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16th International conference on Signal processing systems, ICSPS 2024

作者： Peng, Yishu Zhang, Sheng Zhou, Zhengchun Han, Hongyu School of Information Science and Technology Southwest Jiaotong University Chengdu China School of Computer Science Sichuan Normal University Chengdu China

ISBN: (纸本)9781510689251

In energy- and bandwidth-limited networks, frequency-domain diffusion algorithms based on periodic communication are attractive candidates because of their low computational complexity and communication overhead. Nevertheless, these algorithms assume that intermediate estimates are transmitted exactly in each communication round, which is not realistic in practice. To this end, this paper proposes novel frequency-domain diffusion algorithms with quantized communication. Using the randomized quantizer to compress the shared information, we first develop the quantized frequency-domain diffusion least mean square (Q-FDLMS) algorithm. To mitigate the performance degradation incurred by quantization errors, the improved Q-FDLMS (IQ-FDLMS) algorithm is also designed. This enhanced version modifies the combination step and incorporates the moving-average scheme. Additionally, the mean-square stability conditions and steady-state error bound of the IQ-FDLMS algorithm are derived. Experiment results indicate that our proposed algorithms are superior to other competing algorithms. © 2025 SPIE.

关键词： Frequency domain analysis

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Spatial and Temporal Evolution of Wetlands in the Yellow River Delta Based on optical Flow Algorithm

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JOURNAL OF information processing systems 2025年第1期21卷 102-113页

作者： Liu, Chao Mu, Ruolan Wang, Chuanlong Yuan, Xiuhe Miao, Sheng Qingdao Univ Technol Sch Environm & Municipal Engn Qingdao Peoples R China Qingdao Univ Technol Sch Informat & Control Engn Qingdao Peoples R China

The Yellow River Delta (YRD) wetlands are the largest coastal wetlands in China, and it serves to control soil erosion, nourish the climate and protect biodiversity. At present, due to climate change and human activities, the wetlands of the YRD are facing ecological and environmental problems such as species invasion, vegetation degradation, and biodiversity reduction. In order to study the evolution of wetland landscape types more intuitively, this paper proposed a semantic segmentation network based on the encoder and decoder structure of ResNet-18. Then, the wetland landscapes in the YRD were classified into five types by combining the Landsat series of remote sensing images. In addition, this paper used the optical flow algorithm to visualize the identification results, which can represent the evolution pattern of wetland landscape types in different years. The results of this paper have an important significance for the subsequent development planning and protection in the YRD.

关键词： Identification of Wetland Landscape Types optical Flow Algorithm ResNet Spatial and Temporal Evolution

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Fusion feature multi-objective classification algorithm based on adaptive scale feature residual network 16

Fusion feature multi-objective classification algorithm base...

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16th International conference on Signal processing systems, ICSPS 2024

作者： Hua, Chenyang Xie, Renhong Li, Peng Fang, Gan Zhao, Xianya Zhou, Maoyuan School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China

ISBN: (纸本)9781510689251

With the development of science and technology, radar has been widely used in both civil and military fields, and radar target classification and recognition is an important research direction in radar signal processing field. The traditional classification and recognition technology does not utilize all the information of echo, and the recognition rate will decrease obviously when the environment is too complicated and the SNR is low. To solve the above problems, this paper proposes three recognition algorithms. It is a fusion feature multi-objective classification algorithm based on multi-scale feature residual network (MKRN), a fusion feature multi-objective classification algorithm based on adaptive scale feature residual network (AKRN) and a fusion feature multi-objective classification algorithm based on adaptive scale feature multi-level branch residual network (AKSRN). These three algorithms introduce residual structure to avoid network degradation caused by the deepening of network depth, and use convolution kernel of different sizes to enhance the ability of the network to extract multi-scale feature information, which improves the accuracy of network classification and recognition. © 2025 SPIE.

关键词： Radar signal processing

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Quantum image encryption algorithm based on generalized Arnold transform and Lorenz chaotic systems

Quantum image encryption algorithm based on generalized Arno...

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2024 International conference Optoelectronic information and optical Engineering, OIOE 2024

作者： Zhang, Ruqing School of Information Engineering Shanghai Maritime University Shanghai201306 China

ISBN: (数字)9781510688209

ISBN: (纸本)9781510688193

With the rapid development of the internet and information technology, the traditional encryption methods suffer from drawbacks such as poor processing capacity and low security, making them vulnerable to attacks. This paper presents a hybrid encryption algorithm that combines the Lorenz chaotic system and the generalized Arnold transform to enhance information security and confidentiality in digital images. The proposed quantum encryption scheme involves two stages: interfering with pixel coordinate information and grayscale values using the generalized Arnold transform, followed by spatial domain scrambling of pixel positions using the Lorenz chaotic system. MATLAB simulations demonstrate the effectiveness of this algorithm in achieving the desired confusion and diffusion of pixel values, making it a superior encryption method for cryptography. © 2025 SPIE.

关键词： Encryption algorithms

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Decentralized Target Localization in Distributed MIMO Radars 16

Decentralized Target Localization in Distributed MIMO Radars

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16th International conference on Signal processing systems, ICSPS 2024

作者： Qiu, Qinhui Li, Jie Xi, Feng School of Electronic and Optical Engineering Nanjing University of Science and Technology China

ISBN: (纸本)9781510689251

This paper addresses the problem of target estimation in distributed MIMO radars, focusing on the challenge of requiring a robust fusion center to process all the collected data. To overcome this limitation, we propose a solution based on the Alternating Direction Method of Multipliers (ADMM) that eliminates the need for a centralized fusion center. By leveraging the sparsity of target location information within the detection range, the target localization problem is reformulated as a sparse recovery problem, which is then efficiently solved using ADMM. The key innovation of the proposed approach is the transformation of the average value computation, typically required in centralized methods, into a consensus averaging problem, and subsequently into a consensus optimization problem. This transformation allows for fully decentralized processing, where each node only communicates with its neighboring nodes to reach a consensus on the estimation of target location. Simulation results demonstrate that the proposed distributed algorithm achieves performance comparable to that of semi-distributed algorithms while fully exploiting the advantages of decentralization. © 2025 SPIE.

关键词： Consensus algorithm

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A Novel Environment Reconstruction Method based on Distributed NLOS Signals 16

A Novel Environment Reconstruction Method based on Distribut...

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16th International conference on Signal processing systems, ICSPS 2024

作者： Yu, Fangrui Li, Junlin Xie, Wei Zhang, Kehao Wan, Qun School of Information and Communication Engineering Nation Key Laboratory on Blind Signal Processing University of Electronic Science and Technology of China Sichuan Chengdu611731 China

ISBN: (纸本)9781510689251

Integrated Sensing and Communication (ISAC) is a rapidly evolving field with extensive application prospects. Environmental reconstruction (ER) is an important content of ISAC. Existing ER techniques often require large sample sizes or specific signals, which significantly limit their application. User Equipments (UEs) widely distributed in the environment and their communication signals provide an adequate measurement basis. However, there are challenges in utilizing communication signals due to limited research, and difficulties in identifying and extracting Non-Line-of-Sight (NLOS) signals. This paper proposes a distributed ER algorithm based on Angle of Arrival (AOA) and Time of Flight (TOF) information of NLOS signals, enabling identification and utilization of various order NLOS signals under complex NLOS conditions. A series of simulation delays validate the effectiveness of the proposed method. © 2025 SPIE.

关键词： Non line of sight propagation Reflection Reconstruction algorithms Mirror pointing Signal processing Environmental sensing Wireless communications

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Machine Learning for Signal processing: Kernel-extreme Learning Machines with Augmented Complex-valued and Mixture Correntropy Techniques 16

Machine Learning for Signal Processing: Kernel-extreme Learn...

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16th International conference on Signal processing systems, ICSPS 2024

作者： Lin, Bingqing Wang, Yan Guan, Yunhe Qian, Guobing College of Electronic and Information Engineering Southwest University Chongqing400715 China

ISBN: (纸本)9781510689251

In this paper, we propose novel machine learning algorithms for signal processing applications based on kernel-extreme learning machines (KELM) and its variants. Specifically, we focus on the use of complex domain and mixture correntropy techniques to enhance the performance and generalization ability of KELM. Two models are derived by augmenting a complex-valued extreme learning machine with kernel functions, minimizing human intervention while enhancing accuracy. Furthermore, we extend the mixture correntropy-based kernel extreme learning machine (MCKELM) to complex domains, resulting in the MCKCELM algorithm, capable of solving prediction problems in complex domains. To further improve the performance of MCKCELM, we apply an augmented matrix to derive two variant models, and demonstrate their convergence through experiments. All models presented in this paper are regularized models specifically designed for signal processing tasks. © 2025 SPIE.

关键词： Adversarial machine learning

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Design of PMCW Millimeter Wave Radar algorithms on an Embedded DSP 16

Design of PMCW Millimeter Wave Radar Algorithms on an Embedd...

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16th International conference on Signal processing systems, ICSPS 2024

作者： Zhao, Jinghao Liu, Qiuming Tan, Bin School of Software engineering Jiangxi University of Science and Technology Nanchang China Nanchang Key laboratory of Virtual Digital Factory and Cultural Communications Nanchang China Jiangxi Provincial Key Laboratory of Electronic Data Control and Forensics China School of Electronics and Information Engineering Jinggangshan University Ji’an China

ISBN: (纸本)9781510689251

The rapid advancements in autonomous driving technology necessitate the extensive deployment of automotive radars operating within the 77-81 GHz millimeter-wave band in the forthcoming years. In contrast to earlier Frequency Modulated Continuous Wave (FMCW) radars, Phase-Modulated Continuous Wave (PMCW) radars exhibit notable improvements in processing speed and flexibility, offering superior range and velocity resolution. These enhancements are critical for the precise detection and interpretation of dynamic and complex traffic scenarios. This paper initially presents the simulation and testing of algorithms for range and speed measurement using single-input single-output (SISO) PMCW millimeter-wave radar. Building upon these results, further simulations incorporating multi-input multi-output (MIMO) systems utilizing Hadamard codes are conducted to augment PMCW radar performance. The final phase of this study involves implementing the relevant signal processing algorithms on a custom-developed digital signal processor (DSP) named SWIFT. Experimental findings demonstrate that PMCW radar significantly mitigates multipath effects and clutter, maintaining high performance in complex environments. Furthermore, the algorithms executed on the DSP meet the anticipated performance standards. The proposed methodology not only validates the theoretical framework but also establishes a foundation for future hardware implementation. © 2025 SPIE.

关键词： Automotive radar

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Position Determination from CSI Measurements of Multiple UEs under Unaware Environment 16

Position Determination from CSI Measurements of Multiple UEs...

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16th International conference on Signal processing systems, ICSPS 2024

作者： Wang, Chenchen Ma, Yubin Yu, Fangrui Wen, Hao Liu, Weiyu Wan, Qun Nation Key Laboratory on Blind Signal Processing School of Information and Communication Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China

ISBN: (纸本)9781510689251

In urban environment, communication signals encompass non-line-of-sight (NLOS) paths that contain environmental information. This paper investigates utilizing NLOS component signal to estimate the environment parameters, and uses these environmental parameters to achieve blind UEs (User Equipment) positioning. Specifically, this paper first analyzes the relationship between environmental parameters and the time of arrival (TOA) of NLOS signals. Second, an algorithm is proposed that utilizes the TOA of one-order NLOS signals in uplink signals between distributed UEs and base stations (BS) to estimate environmental parameters via maximum likelihood estimation. Subsequently, leveraging the obtained environmental parameters, achieving the positioning of blind UEs. Finally, numerical simulations validate the effectiveness of the proposed method. © 2025 SPIE.

关键词： Reflection Non line of sight propagation Mirrors Reconstruction algorithms Environmental sensing Mirror surfaces Point clouds LIDAR Monte Carlo methods Numerical simulations

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Employing Machine Learning Paradigms for Optimized Hard Disk Failure Prediction in Shipboard systems 4

Employing Machine Learning Paradigms for Optimized Hard Disk...

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4th International Computational Imaging conference, CITA 2024

作者： Ma, C. Li, F. Gao, D. Zhao, Y. Bao, T. Li, X. China Ship Research and Development Academy Beijing China State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Beijing100876 China

ISBN: (纸本)9781510688834

This paper presents an optimized machine learning-based approach for hard disk failure prediction, specifically tailored for shipboard systems. In shipboard systems, unexpected hard disk failures can disrupt operations and lead to critical data loss;therefore, reliable failure prediction is essential. To address the impact of dataset imbalance on predictive performance, we employed the Synthetic Minority Over-sampling Technique (SMOTE) alongside a fault-backtracking method to transform the imbalanced dataset into a balanced one, thereby enhancing the algorithm's predictive accuracy. Four machine learning algorithms were utilized in this study: K-Nearest Neighbors (KNN), Logistic Regression (LR), Extreme Gradient Boosting (XGBoost), and Random Forest (RF). Experimental results demonstrated that the RF algorithm achieved an accuracy of 97.1%, outperforming the other algorithms across all evaluation metrics. The contribution of this work lies in providing a feasible solution for hard disk failure prediction in shipboard systems through effective data processing strategies and algorithm selection, ultimately enhancing shipboard safety and reliability. © 2025 SPIE.

关键词： Hard disk storage

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