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Advanced signal processing Methods for Characterization of Schizophrenia

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2021年第4期68卷 1123-1130页

作者： Masychev, Kirill Ciprian, Claudio Ravan, Maryam Reilly, James P. MacCrimmon, Duncan New York Inst Technol Dept Comp Sci New York NY USA New York Inst Technol Dept Elect & Comp Engn New York NY 10023 USA McMaster Univ Dept Elect & Comp Engn Hamilton ON L8S 4L8 Canada McMaster Univ Dept Psychiat Hamilton ON Canada

Objective: Schizophrenia is a severe mental disorder associated with nerobiological deficits. Auditory oddball P300 have been found to be one of the most consistent markers of schizophrenia. The goal of this study is to find quantitative features that can objectively distinguish patients with schizophrenia (SCZs) from healthy controls (HCs) based on their recorded auditory odd-ball P300 electroencephalogram (EEG) data. Methods: Using EEG dataset, we develop a machine learning (ML) algorithm to distinguish 57 SCZs from 66 HCs. The proposed ML algorithm has three steps. In the first step, a brain source localization (BSL) procedure using the linearly constrained minimum variance (LCMV) beamforming approach is employed on EEG signals to extract source waveforms from 30 specified brain regions. In the second step, a method for estimating effective connectivity, referred to as symbolic transfer entropy (STE), is applied to the source waveforms. In the third step the ML algorithm is applied to the STE connectivity matrix to determine whether a set of features can be found that successfully discriminate SCZ from HC. Results: The findings revealed that the SCZs have significantly higher effective connectivity compared to HCs and the selected STE features could achieve an accuracy of 92.68%, with a sensitivity of 92.98% and specificity of 92.42%. Conclusion: The findings imply that the extracted features are from the regions that are mainly affected by SCZ and can be used to distinguish SCZs from HCs. Significance: The proposed ML algorithm may prove to be a promising tool for the clinical diagnosis of schizophrenia.

关键词： Entropy Electroencephalography Feature extraction Brain Standards signal processing algorithms Location awareness Effective connectivity electroencephalography (EEG) EEG beamforming machine learning odd-ball paradigm schizophrenia symbolic transfer entropy

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Algorithm Unrolling: Interpretable, Efficient Deep Learning for signal and Image processing

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IEEE signal processing MAGAZINE 2021年第2期38卷 18-44页

作者： Monga, Vishal Li, Yuelong Eldar, Yonina C. Penn State Univ Sch Elect Engn University Pk PA 16802 USA Penn State Univ Elect Engn & Comp Sci Fac University Pk PA 16802 USA Amazon Lab 126 San Jose CA 94089 USA Weizmann Inst Sci Dept Math & Comp Sci IL-7610001 Rehovot Israel Weizmann Inst Sci Ctr Biomed Engn & Signal Proc IL-7610001 Rehovot Israel MIT Cambridge MA 02139 USA Broad Inst Cambridge MA USA Duke Univ Durham NC 27706 USA Israel Acad Sci & Humanities Jerusalem Israel

Deep neural networks provide unprecedented performance gains in many real-world problems in signal and image processing. Despite these gains, the future development and practical deployment of deep networks are hindered by their black-box nature, i.e., a lack of interpretability and the need for very large training sets. An emerging technique called algorithm unrolling, or unfolding, offers promise in eliminating these issues by providing a concrete and systematic connection between iterative algorithms that are widely used in signal processing and deep neural networks. Unrolling methods were first proposed to develop fast neural network approximations for sparse coding. More recently, this direction has attracted enormous attention, and it is rapidly growing in both theoretic investigations and practical applications. The increasing popularity of unrolled deep networks is due, in part, to their potential in developing efficient, high-performance (yet interpretable) network architectures from reasonably sized training sets.

关键词： Training data Systematics Neural networks signal processing algorithms Performance gain Network architecture Deep learning Machine learning

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Hybrid Geometric Algebra Correntropy: Definition and Application to Robust Adaptive Filtering

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2024年第2期71卷 952-956页

作者： Long, Xiaoqiang Zhao, Haiquan Hou, Xinyan Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China Southwest Jiaotong Univ Key Lab Magnet Suspens Technol & Maglev Vehicle Minist Educ Chengdu 610031 Peoples R China

In order to address the performance degradation of the geometric algebra LMS algorithms (GA-LMS) in the face of non-Gaussian disturbances, this brief proposes a robust adaptive filtering algorithm based on hybrid maximum geometric algebra correntropy (HMGACC) perspective. Specially, we define the hybrid geometric algebra correntropy (HGAC) cost function, which can increase the flexibility of the correntropy. Secondly, we introduce a proportionality parameter to make the HGAC algorithm more general, which can be changed into a traditional single kernel GAC algorithm by changing the proportionality parameter. Furthermore, the addition of the proportionality parameter also allows for a non-fixed form of step-size, i.e., the step-size of the proposed algorithm in this brief is a hybrid step-size, which will be more useful for us to improve the performance of the algorithm. Finally, in the experimental simulations, the proposed algorithm can achieve better convergence and steady-state performance in comparison with other competing algorithms under different non-Gaussian disturbances, which confirms the feasibility of the proposed method.

关键词： Algebra signal processing algorithms Optimized production technology Manganese Filtering algorithms Steady-state signal processing Geometric algebra hybrid maximum correntropy proportionality parameter hybrid step-size

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Robust DOA Estimation Against Outliers via Joint Sparse Representation

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IEEE signal processing LETTERS 2024年 31卷 2015-2019页

作者： Xiao, Wudang Li, Yingsong Zhao, Luyu de Lamare, Rodrigo C. Anhui Univ Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230601 Peoples R China Pontifical Catholic Univ Rio de Janeiro PUC Rio Ctr Telecommun Res CETUC BR-22451900 Gavea Brazil Univ York Dept Elect Engn York YO10 5DD England

Several approaches for estimating the direction of arrival (DOA) are traditionally developed assuming Gaussian noise, making them highly sensitive to outliers. Therefore, when confronted with impulsive noise, the performance of these methods may significantly deteriorate. In this letter, we characterize impulsive noise as Gaussian noise mixed sparse outliers. By exploiting their statistical differences, we propose an innovative DOA estimation technique within the framework of sparse signal recovery (SSR). Unlike common robust loss functions, such as $\ell _{1}$ and $\ell _{p}$ norms, we combine the $\ell _{2}$-norm with the Minimax Logarithmic Concave function as the loss function. Furthermore, to address the issue of grid mismatch, we utilize an alternating optimization approach to acquire the grid deviations, with the aid of rough DOA estimations and estimated outliers. Simulation results indicate that the proposed technique exhibits robustness against large outliers.

关键词： Estimation Direction-of-arrival estimation signal processing algorithms Optimization Gaussian noise Linear programming Array signal processing DOA estimation outliers sparse signal recovery

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Deep Inertia Lp Half-Quadratic Splitting Unrolling Network for Sparse View CT Reconstruction

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IEEE signal processing LETTERS 2024年 31卷 2030-2034页

作者： Guo, Yu Wu, Caiying Li, Yaxin Jin, Qiyu Zeng, Tieyong Inner Mongolia Univ Sch Math Sci Hohhot 010020 Peoples R China Chinese Univ Hong Kong Dept Math Satin Hong Kong Peoples R China

Sparse view computed tomography (CT) reconstruction poses a challenging ill-posed inverse problem, necessitating effective regularization techniques. In this letter, we employ L-p-norm (0 < p< 1) regularization to induce sparsity and introduce inertial steps, leading to the development of the inertial L-p-norm half-quadratic splitting algorithm. We rigorously prove the convergence of this algorithm. Furthermore, we leverage deep learning to initialize the conjugate gradient method, resulting in a deep unrolling network with theoretical guarantees. Our extensive numerical experiments demonstrate that our proposed algorithm surpasses existing methods, particularly excelling in fewer scanned views and complex noise conditions.

关键词： signal processing algorithms Convergence Computed tomography Deep learning Image reconstruction Noise Gold Inertial L-p-norm wavelet sparse view unrolling

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Block-Level Interference Exploitation Precoding for MU-MISO: An ADMM Approach

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IEEE TRANSACTIONS ON COMMUNICATIONS 2025年第5期73卷 3482-3496页

作者： Wang, Yiran Wen, Yunsi Li, Ang Hu, Xiaoyan Masouros, Christos Xi An Jiao Tong Univ Fac Elect & Informat Engn Sch Informat & Commun Engn Xian 710049 Shaanxi Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 211189 Peoples R China UCL Dept Elect & Elect Engn London WC1E 7JE England

We study constructive interference based block-level precoding (CI-BLP) in the downlink of multi-user multiple-input single-output (MU-MISO) systems. Specifically, our aim is to extend the analysis on CI-BLP to the case when the number of symbol slots in a given transmission block is smaller than the number of users. To this end, we mathematically prove the feasibility of using the pseudo-inverse to obtain a closed-form structure of the optimal CI-BLP precoding matrix. Similar to the case when the number of symbol slots in a given transmission block is not smaller than the number of users, we show that a quadratic programming (QP) optimization on simplex can be constructed. We also design a low-complexity algorithm based on the alternating direction method of multipliers (ADMM) framework, which can achieve a flexible trade-off between communication performance and execution time by modifying the maximum number of iterations. We further analyze the convergence and complexity of the proposed algorithm. Numerical results validate our analysis and the optimality of the QP optimization, and further show that the proposed ADMM algorithm can provide satisfactory results in dozens of iterations, which motivates the use of CI-BLP in practical wireless systems.

关键词： Precoding Symbols Optimization Interference Measurement signal to noise ratio signal processing algorithms Wireless communication Quadrature amplitude modulation Modulation MIMO constructive interference (CI) block-level precoding (BLP) quadratic programming (QP) optimization alternating direction method of multipliers (ADMM)

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Flexible High-Level Synthesis Library for Linear Transformations

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2024年第7期71卷 3348-3352页

作者： Zhao, Wuqiong Li, Changhan Ji, Zhenhao Guo, Zhichen Chen, Xuanbo You, You Huang, Yongming You, Xiaohu Zhang, Chuan Southeast Univ LEADS Natl Mobile Commun Res Lab Nanjing 211100 Peoples R China Southeast Univ Frontiers Sci Ctr Mobile Informat Commun & Secur Nanjing 211100 Peoples R China

Despite decades pursuing efficient hardware design for signal processing based on linear algebra, traditional hardware description languages (HDLs)-based design workflows remain challenging and time-consuming. High-level synthesis (HLS) provides an easier approach but still requires thorough designs of basic modules concerning linear transformations to achieve acceptable hardware efficiency. To simplify the HLS workflow, we propose the FLAMES library, which provides efficient ready-to-use linear transformation modules. Users can implement algorithms with significantly higher code-writing efficiency via the FLAMES library. We demonstrate its effectiveness by implementing the orthogonal matching pursuit list (OMPL) algorithm for compressed sensing in FPGA, achieving 1.56 x and 1.12 x throughput/slice compared with traditional HLS for the sequential and parallel architecture, respectively.

关键词： Libraries Hardware Fires Random access memory signal processing algorithms C plus plus languages Tensors High-level synthesis (HLS) linear transformations compressed sensing hardware implementation field programmable gate array (FPGA)

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Spatio-Temporal Multi-Image Reflection Removal

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IEEE signal processing LETTERS 2024年 31卷 2345-2349页

作者： Zhang, Xinxin Shang, Wenjing Wang, Qiangchang Gong, Yongshun Liu, Qifang Shandong Univ Sch Software Jinan 250100 Peoples R China Yantai Univ Sch Phys & Elect Informat Yantai 264005 Peoples R China Shandong Univ Sch Civil Engn Jinan 250100 Peoples R China

In this letter, we propose a precise algorithm to eliminate reflections from two images by utilizing temporal and spatial priors. For the temporal prior, we compute the motion information between reflection layers in the two input reflection-contaminated images. Different from numerous popular multi-image reflection removal methods, our proposed algorithm does not assume that two input images are captured under similar lighting conditions and the same camera settings. Furthermore, the proposed algorithm is robust to the difference between the two reflection layers, such as moving objects and different reflections. For the spatial term, a sparsity gradient regularization is adopted to enforce the spatial smoothness of transmission layers and reflection layers. Importantly, the proposed algorithm does not rely on additional training data or high-performance computing devices. Experimental results on both synthetic images and real-world photographs demonstrate that the proposed algorithm achieves State-of-the-Art performance.

关键词： Reflection signal processing algorithms Cameras Synthetic data Glass Accuracy Polynomials Image registration local all-pass filters multiple images reflection removal spatio-temporal

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Gait Symmetry Analysis With FMCW MIMO Radar

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IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES 2025年

作者： Lopez-Delgado, Ignacio E. Wang, Dingyang Fioranelli, Francesco Grajal, Jesus Univ Politecn Madrid Informat Proc & Telecommun Ctr Madrid 28040 Spain Delft Univ Technol Microwave Sensing Signals & Syst Grp MS3 NL-2628 CD Delft Netherlands

Monitoring gait symmetry reliably is crucial, as it is an early indicator of Parkinson's disease (PD). In this work, a method is presented to analyze gait asymmetries using a 24-GHz frequency-modulated continuous-wave (FMCW) multiple-input-multiple-output (MIMO) radar in a nonclinical environment. The proposed method is validated by analyzing the gait of 60 people recorded in an environment that presents multiple challenges such as multipath, gait interruption events, and different trajectories, which cause aspect angle variability. This article presents algorithms to address these challenges to extract useful gait information from the radar data and discusses the performance of the proposed system, analyzing the ratio of correct feet identifications and the accuracy of the asymmetry gait parameters. It achieved a mean absolute error of the symmetry ratio below 8% for all gait parameters. This shows that the proposed system and algorithms are robust for both clinical and in-home implementations.

关键词： Radar Foot MIMO radar Azimuth signal processing algorithms Diseases Data mining Accuracy Transmitters Receivers Frequency-modulated continuous-wave (FMCW) radar gait analysis gait symmetry multiple-input-multiple-output (MIMO) radar Parkinson's disease (PD)

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Greedy Capon Beamformer

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IEEE signal processing LETTERS 2024年 31卷 2775-2779页

作者： Ollila, Esa Aalto Univ Dept Informat & Commun Engn FI-00076 Aalto Finland

We propose greedy Capon beamformer (GCB) for direction finding of narrow-band sources present in the array's viewing field. After defining the grid covering the location search space, the algorithm greedily builds the interference-plus-noise covariance matrix by identifying a high-power source on the grid using Capon's principle of maximizing the signal to interference plus noise ratio while enforcing unit gain towards the signal of interest. An estimate of the power of the detected source is derived by exploiting the unit power constraint, which subsequently allows to update the noise covariance matrix by simple rank-1 matrix addition composed of outerproduct of the selected steering matrix with itself scaled by the signal power estimate. Our numerical examples demonstrate effectiveness of the proposed GCB in direction finding where it performs favourably compared to the state-of-the-art algorithms under a broad variety of settings. Furthermore, GCB estimates of direction-of-arrivals (DOAs) are very fast to compute.

关键词： Covariance matrices Vectors signal to noise ratio Interference Direction-of-arrival estimation Complexity theory Multiple signal classification signal processing algorithms White noise Power generation Beamforming MVDR beamforming direction finding source localization greedy pursuit

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