- Signal and System Modeling, Representation and Estimation
- Multirate Signal Processing
- Sampling and Reconstruction
- Nonlinear Systems and Signal Processing
- Filter Design
- Adaptive Signal Processing
- Statistical Signal Processing

- Read more about A Novelty in Blahut-Arimoto Type Algorithms: Optimal Control over Noisy Communication Channels
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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 .....................

## Comsoc.pdf

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- Read more about Sampling Signals on Meet/Join Lattices
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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.

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- Read more about ACCELERATING NONNEGATIVE MATRIX FACTORIZATION OVER POLYNOMIAL SIGNALS WITH FASTER PROJECTIONS
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- Read more about Computing Vessel Velocity from Single Perspective Projection Images
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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.

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- Read more about PERFORMANCE ANALYSIS OF DISCRETE-VALUED VECTOR RECONSTRUCTION BASED ON BOX-CONSTRAINED SUM OF L1 REGULARIZERS
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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.

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- Read more about Robust least squares estimation of graph signals
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Recovering a graph signal from samples is a central problem in graph signal processing. Least mean squares (LMS) method for graph signal estimation is computationally efficient adaptive method. In this paper, we introduce a technique to robustify LMS with respect to mismatches in the presumed graph topology. It builds on the fact that graph LMS converges faster when the graph topology is specified correctly. We consider two measures of convergence speed, based on which we develop randomized greedy algorithms for robust interpolation of graph signals.

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Community detection from graphs has many applications

in machine learning, biological and social sciences. While

there is a broad spectrum of literature based on various

approaches, recently there has been a significant focus on

inference algorithms for statistical models of community

structure. These algorithms strive to solve an inference

problem based on a generative model of the network. Recent

advances in stochastic gradient MCMC have played a crucial

role in improving the scalability of these techniques. In this

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- Read more about POTENTIAL GAMES FOR DISTRIBUTED PARAMETER ESTIMATION IN NETWORKS WITH AMBIGUOUS MEASUREMENTS
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Distributed estimation of a parameter vector in a network of sensor nodes with ambiguous measurements is considered. The ambiguities are modelled by following a set-theoretic approach, that leads to each sensor employing a non-convex constraint set on the parameter vector. Consensus can be used to reach an estimate consistent with the measurements of all nodes, assuming that such an estimate exists, but unfortunately, such an approach leads to a non-convex problem.

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- Read more about Provably Accelerated Randomized Gossip Algorithms
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In this work we present novel provably accelerated gossip algorithms for solving the average consensus problem. The proposed protocols are inspired from the recently developed accelerated variants of the randomized Kaczmarz method - a popular method for solving linear systems. In each gossip iteration all nodes of the network update their values but only a pair of them exchange their private information. Numerical experiments on popular wireless sensor networks showing the benefits of our protocols are also presented.

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- Read more about A Non-Convex Approach to Non-negative Super-Resolution: Theory and Algorithm
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## QIAO_HENG.pdf

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