This brief derives a 2-D spectrum estimator from some recent results on the statistical properties of wavelet packet coefficients of random processes. It provides an analysis of the bias of this estimator with respect to the wavelet order. This brief also discusses the performance of this wavelet-based estimator, in comparison with the conventional 2-D Fourier-based spectrum estimator on texture analysis and content-based image retrieval. It highlights the effectiveness of the wavelet-based spectrum estimation.

Download full-text PDF

Source
http://dx.doi.org/10.1109/TIP.2013.2246524DOI Listing

Publication Analysis

Top Keywords

wavelet packet
8
texture analysis
8
spectrum estimator
8
2-d wavelet
4
spectrum
4
packet spectrum
4
spectrum texture
4
analysis derives
4
derives 2-d
4
2-d spectrum
4

Similar Publications

Objectives: The actions and decisions of pilots are directly related to aviation safety. Therefore, understanding the neurological and cognitive processes of pilots during flight is essential. This study aims to investigate the EEG signals of pilots to understand the characteristic changes during the climb and descent stages of flight.

View Article and Find Full Text PDF

When using a fiber optic gyroscope as the core measurement element in an inertial navigation system, its work stability and reliability directly affect the accuracy of the navigation system. The modeling and fault diagnosis of the gyroscope is of great significance in ensuring the high accuracy and long endurance of the inertial system. Traditional diagnostic models often encounter challenges in terms of reliability and accuracy, for example, difficulties in feature extraction, high computational cost, and long training time.

View Article and Find Full Text PDF

To address the challenge of accurately capturing tool wear states in small sample scenarios, this paper proposes a tool wear prediction method that combines XGBoost feature selection with a PSO-BP network. In order to solve the problem of input feature selection and parameter selection in BP neural network, a double-layer programming model of input feature and parameter selection is established, which is solved by XGBoost and PSO. Initially, vibration and cutting force signals from CNC machining are preprocessed using time-domain segmentation, Hampel filtering, and wavelet denoising.

View Article and Find Full Text PDF

Energy feature extraction and visualization of voltage sags using wavelet packet analysis for enhanced power quality monitoring.

Sci Rep

January 2025

Department of Theoretical Electrical Engineering and Diagnostics of Electrical Equipment, Institute of Electrodynamics, National Academy of Sciences of Ukraine, Beresteyskiy, 56, Kyiv-57, Kyiv, 03680, Ukraine.

Power quality (PQ) disturbances, such as voltage sags, are significant issues that can lead to damage in electrical equipment and system downtime. Detecting and classifying these disturbances accurately is essential for maintaining reliable power systems. This paper introduces a novel approach to voltage sag analysis by employing wavelet packet analysis combined with energy-based feature extraction to enhance PQ monitoring.

View Article and Find Full Text PDF
Article Synopsis
  • The study develops a method for detecting debonding defects in concrete-filled steel tube (CFST) structures using piezoelectric sensors and wave analysis.
  • Experimental and numerical tests compare the effectiveness of flat and oblique measurement methods, finding that flat measurements are best for height detection, while oblique measurements excel at length detection.
  • A new mathematical model linking wavelet packet energy to debonding size enhances the detection process, aiding in maintenance and repair of CFST structures through improved accuracy in defect analysis.
View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!