Abstract: 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 l2-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.

Abstract: 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 l2-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.

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

Abstract: 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 (?s (k)), P after a next step (P(k+1)) and its elevation angle at k+1 (?s (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 ?S (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 ?S (k+1).

Abstract: 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 (?s (k)), P after a next step (P(k+1)) and its elevation angle at k+1 (?s (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 ?S (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 ?S (k+1).

Abstract: 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.