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2023 IEEE Statistical Signal Processing Workshop (SSP)最新文献

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A Two-Stage Sparsity-Based Method for Location and Doppler Estimation in Bistatic Automotive Radar 基于两级稀疏度的双基地汽车雷达定位与多普勒估计方法
2023 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2023-07-02 DOI: 10.1109/SSP53291.2023.10207941
A. Moussa, Wei Liu
{"title":"A Two-Stage Sparsity-Based Method for Location and Doppler Estimation in Bistatic Automotive Radar","authors":"A. Moussa, Wei Liu","doi":"10.1109/SSP53291.2023.10207941","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10207941","url":null,"abstract":"Recently, sparse representation of radar signals has attracted a lot of interest when source signals carry a sparse structure, typically used for direction-of-arrival (DOA) estimation. It offers the ability of manipulating the signal model to fit the application’s needs and it may have super-resolution capability. However, signals carrying range and Doppler information can also have a sparse representation, which is an area of research that is often overlooked. In this paper, we propose a method for two-dimensional (2D)-localisation and Doppler estimation in a bistatic automotive application, by adopting the concept of group-sparsity (GS). We show through computer simulations the success of the proposed method in outperforming the state-of-art.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"263 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124293958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
EEG-Based Neurodegenerative Disease Classification using LSTM Neural Networks 基于脑电图的LSTM神经网络神经退行性疾病分类
2023 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2023-07-02 DOI: 10.1109/SSP53291.2023.10208023
Michele Alessandrini, G. Biagetti, P. Crippa, L. Falaschetti, S. Luzzi, C. Turchetti
{"title":"EEG-Based Neurodegenerative Disease Classification using LSTM Neural Networks","authors":"Michele Alessandrini, G. Biagetti, P. Crippa, L. Falaschetti, S. Luzzi, C. Turchetti","doi":"10.1109/SSP53291.2023.10208023","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208023","url":null,"abstract":"In recent years, the use of electroencephalography (EEG) for the clinical diagnosis of neurodegenerative diseases, such as Alzheimer’s disease, frontotemporal dementia and dementia with Lewy bodies, has been extensively studied. The classification of these different neurodegenerative diseases can benefit from machine learning techniques which, compared to manual diagnosis methods, have higher reliability and higher recognition performance, being able to handle large amounts of data. The purpose of this work is to develop an automatic classification method that can recognize a number of different neurodegenerative diseases such the aforementioned ones, having similar corresponding EEGs or being difficult to discern by inspection from a human operator. We show how a recurrent neural network (RNN) based on long short-term memory (LSTM) elements can successfully perform the task of classification, when the data are properly pre-processed.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123426368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Arithmetic Mean May Offer Fixed Points When Expected Mean Fails in Probabilistic Asynchronous Affine Inference 在概率异步仿射推理中，当期望均值失效时，算术均值可以提供不动点
2023 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2023-07-02 DOI: 10.1109/SSP53291.2023.10208025
Georgios Apostolakis, A. Bletsas
{"title":"Arithmetic Mean May Offer Fixed Points When Expected Mean Fails in Probabilistic Asynchronous Affine Inference","authors":"Georgios Apostolakis, A. Bletsas","doi":"10.1109/SSP53291.2023.10208025","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208025","url":null,"abstract":"Distributed computing over multiple terminals is recently regaining increased popularity. This work studies the probabilistic asynchronous affine model, which can be applied in a vast range of message passing (inference) algorithms; moreover, the probability of a terminal failing to exchange messages can be also modeled. This work complements recent prior art by analyzing the state vector’s arithmetic mean instead of the expected mean, since there are cases where valid fixed points can be retrieved from the arithmetic mean (exploiting a finite number of experiments), even if the expected mean diverges. This work highlights this fact and offers a sufficient criterion for arithmetic mean convergence to a fixed point, for the first time in the literature; the criterion also covers cases where the individual experiments do not converge but their arithmetic mean does. Simulation results verify the theoretical findings.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123245738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Tensor Chain Decomposition and Function Interpolation 张量链分解与函数插值
2023 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2023-07-02 DOI: 10.1109/SSP53291.2023.10207942
P. Tichavský, A. Phan
{"title":"Tensor Chain Decomposition and Function Interpolation","authors":"P. Tichavský, A. Phan","doi":"10.1109/SSP53291.2023.10207942","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10207942","url":null,"abstract":"Tensor Chain (TC) decomposition represents a given tensor as a chain (circle) of order-3 tensors (wagons) connected through tensor contractions. In this paper, we show the link between the TC decomposition and a structured Tucker decompositions, and propose a variant of the Krylov-Levenberg-Marquardt optimization, tailored for this problem. Many extensions can be considered, here we only mention decomposition of tensor with missing entries, which enables the tensor completion. Performance of the proposed algorithm is demonstrated on tensor decomposition of the sampled Rosenbrock function. It can be better modeled both as TC and canonical polyadic (CP) decomposition, but with TC, the reconstruction is possible with a lower number of function values.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125422604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
SSP 2023 Blank Page SSP 2023空白页
2023 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2023-07-02 DOI: 10.1109/ssp53291.2023.10207928
{"title":"SSP 2023 Blank Page","authors":"","doi":"10.1109/ssp53291.2023.10207928","DOIUrl":"https://doi.org/10.1109/ssp53291.2023.10207928","url":null,"abstract":"","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"2012 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127398072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Fast Adaptive Cross Tubal Tensor Approximation 快速自适应交叉管张量近似
2023 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2023-07-02 DOI: 10.1109/SSP53291.2023.10208018
S. Ahmadi-Asl, A. Phan, A. Cichocki, Ashish Jha, Anastasia Sozykina, Jun Wang, I. Oseledets
{"title":"Fast Adaptive Cross Tubal Tensor Approximation","authors":"S. Ahmadi-Asl, A. Phan, A. Cichocki, Ashish Jha, Anastasia Sozykina, Jun Wang, I. Oseledets","doi":"10.1109/SSP53291.2023.10208018","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208018","url":null,"abstract":"This paper deals with proposing a new efficient adaptive algorithm for the computation of tensor SVD (t-SVD). The proposed algorithm can estimate the tubal-rank of a given third-order tensor and the corresponding low tubal-rank approximation given an approximation tolerance. The main advantage of the proposed algorithm is using only a part of lateral and a horizontal slices at each iteration in its computations. So, it is applicable for decomposing large-scale data tensors. Simulations on synthetics and real-world datasets are provided and in some cases, we achieve more than one order of magnitude acceleration compared with the classical truncated t-SVD. It is shown that the proposed approach can potentially be used in deep learning and internet of things applications.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127284240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Efficient Sparse Reduced-Rank Regression With Covariance Estimation 基于协方差估计的高效稀疏降秩回归
2023 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2023-07-02 DOI: 10.1109/SSP53291.2023.10208069
Fengpei Li, Ziping Zhao
{"title":"Efficient Sparse Reduced-Rank Regression With Covariance Estimation","authors":"Fengpei Li, Ziping Zhao","doi":"10.1109/SSP53291.2023.10208069","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208069","url":null,"abstract":"Multivariate linear regression is a fundamental model widely used in many fields of signal processing and machine learning. To enhance its interpretability and predicting performance, many approaches have been developed. Among them, the sparse reduced-rank regression with covariance estimation (SRRRCE) method has been shown to be promising. SRRRCE is powerful, which jointly considers the dimension reduction and variable selection of the regression coefficient, as well as a covariance selection target. In this paper, we will propose a new optimization formulation for SRRRCE by modifying the variable coupling constraint in the existing formulation. For efficient problem solving, a convergent single-loop algorithm based on the block majorization-minimization algorithmic framework is developed. Numerical experiments demonstrate the proposed estimation method possesses better prediction performance and faster convergence speed compared to the existing one.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126572692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
OTFS-IM channel estimation and data detection algorithm with a superimposed pilot pattern 基于叠加导频模式的OTFS-IM信道估计和数据检测算法
2023 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2023-07-02 DOI: 10.1109/SSP53291.2023.10207981
Rabah Ouchikh, A. Aïssa-El-Bey, T. Chonavel, M. Djeddou
{"title":"OTFS-IM channel estimation and data detection algorithm with a superimposed pilot pattern","authors":"Rabah Ouchikh, A. Aïssa-El-Bey, T. Chonavel, M. Djeddou","doi":"10.1109/SSP53291.2023.10207981","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10207981","url":null,"abstract":"Orthogonal time frequency space modulation (OTFS) has been proved to have better performance over orthogonal frequency division multiplexing (OFDM) under high-mobility environments. In this manuscript, we address the challenging problem of channel estimation in high-mobility scenarios for 5G and beyond. To improve the spectral efficiency of the system, we combine superimposed pilots with index modulation paradigm. Indeed, we propose an iterative algorithm for channel estimation and symbol detection with superimposed pilot pattern for OTFS-IM systems. The proposed algorithm iterates between LMMSE-based data detection and data-aided channel estimation. Performance in terms of spectral efficiency and bit error rate are evaluated and compared against state-of-the-art methods.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124627970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Speech Distortion Weighted Single-Channel Wiener Filter Based STFT-Domain Noise Reduction 基于stft域降噪的语音失真加权单通道维纳滤波器
2023 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2023-07-02 DOI: 10.1109/SSP53291.2023.10208040
Jie Zhang, Rui Tao, Lirong Dai
{"title":"A Speech Distortion Weighted Single-Channel Wiener Filter Based STFT-Domain Noise Reduction","authors":"Jie Zhang, Rui Tao, Lirong Dai","doi":"10.1109/SSP53291.2023.10208040","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208040","url":null,"abstract":"In this work, we focus on the single-channel noise reduction (NR) in the short-time Fourier transform (STFT) domain from the traditional signal processing perspective. As conventional single-channel NR methods suffer from a serious speech distortion (SD), we propose an SD weighted single-channel Wiener filter (SDW-SWF), where an auxiliary parameter µ is exploited to trade-off the SD and residual noise variance. In the subspace, the obtained SDW-SWF can be formulated as a function of µ and a set of generalized eigenvectors of correlation matrices. In addition, we theoretically analyze the impacts of the trade-off factor and the rank on the SD, residual noise power and the output signal-to-noise ratio (SNR). Finally, numerical results validate the effectiveness of the proposed method, exhibiting a consistency with the theoretical findings. It can be concluded that the SDW-SWF approach enables more degrees-of-freedom to improve the speech intelligibility at a sacrifice of SNR.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125120577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Multilinear singular value decomposition of a tensor with fibers observed along one mode* 具有沿单模态观察到的纤维张量的多线性奇异值分解
2023 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2023-07-02 DOI: 10.1109/SSP53291.2023.10207967
Stijn Hendrikx, Mikael Sørensen, L. D. Lathauwer
{"title":"Multilinear singular value decomposition of a tensor with fibers observed along one mode*","authors":"Stijn Hendrikx, Mikael Sørensen, L. D. Lathauwer","doi":"10.1109/SSP53291.2023.10207967","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10207967","url":null,"abstract":"We introduce an algorithm that uses only standard linear algebra operations for computing the multilinear singular value decomposition of an incomplete tensor with fibers observed along a single mode. This setting is very relevant for applications. For example, in an application where the tensor has a \"time\" mode, obtaining a fiber along this mode may be considerably easier than doing so along other modes. In the noise-free case, the algorithm is guaranteed to retrieve the exact solution, if the observed fibers satisfy certain deterministic conditions. As such, the approach reveals an interesting feature of the tensor setting that is not present at the matrix level. In the presence of noise, a solution obtained with this algorithm serves as a good initial point for further optimization. We illustrate, both on synthetic and real-life data, that this initialization strategy is fast and significantly reduces the number of iterations needed by an optimization algorithm. One possible use of the approach is as a linear algebra-based orthogonal compression of an incomplete tensor, after which the low multilinear rank approximation can be used as a \"complete\" proxy of the data for further analysis.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"16 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134360157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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