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We present new first order algorithm for matrix completion and show by numerical tests that they are able to recover rank r matrices of size m × n from only Const. · (m + n − r)r entries with Const. close to one. This evidence shows that matrix completion is achieved closer to the oracle limit than in compressed sensing.
As the field of bioimage informatics matures, the issue of the validation of image reconstruction algorithms and the definition of proper performance criteria becomes more pressing. In this work, we discuss benchmarking aspects of fluorescence microscopy quantitative tools. We point out the importance of generating realistic datasets and describe our approach to this task. We rely on our experience...
Energy detection is a simple non-coherent approach used for spectrum sensing that offers linear computational complexity and low latency. Its main shortcoming is the well-known noise uncertainty problem, which results in failed detection in the very low signal-to-noise ratio. In this paper, we give a theoretical analysis of the noise uncertainty modeling with energy detection to drive a relationship...
A recent rise of compressive sensing (CS) algorithms has prompted many questions about the analysis of such sensed signals. Specifically, calculating a time-frequency representation (TFR) of these signals is an open question. In this paper, we propose an approach for calculating TFRs of compressed sensed signals based on recently proposed CS algorithm using modulated discrete prolate spheroidal sequences...
Uncovering transcription factor (TF) mediated regulatory networks from microarray expression data and prior knowledge is considered in this paper. Bayesian factor models that model direct TF regulation are formulated. To address the enormous computational complexity of the model in large networks, a novel, efficient basis-expansion factor model (BEFaM) has been proposed, where the loading (regulatory)...
In this paper, we propose a new algorithm for learning overcomplete dictionaries. The proposed algorithm is actually a new approach for optimizing a recently proposed cost function for dictionary learning. This cost function is regularized with a term that encourages low similarity between different atoms. While the previous approach needs to run an iterative limited-memory BFGS (l-BFGS) algorithm...
The neuronal content of an organism, the individual morphology of each neuron and the variability of these components constitute the atlas of the neurome. The description of such an atlas will be critical in determining the complex neural system of a given organism, eventually providing clues to how animals think and function. As the organisms under investigation scale from the worm to the human,...
This paper deals with the restoration of Positron Emission Tomography images. The partial volume effect creates blurring in such images and causes inaccurate quantization. This artefact is due to the complex geometry of the acquisition system. We propose to represent this complexity by a spatially variable point spread function. The PSF is first measured at a set of locations in the field of view...
This article provides a methodology for designing a proposal distribution in the context of particle filtering for terrain navigation. The suggested method is based on the use of an importance distribution centered around an estimate of the maximum a posteriori (MAP). By assuming a Gaussian prior, we show that the computation of the MAP can be reduced to an optimization problem in a space of lower...
This paper proposes impact noise suppression with a new phase-based detection. Different from any other conventional algorithms which rely on magnitude-based or model-based detection, a new impact-noise detection utilizing phase linearity of the input noisy signal is developed based on an analysis of an impulse. Phase slopes of the input noisy signal is compared with an ideal phase slope obtained...
Non-negativity is a widely used constraint in parameter estimation procedures due to physical characteristics of systems under investigation. In this paper, we consider an LMS-type algorithm for system identification subject to non-negativity constraints, called Non-Negative Least-Mean-Square algorithm, and its normalized variant. An important contribution of this paper is that we study the stochastic...
This paper focuses on the application of particle filtering to tracking a single target by a network of mobile agents using measurements affected by dynamic interferences. The proposed solution is of general value and can be applied to systems with limited resources, and constraints on sensing modes, power and bandwidth utilization, and algorithm complexity. The highlight of the method is on its ability...
Most existing analysis dictionary learning (ADL) algorithms, such as the Analysis K-SVD, assume that the original signals are known or can be correctly estimated. Usually the signals are unknown and need to be estimated from its noisy versions with some computational efforts. When the noise level is high, estimation of the signals becomes unreliable. In this paper, a simple but effective ADL algorithm...
In this paper, we investigate how data embedding capacity and extraction fidelity can be enhanced keeping imperceptibility of the host audio unaltered. To meet this end, we propose a new blind watermarking system for two-channel audio signals which is based on Code Division Multiple Access (CDMA) and Independent Components Analysis (ICA) techniques. At the emitter, CDMA is used to increase the watermark...
This work presents an exact tracking analysis of the Normalized Least Mean Square (NLMS) algorithm for circular complex correlated Gaussian inputs. Unlike the existing works, the analysis presented neither uses separation principle nor small step-size assumption. The approach is based on the derivation of a closed form expression for the cumulative distribution function (CDF) of random variables of...
In this work, we approach the blind separation of dependent sources based only on a set of their linear mixtures. We prove that, when the sources have a pairwise dependence characterized by the linear conditional expectation (LCE) law, i.e. E[Si|Sj] =ρijSj for i ≠ j, with ρij = E[SiSj] (correlation coefficient), we are able to separate them by maximizing or minimizing a Generic Order Moment (GOM)...
We study the two-user MIMO block fading two-way relay channel in the non-coherent setting, where neither the terminals nor the relay have knowledge of the channel realizations. We analyze the achievable sum-rate when the users employ independent, isotropically distributed, unitary input signals, with amplify-and-forward (AF) strategy at the relay node. As a byproduct, we present an achievable pre-log...
This paper proves the asymptotic convergence of the meansquared error (MSE) of a distributed Kalman filter that we have previously proposed. This result shows that our distributed Kalman filter can track with bounded MSE any arbitrary linear dynamics.
Spatial Spectrum estimation is a key technique used in a wide variety of problems arising in signal processing and communication, particularly those employing multiple antennas. In many scenarios such as direction finding using antenna arrays, it is crucial to estimate which directions in space contribute to active sources (indicated by a non zero power). It has been recently shown that if the sources...
Independent vector analysis (IVA) can theoretically avoid the permutation ambiguity present in frequency domain independent component analysis by using a multivariate source prior to retain the dependency between different frequency bins of each source. The auxiliary function based independent vector analysis (AuxIVA) is a stable and fast update IVA algorithm which includes no tuning parameters. In...
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