The complex backpropagation algorithm

The backpropagation (BP) algorithm that provides a popular method for the design of a multilayer neural network to include complex coefficients and complex signals so that it can be applied to general radar signal processing and communications problems. It is shown that the network can classify complex signals. The generalization of the BP to deal with complex signals should make it possible to expand the line of applications of this powerful nonlinear signal processing algorithm     

Stochastic correlative learning algorithms

This paper addresses stochastic correlative learning as the basis for a broadly defined class of statistical learning algorithms known collectively as the algorithm of pattern extraction (ALOPEX) family. Starting with the neurobiologically motivated Hebb's rule, the two conventional forms of the ALOPEX algorithm are derived, followed by a modified variant designed to improve the convergence speed. We next describe two more elaborate versions of the ALOPEX algorithm, which incorporate particle filtering that exemplifies a form of Monte Carlo simulation, to exchange computational complexity for an improved convergence and tracking behavior. In support of the different forms of the ALOPEX algorithm developed herein, we present three different experiments using synthetic and real-life data on binocular fusion of stereo images, on-line prediction, and system identification.     

Impact of linear array geometry on direction-of-arrival estimationfor a single source

The analysis is conducted using the Cramer-Rao lower bound simulations and performance modeling of maximum-likelihood estimation (MLE) while assuming a single source of illumination and additive white Gaussian noise. Particular attention is paid to the implementation of the MLE, the threshold signal-to-noise ratio (SNR), probability of outlier, and high SNR mean-squared-error (MSE) performance, which are evaluated and compared for uniform and nonuniform arrays. The conditions under which tradeoffs exist in choosing a particular geometry and their significance are determined     

Monitoring neuronal oscillations and signal transmission betweencortical regions using time-frequency analysis ofelectroencephalographic activity

Oscillatory states in the electroencephalogram (EEG) reflect the rhythmic synchronous activity in large networks of neurons. Time-frequency (TF) methods, which quantify the spectral content of the EEG as a function of time, are well suited as tools for the study of spontaneous and induced changes in oscillatory states. The use of these methods provides insights into the temporal dynamics of EEG activity in both humans and experimental animals, and aids the study of the neuronal mechanisms that generate rhythmic EEG activity. Further the use of TF coherence analysts, which quantifies the consistency of phase relationships in multichannel EEG recordings, may contribute to the understanding of signal transmission between neuronal populations in different parts of the brain. We have used TF techniques to analyze the flow of activity patterns between two strongly connected brain structures: the entorhinal cortex and the hippocampus. Both of these structures are believed to be involved in information storage. By applying various frequencies of stimulation, we have found a peak in the spectral power in both sites at around 18 Hz, but the coherence between the EEG signals recorded from these sites was found to increase monotonically up to about 35 Hz. We have also found that long-term potentiation, a strong increase in the efficacy of excitatory synapses between these sites, either had no effect or decreased coherence     

Adaptive digital communication receivers

We present a tutorial review of adaptive signal processing applied to a digital communication receiver operating in a nonstationary environment. The topics covered include feedforward and recurrent neural networks and their applications to communication systems. The article also includes a discussion of turbo decoding, which involves a form of recurrent learning. It concludes with a discussion of the various aspects of adaptive signal processing as they relate to digital communication receivers.     

Turbo-MIMO for wireless communications

This article reviews an important class of MIMO wireless communications, known collectively as turbo-MIMO systems. A distinctive property of turbo-MIMO wireless communication systems is that they can attain a channel capacity close to the Shannon limit and do so in a computationally manageable manner. The article focuses attention on a subclass of turbo-MIMO systems that use space-time coding based on bit-inter-leaved coded modulation. Different computationally manageable decoding (detection) strategies are briefly discussed. The article also includes computer experiments that are intended to improve the understanding of specific issues involved in the design of turbo-MIMO systems.     

Error bound method and its application to the LMS algorithm

A method to determine a bound on the error performance of an adaptive filter due to roundoff effects is described. The method converts the analysis of a recursive algorithm into two much simpler sub-problems: convergence and momentary error. To apply the method, the input data has to be bounded. By classifying convergence into different categories according to their rates, it is observed that adaptive filtering algorithms that belong to a particular class share similar behavior due to roundoff error or other perturbation effects. The merit of the method is its simplicity and general applicability. Based on this method, a sufficient condition for the numerical stability of an adaptive filter is derived. Application of the method to the least mean square (LMS) algorithm is described. The analysis may also be generalized to include other perturbation effects     

Cognitive radio: brain-empowered wireless communications

Cognitive radio is viewed as a novel approach for improving the utilization of a precious natural resource: the radio electromagnetic spectrum. The cognitive radio, built on a software-defined radio, is defined as an intelligent wireless communication system that is aware of its environment and uses the methodology of understanding-by-building to learn from the environment and adapt to statistical variations in the input stimuli, with two primary objectives in mind: /spl middot/ highly reliable communication whenever and wherever needed; /spl middot/ efficient utilization of the radio spectrum. Following the discussion of interference temperature as a new metric for the quantification and management of interference, the paper addresses three fundamental cognitive tasks. 1) Radio-scene analysis. 2) Channel-state estimation and predictive modeling. 3) Transmit-power control and dynamic spectrum management. This work also discusses the emergent behavior of cognitive radio.