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In Nuclear Magnetic Resonance (NMR) spectroscopy, an efficient analysis and a relevant extraction of different molecule properties from a given chemical mixture are important tasks, especially when processing bidimensional NMR data. To that end, using a blind source separation approach based on a variational formulation seems to be a good strategy. However, the poor resolution of NMR spectra and their large dimension require a new and modern blind source separation method.


Signed graphs have recently been found to offer advantages over unsigned graphs in a variety of tasks. However, the problem of learning graph topologies has only been considered for the unsigned case. In this paper, we propose a conceptually simple and flexible approach to signed graph learning via signed smoothness metrics. Learning the graph amounts to solving a convex optimization problem, which we show can be reduced to an efficiently solvable quadratic problem. Applications to signal reconstruction and clustering corroborate the effectiveness of the proposed method.


Graph signal processing on directed graphs poses theoretical challenges since an eigendecomposition of filters is in general not available. Instead, Fourier analysis requires a Jordan decomposition and the frequency response is given by the Jordan normal form, whose computation is numerically unstable for large sizes. In this paper, we propose to replace a given adjacency shift A by a diagonalizable shift A D obtained via the Jordan-Chevalley decomposition.


A probability-theoretic problem under information
constraints for the concept of optimal control over a noisymemoryless channel is considered. For our Observer-Controller
block, i.e., the lossy joint-source-channel-coding (JSCC) scheme,
after providing the relative mathematical expressions, we propose a Blahut-Arimoto-type algorithm − which is, to the best
of our knowledge, for the first time. The algorithm efficiently finds the probability-mass-functions (PMFs) required for .....................


We present a novel sampling theorem, and prototypical applications, for Fourier-sparse lattice signals, i.e., data indexed by a finite semi-lattice. A semilattice is a partially ordered set endowed with a meet (or join) operation that returns the greatest lower bound (smallest upper bound) of two elements. Semilattices can be viewed as a special class of directed graphs with a strictly triangular adjacency matrix , which thus cannot be diagonalized.


We present an image-based approach to estimate the velocity of moving vessels from their traces on the water surface. Vessels moving at constant heading and speed display a familiar V-shaped pattern which only differs from one to another by the wavelength of their transverse and divergent components. Such wavelength is related to vessel velocity. We use planar homography and natural constraints on the geometry of ships’ wake crests to compute vessel velocity from single optical images acquired by conventional cameras.


In this paper, we analyze the asymptotic performance of a convex optimization-based discrete-valued vector reconstruction from linear measurements. We firstly propose a box-constrained version of the conventional sum of absolute values (SOAV) optimization, which uses a weighted sum of L1 regularizers as a regularizer for the discrete-valued vector. We then derive the asymptotic symbol error rate (SER) performance of the box-constrained SOAV (Box-SOAV) optimization theoretically by using convex Gaussian min-max theorem.