University of Central Florida Research Foundation Inc

Dme Corporation

Wasfy Mikhael

Raghuram Ranganathan

Issa E Batarseh

Nasser Kutkut

Venkatesh Krishnan

Matthew T Hunter

Ahmed Aldhahab

Moataz M Abdelwahab

Manal M Abdelwahab

A computer executed method for supervised facial recognition comprising the operations of preprocessing, feature extraction and recognition. Preprocessing may comprise dividing received face images into several subimages, converting the different face image (or subimage) dimensions into a common dimension and/or converting the datatypes of all of the face images (or subimages) into an appropriate datatype. In feature extraction, 2D DMWT is used to extract information from the face images. Application of the 2D DMWT may be followed by FastICA. FastICA, or, in cases where FastICA is not used, 2D DMWT, may be followed by application of the l-norm and/or eigendecomposition to obtain discriminating and independent features. The resulting independent features are fed into the recognition phase, which may use a neural network, to identify an unknown face image.

Supervised facial recognition system and method

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 adaptive 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 (&#x3b8;(k)), P after a next step (P(k+1)) and an elevation angle of the PV panel at k+1 (&#x3b8;(k+1)). The algorithm generates a perturbed power value P(k+1) to provide a power perturbation to P(k), and calculates &#x3b8;(k+1) using P(k+1). The motor control signals cause the motor to position the PV panel to achieve &#x3b8;(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 &#x2267; the change limit, again sensing P, and repeating the generating, calculating and positioning.

Method of adaptive solar tracking using 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 signals 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 (&#x3b8;(k)), P after a next step (P(k+1)) and its elevation angle at k+1 (&#x3b8;(k+1)). The IAC algorithm generates a perturbed power value P(k+1) to provide a power perturbation to P(k), and calculates a position angle &#x3b8;(k+1) of the PV panel using the perturbed power value. The motor control signals from the computing device cause the motor to position the PV panel to achieve &#x3b8;(k+1).

Iterative adaptive solar tracking having variable step size

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 square 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.

Adaptive methods employing optimal convergence factors for processing complex signals and systems

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 output 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 from the waveform memory. The interpolated digital signal is sampled at the fixed DAC sample rate independent of the sample rate of digital waveform signal.

AWG having arbitrary factor interpolator and fixed frequency DAC sampling clock

Methods, system apparatus and devices for classification and recognition that is based on principal component analysis and is implemented in the transform domain using the fast two-dimensional PCA to processes the signal in the transform domain. The signal is represented with a reduced number of coefficients, therefore reducing the storage requirements and computational complexity while yielding high recognition accuracy.

Recognition and classification based on principal component analysis in the transform domain

The invention relates to representation of one and multidimensional signal vectors in multiple nonorthogonal 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 multiple 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.

Energy based split vector quantizer employing signal representation in multiple transform domains

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.

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Winter Springs, FL, United States of America

Wasfy Mikhael