Background: The aim of the Endocardial T-Wave Alternans Study was to prospectively assess the presence of T-wave alternans (TWA) or beat-to-beat repolarization changes on implantable cardioverterdefibrillator (ICD)-stored electrograms (EGMs) immediately preceding the onset of spontaneous ventricular tachycardia (VT) or fibrillation (VF). Methods: Thirty-seven VT/VF episodes were compared to 116 baseline reference EGMs from the same 57 patients. A Bayesian model was used to estimate the T-wave waveform in each cardiac beat and a set of 10 parameters was selected to segment each detected T wave. Beat-by-beat differences in each T-wave parameter were computed using the absolute value of the difference between each beat and the following one. Fisher criterion was used for determining the most discriminant T-wave parameters, then top-M ranked parameters yielding a normalized cumulative Fisher score > 95% were selected, and analysis was applied on these selected parameters. Simulated TWA EGMs were used to validate the algorithm. Results: In the simulation study, TWA was detectable even in the case of the smallest simulated alternans of 25 μV. In 13 of the 37 episodes (35%) occurring in nine of 16 patients, significant larger beat-to-beat variations before arrhythmia onset were detected compared to their respective references (median one positive episode per patient). Parameters including the T-wave apex amplitude seem the more discriminant parameters. Conclusions: Detection of beat-by-beat repolarization variations in ICD-stored EGMs is feasible in a significant subset of cases and may be used for predicting the onset of ventricular arrhythmias.

Spectral estimation is an important classical problem that has received considerable attention in the signal processing literature. In this contribution, we propose a novel method for estimating the parameters of sums of complex exponentials embedded in additive noise from regularly or irregularly spaced samples. The method relies on Kronecker’s theorem for Hankel operators, which enables us to formulate the nonlinear least squares problem associated with the spectral estimation problem in terms of a rank constraint on an appropriate Hankel matrix. This matrix is generated by sequences approximating the underlying sum of complex exponentials. Unequally spaced sampling is accounted for through a proper choice of interpolation matrices. The resulting optimization problem is then cast in a form that is suitable for using the alternating direction method of multipliers (ADMM). The method can easily include either a nuclear norm or a finite rank constraint for limiting the number of complex exponentials. The usage of a finite rank constraint makes, in contrast to the nuclear norm constraint, the method heuristic in the sense that the problem is non-convex and convergence to a global minimum can not be guaranteed. However, we provide a large set of numerical experiments that indicate that usage of the finite rank constraint nevertheless makes the method converge to minima close to the global minimum for reasonably high signal to noise ratios, hence essentially yielding maximum-likelihood parameter estimates. Moreover, the method does not seem to be particularly sensitive to initialization and performs substantially better than standard subspace-based methods.

We address the localization of sources with known waveforms in frequency-selective channels. Conventional localization by multilateration is an indirect approach that is suboptimal at lower SNR, and breaks down in the presence of multipath. Here, we propose a direct localization method (DLM) that exploits the sparsity of the emitters, as well as differences in the properties of the line of sight (LOS) versus multipath components of the signals received at the sensors. It is shown that the proposed method has superior performance relative to other known localization techniques and is robust to sensors with blocked LOS.

In a communication scheme, there exist points at the transmitter and at the receiver where the wave is reduced to a finite set of functions of time which describe amplitudes and phases. For instance, the information is summarized in electrical cables which preceed or follow antennas. In many cases, a random propagation time is sufficient to explain changes induced by the medium. In this paper we study models based on stable probability laws which explain power spectra due to propagation of different kinds of waves in different media, for instance, acoustics in quiet or turbulent atmosphere, ultrasonics in liquids or tissues, or electromagnetic waves in free space or in cables. Physical examples show that a sub-class of probability laws appears in accordance with the causality property of linear filters.

For ECG interpretation, the detection and delineation of P and T waves are challenging tasks. This paper proposes sequential Bayesian methods for simultaneous detection, threshold-free delineation, and waveform estimation of P and T waves on a beat-to-beat basis. By contrast to state-of-the-art methods that process multiple-beat signal blocks, the proposed Bayesian methods account for beat-to-beat waveform variations by sequentially estimating the waveforms for each beat. Our methods are based on Bayesian signal models that take into account previous beats as prior information. To estimate the unknown parameters of these Bayesian models, we first propose a block Gibbs sampler that exhibits fast convergence in spite of the strong local dependencies in the ECG signal. Then, in order to take into account all the information contained in the past rather than considering only one previous beat, a sequential Monte Carlo method is presented, with a marginalized particle filter that efficiently estimates the unknown parameters of the dynamic model. Both methods are evaluated on the annotated QT database and observed to achieve significant improvements in detection rate and delineation accuracy compared to state-of-the-art methods, thus providing promising approaches for sequential P and T wave analysis.

Mobile systems monitoring is an application area for Mobile Wireless Sensor Networks (MWSN), which introduces some specific challenges. Delay/Disruption architecture tackles some of these issues, such as delay and connectivity disruptions, and thus has already been used in this context. However, WSN nodes have severe limitations, concerning storage and processing capabilities. This performance problem has not been investigated as it deserves and this is the purpose of this paper. We propose the FREAK scheme which aims at reducing the computation while performance remains high. This scheme relies on the mean frequency of past encounter with the base station. Transmissions are driven by this metric. The FREAK solution is keen because we assume that future can be predicted from the past events. We also analyse the acknowledgements effects on performance. Our proposition is evaluated through simulations based on real traces. FREAK is compared to several replication and quota-based mainstream DTN solutions and achieves quite better performance in realistic scenarios.

We study the impact of reliability mechanisms introduced at the link layer on the performance of transport protocols in the context of 4G satellite links. Specifically, we design a software module that performs realistic analysis of the network performance, by utilizing real physical layer traces of a 4G satellite service. Based on these traces, our software module produces equivalent link layer traces, as a function of the chosen link layer reliability mechanism. We further utilize the link layer traces within the ns-2 network simulator to evaluate the impact of link layer schemes on the performance of selected TCP variants. We consider erasure coding, ARQ and Hybrid-ARQ link layer mechanisms, and TCP Cubic, Compound, Hybla, New Reno and Westwood. We show that, for all target TCP variants, when the throughput of the transport protocol is close to the channel capacity, using the ARQ mechanism is most beneficial for TCP performance improvement. In conditions where the physical channel error rate is high, Hybrid-ARQ results in the best performance for all TCP variants considered, with up to 22% improvements compared to other schemes.

This paper introduces a new dictionary learning strategy based on atoms obtained by translating the composition of K convolutions with S-sparse kernels of known support. The dictionary update step associated with this strategy is a non-convex optimization problem. We propose a practical formulation of this problem and introduce a Gauss–Seidel type algorithm referred to as alternative least square algorithm for its resolution. The search space of the proposed algorithm is of dimension KS, which is typically smaller than the size of the target atom and much smaller than the size of the image. Moreover, the complexity of this algorithm is linear with respect to the image size, allowing larger atoms to be learned (as opposed to small patches). The conducted experiments show that we are able to accurately approximate atoms such as wavelets, curvelets, sinc functions or cosines for large values of K. The proposed experiments also indicate that the algorithm generally converges to a global minimum for large values of K and S.

This paper studies a new Bayesian algorithm for fusing hyperspectral and multispectral images. The observed images are related to the high spatial resolution hyperspectral image to be recovered through physical degradations, e.g., spatial and spectral blurring and/or subsampling defined by the sensor characteristics. In this work, we assume that the spectral response of the multispectral sensor is unknown as it may not be available in practical applications. The resulting fusion problem is formulated within a Bayesian estimation framework, which is very convenient to model the uncertainty regarding the multispectral sensor characteristics and the scene to be estimated. The high spatial resolution hyperspectral image is then inferred from its posterior distribution. More precisely, to compute the Bayesian estimators associated with this posterior, a Markov chain Monte Carlo algorithm is proposed to generate samples asymptotically distributed according to the distribution of interest. Simulation results demonstrate the efficiency of the proposed fusion method when compared with several state-of-the-art fusion techniques.