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Full-Text Articles in Physical Sciences and Mathematics
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. …
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 …
Design And Implementation Of Two Text Recognition Algorithms, Madhumathi Yendamuri
Design And Implementation Of Two Text Recognition Algorithms, Madhumathi Yendamuri
Theses
This report presents two algorithms for text recognition. One is a neural-based orthogonal vector with pseudo-inverse approach for pattern recognition. A method to generate N orthogonal vectors for an N-neuron network is also presented. This approach converges the input to the corresponding orthogonal vector representing the prototype vector. This approach can restore an image to the original image and thus has error recovery capacility. Also, the concept of sub-networking is applied to this approach to enhance the memory capacity of the neural network. This concept drastically increases the memory capacity of the network and also causes a reduction of the …
Design Of An Artificial Neural Network Based Tactile Sensor For The Utah/Mit Dexterous Hand, Jeffery D. Nering
Design Of An Artificial Neural Network Based Tactile Sensor For The Utah/Mit Dexterous Hand, Jeffery D. Nering
Theses and Dissertations
The Neural Tactile Sensor (NTS) is a high resolution, easily manufactured tactile sensor consisting of electrodes, a thin resistive 'skin', and pattern recognition circuitry that is capable of resolving dynamic and static contact location, force, and slip throughout the continuum of the sensor's active region. The sensor operates by means of a resistive 'skin' harboring the electric field generated when a current is injected into it, and a plurality of electrodes for taking measurements of said electric field. When current flows through the resistive medium from the location of tactile contact, an electric field within the resistive medium is established, …
Searching For Orthogonal States Of Neural Networks, Heng Wang
Searching For Orthogonal States Of Neural Networks, Heng Wang
Theses
Two approaches to find orthogonal states of neural network are presented in the paper. The first approach is a recursive one, it builds N orthogonal vectors based on N /2 orthogonal vectors. The second approach is a formula approach, in which orthogonal vectors can be obtained using a formula. Using these approaches, orthogonal states of neural network are found. Some properties of the neural network built on these orthogonal vectors are presented in Appendix A and some examples are given in Appendix B.