Energy Based Split Vector Quantizer Employing Signal Representation in Multiple Transform Domains.

This invention relates to representation of one and multidimensional signal vectors in nonorgothonal domains and design of Vector Quantizers that can be chosen among these representations. There is presented a Vector Quantization technique in multiple nonorthogonal domains for both waveform and model based signal characterization. An iterative codebook accuracy enhancement algorithm, applicable to both waveform and model based Vector Quantization in multiple nonorthogonal domains, which yields further improvement in signal coding performance, is disclosed. Further, Vector Quantization in in nonorthogonal domains is applied to speech and exhibits clear performance improvements of reconstruction quality for the same bit rate compared to existing single domain Vector Quantization techniques. The technique disclosed herein can be easily extended to several other one and multidimensional signal classes

AWG Having Arbitrary Factor Interpolator and Fixed Frequency DAC Sampling Clock

An AWG includes a waveform memory providing a digital waveform signal at a sample rate and an arbitrary factor interpolator (AFI) coupled to receive the digital waveform signal or a processed digital waveform signal. A complex mixer for carrier modulation is coupled to the AFI which outputs a complex band pass signal. A DAC is coupled to an ouput of the complex mixer for receiving the complex band pass signal to provide an analog output signal. A fixed frequency sample clock clocks the DAC to provide a fixed DAC sample rate. The DAC provides a data clock signal to a sample request controller that generates a sample request signal that is coupled to the waveform memory for requesting the digital waveform signal form the waveform memory. The interpolated digital signal is sampled at the fixed DAC sample rate independent of the sample rate of digital waveform signal

Adaptive Methods Employing Optimal Convergence Factors For Processing Complex Signals and Systems

Complex adaptive methods for complex information processing employ optimal individual convergence factors for real and imaginary components of the weight vector. For wireless receivers operating on QPSK, a Complex IA-ICA performs better than existing Complex Fast-ICA methods in terms of accuracy and convergence speed, can process such complex signals in time-varying channels, and employs time-varying and time-invariant convergence factors, independent for the real and imaginary components of the system parameters, and provide individual or group system parameter adjustments. Such systems employ the within complex adaptive ICA with individual element adaptation (Complex IA-ICA). In adaptive beamforming, system identification and other adaptive systems based on the Least Squares method, complex least mean squares methods, with optimally and automatically derived convergence factors, are employed and which perform much better in terms of convergence speed and accuracy, when compared to the traditional Complex LMS and Block Complex LMS methods

A gradient-based optimum block adaptation ICA technique for interference suppression in highly dynamic communication channels

The fast fixed-point independent component analysis (ICA) algorithm has been widely used in various applications because of its fast convergence and superior performance. However, in a highly dynamic environment, real-time adaptation is necessary to track the variations of the mixing matrix. In this scenario, the gradient-based online learning algorithm performs better, but its convergence is slow, and depends on a proper choice of convergence factor. This paper develops a gradient-based optimum block adaptive ICA algorithm (OBA/ICA) that combines the advantages of the two algorithms. Simulation results for telecommunication applications indicate that the resulting performance is superior under time-varying conditions, which is particularly useful in mobile communications. Copyright (C) 2006 Hindawi Publishing Corporation. All rights reserved

Self Designing Intelligent Signal Processing System capable of evolutional Learning for Classification/Recognition of One and Multidimensional Signals

A Self-Designing Intelligent Signal Processing System Capable of Evolutional Learning for Classification/Recognition of One and Multidimensional Signals is described which classifies data by an evolutionary learning environment that develops the features and algorithms that are best suited for the recognition problem under consideration. The System adaptively learns what data need to be extracted in order to recognize the given pattern with the least amount of processing. The System decides what features need to be selected for classification and/or recognition to fit a certain structure that leads to the least amount of processing according to the nature of the given data. The System disclosed herein is capable of recognizing an enormously large number of patterns with a high accuracy

Optical Profilometers Using Adaptive Signal Processing

A method of adaptive signal processing has been proposed as the basis of a new generation of interferometric optical profilometers for measuring surfaces. The proposed profilometers would be portable, hand-held units. Sizes could be thus reduced because the adaptive-signal-processing method would make it possible to substitute lower-power coherent light sources (e.g., laser diodes) for white light sources and would eliminate the need for most of the optical components of current white-light profilometers. The adaptive-signal-processing method would make it possible to attain scanning ranges of the order of decimeters in the proposed profilometers

Method of adaptive solar tracking using variable step size

A method for controlling a photovoltaic (PV) panel in a PV system including a computing device that provides motor control signals and implements an iterative adaprtive control (IAC) algorithm for adjusting an angle of the PV panel. The IAC algorithm relates P at a current time k (P(k)), an elevation angle of the PV panel at k (0s(k)), P after a next step (P(k+1)) and an elevation angle of the PV panel at k+1 (0s(k+1)). The algorithm generates a perturbed power value P(k+1) to provide a power perturbation to P(k), and calculates 0s(k+1) using P(k+1). The motor control signals cause the motor to position the PV panel to achieve 0s(k+1). A change in P resulting from the positioning is compared to a predetermined change limit, and only if the change in P is greater than/equal to the change limit, again sensing P, and repeating the generating, calculating and positioning

Iterative Adaptive Solar Tracking Having Variable Step Size

A system controller for position controlling a photovoltaic (PV) panel in a PV system including a power sensor sensing output power (P), and a motor for positioning the PV panel. The system controller includes a computing device having memory that provides motor control signal and implements an iterative adaptive control (IAC) algorithm stored in the memory for adjusting an angle of the PV panel. The IAC algorithm includes an iterative relation that relates P at current time k (P(k)), its elevation angle at k (?s(k)), P after a next step (P(k+1)) and its elevation angle at k+1(?s(k+1)). The IAV algorithm generates a perturbed power value P(k+1) to provide a power perturbation to P(k), and calculates a position angle ?s(k+1) of the PV panel using the perturbed power value. The motor control signals from the computing device cause the motor to oposition the PV panel to achieve ?s(k+1)