The application of wavelets vector quantization of Polish speech

The paper presents a concept of vector quantization of words uttered in the Polish language. Columns or rows of the matrix obtained as a result of time-frequency analysis of chosen words are vectors used for the further analysis. As a tool in the process of vector quantization was used the Wavelet Packet Transform (WPT) in which the signal decomposition scale is similar to the mel frequency scale (see method - Mel Frequency Cepstral Coefficients – MFCC). Such analysis allowed us to choose the best useful properties for the word recognition. Both column (in time) and row (in frequency) analysis are formulated in the form of computer procedures and compared. We hope such studies will be a starting point for further work on the system Automatic Speech Recognition (ASR)

Discrete Wavelet Transforms

The discrete wavelet transform (DWT) algorithms have a firm position in processing of signals in several areas of research and industry. As DWT provides both octave-scale frequency and spatial timing of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Algorithms and Applications reviews the recent progress in discrete wavelet transform algorithms and applications. The book covers a wide range of methods (e.g. lifting, shift invariance, multi-scale analysis) for constructing DWTs. The book chapters are organized into four major parts. Part I describes the progress in hardware implementations of the DWT algorithms. Applications include multitone modulation for ADSL and equalization techniques, a scalable architecture for FPGA-implementation, lifting based algorithm for VLSI implementation, comparison between DWT and FFT based OFDM and modified SPIHT codec. Part II addresses image processing algorithms such as multiresolution approach for edge detection, low bit rate image compression, low complexity implementation of CQF wavelets and compression of multi-component images. Part III focuses watermaking DWT algorithms. Finally, Part IV describes shift invariant DWTs, DC lossless property, DWT based analysis and estimation of colored noise and an application of the wavelet Galerkin method. The chapters of the present book consist of both tutorial and highly advanced material. Therefore, the book is intended to be a reference text for graduate students and researchers to obtain state-of-the-art knowledge on specific applications

Models and analysis of vocal emissions for biomedical applications

This book of Proceedings collects the papers presented at the 3rd International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2003, held 10-12 December 2003, Firenze, Italy. The workshop is organised every two years, and aims to stimulate contacts between specialists active in research and industrial developments, in the area of voice analysis for biomedical applications. The scope of the Workshop includes all aspects of voice modelling and analysis, ranging from fundamental research to all kinds of biomedical applications and related established and advanced technologies

Multi-scale texture segmentation of synthetic aperture radar images

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Detection and localization of aerosol releases from sparse sensor measurements

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 111-114).In this thesis we focus on addressing two aspects pertinent to biological release detection. The first is that of detecting and localizing an aerosolized particle release using a sparse array of sensors. The problem is challenging for several reasons. It is often the case that sensors are costly and consequently only a sparse deployment is possible. Additionally, while dynamic models can be formulated in many environmental conditions, the underlying model parameters may not be precisely known. The combination of these two issues impacts the effectiveness of inference approaches. We restrict ourselves to propagation models consisting of diffusion plus transport according to a Gaussian puff model. We derive optimal inference algorithms utilizing sparse sensor measurements, provided the model parameterization is known precisely. The primary assumptions are that the mean wind field is deterministically known and that the Gaussian puff model is valid. Under these assumptions, we characterize the change in performance of detection, time-to-detection and localization as a function of the number of sensors. We then examine some performance impacts when the underlying dynamical model deviates from the assumed model. In addition to detecting an abrupt change in particles in an environment, it is also important to be able to classify the releases as not all contaminants are of interest. For this reason, the second aspect of addressed is feature extraction, a stage where sensor measurements are reduced to a set of pertinent features that can be used as an input to the classifier.(cont.) Shift invariance of the feature set is critical and thus the Dual Tree Complex Wavelet Transform (DT CWT) is proposed as the wavelet feature domain.by Emily Beth Fox.M.Eng

Selection of the Most Suitable Decomposition Filter for the Measurement of Fluctuating Harmonics

The proliferation of nonlinear loads in both industrial and residential distribution grids leads to undesirable nonsinusoidal and fluctuating harmonic pollution on voltage and current waveforms. New analysis tools, such as wavelets, are being used to overcome the problems posed by the use of the Fourier transform when analyzing complex waveforms. Nevertheless, the selection of the wavelet basis must be done carefully to minimize spectral leakage due to the nonexact frequency discrimination. In this context, this paper proposes an objective method for comparing different wavelet families for the measurement of harmonic contents. This methodology is applicable for determining the best filter among the 53 preselected structures according to the following requirements: frequency selectivity, computational complexity, convolution results, and observed spectral leakage. With all these considerations, the Butterworth infinite-impulse response filter of order 29 was found to be the best wavelet decomposition structure to achieve an effective harmonic analysis up to the 50th order

Tumour grading and discrimination based on class assignment and quantitative texture analysis techniques

Medical imaging represents the utilisation of technology in biology for the purpose of noninvasively revealing the internal structure of the organs of the human body. It is a way to improve the quality of the patient's life through a more precise and rapid diagnosis, and with limited side-effects, leading to an effective overall treatment procedure. The main objective of this thesis is to propose novel tumour discrimination techniques that cover both micro and macro-scale textures encountered in computed tomography (CI') and digital microscopy (DM) modalities, respectively. Image texture can provide significant information on the (ab)normality of tissue, and this thesis expands this idea to tumour texture grading and classification. The fractal dimension (FO) as a texture measure was applied to contrast enhanced CT lung tumour images in an aim to improve tumour grading accuracy from conventional CI' modality, and quantitative performance analysis showed an accuracy of 83.30% in distinguishing between advanced (aggressive) and early stage (non-aggressive) malignant tumours. A different approach was adopted for subtype discrimination of brain tumour OM images via a set of statistical and model-based texture analysis algorithms. The combined Gaussian Markov random field and run-length matrix texture measures outperformed all other combinations, achieving an overall class assignment classification accuracy of 92.50%. Also two new histopathological multi resolution approaches based on applying the FO as the best bases selection for discrete wavelet packet transform, and when fused with the Gabor filters' energy output improved the accuracy to 91.25% and 95.00%, respectively. While noise is quite common in all medical imaging modalities, the impact of noise on the applied texture measures was assessed as well. The developed lung and brain texture analysis techniques can improve the physician's ability to detect and analyse pathologies leading for a more reliable diagnosis and treatment of disease

Cognitive Radio Systems

Cognitive radio is a hot research area for future wireless communications in the recent years. In order to increase the spectrum utilization, cognitive radio makes it possible for unlicensed users to access the spectrum unoccupied by licensed users. Cognitive radio let the equipments more intelligent to communicate with each other in a spectrum-aware manner and provide a new approach for the co-existence of multiple wireless systems. The goal of this book is to provide highlights of the current research topics in the field of cognitive radio systems. The book consists of 17 chapters, addressing various problems in cognitive radio systems