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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
Joint Channel Estimation and Symbol Detection in Overloaded MIMO Using ADMM 基于ADMM的超载MIMO联合信道估计与符号检测
2023 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2023-07-02 DOI: 10.1109/SSP53291.2023.10208046
Swati Bhattacharya, K. Hari, Y. Eldar
{"title":"Joint Channel Estimation and Symbol Detection in Overloaded MIMO Using ADMM","authors":"Swati Bhattacharya, K. Hari, Y. Eldar","doi":"10.1109/SSP53291.2023.10208046","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208046","url":null,"abstract":"This paper proposes a Joint Channel Estimation and Symbol Detection (JED) scheme for overloaded multiple-input multiple-output (MIMO) wireless communication systems, with the number of receive antennas being less than or equal to the number of transmit antennas. Our proposed method for JED using Alternating Direction Method of Multipliers (JED-ADMM) markedly improves the symbol detection performance by yielding 12-16 dB gain in signal-to-noise ratio (SNR) for a bit error rate (BER) of 10−3 over state-of-the-art JED using Alternating Minimization (JED-AM). This gain in BER for the proposed JED-ADMM is also accompanied by a significant reduction in computational complexity (1/4 times) as compared to JED-AM.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"40 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":"114574845","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
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
Fusion of images and clinical features for the prediction of Pulmonary embolism in Ultrasound imaging 超声影像影像与临床特征的融合预测肺栓塞
2023 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2023-07-02 DOI: 10.1109/SSP53291.2023.10208034
Aurélien Olivier, C. Hoffmann, A. Mansour, L. Bressollette, Benoit Clement
{"title":"Fusion of images and clinical features for the prediction of Pulmonary embolism in Ultrasound imaging","authors":"Aurélien Olivier, C. Hoffmann, A. Mansour, L. Bressollette, Benoit Clement","doi":"10.1109/SSP53291.2023.10208034","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208034","url":null,"abstract":"Venous Thromboembolism (VTE) is a life-threatening disease encompassing pulmonary embolism and deep venous thrombosis (DVT). Pulmonary embolism occurs in 50% of patients with a proximal deep venous thrombosis. We aimed to predict the occurrence of a pulmonary embolism in patients with a DVT from clinical data and Ultrasound images of proximal thrombosis. To address this task, we proposed to use a Deep learning model that uses both images and 5 clinical factors as input and we aimed to measure the contributions compared to using only images. Promising results were obtained with both models compared to the state-of-art. The contribution of the clinical factors remains unclear but a gain in accuracy was observed when using smaller models.","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":"125394108","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 Simple and Tight Bayesian Lower Bound for Direction-of-Arrival Estimation 到达方向估计的简单严密贝叶斯下界
2023 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2023-07-02 DOI: 10.1109/SSP53291.2023.10207970
Ori Aharon, J. Tabrikian
{"title":"A Simple and Tight Bayesian Lower Bound for Direction-of-Arrival Estimation","authors":"Ori Aharon, J. Tabrikian","doi":"10.1109/SSP53291.2023.10207970","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10207970","url":null,"abstract":"In this paper, a class of tight Bayesian bounds on the mean-squared-error is proposed. Tight bounds account for the contribution of sidelobes in the likelihood ratio or the ambiguity function. Since the distances between the main lobe and the sidelobes in the likelihood function may depend on the unknown parameter, a single, parameter-independent test-point may not be enough to provide a tight bound. In the proposed class of bounds, the shift test-points are substituted with arbitrary transformations, such that the same test-point can be uniformly optimal for the entire parameter space. The use of single testpoint simplifies the bound and allows providing insight into the considered problem. The proposed bound is applied to the problem of direction-of-arrival estimation using a linear array. Simulations show that the proposed bound accurately predicts the threshold phenomenon of the maximum a-posteriori probability estimator, and is tighter than the Weiss-Weinstein bound.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"62 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":"116350581","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
Recursive Spatial Covariance Estimation with Sparse Priors for Sound Field Interpolation 基于稀疏先验的递归空间协方差估计用于声场插值
2023 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2023-07-02 DOI: 10.1109/SSP53291.2023.10208010
David Sundström, J. Lindström, A. Jakobsson
{"title":"Recursive Spatial Covariance Estimation with Sparse Priors for Sound Field Interpolation","authors":"David Sundström, J. Lindström, A. Jakobsson","doi":"10.1109/SSP53291.2023.10208010","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208010","url":null,"abstract":"Recent advances have shown that sound fields can be accurately interpolated between microphone measurements when the spatial covariance matrix is known. This matrix may be estimated in various ways; one promising approach is to use a plane wave formulation with sparse priors, although this may require the use of a many microphones to suppress the noise. To overcome this, we introduce a time domain formulation exploiting multiple time samples, posing the problem as an identification problem of a recursively estimated sample covariance matrix. A computationally efficient method is proposed to solve the resulting identification problem. Using both numerical experiments and anechoic data, the proposed method is shown to yield preferable performance as compared to current state of the art methods, notably for high frequencies sources and/or in cases when using few microphones.","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":"129710213","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
Communication Quality Optimization for UAV Trajectory in Irregular Topography 不规则地形下无人机轨迹通信质量优化
2023 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2023-07-02 DOI: 10.1109/SSP53291.2023.10207935
Jad Abou Chaaya, A. Coatanhay, A. Mansour, T. Marsault
{"title":"Communication Quality Optimization for UAV Trajectory in Irregular Topography","authors":"Jad Abou Chaaya, A. Coatanhay, A. Mansour, T. Marsault","doi":"10.1109/SSP53291.2023.10207935","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10207935","url":null,"abstract":"Unmanned Aerial Vehicles (UAVs) are becoming increasingly popular for both civil and military missions, and communication link establishment between the UAV and ground/aerial stations is a crucial factor for mission success. However, topography greatly affects the communication link, particularly when the UAV is flying at a low altitude between mountains of varying elevations. This paper proposes a system model based on the diffraction phenomenon with Multiple Knife Edges (MKE) to model the UAV-station channel when the Line of Sight (LoS) is absent. The objective is to optimize the trajectory of low/mid-altitude flying UAVs in complex propagation environments. To maximize communication quality, the paper also proposes an optimization formulation using Mixed Integer Linear Programming (MILP). The proposed approach is validated through simulations that limit LoS propagation using real terrain profiles. The approach finds the optimal UAV trajectory with the \"best feasible\" communication quality within physical limitations.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"173 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":"132258632","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 Data Efficient Vision Transformer for Robust Human Activity Recognition from the Spectrograms of Wearable Sensor Data 一种基于可穿戴传感器数据谱图的稳健人体活动识别的数据高效视觉转换器
2023 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2023-07-02 DOI: 10.1109/SSP53291.2023.10208059
J. McQuire, Paul Watson, Nick Wright, H. Hiden, M. Catt
{"title":"A Data Efficient Vision Transformer for Robust Human Activity Recognition from the Spectrograms of Wearable Sensor Data","authors":"J. McQuire, Paul Watson, Nick Wright, H. Hiden, M. Catt","doi":"10.1109/SSP53291.2023.10208059","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208059","url":null,"abstract":"This study introduces the Data Efficient Separable Transformer (DeSepTr) architecture, a novel framework for Human Activity Recognition (HAR) that utilizes a light-weight computer vision model to train a Vision Transformer (ViT) on spectrograms generated from wearable sensor data. The proposed model achieves strong results on several HAR tasks, including surface condition recognition and activity recognition. Compared to the ResNet-18 model, DeSepTr outperforms by 5.9% on out-of-distribution test data accuracy for surface condition recognition. The framework enables ViTs to learn from limited labeled training data and generalize to data from participants outside of the training cohort, potentially leading to the development of activity recognition models that are robust to the wider population. The results suggest that the DeSepTr architecture can overcome limitations related to the heterogeneity of individuals’ behavior patterns and the weak inductive bias of transformer algorithms.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"25 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":"133882583","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
Dual-Input Type Convolutional Neural Networks Employing Color Normalized and Nuclei Segmented Data for Histopathology Image Classification 采用颜色归一化和核分割数据的双输入型卷积神经网络用于组织病理图像分类
2023 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2023-07-02 DOI: 10.1109/SSP53291.2023.10208033
Osman Demirel, M. Akhtar
{"title":"Dual-Input Type Convolutional Neural Networks Employing Color Normalized and Nuclei Segmented Data for Histopathology Image Classification","authors":"Osman Demirel, M. Akhtar","doi":"10.1109/SSP53291.2023.10208033","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208033","url":null,"abstract":"Improvements in Convolutional Neural Network (CNN) have been widely successful for histopathology image classification. However, color normalization for data preprocessing and nuclei segmentation for feature extraction should also be considered for further performance boost, data redundancy elimination, and provision of distinguishing information. These techniques are known to improve generalizability. However, there is a need to find ways to use the data obtained from color normalized and segmented data for training. In this work, dual-input CNN (DiCNN), concatenated-input CNN (CiCNN), and ensemble CNN (ECNN) are trained and tested with color normalized and nuclei segmented data. The normalization technique is chosen based on correlation and structural similarity. The segmentation method is chosen based on the best-performing normalization technique for consistency and generalizability. The results show that normalized and segmented inputs results in better binary classification with CiCNN outperforming other methods. However, for multiclass classification raw data training is advantageous for all approaches.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"23 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":"134090656","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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