different data size of each user and the service of APs, the graph neural network method is adopted to deal with the link prediction between the user and APs.

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