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Articles 61 - 71 of 71
Full-Text Articles in Engineering
A Committee Of Neural Networks For Automatic Speaker Recognition (Asr) Systems, Viresh Moonasar, Ganesh K. Venayagamoorthy
A Committee Of Neural Networks For Automatic Speaker Recognition (Asr) Systems, Viresh Moonasar, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
This paper describes how the results of speaker verification systems can be improved and made robust with the use of a committee of neural networks for pattern recognition rather than the conventional single-network decision system. It illustrates the use of a supervised learning vector quantization neural network as the pattern classifier. Linear predictive coding and cepstral signal processing techniques are utilized to form hybrid feature parameter vectors to combat the effect of decreased recognition success with increased group size (number of speakers to be recognized)
A Dynamic Parameter Tuning Algorithm For Rbf Neural Networks, Junxu Li
A Dynamic Parameter Tuning Algorithm For Rbf Neural Networks, Junxu Li
Electronic Theses and Dissertations
The objective of this thesis is to present a methodology for fine-tuning the parameters of radial basis function (RBF) neural networks, thus improving their performance. Three main parameters affect the performance of an RBF network. They are the centers and widths of the RBF nodes and the weights associated with each node. A gridded center and orthogonal search algorithm have been used to initially determine the parameters of the RBF network. A parameter tuning algorithm has been developed to optimize these parameters and improve the performance of the RBF network. When necessary, the recursive least square solution may be used …
Data-Driven Homologue Matching For Chromosome Identification, R. Joe Stanley, James M. Keller, Paul D. Gader, Charles William Caldwell
Data-Driven Homologue Matching For Chromosome Identification, R. Joe Stanley, James M. Keller, Paul D. Gader, Charles William Caldwell
Electrical and Computer Engineering Faculty Research & Creative Works
Karyotyping involves the visualization and classification of chromosomes into standard classes. In "normal" human metaphase spreads, chromosomes occur in homologous pairs for the autosomal classes 1-22, and X chromosome for females. Many existing approaches for performing automated human chromosome image analysis presuppose cell normalcy, containing 46 chromosomes within a metaphase spread with two chromosomes per class. This is an acceptable assumption for routine automated chromosome image analysis. However, many genetic abnormalities are directly linked to structural or numerical aberrations of chromosomes within the metaphase spread. Thus, two chromosomes per class cannot be assumed for anomaly analysis. This paper presents the …
Voice Recognition Using Neural Networks, Ganesh K. Venayagamoorthy, Viresh Moonasar, K. Sandrasegaran
Voice Recognition Using Neural Networks, Ganesh K. Venayagamoorthy, Viresh Moonasar, K. Sandrasegaran
Electrical and Computer Engineering Faculty Research & Creative Works
One solution to the crime and illegal immigration problem in South Africa may be the use of biometric techniques and technology. Biometrics are methods for recognizing a user based on unique physiological and/or behavioural characteristics of the user. This paper presents the results of ongoing work into using neural networks for voice recognition
The General Approximation Theorem, Donald C. Wunsch, Alexander N. Gorban
The General Approximation Theorem, Donald C. Wunsch, Alexander N. Gorban
Electrical and Computer Engineering Faculty Research & Creative Works
A general approximation theorem is proved. It uniformly envelopes both the classical Stone theorem and approximation of functions of several variables by means of superpositions and linear combinations of functions of one variable. This theorem is interpreted as a statement on universal approximating possibilities ("approximating omnipotence") of arbitrary nonlinearity. For the neural networks, our result states that the function of neuron activation must be nonlinear, and nothing else
Fault Classification Using Kohonen Feature Mapping, Badrul H. Chowdhury, Kunyu Wang
Fault Classification Using Kohonen Feature Mapping, Badrul H. Chowdhury, Kunyu Wang
Electrical and Computer Engineering Faculty Research & Creative Works
Applications of neural networks to power system fault diagnosis have provided positive results and shown advantages in process speed over conventional approaches. This paper describes the application of a Kohonen neural network to fault detection and classification using the fundamental components of currents and voltages. The Electromagnetic Transients Program is used to obtain fault patterns for the training and testing of neural networks. Accurate classifications are obtained for all types of possible short circuit faults on test systems representing high voltage transmission lines. Short training time makes the Kohonen network suitable for on-line power system fault diagnosis. The method introduced …
Terrain Classification In Sar Images Using Principal Components Analysis And Neural Networks, Mahmood R. Azimi-Sadjadi, Saleem Ghaloum, R. Zoughi
Terrain Classification In Sar Images Using Principal Components Analysis And Neural Networks, Mahmood R. Azimi-Sadjadi, Saleem Ghaloum, R. Zoughi
Electrical and Computer Engineering Faculty Research & Creative Works
The development of a neural network-based classifier for classifying three distinct scenes (urban, park and water) from several polarized SAR images of San Francisco Bay area is discussed. The principal component (PC) scheme or Karhunen-Loeve (KL) transform is used to extract the salient features of the input data, and to reduce the dimensionality of the feature space prior to the application to the neural networks. Employing PC scheme along with polarized images used in this study, led to substantial improvements in the classification rates when compared with previous studies. When a combined polarization architecture is used the classification rate for …
Detection, Location, And Quantification Of Structural Damage By Neural-Netprocessed Moire Profilometry, Barry G. Grossman, Frank S. Gonzalez, Joel H. Blatt, Jeffery A. Hooker
Detection, Location, And Quantification Of Structural Damage By Neural-Netprocessed Moire Profilometry, Barry G. Grossman, Frank S. Gonzalez, Joel H. Blatt, Jeffery A. Hooker
Electrical Engineering and Computer Science Faculty Publications
The development of efficient high speed techniques to recognize, locate, and quantify damage is vitally important for successful automated inspection systems such as ones used for the inspection of undersea pipelines. Two critical problems must be solved to achieve these goals: the reduction of nonuseful information present in the video image and automatic recognition and quantification of extent and location of damage. Artificial neural network processed moire profilometry appears to be a promising technique to accomplish this. Real time video moire techniques have been developed which clearly distinguish damaged and undamaged areas on structures, thus reducing the amount of extraneous …
Composite Damage Assessment Employing An Optical Neural Network Processor And An Embedded Fiberoptic Sensor Array, Barry G. Grossman, Xing Gao, Michael H. Thursby
Composite Damage Assessment Employing An Optical Neural Network Processor And An Embedded Fiberoptic Sensor Array, Barry G. Grossman, Xing Gao, Michael H. Thursby
Electrical Engineering and Computer Science Faculty Publications
This paper discusses a novel approach for composite damage assessment with potential for DoD, NASA, and commercial applications. We have analyzed and modeled a two dimensional composite damage assessment system for real-time monitoring and determination of damage location in a composite structure. The system combines two techniques: a fiberoptic strain sensor array and an optical neural network processor. A two dimensional fiberoptic sensor array embedded in the composite structure during the manufacturing process can be used to detect changes in the mechanical strain distribution caused by subsequent damage to the structure. The optical processor, a pre-trained Kohonen neural network, has …
An Industrial Application To Neural Networks To Reusable Design, Donald C. Wunsch, R. Escobedo, T. P. Caudell, S. D. G. Smith, G. C. Johnson
An Industrial Application To Neural Networks To Reusable Design, Donald C. Wunsch, R. Escobedo, T. P. Caudell, S. D. G. Smith, G. C. Johnson
Electrical and Computer Engineering Faculty Research & Creative Works
Summary form only given, as follows. The feasibility of training an adaptive resonance theory (ART-1) network to first cluster aircraft parts into families, and then to recall the most similar family when presented a new part has been demonstrated, ART-1 networks were used to adaptively group similar input vectors. The inputs to the network were generated directly from computer-aided designs of the parts and consist of binary vectors which represent bit maps of the features of the parts. This application, referred to as group technology, is of large practical value to industry, making it possible to avoid duplication of design …
Optical Processors For Smart Structures, Barry G. Grossman, Howard Hou, Ramzi H. Nassar
Optical Processors For Smart Structures, Barry G. Grossman, Howard Hou, Ramzi H. Nassar
Electrical Engineering and Computer Science Faculty Publications
For underwater fiber-optic sensor arrays containing hundreds of sensors as well as smart aerospace structures and skins using fiber-optic strain sensor arrays, the output of the sensors are optical signals that are a function of the measurand. We are also employing optical signals to energize smart-structure actuators (shape-memory alloys). An all-optical processor would thus seem to be logical choice for the processor since we must simultaneously process, in real time, multiple optical input (sensor) signals and generate multiple output (actuator) signals. In addition, with an all-optical processor, there would be no reduction in processor performance due to converting between optical …