Feedback Acquisition and Reconstruction of Spectrum-Sparse Signals by Predictive Level Comparisons

In this letter, we propose a sparsity promoting feedback acquisition and reconstruction scheme for sensing, encoding and subsequent reconstruction of spectrally sparse signals. In the proposed scheme, the spectral components are estimated utilizing a sparsity-promoting, sliding-window algorithm in a feedback loop. Utilizing the estimated spectral components, a level signal is predicted and sign measurements of the prediction error are acquired. The sparsity promoting algorithm can then estimate the spectral components iteratively from the sign measurements. Unlike many batch-based Compressive Sensing (CS) algorithms, our proposed algorithm gradually estimates and follows slow changes in the sparse components utilizing a sliding-window technique. We also consider the scenario in which possible flipping errors in the sign bits propagate along iterations (due to the feedback loop) during reconstruction. We propose an iterative error correction algorithm to cope with this error propagation phenomenon considering a binary-sparse occurrence model on the error sequence. Simulation results show effective performance of the proposed scheme in comparison with the literature

New Features Using Robust MVDR Spectrum of Filtered Autocorrelation Sequence for Robust Speech Recognition

This paper presents a novel noise-robust feature extraction method for speech recognition using the robust perceptual minimum variance distortionless response (MVDR) spectrum of temporally filtered autocorrelation sequence. The perceptual MVDR spectrum of the filtered short-time autocorrelation sequence can reduce the effects of residue of the nonstationary additive noise which remains after filtering the autocorrelation. To achieve a more robust front-end, we also modify the robust distortionless constraint of the MVDR spectral estimation method via revised weighting of the subband power spectrum values based on the sub-band signal to noise ratios (SNRs), which adjusts it to the new proposed approach. This new function allows the components of the input signal at the frequencies least affected by noise to pass with larger weights and attenuates more effectively the noisy and undesired components. This modification results in reduction of the noise residuals of the estimated spectrum from the filtered autocorrelation sequence, thereby leading to a more robust algorithm. Our proposed method, when evaluated on Aurora 2 task for recognition purposes, outperformed all Mel frequency cepstral coefficients (MFCC) as the baseline, relative autocorrelation sequence MFCC (RAS-MFCC), and the MVDR-based features in several different noisy conditions

Exploiting the pilot pattern orthogonality of ofdma signals for the estimation of base stations number of antennas

International audienceIn a recent work, we proposed a GLR test dedicated to the identification of OFDM systems. In the present paper, we show that the proposed technique can be extended for the estimation of the number of antennas used by a base station. This extension is made possible thanks to the orthogonality property that exhibit the pilot pattern associated to the different antennas. Thanks to a multihypothesis testing we show that the number of transmitting antennas is estimated using only one antenna at the receiver and without any knowledge of the pilot sequence

Multiple Antenna Spectrum Sensing in Cognitive Radios

Abstract-In this paper, we consider the problem of spectrum sensing by using multiple antenna in cognitive radios when the noise and the primary user signal are assumed as independent complex zero-mean Gaussian random signals. The optimal multiple antenna spectrum sensing detector needs to know the channel gains, noise variance, and primary user signal variance. In practice some or all of these parameters may be unknown, so we derive the Generalized Likelihood Ratio (GLR) detectors under these circumstances. The proposed GLR detector, in which all the parameters are unknown, is a blind and invariant detector with a low computational complexity. We also analytically compute the missed detection and false alarm probabilities for the proposed GLR detectors. The simulation results provide the available traded-off in using multiple antenna techniques for spectrum sensing and illustrates the robustness of the proposed GLR detectors compared to the traditional energy detector when there is some uncertainty in the given noise variance

Regional Dominant Frequency: A New Tool for Wave Break Identification During Atrial Fibrillation

Cardiac mapping systems are based on the time/frequency feature analyses of intracardiac electrograms recorded from individual bipolar/unipolar electrodes. Signals from each electrode are processed independently. Such approaches fail to investigate the interrelationship between simultaneously recorded channels of any given mapping catheter during atrial fibrillation (AF). We introduce a novel signal processing technique that reflects regional dominant frequency (RDF) components. We show that RDF can be used to identify and characterize variation and disorganization in wavefront propagation- wave breaks. The intracardiac electrograms from the left atrium of 15 patients were exported to MATLAB and custom software employed to estimate RDF and wave break rate (WBR). We observed a heterogeneous distribution of both RDF and WBR; the two measures were weakly correlated (0.3; p < 0.001). We identified locations of AF or atrial tachycardia (ATach) termination and later compared offline with RDF and WBR maps. We inspected our novel metrics for associations with AF termination sites. Areas associated with AF termination demonstrated high RDF and low WBR (↑RDF,↓WBR). These sites were present in 14 of 15 patients (mean 2.6 ± 1.2 sites per patient; range, 1–4 sites), 43% situated within the pulmonary veins. In nine patients where AF terminated to sinus rhythm (6) or ATach (3), post-hoc analysis demonstrated all ↑RDF,↓WBR sites were ablated and correlated with AF termination sites. The proposed RDF signal processing tools can be used to identify and quantify wave break, and the combined use of these two novel metrics can aid characterization of AF. Further prospective studies are warranted