Topic
Harmonic wavelet transform
About: Harmonic wavelet transform is a research topic. Over the lifetime, 9602 publications have been published within this topic receiving 247336 citations.
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12 Dec 2005
TL;DR: A genetic algorithm systemically evolves a new set of coefficients that significantly reduces mean squared error for various classes of one-dimensional signal reconstruction under lossy conditions due to quantization.
Abstract: This paper describes a genetic algorithm that evolves optimized sets of coefficients for one-dimensional signal reconstruction under lossy conditions due to quantization. Beginning with a population of mutated copies of the set of coefficients describing a standard wavelet-based inverse transform, the genetic algorithm systemically evolves a new set of coefficients that significantly reduces mean squared error (relative to the performance of the selected wavelet) for various classes of one-dimensional signals. The evolved transforms also outperform wavelets when subsequently tested against random signals from the same class
36 citations
23 May 1993
TL;DR: Simulations show that the proposed technique for redundancy removal of quantized coefficents of a wavelet transform performs better than classical methods, while maintaining an efficient implementation complexity.
Abstract: A novel technique for redundancy removal of quantized coefficents of a wavelet transform is discussed. This technique rests on the coding of the address of nonzero coefficients using blocks in both lossy and lossless approach. Simulations show that the proposed technique performs better than classical methods, while maintaining an efficient implementation complexity. >
36 citations
TL;DR: In this article, an implementation of the quantum fast Fourier transform algorithm in an entangled system of multilevel atoms is presented, where wave-packet control of the internal states of the ions in the linear ion-trap scheme for quantum computing is used.
Abstract: We propose an implementation of the quantum fast Fourier transform algorithm in an entangled system of multilevel atoms. The Fourier transform occurs naturally in the unitary time evolution of energy eigenstates and is used to define an alternative wave-packet basis for quantum information in the atom. A change of basis from energy levels to wave packets amounts to a discrete quantum Fourier transform within each atom. The algorithm then reduces to a series of conditional phase transforms between two entangled atoms in mixed energy and wave-packet bases. We show how to implement such transforms using wave-packet control of the internal states of the ions in the linear ion-trap scheme for quantum computing.
36 citations
TL;DR: This new approach is based on reusing the calculations of the STFT at consecutive time instants, which leads to significant savings in hardware components with respect to fast Fourier transform based STFTs.
Abstract: This brief presents the feedforward short-time Fourier transform (STFT). This new approach is based on reusing the calculations of the STFT at consecutive time instants. This leads to significant savings in hardware components with respect to fast Fourier transform based STFTs. Furthermore, the feedforward STFT does not have the accumulative error of iterative STFT approaches. As a result, the proposed feedforward STFT presents an excellent tradeoff between hardware utilization and performance.
36 citations
TL;DR: In this paper, a wavelet subspace Hilbert-Huang transform (WSHHT) algorithm was used to identify spectral patterns of very short genes (below 70 bp) in DNA sequences.
Abstract: This paper presents a new algorithm for the analysis of spectral properties of short genes using the wavelet transform and the Hilbert–Huang transform (HHT). A wavelet subspace algorithm combined with the empirical mode decomposition (EMD) is introduced to create subdivided intrinsic mode functions (IMFs) and a cross-correlation analysis is applied to remove pseudo-spectral components. Experiments are carried out on DNA sequences with the double-base (DB) curve representation and the results show that the signal-to-noise ratio of buried signals can be enhanced using the proposed method, yielding significant patterns that are rarely observed with conventional methods. The wavelet subspace Hilbert–Huang transform (WSHHT) algorithm is able to correctly identify spectral patterns of very short genes (below 70 bp) in DNA sequences.
36 citations