Multiresolution analysis

About: Multiresolution analysis is a research topic. Over the lifetime, 4032 publications have been published within this topic receiving 140743 citations. The topic is also known as: Multiresolution analysis, MRA.

Improved Techniques for an Iris Recognition System with High Performance

Abstract: We describe in this paper efficient techniques for iris recognition system with high performance from the practical point of view. These techniques range every step for an iris recognition system from the image acquisition step to the final step, the pattern matching, and contain as follows: a method of evaluating the quality of an image in the image acquisition step and excluding it from the subsequent processing if it is not appropriate, a bisection-based Hough transform method on the edge components for detecting the center of the pupil and localizing the iris area from an eye image, an elastic body model for transforming the localized iris area into a simple coordination system, and a compact and efficient feature extraction method which is based on 2D multiresolution wavelet transform. By exploiting these techniques, we can improve the system performance in terms of computationally efficient, and more accurate and robust against noises.

Implementation of Wavelet-Based Controller for Battery Storage System of Hybrid Electric Vehicles

Abstract: This paper presents a wavelet-based multiresolution proportional integral derivative (MRPID) controller for temperature control of the ambient air of battery storage system of the hybrid electric vehicles. In the proposed wavelet MRPID controller, the discrete wavelet transform (DWT) is used to decompose temperature error into frequency components at various resolution of the error signal. The wavelet transformed coefficients are scaled by suitable gains and then added together to generate the control signal of thermal system. The proposed wavelet controller is implemented for battery storage system in both simulation and experiments. The digital signal processor board is used for real-time implementation of the proposed controller. The performance of the proposed wavelet-based MRPID controller is compared with conventional proportional-integral-derivative (PID) and adaptive neural network controllers. The proposed wavelet controller for battery storage system is found more robust and quicker than the conventional and adaptive controllers.

Multivariate periodic wavelets

Abstract: A general construction of a multiresolution analysis with a matrix dilation for periodic functions is described, together with a method of finding wavelet biorthogonal bases. The convergence of expansions with respect to these bases is studied. §

Multiresolution wedgelet transform for image processing

Abstract: Edges in images convey a great deal of information, but wavelet transforms do not provide an economical representation. Thus, popular wavelet-based compression and restoration techniques perform poorly in the presence of edges. We present her a new multiresolution wedgelet transform based on the lifting construction. This transform provides an economical edge representation and thus offers the potential for improved image processing. We demonstrate this potential with applications in image denoising.

A Wavelet-Based Approach to Preserve Privacy for Classification Mining*

Abstract: Despite the commercial success of data mining, a major drawback has been acknowledged across academic, industry, and government sectors, namely, the issue of violating the privacy of individuals. We propose a data transformation method based on wavelets to disguise private data while preserving the original classification patterns. Wavelet transformations have been used extensively in signal processing for data reduction, multiresolution analysis, and removing noise from data. In our implementation, two commonly used wavelet transforms, the Haar and the Daub-4 transforms, are tested for pattern and privacy preservation in classification mining tasks. Empirical results confirm that the Haar and the Daub-4 transforms preserve the classification patterns and preserve the privacy for real valued data.