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In this paper, the problem of single-channel blind source separation (SCBSS) of a mixture of two co-frequency phase-shift keying (PSK) signals with unknown carrier frequency offsets (CFOs) is investigated. Two SCBSS algorithms which are robust to CFOs are proposed to perform separation of the mixture signals. In the first algorithm, the phase changes of the received signals caused by CFOs are tracked...
Sign language is important since it permits insight into the deaf culture and allows more opportunities to communicate with those who are deaf or hard of hearing. In this paper, we show that Wi-Fi signals can be used to recognize sign language with sparsely labeled training dataset. The key intuition is that sign language introduces different multi-path distortions in Wi-Fi signals and generates different...
Localization of a viewer's region of interest (ROI) on eye gaze signal trajectories acquired by eye trackers is a widely used approach in scene analysis, image compression, and quality of experience assessment. In this paper, we propose a novel clustering approach for ROI estimation from potentially noisy raw eye gaze data, based on signal processing on graphs. The clustering approach adapts graph...
Novel human gesture recognition and classification technique is suggested and experimentally studied. Suggested strategy is based on exploiting the interactions of human gestures with high-frequency electromagnetic field. Extracting of classification features contained in the wireless radio signal modulated by human gestures is proposed by utilizing bispectrum-based processing of the signal envelope...
On the basis of autoregressive mathematical models of signal-noise situations, methods for synthesizing robust algorithms have been developed, new optimal robust autoregressive signal model parameters estimation algorithms have been developed, their effectiveness has been studied by computer simulation.
A framework for reliable seperation of a low-rank subspace from grossly corrupted multi-dimensional signals is pivotal in modern signal processing applications. Current methods fall short of this separation either due to the radical simplification or the drastic transformation of data. This has motivated us to propose two new robust low-rank tensor models: Tensor Orthonormal Robust PCA (TORCPA) and...
In this paper, a robust algorithm for gait cycle segmentation is proposed based on a peak detection approach. The proposed algorithm is less influenced by noise and outliers and is capable of segmenting gait cycles from different types of gait signals recorded using different sensor systems. The presented algorithm has enhanced ability to segment gait cycles by eliminating the false peaks and interpolating...
This paper addresses the problem of sequential binary hypothesis testing in a multi-agent network to detect a random signal in non-Gaussian noise. To this end, the con-sensus+innovations sequential probability ratio test (ciSPRT) is generalized for arbitrary binary hypothesis tests and a robust version is developed. Simulations are performed to validate the performance of the proposed algorithms in...
By injecting false data through compromised sensors, an adversary can drive the probability of detection in a sensor network-based spatial field surveillance system to arbitrarily low values. As a countermeasure, a small subset of sensors may be secured. Leveraging the theory of Matched Subspace Detection, we propose and evaluate several detectors that add robustness to attacks when such trusted nodes...
We study the problem of sequential binary hypothesis testing in a distributed multi-sensor network in non-Gaussian noise. To this end, we develop three robust extensions of the Consensus+Innovations Sequential Probability Ratio Test (CISPRT), namely, the Median-CISPRT, the M-CISPRT, and the Myriad-CISPRT, and validate their performance in a shift-in-mean as well as a change-in-variance test. Simulations...
In this paper, we develop a robust generalization of the Gaussian quasi score test (GQST) for composite binary hypothesis testing. The proposed test, called measure-transformed GQST (MT-GQST), is based on a transformation applied to the probability distribution of the data. The considered transform is structured by a non-negative function, called MT-function, that weights the data points. By appropriate...
Many of today's signal processing tasks consider sparse models where the number of explanatory variables exceeds the sample size. When dealing with real-world data, the presence of impulsive noise and outliers must also be accounted for. Accurate and robust parameter estimation and consistent variable selection are needed simultaneously. Recently, some popular robust methods have been adapted to such...
We present a method for joint robust design of linear source and relay filters in a MIMO full-duplex link that supports the transmission of several data streams. The system model accounts for self-interference in the relay, limited dynamic range at the transmit and receive sides of the nodes as well as channel uncertainty. The design criterion is to minimize the worst-case mean square error at the...
An information-theoretic approach is described to estimate the determinant of the covariance matrix of a random vector sequence (a common task in a wide range of estimation and detection problems in signal processing for communications). The method is based on a prior entropy-based processing of the data using kernels and offers robustness against small-entropy contamination. The trade-off between...
Monitoring heartbeats takes an important role to ensure a person's health and well-being. Few of the existing systems are accurate, unobtrusive, robust and easy to install at the same time. Thus, we propose a completely unobtrusive system which can detect heartbeats during sleep by sensing the weak ballistic vibrations caused by heartbeats on any bed. The system, HB-Phone, is centered around the off-the-shelf...
A novel method is proposed which deals with secure extraction of data by utilizing transform based image watermarking techniques. The image is embedded with a watermark using a combination of Discrete Cosine Transform (DCT), Discrete Fourier Transform (DFT) and Discrete Wavelet Transform (DWT) in this scheme. The conventional transforms are applied in an optimized mode in these schemes in different...
With the increased use of smart devices, digital cameras and abundance of memory in the devices, the pictures of the same scenes have been taken several times, resulting in a number of images consisting of the same or very similar content in memory. Manually selecting the good ones is time-consuming as well as error prone. In this paper, the features of the images in the data sets were extracted and...
Digital signal processing for Global Navigation Satellite System (GNSS) receivers is mostly based on the assumption that the noise at the receiver input is Gaussian. This assumption leads to a non-linear Least Squares (LS) problem where GNSS signal parameters are estimated by minimizing a quadratic cost function. The receiver performance can be however significantly degraded by non-Gaussian phenomena...
Electrical load disaggregation continues to attract new explorations due to its challenging nature as well as utility. When the loads to be separated are characterized by suitable features, there is a possibility to solve the problem by utilizing the techniques from the emerging area of Graph Signal Processing (GSP). In this paper, we propose a three-staged approach comprising of (i) Event Detection...
In this paper, we propose a methodology for the fusion of different modes of speaker verification (SV) operation (fixed-passphrase, text-dependent and text-independent mode), using regression fusion models. The experimental results with and without spoofing attack conditions and using different single mode speaker verification engines, GMM-UBM, HMM-UBM and i-vector, indicated improvement in all the...
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