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Outlier Indicator Based Projection Fuzzy K-Means Clustering for Hyperspectral Image

IEEE signal processing LETTERS 2025年 32卷 496-500页

作者： Liu, Xinze Yang, Xiaojun Zhang, Jiale Wang, Jing Nie, Feiping Guangdong Univ Technol Sch Informat Engn Guangzhou 510006 Peoples R China Guangdong Univ Technol Guangdong Prov Key Lab Informat Photon Technol China Key Lab Photon Technol Integrated Sensing & Minist Educ China Guangzhou 510006 Peoples R China Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Northwestern Polytech Univ Ctr OPT IMagery Anal & Learning OPTIMAL Xian 710072 Peoples R China

The application of hyperspectral image (HSI) clustering has become widely used in the field of remote sensing. Traditional fuzzy K-means clustering methods often struggle with HSI data due to the significant levels of noise, consequently resulting in segmentation inaccuracies. To address this limitation, this letter introduces an innovative outlier indicator-based projection fuzzy K-means clustering (OIPFK) algorithm for clustering of HSI data, enhancing the efficacy and robustness of previous fuzzy K-means methodologies through a two-pronged strategy. Initially, an outlier indicator vector is constructed to identify noise and outliers by computing the distances between each data point in a reduced dimensional space. Subsequently, the OIPFK algorithm incorporates the fuzzy membership relationships between samples and clustering centers within this lower-dimensional framework, along with the integration of the outlier indicator vectors, to significantly mitigates the influence of noise and extraneous features. Moreover, an efficient iterative optimization algorithm is employed to address the optimization challenges inherent to OIPKM. Experimental results from three real-world hyperspectral image datasets demonstrate the effectiveness and superiority of our proposed method.

关键词： Hyperspectral imaging Optimization Noise Vectors Clustering algorithms Robustness Linear programming signal processing algorithms Photonics Indexes Projection fuzzy K-means hyperspectral image robustness outliers clustering

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Parametric Minimum Error Entropy Criterion: A Case Study in Blind Sensor Fusion and Regression Problems

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 5091-5106页

作者： Lopez, Carlos Alejandro Riba, Jaume Univ Politecn Catalunya UPC Dept Teoria Senyal & Comunicac Signal Proc & Commun Grp SPCOM Barcelona 08034 Spain

The purpose of this article is to present the Parametric Minimum Error Entropy (PMEE) principle and to show a case study of the proposed criterion in a blind sensor fusion and regression problem. This case study consists on the estimation of a temporal series with a certain temporal invariance, which is measured from multiple independent sensors with unknown variances and unknown mutual correlations of the measurement errors. In this setting, we show that a particular case of the PMEE criterion is obtained from the Conditional Maximum Likelihood (CML) principle of the measurement model, leading to a semi-data-driven solution. Despite the fact that Information Theoretic Criteria (ITC) are inherently robust, they often result in difficult non-convex optimization problems. Our proposal is to address the non-convexity by means of a Majorization-Minimization (MM) based algorithm. We prove the conditions in which the resulting solution of the proposed algorithm reaches a stationary point of the original problem. In fact, the aforementioned global convergence of the proposed algorithm is possible thanks to a reformulation of the original cost function in terms of a variable constrained in the Grassmann manifold. As shown in this paper, the latter procedure is possible thanks to a homogeneity property of the PMEE cost function.

关键词： Entropy signal processing algorithms Cost function Pollution measurement Minimization Data integration Sensor fusion Robustness Probability density function Manifolds Non-convex optimization conditional maximum likelihood majorization-minimization minimum error entropy Grassmann manifold

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Single-Trial EEG Classification Using Spatio-Temporal Weighting and Correlation Analysis for RSVP-Based Collaborative Brain Computer Interface

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2024年第2期71卷 553-562页

作者： Zhao, Ziwei Lin, Yanfei Wang, Yijun Gao, Xiaorong Beijing Inst Technol Sch Integrated Circuits & Elect Beijing 100081 Peoples R China Chinese Acad Sci Inst Semicond State Key Lab Integrated Optoelect Beijing 100083 Peoples R China Tsinghua Univ Sch Med Beijing Peoples R China

Objective: Since single brain computer interface (BCI) is limited in performance, it is necessary to develop collaborative BCI (cBCI) systems which integrate multi-user electroencephalogram (EEG) information to improve system performance. However, there are still some challenges in cBCI systems, including effective discriminant feature extraction of multi-user EEG data, fusion algorithms, time reduction of system calibration, etc. Methods: This study proposed an event-related potential (ERP) feature extraction and classification algorithm of spatio-temporal weighting and correlation analysis (STC) to improve the performance of cBCI systems. The proposed STC algorithm consisted of three modules. First, source extraction and interval modeling were used to overcome the problem of inter-trial variability. Second, spatio-temporal weighting and temporal projection were utilized to extract effective discriminant features for multi-user information fusion and cross-session transfer. Third, correlation analysis was conducted to match target/non-target templates for classification of multi-user and cross-session datasets. Results: The collaborative cross-session datasets of rapid serial visual presentation (RSVP) from 14 subjects were used to evaluate the performance of the EEG classification algorithm. For single-user/collaborative EEG classification of within-session and cross-session datasets, STC had significantly higher performance than the existing state-of-the-art machine learning algorithms. Conclusion: It was demonstrated that STC was effective to improve the classification performance of multi-user collaboration and cross-session transfer for RSVP-based BCI systems, and was helpful to reduce the system calibration time.

关键词： Feature extraction Electroencephalography Classification algorithms Correlation Collaboration Visualization signal processing algorithms Collaborative BCI correlation analysis cross-session transfer rapid serial visual presentation (RSVP) spatio-temporal projection

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Inpainting-Driven Graph Learning via Explainable Neural Networks

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IEEE signal processing LETTERS 2025年 32卷 111-115页

作者： Batreddy, Subbareddy Mishra, Pushkal Kakarla, Yaswanth Siripuram, Aditya Indian Inst Technol Hyderabad Hyderabad 502284 India

Given partial measurements of a time-varying graph signal, we propose an algorithm to simultaneously estimate both the underlying graph topology and the missing measurements. The proposed algorithm operates by training an interpretable neural network, designed from the unrolling framework. The proposed technique can be used as a graph learning and/or a graph signal reconstruction algorithm. This work builds on prior work in graph learning by tailoring the learned graph to the signal reconstruction task;and also enhances prior work in graph signal reconstruction by allowing the underlying graph to be unknown.

关键词： signal processing algorithms Neural networks Optimization Laplace equations Data models Covariance matrices Biological neural networks Training Minimization Topology Graph temporal data graph signal processing graph topology inference unrolling

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Automatic Dual-Polarized Ground Penetrating Radar for Enhanced 3-D Tree Roots System Architecture Reconstruction

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷

作者： Luo, Wenhao Lee, Yee Hui Hao, Tong Yusof, Mohamed Lokman Mohd Yucel, Abdulkadir C. Tongji Univ Coll Surveying & Geoinformat Shanghai 200092 Peoples R China Tongji Univ Ctr Spatial Informat Sci & Sustainable Dev Applica Shanghai 200092 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore Natl Pk Board Singapore 259569 Singapore

Tree root systems are crucial for providing structural support and stability to trees. However, in urban environments, they can pose challenges due to potential conflicts with the foundations of roads and infrastructure, leading to significant damage. Therefore, there is a pressing need to investigate the subsurface tree root system architecture (RSA). Ground-penetrating radar (GPR) has emerged as a powerful tool for this purpose, offering high-resolution and nondestructive testing (NDT) capabilities. One of the primary challenges in enhancing GPR's ability to detect roots lies in accurately reconstructing the 3-D structure of complex RSAs. This challenge is exacerbated by subsurface heterogeneity and intricate interlacement of root branches, which can result in erroneous stacking of 2-D root points during 3-D reconstruction. This study introduces a novel approach using our developed wheel-based dual-polarized GPR system capable of capturing four polarimetric scattering parameters at each scan point through automated zigzag movements. A dedicated radar signal processing framework analyzes these dual-polarized signals to extract essential root parameters. These parameters are then used in an optimized slice relation clustering (OSRC) algorithm, specifically designed for improving the reconstruction of complex RSA. The efficacy of integrating root parameters derived from dual-polarized GPR signals into the OSRC algorithm is initially evaluated through simulations to assess its capability in RSA reconstruction. Subsequently, the GPR system and processing methodology are validated under real-world conditions using natural Angsana tree root systems. The findings demonstrate a promising methodology for enhancing the accurate reconstruction of intricate 3-D tree RSA structures.

关键词： Three-dimensional displays Image reconstruction Radar antennas Vegetation signal processing algorithms Accuracy Surveys Permittivity Ground penetrating radar Clustering algorithms 3-D reconstruction dual-polarized ground-penetrating radar (GPR) optimized slice relation clustering (OSRC) radar signal processing framework root system architecture (RSA)

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Robust Gaussian Mixture Modeling: A K-Divergence Based Approach

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 3578-3594页

作者： Kenig, Ori Todros, Koby Adali, Tulay Ben Gurion Univ Negev Sch Elect & Comp Engn IL-84105 Beer Sheva Israel Univ Maryland Baltimore Cty Dept Comp Sci & Elect Engn Baltimore MD 21250 USA

This paper addresses the problem of robust Gaussian mixture modeling in the presence of outliers. We commence by introducing a general expectation-maximization (EM)-like scheme, called K -BM, for iterative numerical computation of the minimum K -divergence estimator (MKDE). This estimator leverages Parzen's non-parametric K ernel density estimate to down-weight low density regions associated with outlying measurements. Akin to the conventional EM, the K -BM involves successive M aximizations of lower B ounds on the objective function of the M K DE. However, differently from EM, these bounds are not exclusively reliant on conditional expectations. The K -BM algorithm is applied to robust parameter estimation of a finite-order multivariate Gaussian mixture model (GMM). We proceed by introducing a new robust variant of the Bayesian information criterion (BIC) that penalizes the MKDE's objective function. The proposed criterion, called K -BIC, is conveniently applied for robust GMM order selection. In the paper, we also establish a data-driven procedure for selection of the kernel's bandwidth parameter. This procedure operates by minimizing an empirical asymptotic approximation of the mean-integrated-squared-error (MISE) between the underlying density and the estimated GMM density. Lastly, the K -BM, the K -BIC, and the MISE based selection of the kernel's bandwidth are combined into a unified framework for joint order selection and parameter estimation of a GMM. The advantages of the K -divergence based framework over other robust approaches are illustrated in simulation studies involving synthetic and real data.

关键词： Parameter estimation Numerical models Kernel Computational modeling Bandwidth Maximum likelihood estimation signal processing algorithms Divergences estimation theory robust statistics

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Real-Time Deployment of Pruned Volterra Algorithm for PAM4 signals in a W-Band Wireless System

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2024年第20期42卷 7193-7201页

作者： Zhu, Zonghui Wang, Kaihui Sun, Xinda Zhang, Long Gou, Yumeng Meng, Yuanxiao Chen, Yun Yu, Jianjun Fudan Univ Dept Commun Sci & Engn Shanghai 200433 Peoples R China Fudan Univ State Key Lab ASlC & Syst Key Lab Informat Sci Electromagnet Waves MoE Shanghai 200433 Peoples R China Fudan Univ Sch Informat Sci & Technol Shanghai 200433 Peoples R China Fudan Univ Sch Microelect Zhangjiang Campus Shanghai 201203 Peoples R China

Nonlinear equalization technology is widely used in signal impairment compensation, among which Volterra equalizer is a commonly used method that can model and compensate for the linear and nonlinear parts of the system. However, the Volterra equalizer has high complexity and is difficult to implement in a field programmable gate array (FPGA). This paper proposes a decision-oriented least mean square (DD-LMS) Volterra algorithm and performs pruning operations on it. Compared with the traditional Volterra algorithm, this algorithm has faster convergence speed and does not require training sequences. Through pruning operations, the complexity and resource overhead of the Volterra-DDLMS algorithm are greatly reduced. This paper demonstrates an FPGA-based W-band wireless millimeter wave system with DD-LMS Volterra algorithm to improve performance. The receiving end uses 32-channel parallel Resample, Constant Module Algorithm (CMA) and pruned Volterra-DDLMS equalizer to process high-speed parallel PAM4 signals. The 14.7456 Gbit/s PAM4 signal was successfully transmitted within 40 meters of free space and met the 2.4 x 10(-2) soft decision forward error correction (SD-FEC) standard. Finally, PAM4 signal transmission system based on pruned Volterra showed good stability within a 1-hour measurement period, and its real-time measurement bit error rate (BER) performance was verified.

关键词： Complexity theory signal processing algorithms Real-time systems Interference Clocks Adaptation models Wireless communication Millimeter-wave systems pruned Volterra algorithm real-time

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SSA: A Uniformly Recursive Bidirection-Sequence Systolic Sorter Array

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2024年第10期35卷 1721-1734页

作者： Gao, Teng Huang, Lan Gao, Shang Wang, Kangping Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China

The use of reconfigurable circuits with parallel computing capabilities has been explored to enhance sorting performance and reduce power consumption. Nonetheless, most sorting algorithms utilizing dedicated processors are designed solely based on the parallelization of the algorithm, lacking considerations of specialized hardware structures. This leads to problems, including but not limited to the consumption of excessive I/O interface resources, on-chip storage resources, and complex layout wiring. In this paper, we propose a Systolic Sorter Array, implemented by a Uniform Recurrence Equation (URE) with highly parameterised in terms of data size, bit width and type. Leveraging this uniformly recursive structure, the sorter can simultaneously sort two independent sequences. In addition, we implemented global and local control modes on the FPGA to achieve higher computational frequencies. In our experiments, we have demonstrated the speed-up ratio of SSA relative to other state of the art (SOTA) sorting algorithms using C++ std::sort() as benchmark. Inheriting the benefits from the Systolic Array architecture, the SSA reaches up to 810 Mhz computing frequency on the U200. The results of our study show that SSA outperforms other sorting algorithms in terms of throughput, speed-up ratio, and computation frequency.

关键词： Sorting Systolic arrays Hardware Field programmable gate arrays signal processing algorithms Complexity theory Arrays Systolic array Uniform recurrence equations FPGA Parallel sorting

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Magnitude Matters: Fixing signSGD Through Magnitude-Aware Sparsification and Error Feedback in the Presence of Data Heterogeneity

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 5140-5155页

作者： Jin, Richeng He, Xiaofan Zhong, Caijun Zhang, Zhaoyang Quek, Tony Q. S. Dai, Huaiyu Zhejiang Univ Dept Informat & Commun Engn Hangzhou 310007 Peoples R China Wuhan Univ Elect Informat Sch Wuhan 430072 Peoples R China Singapore Univ Technol & Design Singapore 487372 Singapore Yonsei Univ Yonsei Frontier Lab Seoul 03722 South Korea North Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA

Communication overhead has become one of the major bottlenecks in the distributed training of deep neural networks. To alleviate the concern, various gradient compression methods have been proposed, and sign-based algorithms are of surging interest. However, signSGD fails to converge in the presence of data heterogeneity, which is commonly observed in the emerging federated learning (FL) paradigm. Error feedback has been proposed to address the non-convergence issue. Nonetheless, it requires the workers to locally keep track of the compression errors, which renders it not suitable for FL since the workers may not participate in the training throughout the learning process. In this paper, we propose a magnitude-driven sparsification scheme, which addresses the non-convergence issue of signSGD while further improving communication efficiency. Moreover, the local update and the error feedback schemes are further incorporated to improve the learning performance (i.e., test accuracy and communication efficiency), and the convergence of the proposed method is established. The effectiveness of the proposed scheme is validated through extensive experiments on Fashion-MNIST, CIFAR-10, CIFAR-100, Tiny-ImageNet, and Mini-ImageNet datasets.

关键词： Training Convergence Servers Quantization (signal) Vectors Training data signal processing algorithms Data heterogeneity federated learning gradient compression sparsification

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Beam Alignment in Multipath Environments for Integrated Sensing and Communication Using Bandit Learning

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IEEE JOURNAL OF SELECTED TOPICS IN signal processing 2024年第5期18卷 871-885页

作者： Sneh, Akanksha Ram, Shobha Sundar Darak, Sumit J. Tewari, Aakanksha IIIT Delhi ECE Dept Delhi 110020 India

Prior works have explored multi-armed bandit (MAB) algorithms for the selection of optimal beams for millimeter-wave (mmW) communications between base station and mobile users. However, when the number of beams is large, the existing MAB algorithms are characterized by long exploration times, resulting in poor overall communication throughput. In this work, we propose augmenting the upper confidence bound (UCB) based MAB with integrated sensing and communication (ISAC) to address this limitation. The premise of the work is that the radar and communication functionalities share the same field-of-view and that communication mobile users are detected by the radar as mobile targets. The radar information is used for significantly reducing the number of candidate beams for the UCB, resulting in an overall reduction in the exploration time. Further, the radar information is used to estimate the realignment time in quasi-stationary scenarios. We have realized the MAB and radar signal processing algorithms on the system on chip (SoC) via hardware-software co-design (HSCD) and fixed-point analysis. We demonstrate the significant gain in execution time using accelerators. The simulations consider complex propagation channels involving direct and multipath, with simple and extended radar targets in the presence of significant static clutter. The resulting experiments show that the proposed ISAC-based MAB achieves a 35% reduction in the overall exploration time and 1.4 factor higher throughput as compared to the conventional MAB that is based only on communications.

关键词： Radar Sensors signal processing algorithms Array signal processing Robot sensing systems Radar detection Hardware Analog beamforming joint radar communication multi-armed bandit upper confidence bound

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