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Multi-objective Deployment of WSNs in Underground Sheltered Space 19th

Multi-objective Deployment of WSNs in Underground Sheltere...

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

作者： Wan, Liangtian Wang, Caiyun Sun, Lu Chen, Boyu Zheng, Jibin Wang, Xianpeng Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Dalian116620 China Department of Communication Engineering Institute of Information Science Technology Dalian Maritime University Dalian116026 China School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China National Laboratory of Radar Signal Processing Xidian University Xi’an710071 China State Key Laboratory of Marine Resource Utilization in South China Sea and School of Information and Communication Engineering Hainan University Haikou570228 China

ISBN: (纸本)9783031651250

Given the increasing population, the underground spaces are widely used. The existing research on wireless sensor networks (WSNs) development has mainly focused on overground space;also, the existing work was based on an ideal deployment environment and simple sensor behaviour. As a consequence, they do not satisfy requirements for practical 3D environment. The 3D wireless sensor networks (WSNs) deployment has new difficulties in the underground space. The signal transmission is impacted by obstacles in the underground space. Thus, we propose a geometry-based 3D signal propagation model to calculate the signal path loss caused by obstacles. Then, taking both coverage and connectivity into account, we transform the WSNs deployment problem into a multi-objective optimization problem (MOP). Finally, we compare some state-of-the-art multi-objective evolutionary algorithms (MOEAs) for the WSNs deployment problem in underground sheltered space. By the experimental results, we optimize the multiobjective deployment problem of the WSNs in the underground sheltered space. © ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Wireless sensor networks

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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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Knowledge-Aided Covariance Matrix Estimation Algorithm Based on Geometric Mean in STAP for Airborne Radar

Knowledge-Aided Covariance Matrix Estimation Algorithm Based...

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IET International Radar Conference 2023, IRC 2023

作者： Jing, Yang Du, Xiaolin Cui, Guolong Zheng, Jibin Li, Jianbo School of Computer and Control Engineering Yantai University Yantai China 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 School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781839539954

The problem of knowledge-aided (KA) covariance matrix estimation in airborne radar space-time adaptive processing (STAP) is studied in this paper. In the traditional KA covariance matrix estimation methods, the insufficient number of available samples and inaccurate prior knowledge can cause serious deterioration of STAP performance. To tackle this problem, a new KA covariance matrix estimation algorithm based on geometric mean is proposed. Specifically, by utilizing the persymmetric structure information as well as the geometric mean in the space of positive definite matrices, a geometric mean estimator is constructed, which is then linearly combined with the prior covariance matrix to obtain a new type of CCM estimators. Simulation results illustrate that the proposed algorithm has good STAP performance with small samples and inaccurate prior knowledge. © The Institution of Engineering & technology 2023.

关键词： Covariance matrix

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Two-dimensional spatial-variant compensation frequency domain imaging method for bistatic forward-looking radar

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Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics 2023年第7期45卷 1990-2001页

作者： Wang, Pengfei Zhan, Hengyi Sun, Hongzhong School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China China Airborne Missile Academy Luoyang471000 China National Key Laboratory of Radar Signal Processing Xidian University Xi'an710071 China

To solve the problem of the strong 2D spatial-variant of echo Doppler parameters in the airborne bistatic forward-looking radar under the condition of wide-swath and long synthetic aperture time, a 2D spatial-variant order compensation imaging method is proposed. Based on the best uniform approximation characteristics of Chebyshev orthogonal polynomials, the high-precision approximation of slant-distance and the high-quality decoupling of 2D spectrum is realized, then the spatial-variant linear range cell migration(LRCM) is corrected by keystone transform. On this basis, the 2D spatial-variant order of Doppler parameters is quantitatively analyzed. Taking π/4 phase threshold as the standard, the 2D spatial-variant correction function suitable for the spatial-variant order is designed by using the least square fitting algorithm, and the azimuth extended nonlinear chirp scaling accuracy imaging method is proposed to accurately and efficiently correct the Doppler parameters of different spatial-variant order. The results of point target simulation and real data processing verify the good focusing performance of the proposed method. © 2023 Chinese Institute of Electronics. All rights reserved.

关键词： Synthetic aperture radar

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Clutter Compensation for Space-Air Bistatic Radar Based on Unitary Subspace Transformation

Clutter Compensation for Space-Air Bistatic Radar Based on U...

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IEEE International Conference on Radar

作者： Qingyun Kan Jingwei Xu Guisheng Liao keyi Wang Yanhong Xu National Key Laboratory of Radar Signal Processing Xidian University Xi’an China The School of Communication and Information Engineering Xi’an University of Science and Technology Xi’an China

Since the space-air bistatic radar (SABR) system works in down-looking mode, it encounters a severely broadened clutter spectrum and the clutter is range dependent. Thereby diminishing the performance of space-time adaptive processing (STAP) technology through covariance matrix estimation error. It is necessary to perform clutter compensation before STAP clutter suppression. Inspired by the thinking of coherent signal subspace method for estimating the directions of arrival of wideband signals, we propose the range dependence compensation method which transforms the clutter subspace of training samples into the clutter subspace of reference range bin by unitary matrix transformation. Simulation examples are conducted to validate the effectiveness of the proposed algorithm.

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Adaptive Gaussian Regularization Constrained Sparse Subspace Clustering for Image Segmentation

Adaptive Gaussian Regularization Constrained Sparse Subspace...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Sensen Song Dayong Ren Zhenhong Jia Fei Shi School of Computer Science and Technology Xinjiang University Urumqi China Key Laboratory of Signal Detection and Processing Xinjiang Uygur Autonomous Region Urumqi China College of Mathematics and System Science Xinjiang University Urumqi China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Sparse Subspace Clustering (SSC) is integral to image processing, drawing from spectral clustering foundations. However, prevalent methods, relying on an l 1 -norm constraint, fail to capture nuanced inter-region correlations, affecting segmentation efficacy. To remedy this, we introduce an Adaptive Gaussian Regularization Constrained SSC for enhanced image segmentation. This method begins with superpixel preprocessing to enrich local information. Given the Gaussian nature of the SSC’s sparse coefficient matrix, a Gaussian probability density function is infused as a regularization term, reinforcing regional image ties and facilitating similarity matrix creation. Using spectral clustering, we then define superpixel clusters leading to the final segmentation. When tested against the BSDS500 and SBD datasets and other leading algorithms, our model showcases marked improvements in natural image segmentation.

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LE-CAM++: A Lighter and More Efficient CAM++ for Speaker Verification

LE-CAM++: A Lighter and More Efficient CAM++ for Speaker Ver...

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International Symposium on Chinese Spoken Language processing

作者： Shuanghong Liu Zhida Song Zhihua Fang Liang He School of Computer Science and Technology Xinjiang University Urumqi Xinjiang Key Laboratory of Signal Detection and Processing Urumqi School of Intelligence Science and Technology Xinjiang University Urumqi Department of Electronic Engineering and Beijing National Research Center for Information Science and Technology Tsinghua University Beijing

ISBN: (数字)9798331516826

ISBN: (纸本)9798331516833

Due to its superior performance and fewer parameters, CAM++ has become the state-of-the-art model for speaker verification tasks. This model uses 2D convolutional blocks to extract front-end features, which are then fed into a densely connected time-delay neural network backbone to extract deep features. However, the simple stacking of 2D convolutions may lead to the generation of a significant amount of redundant features, which is detrimental to efficient feature extraction. Furthermore, although CAM++ already has a relatively small number of parameters, there is still room for further optimization. To address these issues, this paper first employs depthwise separable convolutions to replace the dilated convolutions in the back-end network of CAM++, making the model more lightweight. Next, we introduce spatial and channel reconstruction convolution (SCConv) in the ResBlock module of CAM++ to reduce redundant features and optimize the feature extraction process. Finally, after SCConv, we apply squeeze and excitation attention mechanism to model the interdependencies between channels and recalibrate each channel, further enhancing the model's representational capacity. We name the resulting model LE-CAM++. Our proposed model achieves an EER of 0.686 and a minDCF of 0.084 on the VoxCeleb1–O dataset. Compared to the baseline model CAM ++, the EER is reduced by 11%, and the minDCF is reduced by 28%. Additionally, the model parameters are reduced by 8%.

关键词： Attention mechanisms Stacking Neural networks Feature extraction Optimization

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Simplified Skip-Connected UNet for Robust Speaker Verification Under Noisy Environments

Simplified Skip-Connected UNet for Robust Speaker Verificati...

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International Symposium on Chinese Spoken Language processing

作者： Zonghui Wang Zhihua Fang Zhida Song Liang He School of Computer Science and Technology Xinjiang University Urumqi Xinjiang Key Laboratory of Signal Detection and Processing Urumqi School of Intelligence Science and Technology Xinjiang University Urumqi Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing

ISBN: (数字)9798331516826

ISBN: (纸本)9798331516833

In recent years, deep neural network based methods for speaker verification have made remarkable progress in clean environments. However, background noise significantly reduces the accuracy and reliability of speaker verification systems by masking or changing the voice characteristics of the speaker. In this paper, we propose a cascaded framework optimized with multiobjective loss to mitigate the interference of different levels and types of noise on the speaker verification task. The proposed architecture consists of two components: a speech enhancement module based on improved 2D-UNet, which reduces the structural limitations of directly using classical UNet for noise reduction, and a back-end speaker embedding extraction module. We carry our experiments on the VoxCeleb1 and VOiCES datasets, as well as in the presence of out-of-domain noise conditions. The evaluations have demonstrated this method shows great potential for speaker verification in noisy environments.

关键词： Training Semantics Noise reduction Interference Speech enhancement Complexity theory Noise measurement Reliability Optimization signal to noise ratio

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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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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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