George A. Tsihrintzis

Performance of optimum and suboptimum receivers in the presence of impulsive noise modeled as an alpha-stable process

Impulsive noise bursts in communication systems are traditionally handled by incorporating in the receiver a limiter which clips the received signal before integration. An empirical justification for this procedure is that it generally causes the signal-to-noise ratio to increase. Recently, very accurate models of impulsive noise were presented, based on the theory of symmetric /spl alpha/-stable probability density functions. We examine the performance of optimum receivers, designed to detect signals embedded in impulsive noise which is modeled as an infinite variance symmetric /spl alpha/-stable process, and compare it against the performance of several suboptimum receivers. As a measure of receiver performance, we compute an asymptotic expression for the probability of error for each receiver and compare it to the probability of error calculated by extensive Monte-Carlo simulation. >

Fast estimation of the parameters of alpha-stable impulsive interference

We address the problem of estimation of the parameters of the recently proposed symmetric, alpha-stable model for impulsive interference. We propose new estimators based on asymptotic extreme value theory, order statistics, and fractional lower order moments, which can be computed fast and are, therefore, suitable for the design of real-time signal processing algorithms. The performance of the new estimators is theoretically evaluated, verified via Monte Carlo simulation, and compared with the performance of maximum-likelihood estimators.

Maximum likelihood estimation of object location in diffraction tomography

The problem is formulated within the context of diffraction tomography, where the complex phase of the diffracted wavefield is modeled using the Rytov approximation and the measurements consist of noisy renditions of this complex phase at a single frequency. The log likelihood function is computed for the case of additive zero mean Gaussian white noise and shown to be expressible in the form of the filtered backpropagation algorithm of diffraction tomography. In this form however, the filter function is no longer the rho filter appropriate to least square reconstruction but is now the generalized projection (propagation) of the object (centered at the origin) onto the line(s) parallel to the measurement line(s), but passing through the origin. This result allows the estimation problem to be solved via a diffraction tomographic imaging procedure where the noisy data is filtered and backpropagated in a first step, and the point of maximum value of the resulting image is then the maximum likelihood (ML) estimate of the objects location. The authors include a calculation of the Cramer-Rao bound for the estimation error and a computer simulation study illustrating the estimation procedure. >

Incoherent receivers in alpha-stable impulsive noise

We compute an incoherent receiver for demodulation of signals with random phase in additive impulsive noise modeled as a bivariate isotropic Cauchy process. Monte-Carlo simulation clearly shows that the proposed Cauchy receiver has a significantly improved operating characteristic over the corresponding Gaussian receiver. Moreover, the Cauchy receiver is very robust in the entire class of bivariate isotropic symmetric alpha-stable impulsive noises. >

Higher-order (nonlinear) diffraction tomography: reconstruction algorithms and computer simulation

The usual propagation transform of diffraction tomography is generalized into higher-order (nonlinear) propagation transforms via use of the Born series as the data-generating model in scattering experiments. Nonlinear tomographic reconstruction algorithms are developed for inversion of scattered field data modeled up to an arbitrarily large (possibly infinite) number of terms in the Born series. A computer simulation study is included to illustrate the performance of the algorithms for the case of scattering objects with cylindrical symmetry.

Maximum likelihood estimation of object location in diffraction tomography. II. Strongly scattering objects

For pt.I see ibid., vol.39, no.3, p.672-82, 1991. The problem of estimating the location of a known scattering object from noisy, limited scattering data is solved within exact, scalar wave scattering theory. The estimation procedure is shown to be a generalization of an earlier procedure developed within the Born and Rytov approximations of weak scattering theory and is compared with that earlier procedure in a computer simulation study of electromagnetic tomography. >

Higher order (nonlinear) diffraction tomography: inversion of the Rytov series

Nonlinear tomographic reconstruction algorithms are developed for inversion of data measured in scattering experiments in which the complex phase of the wavefields is modeled by an arbitrarily large (possibly infinite) number of terms in the Rytov series. The algorithms attain the form of a Volterra series of nonlinear operators, with the usual filtered backpropagation algorithm of diffraction tomography as the leading linear term. A computer simulation study is included to illustrate the performance of the algorithms for the case of scattering objects with cylindrical symmetry.

MUSIPER: a system for modeling music similarity perception based on objective feature subset selection

We explore the use of objective audio signal features to model the individualized (subjective) perception of similarity between music files. We present MUSIPER, a content-based music retrieval system which constructs music similarity perception models of its users by associating different music similarity measures to different users. Specifically, a user-supplied relevance feedback procedure and related neural network-based incremental learning allows the system to determine which subset of a set of objective features approximates more accurately the subjective music similarity perception of a specific user. Our implementation and evaluation of MUSIPER verifies the relation between subsets of objective features and individualized music similarity perception and exhibits significant improvement in individualized perceived similarity in subsequent music retrievals.

Data-adaptive algorithms for signal detection in sub-Gaussian impulsive interference

We address the problem of coherent detection of a signal embedded in heavy-tailed noise modeled as a sub-Gaussian, alpha-stable process. We assume that the signal is a complex-valued vector of length L, known only within a multiplicative constant, while the dependence structure of the noise, i.e. the underlying matrix of the sub-Gaussian process, is not known. We implement a generalized likelihood ratio detector that employs robust estimates of the unknown noise underlying matrix and the unknown signal strength. The performance of the proposed adaptive detector is compared with that of an adaptive matched filter that uses Gaussian estimates of the noise-underlying matrix and the signal strength and is found to be clearly superior. The proposed new algorithms are theoretically analyzed and illustrated in a Monte-Carlo simulation.

Towards Improving Visual-Facial Emotion Recognition through Use of Complementary Keyboard-Stroke Pattern Information

In this paper, we investigate the possibility of improving the accuracy of visual-facial emotion recognition through use of additional (complementary) keyboard-stroke information. The investigation is based on two empirical studies that we have conducted involving human subjects and human observers. The studies were concerned with the recognition of emotions from a visual-facial modality and keyboard-stroke information, respectively. They were inspired by the relative shortage of such previous research in empirical work concerning the strengths and weaknesses of each modality so that the extent can be determined to which the keyboard-stroke information complements and improves the emotion recognition accuracy of the visual-facial modality. Specifically, our research focused on the recognition of six basic emotion states, namely happiness, sadness, surprise, anger and disgust as well as the emotionless state which we refer to as neutral. We have found that the visual-facial modality may allow the recognition of certain states, such as neutral and surprise, with sufficient accuracy. However, its accuracy in recognizing anger and happiness can be improved significantly if assisted by keyboard-stroke information.