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A Novel Tensor Tracking Algorithm for Block-Term Decomposition of Streaming Tensors 一种新的流张量块项分解张量跟踪算法
2023 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2023-07-02 DOI: 10.1109/SSP53291.2023.10208007
Thanh Trung LE, K. Abed-Meraim, P. Ravier, O. Buttelli
{"title":"A Novel Tensor Tracking Algorithm for Block-Term Decomposition of Streaming Tensors","authors":"Thanh Trung LE, K. Abed-Meraim, P. Ravier, O. Buttelli","doi":"10.1109/SSP53291.2023.10208007","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208007","url":null,"abstract":"Block-term decomposition (BTD), which factorizes a tensor (aka a multiway array) into block components of low rank, has been a powerful processing tool for multivariate and high-dimensional data analysis. In this paper, we propose a novel tensor tracking method called SBTD for factorizing tensors derived from multidimensional data streams under the BTD format. Thanks to the alternating optimization framework, SBTD first applies a regularized least-squares solver to estimate the temporal factor of the underlying streaming tensor. Then, SBTD adopts an adaptive filter to track the non-temporal tensor factors over time by minimizing a weighted least-squares cost function. Numerical experiments indicate that SBTD is capable of tensor tracking with competitive performance compared to the state-of-the-art BTD algorithms.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"32 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":"133055528","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
Machine Learning Methods for Neonatal Heart Rate Prediction using Respiratory Signals 使用呼吸信号预测新生儿心率的机器学习方法
2023 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2023-07-02 DOI: 10.1109/SSP53291.2023.10208073
Maharaj Faawwaz A Yusran, Tengku Siti Aisha Tengku Mohd Azzman, S. Saw, Zati Hakim Azizul Hasan
{"title":"Machine Learning Methods for Neonatal Heart Rate Prediction using Respiratory Signals","authors":"Maharaj Faawwaz A Yusran, Tengku Siti Aisha Tengku Mohd Azzman, S. Saw, Zati Hakim Azizul Hasan","doi":"10.1109/SSP53291.2023.10208073","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208073","url":null,"abstract":"Approximately 10% of neonates require assistance transitioning from intrauterine to extrauterine environments. Applying these interventions requires accurate monitoring of vitals such as heart and respiratory rates. However, the current methods of these vital measurements require many devices to be attached to the neonates, resulting in rather intrusive methods that could even harm the neonates if not administered properly. This pilot study investigates the possibility of applying signal processing along with automated machine learning and deep learning models to estimate heart rate from respiratory signals recorded using inductance bands. The best machine learning model can get an average MAE of 10.15 BPM, and the best deep learning model at 10.88 BPM. The advantage of applying such a method would be reducing devices attached to neonates while preserving estimation accuracy.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"441 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":"125781394","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
DOA Estimation using Planar Sparse Fractal Array 基于平面稀疏分形阵列的DOA估计
2023 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2023-07-02 DOI: 10.1109/SSP53291.2023.10208047
Kretika Goel, M. Agrawal, Subrat Kar
{"title":"DOA Estimation using Planar Sparse Fractal Array","authors":"Kretika Goel, M. Agrawal, Subrat Kar","doi":"10.1109/SSP53291.2023.10208047","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208047","url":null,"abstract":"The term fractal refers to the fractional dimensions that have recursive nature and when clubbed with the properties of sparse arrays leads to the generation of a novel array called a sparse fractal array. In this paper, we extend our research to the 2D domain by introducing planar sparse arrays which generate hole-free difference coarray and have OpN2q elements just like the OBA but here in the new closed box form, with the additional property of fractal arrays along with sparseness. To estimate azimuth and elevation angle we have designed planar sparse fractal arrays using nested arrays and coprime arrays as the fundamental basic generating array which helps in achieving a high degree of freedom which makes it useful for DOA estimation. Simulations show that the proposed planar arrays have the better estimation performance when compared with existing planar arrays like URA, OBA, and CPA.","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":"126694935","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
Inexact higher-order proximal algorithms for tensor factorization 张量分解的非精确高阶近端算法
2023 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2023-07-02 DOI: 10.1109/SSP53291.2023.10208064
V. Leplat, A. Phan, A. Ang
{"title":"Inexact higher-order proximal algorithms for tensor factorization","authors":"V. Leplat, A. Phan, A. Ang","doi":"10.1109/SSP53291.2023.10208064","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208064","url":null,"abstract":"This paper explores Higher-order Methods (HoM) for Matrix Factorization (MF) and Tensor Factorization (TF) models, which are powerful tools for high dimensional data analysis and feature extraction. Unlike First-order Methods (FoM), which use gradients, HoM use higher-order derivatives of the objective function, which makes them faster but more costly per iteration. We develop efficient and implementable higher-order proximal point methods within the BLUM framework for large-scale problems. We introduce the appropriate objective functions, the algorithm, and the experimental results that demonstrate the advantages of our HoM-based algorithms over FoM-based algorithms for MF and TF models. We show that our HoM-based algorithms have a lower number of iterations with respect to their per-iteration cost than FoM-based algorithms.","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":"126739680","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
AI-assisted monitoring of COVID-19 community isolation in Thailand 人工智能辅助监测泰国COVID-19社区隔离
2023 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2023-07-02 DOI: 10.1109/SSP53291.2023.10208057
Natthanan Ruengchaijatuporn, Parin Kittipongdaja, Tagon Sompong, Pasit Jakkrawankul, P. Torvorapanit, N. Chantasrisawad, Wariya Chintanapakdee, Thanisa Tongbai, A. Petchlorlian, Wiroon Sriborrirux, C. Chunharas, O. Putcharoen, E. Chuangsuwanich, S. Sriswasdi
{"title":"AI-assisted monitoring of COVID-19 community isolation in Thailand","authors":"Natthanan Ruengchaijatuporn, Parin Kittipongdaja, Tagon Sompong, Pasit Jakkrawankul, P. Torvorapanit, N. Chantasrisawad, Wariya Chintanapakdee, Thanisa Tongbai, A. Petchlorlian, Wiroon Sriborrirux, C. Chunharas, O. Putcharoen, E. Chuangsuwanich, S. Sriswasdi","doi":"10.1109/SSP53291.2023.10208057","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208057","url":null,"abstract":"By minimizing human movement and contact, community isolation is an effective containment measure for the COVID-19 pandemic, especially against later strains that cause less severe symptoms. Nonetheless, a significant number of patients who enter community isolation with mild symptoms eventually develop severe pneumonias and require hospitalization. Therefore, the ability to foresee severe cases would be indispensable for managing limited medical resources. Here, we developed a proof-of-concept machine learning model, using daily vital signs data from 1,123 community isolation patients in Bangkok, Thailand, that can predict future hospitalization events up to 3 days in advance with an area under the precision-recall curve of 0.95. The model requires simple inputs, including body temperature, pulse rate, peripheral oxygen saturation, and shortness of breath, that the patients can self-perform and report. Hence, our approach can aid clinicians in providing remote, proactive healthcare service in broad settings","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":"126857972","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
Bayesian Compressed Sensing-Based Hybrid Models for Stock Price Forecasting 基于贝叶斯压缩感知的股票价格预测混合模型
2023 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2023-07-02 DOI: 10.1109/SSP53291.2023.10207939
Somaya Sadik, Mohamed Et-tolba, B. Nsiri
{"title":"Bayesian Compressed Sensing-Based Hybrid Models for Stock Price Forecasting","authors":"Somaya Sadik, Mohamed Et-tolba, B. Nsiri","doi":"10.1109/SSP53291.2023.10207939","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10207939","url":null,"abstract":"Nowadays, conventional statistical approaches to stock price forecasting fail to provide accurate predictions because financial data are affected by noise from different sources. To deal with this issue, we propose to apply Bayesian compressed sensing (BCS) for noise removal before performing any prediction. This results in a hybrid forecasting model combining BCS, denoising, and a prediction technique. The BCS approach was chosen instead of the traditional compressed sensing (CS) due to its superiority in terms of signal recovery accuracy. In the prediction step, we consider three models namely, autoregressive integrated moving average (ARIMA), long short-term memory (LSTM), and forward neural networks (FNN). The Standard & Poor 500 index (SP500), the Hang Seng index (HSI), and the Euro Stock 50 index (EU50) series are used as sample data for validation. In terms of accuracy, numerical results show that the proposed BCS-based hybrid models provide better performance compared to their single counterparts.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"96 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":"127543657","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
Intelligent Spectrum Sensing with ConvNet for 5G and LTE Signals Identification 基于ConvNet的5G和LTE信号识别智能频谱感知
2023 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2023-07-02 DOI: 10.1109/SSP53291.2023.10208054
Thien Huynh-The, Viet Quoc Pham, Thai-Hoc Vu, D. B. D. Costa, Van‐Phuc Hoang
{"title":"Intelligent Spectrum Sensing with ConvNet for 5G and LTE Signals Identification","authors":"Thien Huynh-The, Viet Quoc Pham, Thai-Hoc Vu, D. B. D. Costa, Van‐Phuc Hoang","doi":"10.1109/SSP53291.2023.10208054","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208054","url":null,"abstract":"The paper presents an intelligent spectrum sensing approach for next-generation wireless networks by exploiting deep learning, in which we develop a deep convolutional network (ConvNet) to automatically identify Fifth Generation New Radio (5G NR) and Long-Term Evolution (LTE) signals under standards-specified channel models with diversified RF impairments. In particular, we design a semantic segmentation ConvNet to detect and localize the spectral content of 5G NR and LTE in a synthetic signal featured by spectrum occupancy. A received signal is first converted by a short-time Fourier transform and represented as a wideband spectrogram image which is then passed through the ConvNet, incorporated by DeepLabv3+ and ResNet18 to improve the accuracy of pixel-wise segmentation to further increase the accuracy of signal identification. In the simulations, our ConvNet achieves around 95% mean accuracy and 91% mean intersection-over-union (IoU) at medium SNR level and demonstrates robustness under various practical channel impairments.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"7 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":"129130475","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
Resource-Efficient Federated Learning Robust to Communication Errors 对通信错误具有鲁棒性的资源高效联邦学习
2023 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2023-07-02 DOI: 10.1109/SSP53291.2023.10208024
Ehsan Lari, Vinay Chakravarthi Gogineni, R. Arablouei, Stefan Werner
{"title":"Resource-Efficient Federated Learning Robust to Communication Errors","authors":"Ehsan Lari, Vinay Chakravarthi Gogineni, R. Arablouei, Stefan Werner","doi":"10.1109/SSP53291.2023.10208024","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10208024","url":null,"abstract":"The effectiveness of federated learning (FL) in leveraging distributed datasets is highly contingent upon the accuracy of model exchanges between clients and servers. Communication errors caused by noisy links can negatively impact learning accuracy. To address this issue, we present an FL algorithm that is robust to communication errors while reducing the communication load on clients. To derive the proposed algorithm, we consider a weighted least-squares regression problem as a motivating example. We cast the considered problem as a distributed optimization problem over a federated network, which employs random scheduling to enhance communication efficiency, and solve it using the alternating direction method of multipliers. To improve robustness, we eliminate the local dual parameters and reduce the number of global model exchanges via a change of variable. We analyze the mean convergence of our proposed algorithm and demonstrate its effectiveness compared with related existing algorithms via simulations.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"128 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":"131557684","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 Deterministic Usage of the Elliptic Curve Digital Signature Algorithm with Scrypt 基于Scrypt的椭圆曲线数字签名算法的改进确定性应用
2023 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2023-07-02 DOI: 10.1109/SSP53291.2023.10207927
D. Tran, Ba Linh Vu, Xuan Nguyen Tien
{"title":"Improved Deterministic Usage of the Elliptic Curve Digital Signature Algorithm with Scrypt","authors":"D. Tran, Ba Linh Vu, Xuan Nguyen Tien","doi":"10.1109/SSP53291.2023.10207927","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10207927","url":null,"abstract":"In this paper, we propose an improved deterministic usage of the Elliptic Curve Digital Signature Algorithm (ECDSA) with the key derivation function scrypt. In particular, the scrypt function generates a batch of random bits where the random bits needed for the signing process are selected. As a certain number of bits is chosen from a bigger set, the reuse of the secret random number for each signing process is avoided, which is against fault and side-channel attacks. Numerical results are provided for five different-length messages and seventeen private keys considered as inputs for deterministic ECDSA and our proposed method. The random quality assessment using a statistical test suite of the National Institute of Standards and Technology (NIST) shows that our proposed method generates higher-quality random bit sequences, which can be seen clearly with one- and two-million-bit lengths respectively.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"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":"132775037","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
False Discovery Rate Control for Fast Screening of Large-Scale Genomics Biobanks 大规模基因组生物库快速筛选中的错误发现率控制
2023 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2023-07-02 DOI: 10.1109/SSP53291.2023.10207957
Jasin Machkour, Michael Muma, D. Palomar
{"title":"False Discovery Rate Control for Fast Screening of Large-Scale Genomics Biobanks","authors":"Jasin Machkour, Michael Muma, D. Palomar","doi":"10.1109/SSP53291.2023.10207957","DOIUrl":"https://doi.org/10.1109/SSP53291.2023.10207957","url":null,"abstract":"Genomics biobanks are information treasure troves with thousands of phenotypes (e.g., diseases, traits) and millions of single nucleotide polymorphisms (SNPs). The development of methodologies that provide reproducible discoveries is essential for the understanding of complex diseases and precision drug development. Without statistical reproducibility guarantees, valuable efforts are spent on researching false positives. Therefore, scalable multivariate and high-dimensional false discovery rate (FDR)-controlling variable selection methods are urgently needed, especially, for complex polygenic diseases and traits. In this work, we propose the Screen-T-Rex selector, a fast FDR-controlling method based on the recently developed T-Rex selector. The method is tailored to screening large-scale biobanks and it does not require choosing additional parameters (sparsity parameter, target FDR level, etc). Numerical simulations and a real-world HIV-1 drug resistance example demonstrate that the performance of the Screen-T-Rex selector is superior, and its computation time is multiple orders of magnitude lower compared to current benchmark knockoff methods.","PeriodicalId":296346,"journal":{"name":"2023 IEEE Statistical Signal Processing Workshop (SSP)","volume":"9 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":"131903535","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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