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Articles 1 - 6 of 6

Full-Text Articles in Engineering

Algorithms For Enhancing Pattern Separability, Feature Selection And Incremental Learning With Applications To Gas Sensing Electronic Nose Systems , Robi Polikar Jan 2000

Algorithms For Enhancing Pattern Separability, Feature Selection And Incremental Learning With Applications To Gas Sensing Electronic Nose Systems , Robi Polikar

Retrospective Theses and Dissertations

Three major issues in pattern recognition and data analysis have been addressed in this study and applied to the problem of identification of volatile organic compounds (VOC) for gas sensing applications. Various approaches have been proposed and discussed. These approaches are not only applicable to the VOC identification, but also to a variety of pattern recognition and data analysis problems. In particular, (1) enhancing pattern separability for challenging classification problems, (2) optimum feature selection problem, and (3) incremental learning for neural networks have been investigated;Three different approaches are proposed for enhancing pattern separability for classification of closely spaced, or ...


3-D Defect Profile Reconstruction From Magnetic Flux Leakage Signatures Using Wavelet Basis Function Neural Networks , Kyungtae Hwang Jan 2000

3-D Defect Profile Reconstruction From Magnetic Flux Leakage Signatures Using Wavelet Basis Function Neural Networks , Kyungtae Hwang

Retrospective Theses and Dissertations

The most popular technique for inspecting natural gas pipelines involves the use of magnetic flux leakage (MFL) methods. The measured MFL signal is interpreted to obtain information concerning the structural integrity of the pipe. Defect characterization involves the task of calculating the shape and size of defects based on the information contained in the signal. An accurate estimate of the defect profile allows assessment of the safe operating pressure of the pipe. Artificial neural networks (ANNs) have been employed for characterizing defects in the past. However, conventional neural networks such as radial basis function neural networks are not always suitable ...


Multistage Adaptive Noise Cancellation And Multi-Dimensional Signal Processing For Ultrasonic Nondestructive Evaluation , Jae-Joon Kim Jan 2000

Multistage Adaptive Noise Cancellation And Multi-Dimensional Signal Processing For Ultrasonic Nondestructive Evaluation , Jae-Joon Kim

Retrospective Theses and Dissertations

Ultrasonic signal processing presents several challenges with respect to both noise removal and interpretation. The interference of unwanted reflections from material grain structure can render the data extremely noisy and mask the detection of small flaws. It is therefore imperative to separate the flaw reflections from grain noise. The interpretation or classification of ultrasonic signals in general is relatively difficult due to the complexity of the physical process and similarity of signals from various classes of reflectors;Adaptive noise cancellation techniques are ideally suited for reducing spatially varying noise due to the grain structure of material in ultrasonic nondestructive evaluation ...


Image Analysis Using Multiscale Boundary Extraction Algorithm , Nawapak Eua-Anant Jan 2000

Image Analysis Using Multiscale Boundary Extraction Algorithm , Nawapak Eua-Anant

Retrospective Theses and Dissertations

The complete analysis and interpretation of the information in image data is a complex process. This dissertation presents 3 major contributions to image analysis, namely, global multiscale detection, local scale analysis, and boundary extraction. Global scale analysis is related to identification of the various scales presented in the image. A new approach for global scale analysis is developed based on the differential power spectrum normalized variance ratio (DPSNVR). The DPSNVR is the ratio of the second order normalized central moment of the power spectrum of the image to that of the multiscale differential mask. Local maxima in DPSNVR graph directly ...


Texture Representation Using Wavelet Filterbanks , Nam-Deuk Kim Jan 2000

Texture Representation Using Wavelet Filterbanks , Nam-Deuk Kim

Retrospective Theses and Dissertations

Texture analysis is a fundamental issue in image analysis and computer vision. While considerable research has been carried out in the texture analysis domain, problems relating to texture representation have been addressed only partially and active research is continuing. The vast majority of algorithms for texture analysis make either an explicit or implicit assumption that all images are captured under the same measurement conditions, such as orientation and illumination. These assumptions are often unrealistic in many practical applications;This dissertation addresses the viewpoint-invariance problem in texture classification by introducing a rotated wavelet filterbank. The proposed filterbank, in conjunction with a ...


Wavelet Based Multiresolution Zero-Crossing Representations , Muhammad Akbar Khan Afzal Jan 2000

Wavelet Based Multiresolution Zero-Crossing Representations , Muhammad Akbar Khan Afzal

Retrospective Theses and Dissertations

This research proposes a new signal representation based an multiscale zero-crossings of the signal. It is shown that the original signal is uniquely characterized by its zero-crossing representation. The representation is insensitive to translation of the input signal. In this approach, the zero-crossing information is supplemented with the first moment of the signal to stabilize the representation. This technique, initially suggested by others for wavelet transform zero-crossings, results in a method for reconstructing the original signal from its multiscale zero-crossings. The signal recovery algorithm is fast and efficient, and suggests completeness of the representation. The representation has been successfully applied ...