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Learning-Based Rainfall Estimation via Communication Satellite Links 基于学习的基于通信卫星链路的降雨估计
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-10 DOI: 10.1109/SSP.2018.8450726
A. Gharanjik, K. Mishra, B. Shankar, B. Ottersten
{"title":"Learning-Based Rainfall Estimation via Communication Satellite Links","authors":"A. Gharanjik, K. Mishra, B. Shankar, B. Ottersten","doi":"10.1109/SSP.2018.8450726","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450726","url":null,"abstract":"We present a method for estimating rainfall by opportunistic use of Ka-band satellite communication network. Our approach is based on the attenuation of the satellite link signal in the rain medium and exploits the nearly linear relation between the rain rate and the specific attenuation at Ka-band frequencies. Although our experimental setup is not intended to achieve high resolutions as millimeter wavelength weather radars, it is instructive because of easy availability of millions of satellite ground terminals throughout the world. The received signal is obtained over a passive link. Therefore, traditional weather radar signal processing to derive parameters for rainfall estimation algorithms is not feasible here. We overcome this disadvantage by employing neural network learning algorithms to extract relevant information. Initial results reveal that rainfall accumulations obtained through our method are 85% closer to the in situ rain gauge estimates than the nearest C-band German weather service Deutscher Wetterdienst (DWD) radar.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131102403","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: 10
Simultaneous Sparsity and Parameter Tying for Deep Learning Using Ordered Weighted ℓ1 Regularization 基于有序加权1正则化的深度学习的同时稀疏性和参数绑定
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-10 DOI: 10.1109/SSP.2018.8450819
Dejiao Zhang, Julian Katz-Samuels, Mário A. T. Figueiredo, L. Balzano
{"title":"Simultaneous Sparsity and Parameter Tying for Deep Learning Using Ordered Weighted ℓ1 Regularization","authors":"Dejiao Zhang, Julian Katz-Samuels, Mário A. T. Figueiredo, L. Balzano","doi":"10.1109/SSP.2018.8450819","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450819","url":null,"abstract":"A deep neural network (DNN) usually contains millions of parameters, making both storage and computation extremely expensive. Although this high capacity allows DNNs to learn sophisticated mappings, it also makes them prone to over-fitting. To tackle this issue, we adopt a recently proposed sparsity-inducing regularizer called OWL (ordered weighted ℓ1, which has proven effective in sparse linear regression with strongly correlated covariates. Unlike the conventional sparsity-inducing regularizers, OWL simultaneously eliminates unimportant variables by setting their weights to zero, while also explicitly identifying correlated groups of variables by tying the corresponding weights to a common value. We evaluate the OWL regularizer on several deep learning benchmarks, showing that it can dramatically compress the network with slight or even no loss on generalization accuracy.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130469702","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
Detection of Pilot Spoofing Attack Over Frequency Selective Channels 频选信道导频欺骗攻击检测
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-10 DOI: 10.1109/SSP.2018.8450703
Jitendra Tugnait
{"title":"Detection of Pilot Spoofing Attack Over Frequency Selective Channels","authors":"Jitendra Tugnait","doi":"10.1109/SSP.2018.8450703","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450703","url":null,"abstract":"In a time-division duplex (TDD) multiple antenna system, the channel state information (CSI) can be estimated using reverse training. A pilot contamination (spoofing) attack occurs when during the training phase, an adversary also sends identical training (pilot) signal as that of the legitimate receiver. This contaminates channel estimation and alters the legitimate precoder/beamformimg design, facilitating eavesdropping. Past approaches to pilot spoofing detection are limited to flat fading channels. In this paper we propose a novel approach for detection of pilot spoofing attack over frequency selective channels, with unknown channels and channel lengths, except that an upperbound on the number of channel taps is assumed to be known. The proposed approach is illustrated by numerical examples and they show the efficacy of the proposed approach. A method to estimate Bob’s channel regardless of the spoofing attack, is also presented and illustrated via simulations.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125521026","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: 2
Detecting and Estimating Multivariate Self-Similar Sources in High-Dimensional Noisy Mixtures 高维噪声混合中多元自相似源的检测与估计
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450758
P. Abry, H. Wendt, G. Didier
{"title":"Detecting and Estimating Multivariate Self-Similar Sources in High-Dimensional Noisy Mixtures","authors":"P. Abry, H. Wendt, G. Didier","doi":"10.1109/SSP.2018.8450758","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450758","url":null,"abstract":"Nowadays, because of the massive and systematic deployment of sensors, systems are routinely monitored via a large collection of time series. However, the actual number of sources driving the temporal dynamics of these time series is often far smaller than the number of observed components. Independently, self-similarity has proven to be a relevant model for temporal dynamics in numerous applications. The present work aims to devise a procedure for identifying the number of multivariate self-similar mixed components and entangled in a large number of noisy observations. It relies on the analysis of the evolution across scales of the eigenstructure of multivariate wavelet representations of data, to which model order selection strategies are applied and compared. Monte Carlo simulations show that the proposed procedure permits identifying the number of multivariate self-similar mixed components and to accurately estimate the corresponding self-similarity exponents, even at low signal to noise ratio and for a very large number of actually observed mixed and noisy time series.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"201 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115703459","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: 7
A Probabilistic Approach for Heart Rate Variability Analysis Using Explicit Duration Hidden Markov Models 基于显式持续时间隐马尔可夫模型的心率变异性概率分析方法
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450781
Ju Gao, Diyan Teng, Emre Ertin
{"title":"A Probabilistic Approach for Heart Rate Variability Analysis Using Explicit Duration Hidden Markov Models","authors":"Ju Gao, Diyan Teng, Emre Ertin","doi":"10.1109/SSP.2018.8450781","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450781","url":null,"abstract":"Monitoring of the temporal dynamics of the beat-to-beat intervals offers a non-invasive method for assessing autonomous nervous system activity. Recently it became feasible to continuously monitor cardiac activity through the pulse wave signal collected using wrist based sensors employing photoplethysmography (PPG). However, wearable sensor data collected in ambulatory setting is full of motion artifacts, baseline drift, and noise. New computational techniques are required to make reliable high level inferences from wearable sensor data. In this paper, we propose a probabilistic method for computing heart rate variability indices from noisy PPG sensor data collected in the natural environment. We model the joint distribution of beat labels and sensor data using an Explicit Duration Hidden Markov Model (EDHMM) and sample likely beat sequences from the posterior distribution conditioned on measured sensor data. Beat sequences produced by the EDHMM sampler can be used to calculate posterior distribution of arbitrary heart rate variability indices to form Bayesian estimates. Experimental validation with IEEE Signal Processing Cup data shows that our proposed framework can outperform state-of-the art methods in PPG signal analysis in continuous heart rate estimation.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127107858","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: 2
Asymptotic Behavior Of Margin-Based Classification Methods 基于边缘的分类方法的渐近行为
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450750
Hanwen Huang
{"title":"Asymptotic Behavior Of Margin-Based Classification Methods","authors":"Hanwen Huang","doi":"10.1109/SSP.2018.8450750","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450750","url":null,"abstract":"We investigate the asymptotic behavior of the margin-based classification methods in the limit of large dimension $ prightarrow infty $ and large sample size $n rightarrow infty $ at fixed rate $alpha = n/p$. Under spiked population model, we first derive a general framework for describing the performance of a class of classification methods. Then we apply this framework to two commonly used classification methods: Support Vector Machine (SVM) and Distance Weighted Discrimination (DWD). Our analytical results show that DWD is less sensitive to the tuning parameter and achieves better performance than SVM in situations where $nlt p$. This finding provides a theoretical confirmation to the empirical results that have been observed in many previous simulation and real data studies.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125018616","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: 2
Sparsity Problem Involving Rational Basis Functions 涉及有理基函数的稀疏性问题
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450725
P. Kovács
{"title":"Sparsity Problem Involving Rational Basis Functions","authors":"P. Kovács","doi":"10.1109/SSP.2018.8450725","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450725","url":null,"abstract":"In this paper we consider the problem of sparse signal modeling by means of rational functions. Our dictionary is composed by a finite collection of elementary rational functions. In order to represent the signal with minimal error, we select an optimal number of basis from this set. The mutual coherence is a fundamental attribute of the dictionary. We analyze this quantity and describe its relation to the free parameters, i.e., the inverse poles, of rational functions. Then, we demonstrate the efficiency of sparse rational representations by compressing real electrocardiograms (ECG) including comparisons with other methods.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128318592","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
From Random Matrices to Monte Carlo Integration Via Gaussian Quadrature 从随机矩阵到通过高斯正交的蒙特卡罗积分
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450783
R. Bardenet, A. Hardy
{"title":"From Random Matrices to Monte Carlo Integration Via Gaussian Quadrature","authors":"R. Bardenet, A. Hardy","doi":"10.1109/SSP.2018.8450783","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450783","url":null,"abstract":"We introduced in [1] a new Monte Carlo estimator that relies on determinantal point processes (DPPs). We were initially motivated by peculiar properties of results from random matrix theory. This motivation is absent from the original paper [1], so we develop it here. Then, we give a non-technical overview of the contents of [1], insisting on points that may be of interest to the statistical signal processing audience.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"26 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124565087","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
Estimation Of Spatial Fields Of Nlos/Los Conditions For Improved Localization In Indoor Environments 基于Nlos/Los条件的空间场估计改进室内环境定位
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450840
E. Arias-de-Reyna, D. Dardari, P. Closas, P. Djurić
{"title":"Estimation Of Spatial Fields Of Nlos/Los Conditions For Improved Localization In Indoor Environments","authors":"E. Arias-de-Reyna, D. Dardari, P. Closas, P. Djurić","doi":"10.1109/SSP.2018.8450840","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450840","url":null,"abstract":"A major challenge in indoor localization is the presence or absence of line-of-sight (LOS). The absence of LOS, denoted as non-line-of-sight (NLOS), directly affects the accuracy of any localization algorithm because of the induced bias in ranging. The estimation of the spatial distribution of NLOS-induced ranging bias in indoor environments remains a major challenge. In this paper, we propose a novel crowd-based Bayesian learning approach to the estimation of bias fields caused by LOS/NLOS conditions. The proposed method is based on the concept of Gaussian processes and exploits numerous measurements. The performance of the method is demonstrated with extensive experiments.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129126731","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: 5
Quantized Constant Envelope Precoding for Frequency Selective Channels 频率选择信道的量化恒定包络预编码
2018 IEEE Statistical Signal Processing Workshop (SSP) Pub Date : 2018-06-01 DOI: 10.1109/SSP.2018.8450753
H. Jedda, J. Nossek
{"title":"Quantized Constant Envelope Precoding for Frequency Selective Channels","authors":"H. Jedda, J. Nossek","doi":"10.1109/SSP.2018.8450753","DOIUrl":"https://doi.org/10.1109/SSP.2018.8450753","url":null,"abstract":"The combination of coarse quantization with Constant Envelope (CE) signaling at the transmitter is of paramount importance in massive Multiple-Input Multiple-Output (MIMO) systems due to its power efficiency. In this context, we present a nonlinear precoder design for single-carrier transmission with frequency-selective channels. The optimization problem is formulated for Quadrature Amplitude Modulation (QAM) signaling as a linear programming problem. Simulation results show significant gains compared to the linear precoders.","PeriodicalId":330528,"journal":{"name":"2018 IEEE Statistical Signal Processing Workshop (SSP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124326362","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: 4
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