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Full-Text Articles in Engineering
Signal Approximation With A Wavelet Neural Network, Charles M. Westphal
Signal Approximation With A Wavelet Neural Network, Charles M. Westphal
Theses and Dissertations
This study investigated the use of Wavelet Neural Networks (WNN) for signal approximation. The particular wavelet function used in this analysis consisted of a summation of sigmoidal functions (a sigmoidal wavelet). The sigmoidal wavelet has the advantage of being easily implemented in hardware via specialized electronic devices like the Intel Electronically Trainable Analog Neural Network (ETANN) chip. The WNN representation allows the determination of the number of hidden-layer nodes required to achieve a desired level of approximation accuracy. Results show that a bandlimited signal can be accurately approximated with a WNN trained with irregularly sampled data. Signal approximation, Wavelet neural …
Face Recognition With Neural Networks, Dennis L. Krepp
Face Recognition With Neural Networks, Dennis L. Krepp
Theses and Dissertations
This study investigated neural networks for face verification and classification. The research concentrated on developing a neural network based feature extractor and/or classifier to perform authorized user verification in a realistic work environment. Recognition accuracy, system assumptions, training time, and execution time were analyzed to determine the feasibility of a neural network approach. Data was collected using a camcorder and two segmentation schemes: manual segmentation and motion-based, automatic segmentation. Data consisted of over 2000. 32x32 pixel, 8 bit gray scale images of 52 subjects; each subject had two to ten days worth of images collected. Several training and test sets …
Etann Hardware Implementation For Radar Emitter Identification, James B. Calvin Jr.
Etann Hardware Implementation For Radar Emitter Identification, James B. Calvin Jr.
Theses and Dissertations
This study investigated classification of 30 radar emitters with 16 signal features using Intel's 80170NX chip, the Electronically Trainable Analog Neural Network (ETANN). Software tools were developed to characterize the ETANN sigmoidal transfer function for use in a custom simulator, known as Neural Graphics. Neural Graphics operates on a Silicon Graphics workstation. The Intel Neural Network Training System simulators were used in early experiments, but were found to be inefficient in training on data used in this research. Using a modified Neural Graphics simulator, single chip and multi-chip experiments were performed to provide benchmark results prior to performing chip-in-loop training. …
A New Method To Optimize The Satellite Broadcasting Schedules Using The Mean Field Annealing Of A Neural Network, Youyi Yu
Theses
This thesis reports a new method for optimizing satellite broadcasting schedules based on the Hopfield neural model in combination with the mean field annealing theory. A clamping technique is used with an associative matrix, thus reducing the dimensions of the solution space. A formula for estimating the critical temperature for the mean field annealing procedure is derived, hence enabling the updating of the mean field theory equations to be more economical. Several factors on the numerical implementation of the mean field equations using a straightforward iteration method that may cause divergence are discussed; methods to avoid this kind of divergence …
A Gaze-Addressing Communication System Using Artificial Neural Networks, Gabriel Baud-Bovy
A Gaze-Addressing Communication System Using Artificial Neural Networks, Gabriel Baud-Bovy
Dissertations and Theses
Severe motor disabilities can render a person almost completely incapable of communication. Nevertheless, in many cases, the sensory systems are intact and the eye movements are still under good control. In these cases, one can use a device such as the Brain Response Interface (BRI) to command a remote control (e.g. room temperature, bed position), a word-processor, a speech synthesizer, and so on.