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

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Distributed Quantile Regression with Non-Convex Sparse Penalties 非凸稀疏惩罚的分布分位数回归
2023 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2023-07-02 DOI: 10.1109/SSP53291.2023.10208080
Reza Mirzaeifard, Vinay Chakravarthi Gogineni, Naveen K. D. Venkategowda, Stefan Werner
{"title":"Distributed Quantile Regression with Non-Convex Sparse Penalties","authors":"Reza Mirzaeifard, Vinay Chakravarthi Gogineni, Naveen K. D. Venkategowda, Stefan Werner","doi":"10.1109/SSP53291.2023.10208080","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208080","url":null,"abstract":"The surge in data generated by IoT sensors has increased the need for scalable and efficient data analysis methods, particularly for robust algorithms like quantile regression, which can be tailored to meet a variety of situations, including nonlinear relationships, distributions with heavy tails, and outliers. This paper presents a sub-gradient-based algorithm for distributed quantile regression with non-convex, and non-smooth sparse penalties such as the Minimax Concave Penalty (MCP) and Smoothly Clipped Absolute Deviation (SCAD). These penalties selectively shrink non-active coefficients towards zero, addressing the limitations of traditional penalties like the l1-penalty in sparse models. Existing quantile regression algorithms with non-convex penalties are designed for centralized cases, whereas our proposed method can be applied to distributed quantile regression using non-convex penalties, thereby improving estimation accuracy. We provide a convergence proof for our proposed algorithm and demonstrate through numerical simulations that it outperforms state-of-the-art algorithms in sparse and moderately sparse scenarios.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"29 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":"134258385","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
Semantic Labeling for Point Cloud Detection and Registration Using the Universal Manifold Embedding: Statistical Analysis 使用通用流形嵌入的点云检测和配准的语义标记:统计分析
2023 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2023-07-02 DOI: 10.1109/SSP53291.2023.10208074
J. Francos
{"title":"Semantic Labeling for Point Cloud Detection and Registration Using the Universal Manifold Embedding: Statistical Analysis","authors":"J. Francos","doi":"10.1109/SSP53291.2023.10208074","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208074","url":null,"abstract":"Detection and registration of point cloud observations are elementary problems in 3-D vision. The Universal Manifold Embedding (UME) is a framework for mapping an observation to a matrix representation which is covariant with the rigid coordinate transformation, while its column space is invariant to the transformation. As point clouds are sets of coordinates with no functional relation imposed on them, adapting the UME framework for point cloud registration requires the definition of a function that assigns a value to each point, invariant to the action of the transformation group. Deep learning methods for point cloud semantic labeling have made it easier to incorporate semantic labels information into point cloud detection and registration. We derive analytic tools for evaluating and optimizing the UME performance in point cloud detection and registration tasks in the presence of labeling errors, when semantic labeling is employed as the transformation-invariant function defined on the point cloud.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"33 10","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133076164","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
Cross Density Kernel for Nonstationary Signal Processing 非平稳信号处理的交叉密度核
2023 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2023-07-02 DOI: 10.1109/SSP53291.2023.10208056
Bo Hu, J. Príncipe
{"title":"Cross Density Kernel for Nonstationary Signal Processing","authors":"Bo Hu, J. Príncipe","doi":"10.1109/SSP53291.2023.10208056","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208056","url":null,"abstract":"This paper introduces the cross density kernel function (CDKF), a new positive-definite kernel that quantifies the statistical dependence between random processes, to address the challenge of applying time series prediction and modeling techniques to nonstationary signals. The paper highlights the limited applicability of the Wiener filter and Parzen’s autocorrelation reproducing kernel Hilbert spaces (RKHS) to stationary signals. CDKF extends these methods by capturing properties of probability density functions for random processes in the Hilbert space with a novel bidirectional recursion, and using two neural networks to optimize the kernel function based on realizations. The paper concludes by presenting experimental results that support the effectiveness of CDKF.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"22 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":"133277132","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
Weight sharing for single-channel LMS 单通道LMS的权重共享
2023 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2023-07-02 DOI: 10.1109/SSP53291.2023.10207966
Shamahil Ibunu, Karl Moore, C. C. Took, Danilo P. Mandic
{"title":"Weight sharing for single-channel LMS","authors":"Shamahil Ibunu, Karl Moore, C. C. Took, Danilo P. Mandic","doi":"10.1109/SSP53291.2023.10207966","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10207966","url":null,"abstract":"Constraining a group of taps of an adaptive filter to a single value may seem like a futile task, as weight sharing reduces the degree of freedom of the algorithm, and there are no obvious advantages for implementing such an update scheme. On the other hand, weight sharing is popular in deep learning and underpins the success of convolutional neural networks (CNNs) in numerous applications. To this end, we investigate the advantages of weight sharing in single-channel least mean square (LMS), and propose weight sharing LMS (WSLMS) and partial weight sharing LMS (PWS). In particular, we illustrate how weight sharing can lead to numerous benefits such as an enhanced robustness to noise and a computational cost that is independent of the filter length. Simulations support the analysis.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"76 3 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":"131703526","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
Improving of the interpretation of linear filtering preprocessing-based multiscale permutation entropy 基于线性滤波预处理的多尺度排列熵解释改进
2023 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2023-07-02 DOI: 10.1109/SSP53291.2023.10208030
M. Jabloun, P. Ravier, O. Buttelli
{"title":"Improving of the interpretation of linear filtering preprocessing-based multiscale permutation entropy","authors":"M. Jabloun, P. Ravier, O. Buttelli","doi":"10.1109/SSP53291.2023.10208030","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208030","url":null,"abstract":"Multi-scale permutation entropy (MPE) is an interesting tool for analyzing signal internal structures and quantifying complexity. The most commonly used MPEs involve a linear preprocessing step applied to the original signal prior to the evaluation of the permutation entropy (PE). However, recent research done by Davalos et al has demonstrated that linear filtering preprocessing significantly modifies the PE of Gaussian processes.To build on this work, we conducted a study to investigate the MPE’s behavior across a variety of signal generation models including sinusoidal signals, frequency modulated signals, and colored Gaussian noise. Our findings indicate that the MPE mainly reflects changes in the center frequency of the signal spectrum, independent of signal generation models. It’s important to note that the linear preprocessing step used in MPE calculations can lead to misinterpretation of the results. Therefore, we suggest that a proper interpretation of the MPE values should be done in conjunction with a spectral analysis or a time-frequency representation.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"38 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":"127117364","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
Deep Hand Gesture Recognition: A Wavelet Scattering Alternative to Convolutional Networks 深度手势识别:小波散射替代卷积网络
2023 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2023-07-02 DOI: 10.1109/SSP53291.2023.10208011
Adel Al-Jumaily, R. Khushaba
{"title":"Deep Hand Gesture Recognition: A Wavelet Scattering Alternative to Convolutional Networks","authors":"Adel Al-Jumaily, R. Khushaba","doi":"10.1109/SSP53291.2023.10208011","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208011","url":null,"abstract":"Hand gesture recognition (HGR) is crucial for improving human-computer interaction, aiding people with disabilities, and enhancing industrial efficiency. Radars are a popular choice for HGR, as they can detect hand gestures in various lighting conditions, through obstructions, with low latency, and without line-of-sight. Deep Convolutional Neural Networks (DCNN) are commonly used to analyze radar signals and recognize complex hand gestures. However, training DCNN models requires significant computational resources, making real-time applications challenging. This study proposes using Wavelet Scattering Transform (WST) as a feature extractor to replace DCNN, while relying on lightweight traditional classifiers for identifying the class of hand movement. WST is a non-linear signal representation that preserves high levels of discriminability while maintaining stability under time-warping deformations. To compare DCNN against WST, the study used a publicly available database of ultra-wideband (UWB) impulse radar gestures, collected from eight participants performing twelve hand gestures. The results showed that WST can achieve an average accuracy of 95% across all subjects, making it a reliable, computationally efficient, and accurate alternative to DCNN. This is the first research demonstrating the effectiveness of WST against DCNN for radar HGR applications (to the best of the authors’ knowledge).","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"117 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":"134555890","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
Semantic Communication for Partial Observation Multi-agent Reinforcement Learning 部分观察多智能体强化学习的语义通信
2023 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2023-07-02 DOI: 10.1109/SSP53291.2023.10207979
Hoang Khoi Do, Thi Quynh Khanh Dinh, Minh Duong Nguyen, Tien Hoa Nguyen
{"title":"Semantic Communication for Partial Observation Multi-agent Reinforcement Learning","authors":"Hoang Khoi Do, Thi Quynh Khanh Dinh, Minh Duong Nguyen, Tien Hoa Nguyen","doi":"10.1109/SSP53291.2023.10207979","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10207979","url":null,"abstract":"Effective cooperation and coordination among agents is essential for success in many real-world scenarios, particularly in reinforcement learning challenges. However, partial observation, where agents are not aware of all the observations made by other agents, creates a significant obstacle to coordination. To overcome this challenge, we propose the Shared Online Multi-agent Knowledge Exchange (SOME) framework, which allows agents to learn to anticipate each other’s observations and improve their local learning. In SOME, agents learn to anticipate the observations of other agents to improve their local learning, allowing for better coordination and cooperation. Additionally, using knowledge generators instead of full observations reduces communication costs. Our experimental evaluation demonstrates that agents trained with SOME can not only predict the next observations and actions of opponents and collaborators but also take appropriate actions, making it a promising approach for overcoming the partial observation challenge in multi-agent reinforcement learning.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"36 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":"134146488","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
System Performance for Coherent and Non-Coherent Processing for Distributed Phased Array Radar 分布式相控阵雷达相干与非相干处理系统性能研究
2023 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2023-07-02 DOI: 10.1109/SSP53291.2023.10207945
K. Yu, M. Fernández
{"title":"System Performance for Coherent and Non-Coherent Processing for Distributed Phased Array Radar","authors":"K. Yu, M. Fernández","doi":"10.1109/SSP53291.2023.10207945","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10207945","url":null,"abstract":"There are significant interests in developing coherent distributed apertures for both commercial and defense applications. Such systems generally would consist of multiple distributed apertures that can provide advanced capabilities in terms of reliability, adaptability and scalability. It should be noted that multiple existing radars can also be networked together to provide improved performance. This paper presents a set of architectures and processing schemes with different complexities in transmit and receive functions that can be leveraged to provide coherent gain on transmit and/or receive or diversity gain when using non-coherent processing. Detection and monopulse angle estimation performances are quantified for various target fluctuation models.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"1 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":"132583648","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
Improved Weighted Least Squares Algorithm for Hybrid AOA and TDOA Localization 基于加权最小二乘的AOA和TDOA混合定位算法
2023 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2023-07-02 DOI: 10.1109/SSP53291.2023.10207975
Yanbin Zou, Jingna Fan, Liehu Wu, Huaping Liu
{"title":"Improved Weighted Least Squares Algorithm for Hybrid AOA and TDOA Localization","authors":"Yanbin Zou, Jingna Fan, Liehu Wu, Huaping Liu","doi":"10.1109/SSP53291.2023.10207975","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10207975","url":null,"abstract":"This paper develops a new hybrid AOA and TDOA localization algorithm. The most promising hybrid AOA and TDOA localization algorithm currently available is a weighted least squares (WLS) estimator, in which the AOA measurements are multiplied by the TDOA measurements, yielding a product of five noise terms. However, only the first-order noise terms are kept in the formulation of the WLS algorithm. In other words, the second- and higher-order noise terms are neglected, which results in a significant performance degradation. We develop an improved WLS algorithm, in which the AOA measurements are added to the TDOA measurements, lowering the highest order of the noise term products to two. Consequently, the performance is improved because a less number of noise terms are neglected.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"29 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":"128941791","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 Continuous Representation Of Switching Linear Dynamic Systems For Accurate Tracking 用于精确跟踪的切换线性动态系统的连续表示
2023 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2023-07-02 DOI: 10.1109/SSP53291.2023.10207936
Parisa Karimi, H. Naumer, F. Kamalabadi
{"title":"A Continuous Representation Of Switching Linear Dynamic Systems For Accurate Tracking","authors":"Parisa Karimi, H. Naumer, F. Kamalabadi","doi":"10.1109/SSP53291.2023.10207936","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10207936","url":null,"abstract":"We propose a method for tracking linear representations of a nonlinear dynamic system with time-varying parameters based on a continuous representation of its switching linear dynamic system (SLDS) model. Given approximate linear representations for a finite set of unknown intrinsic parameters of the dynamics, a combination of autoencoder-based dimensionality reduction and cubic curve-fitting are applied to learn the continuous manifold of dynamics embedded in the evolution operator. This representation enables a significant reduction of the squared Frobenius norm of error in maximum likelihood (ML) system identification relative to that of the original SLDS model. Numerical experiments also verify this result.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"172 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":"124202285","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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