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The unceasing demand for continuous situational awareness calls for innovative and large-scale signal processing algorithms, complemented by collaborative and adaptive sensing platforms to accomplish the objectives of layered sensing and control. Towards this goal, the present paper develops a spline-based approach to field estimation, which relies on a basis expansion model of the field of interest. The model entails known bases, weighted by generic functions estimated from the field's noisy samples. A novel field estimator is developed based on a regularized variational least-squares (LS) criterion that yields finite-dimensional (function) estimates spanned by thin-plate splines. Robustness considerations motivate well the adoption of an overcomplete set of (possibly overlapping) basis functions, while a sparsifying regularizer augmenting the LS cost endows the estimator with the ability to select a few of these bases that “better” explain the data. This parsimonious field representation becomes possible, because the sparsity-aware spline-based method of this paper induces a group-Lasso estimator for the coefficients of the thin-plate spline expansions per basis. A distributed algorithm is also developed to obtain the group-Lasso estimator using a network of wireless sensors, or, using multiple processors to balance the load of a single computational unit. The novel spline-based approach is motivated by a spectrum cartography application, in which a set of sensing cognitive radios collaborate to estimate the distribution of RF power in space and frequency. Computer simulations and tests on real data corroborate that the estimated power spectrum density atlas yields the desired RF state awareness, since the maps reveal spatial locations where idle frequency bands can be reused for transmission, even when fading and shadowing effects are pronounced. [ABSTRACT FROM PUBLISHER]

This paper introduces a tensor framework to solve the problem of nonunitary joint block diagonalization (JBD) of a set of real or complex valued matrices. We show that JBD can be seen as a particular case of the block-component-decomposition (BCD) of a third-order tensor. The resulting tensor model fitting problem does not require the block-diagonalizer to be a square matrix: the over- and underdetermined cases can be handled. To compute the tensor decomposition, we build an efficient nonlinear conjugate gradient (NCG) algorithm. In the over- and exactly determined cases, we show that exact JBD can be computed by a closed-form solution based on eigenvalue analysis. In approximate JBD problems, this solution can be used to efficiently initialize any iterative JBD algorithm such as NCG. Finally, we illustrate the performance of our technique in the context of independent subspace analysis (ISA) based on second-order statistics (SOS). [ABSTRACT FROM PUBLISHER]

This paper presents an analysis of the performance and behavior of a blind adaptive carrier phase offset recovery scheme based on dispersion minimization (DM). The algorithm, called the DM-derotator, is a kind of simple equalizer that attempts to correct the carrier phase offset by minimizing the dispersion of the projection of the real part of the (complex) data signal. The recent application of this algorithm to digital broadcasting signals motivates our analysis. This paper classifies the stationary points of the DM-derotator for a variety of source signals including digital vestigial side band (VSB) and quadrature amplitude modulation (QAM) and discusses initialization strategies. The analysis is extended to a variety of situations including i) its behavior in the presence of intersymbol interference, ii) its behavior when there is statistical dependence between the in-phase and quadrature components, and iii) its tracking ability. [ABSTRACT FROM AUTHOR]

There is a fundamental tradeoff between power consumption, data transmission rates, and congestion levels in a wireless network. These three elements influence the performance of rate and power control strategies, and they need to be coordinated judiciously. This paper proposes dynamic rate and power control algorithms for distributed wireless networks that also account for the congestion levels in a network. The design is pursued by formulating state-space models with and without uncertain dynamics and by determining control signals that help meet certain performance criteria (such as robustness and desired levels of signal-to-interference ratio). Simulation results illustrate the performance of the proposed control schemes. [ABSTRACT FROM AUTHOR]

We have developed a new audio-to-visual conversion algorithm that uses a constrained optimization approach to take advantage of dynamics of mouth movements. Based on facial muscle analysis, the dynamics of mouth movements is modeled, and constraints are obtained from it. The obtained constraints are used to estimate visual parameters from speech in a framework of hidden Markov model (HMM)-based visual parameter estimation. To solve the constrained optimization problem, the Lagrangian approach is used to transform the constrained problem into an unconstrained problem in our implementation. The proposed method is tested on various noisy environments to show its robustness and correctness. Our proposed algorithm is favorably compared with the mixture-based HMM method, which also uses audio-visual HMMs and finds optimal estimates based on a joint audio-visual probability distribution. Our proposed algorithm can estimate optimal visual parameters while satisfying the constraints and avoiding performance degradation in noisy environments. [ABSTRACT FROM AUTHOR]

Smoothing in state-space models amounts to computing the conditional distribution of the latent state trajectory, given observations, or expectations of functionals of the state trajectory with respect to this distribution. In recent years there has been an increased interest in Monte Carlo-based methods, often involving particle filters, for approximate smoothing in nonlinear and/or non-Gaussian state-space models. One such method is to approximate filter distributions using a particle filter and then to simulate, using backward kernels, a state trajectory backwards on the set of particles. We show that by simulating multiple realizations of the particle filter and adding a Metropolis-Hastings step, one obtains a Markov chain Monte Carlo scheme whose stationary distribution is the exact smoothing distribution. This procedure expands upon a similar one recently proposed by Andrieu, Doucet, Holenstein, and Whiteley. We also show that simulating multiple trajectories from each realization of the particle filter can be beneficial from a perspective of variance versus computation time, and illustrate this idea using two examples. [ABSTRACT FROM PUBLISHER]

We present a graph theoretical methodology that reduces the implementation complexity of the multiplication of a constant vector and a scalar. The complexity of implementation is defined as the required amount of computations like additions. The proposed approach is called minimally redundant parallel (MRP) optimization and is mainly presented in a finite impluse response (FIR) filtering framework to obtain a low-complexity multiplierless implementation. The key idea is to expand the design space using shift inclusive differential coefficients (SIDCs) together with computation reordering using a graph theoretic approach to obtain maximal computation sharing. The problem is formulated using a graph in which vertices and edges represent coefficients and computational cost (number of resources). The multiplierless solution is obtained by solving a set cover problem on the vertices in the graph. A simple polynomial run time algorithm based on a greedy approach is presented. The proposed approach is compared with common-subexpression elimination to show slightly better results and is combined with it for further reduction of complexity. Simulation results show that 50-60% complexity reduction is achieved by only applying the MRP algorithm, and 70% complexity reduction is obtainable by combining it with common-subexpression elimination under a delay constraint of two or three. [ABSTRACT FROM AUTHOR]

Fast algorithms for computing various discrete cosine transforms and discrete sine transforms in a sliding window are proposed. The algorithms are based on a recursive relationship between three subsequent local transform spectra. Efficient inverse algorithms for signal processing in a sliding window are also presented. The computational complexity of the algorithms is compared with that of known fast discrete sinusoidal transforms and running recursive algorithms. [ABSTRACT FROM AUTHOR]

An alternative method of time-frequency signal analysis is introduced and is compared with the Conventional discrete Fourier transfer (DFT)-based methods. The structure of the proposed algorithm and its mathematical properties are presented. The proposed algorithm has a nonlinear structure that provides a frequency-adaptivity feature. It can be implemented both in analog and in digital form and is particularly suitable for real-time applications where computational efficiency is important. A comparison is made with the DFT in terms of algorithm efficiency, sensitivity, and complexity. It is shown that compared with the DFT, the algorithm is more efficient for real-time applications and is less sensitive to noise and variations in the frequency and sampling rate while maintaining simplicity of the structure and computational efficiency. Slower convergence rate of the algorithm, compared with the DFT, constitutes its main shortcoming. [ABSTRACT FROM AUTHOR]

Provides information on a study on the development of a simple algorithm for the design of a second-order state-space digital filter. State digital filter structures; Sensitivities of second-order state digital filters; Implications of the study results.

Presents information on a study introducing a class of fast recursive European strategic programme for research and development in information technology (ESPRIT) algorithms for adaptive (on-line) source localization base on subspace tracking and adaptive rank reduction. Model underlying the ESPRIT method; Principles of fast subspace tracking; Conclusions.