Samuel D. Stearns

Bio: Samuel D. Stearns is an academic researcher from Sandia National Laboratories. The author has contributed to research in topics: Adaptive filter & Digital signal processing. The author has an hindex of 10, co-authored 28 publications receiving 8914 citations.

Adaptive Signal Processing

Abstract: GENERAL INTRODUCTION. Adaptive Systems. The Adaptive Linear Combiner. THEORY OF ADAPTATION WITH STATIONARY SIGNALS. Properties of the Quadratic Performance Surface. Searching the Performance Surface. Gradient Estimation and Its Effects on Adaptation. ADAPTIVE ALGORITHMS AND STRUCTURES. The LMS Algorithm. The Z-Transform in Adaptive Signal Processing. Other Adaptive Algorithms and Structures. Adaptive Lattice Filters. APPLICATIONS. Adaptive Modeling and System Identification. Inverse Adaptive Modeling, Deconvolution, and Equalization. Adaptive Control Systems. Adaptive Interference Cancelling. Introduction to Adaptive Arrays and Adaptive Beamforming. Analysis of Adaptive Beamformers.

Digital Filters

Abstract: sequency as a generalized frequency is introduced, and the frequency is used as a parameter to distinguish individual functions that belong to sets of nonsinusoidal functions. The sixth chapter is devoted to the study of the Walsh-Hadamard transform (WHT) and algorithms to compute it. The concept of the Walsh spectra and their properties are presented with physical significance. Special attention is given to the analogy between the Walsh-Hadamard and the discrete Fourier transforms. In Chapter 7, a study is made of the generalized Haar, Slant, and discrete cosine transforms. Fast algorithms to compute these transforms

Digital signal analysis

Abstract: Review of least squares, orthogonality and the Fourier series review of continuous transforms transfer functions and convolution sampling and measurement of signals the discrete Fourier transform the fast Fourier transform the z-transform non-recursive digital systems digital and continuous systems simulation of continuous systems analogue and digital filter design review of random functions correlation and power spectra least-squares system design random sequences and spectral estimation.

Signal Processing Algorithms

Abstract: Signal processing algorithms , Signal processing algorithms , مرکز فناوری اطلاعات و اطلاع رسانی کشاورزی

An adaptive IIR structure for sinusoidal enhancement, frequency estimation, and detection

Abstract: An adaptive IIR structure for processing a sinusoidal signal in broad-band noise is introduced. The structure contains three adaptive processors, each of which is computationally very simple. Useful features of the structure include enhancement, frequency estimation, and detection.

ANFIS: adaptive-network-based fuzzy inference system

Abstract: The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference system implemented in the framework of adaptive networks. By using a hybrid learning procedure, the proposed ANFIS can construct an input-output mapping based on both human knowledge (in the form of fuzzy if-then rules) and stipulated input-output data pairs. In the simulation, the ANFIS architecture is employed to model nonlinear functions, identify nonlinear components on-line in a control system, and predict a chaotic time series, all yielding remarkable results. Comparisons with artificial neural networks and earlier work on fuzzy modeling are listed and discussed. Other extensions of the proposed ANFIS and promising applications to automatic control and signal processing are also suggested. >

Cognitive radio: brain-empowered wireless communications

Abstract: 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.

Functional connectivity in the motor cortex of resting human brain using echo-planar MRI.

Abstract: An MRI time course of 512 echo-planar images (EPI) in resting human brain obtained every 250 ms reveals fluctuations in signal intensity in each pixel that have a physiologic origin. Regions of the sensorimotor cortex that were activated secondary to hand movement were identified using functional MRI methodology (FMRI). Time courses of low frequency (< 0.1 Hz) fluctuations in resting brain were observed to have a high degree of temporal correlation (P < 10(-3)) within these regions and also with time courses in several other regions that can be associated with motor function. It is concluded that correlation of low frequency fluctuations, which may arise from fluctuations in blood oxygenation or flow, is a manifestation of functional connectivity of the brain.

Learning to Predict by the Methods of Temporal Differences

Abstract: This article introduces a class of incremental learning procedures specialized for prediction – that is, for using past experience with an incompletely known system to predict its future behavior. Whereas conventional prediction-learning methods assign credit by means of the difference between predicted and actual outcomes, the new methods assign credit by means of the difference between temporally successive predictions. Although such temporal-difference methods have been used in Samuel's checker player, Holland's bucket brigade, and the author's Adaptive Heuristic Critic, they have remained poorly understood. Here we prove their convergence and optimality for special cases and relate them to supervised-learning methods. For most real-world prediction problems, temporal-difference methods require less memory and less peak computation than conventional methods and they produce more accurate predictions. We argue that most problems to which supervised learning is currently applied are really prediction problems of the sort to which temporal-difference methods can be applied to advantage.

Practical Genetic Algorithms

Abstract: Introduction to Optimization The Binary Genetic Algorithm The Continuous Parameter Genetic Algorithm Applications An Added Level of Sophistication Advanced Applications Evolutionary Trends Appendix Glossary Index.