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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 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
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
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
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
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
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
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
Enhancing sleep postures classification by incorporating acceleration sensor and LSTM model 结合加速度传感器和LSTM模型增强睡眠姿势分类
2023 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2023-07-02 DOI: 10.1109/SSP53291.2023.10208083
V. Dieu, D. Tran, Khanh-Ly Can, T. Dao, Dinh-Dat Pham, Duc-Tan Tran
{"title":"Enhancing sleep postures classification by incorporating acceleration sensor and LSTM model","authors":"V. Dieu, D. Tran, Khanh-Ly Can, T. Dao, Dinh-Dat Pham, Duc-Tan Tran","doi":"10.1109/SSP53291.2023.10208083","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208083","url":null,"abstract":"It has been well established that sleep posture plays a key role in sleep quality monitoring. Consequently, many noncontact and wearable devices, whose systems rely on sensors such as cameras, radar, wireless, and accelerometers, have been developed to classify sleep positions and postures. However, noncontact systems were often unsuccessful when facing limited conditions such as low light and physical obstacles. On the other hand, other systems currently in research, which involves wearable devices, may have used machine learning models but have not competently exploited other more accurate deep learning models. Recognizing scope for improvement, we propose an enhanced five-sleep-posture classification system (5-SPCS) where a novel integration of accelerometer and an LSTM deep learning model can classify sleep postures more efficiently than either one of them does separately. Our experiments showed that the 5-SPCS was capable of outperforming the baselines of existing machine learning-accelerometer systems at 99.6% accuracy.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"26 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":"125757445","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
Nonasymptotic Analysis of Direct-Augmentation ESPRIT for Localization of More Sources Than Sensors Using Sparse Arrays 使用稀疏阵列对多于传感器的来源进行定位的直接增强 ESPRIT 非渐近分析
2023 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2023-07-02 DOI: 10.1109/SSP53291.2023.10207996
Zai Yang, Kai Wang
{"title":"Nonasymptotic Analysis of Direct-Augmentation ESPRIT for Localization of More Sources Than Sensors Using Sparse Arrays","authors":"Zai Yang, Kai Wang","doi":"10.1109/SSP53291.2023.10207996","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10207996","url":null,"abstract":"Direction augmentation (DA), followed by a subspace method such as MUSIC or ESPRIT, is a successful approach that enables localization of more uncorrelated sources than sensors with a proper sparse linear array. In this paper, we carry out a nonasymptotic performance analysis of DA-ESPRIT in the practical scenario with finitely many snapshots. We show that more uncorrelated sources than sensors are guaranteed, with overwhelming probability, to be localized using DA-ESPRIT if the number of snapshots is greater than an explicit, problem-dependent threshold. Our result does not require a fixed source separation condition, which makes it unique among existing results. Numerical results corroborating our analysis are provided.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"5 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":"126033738","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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