A continuous wavelet transform and classification method for delirium motoric subtyping

The usefulness of motor subtypes of delirium is unclear due to inconsistency in subtyping methods and a lack of validation with objective measures of activity. The activity of 40 patients was measured over 24 h with a discrete accelerometer-based activity monitor. The continuous wavelet transform (CWT) with various mother wavelets were applied to accelerometry data from three randomly selected patients with DSM-IV delirium that were readily divided into hyperactive, hypoactive, and mixed motor subtypes. A classification tree used the periods of overall movement as measured by the discrete accelerometer-based monitor as determining factors for which to classify these delirious patients. This data used to create the classification tree were based upon the minimum, maximum, standard deviation, and number of coefficient values, generated over a range of scales by the CWT. The classification tree was subsequently used to define the remaining motoric subtypes. The use of a classification system shows how delirium subtypes can be categorized in relation to overall motoric behavior. The classification system was also implemented to successfully define other patient motoric subtypes. Motor subtypes of delirium defined by observed ward behavior differ in electronically measured activity levels.

Use of the statistical properties of the wavelet-transform coefficients for optimization of integration time in Fourier transform spectrometry

We show that an analysis of the mean and variance of discrete wavelet coefficients of coaveraged time-domain interferograms can be used as a specification for determining when to stop coaveraging. We also show that, if a prediction model built in the wavelet domain is used to determine the composition of unknown samples, a stopping criterion for the coaveraging process can be developed with respect to the uncertainty tolerated in the prediction.

Application of two-dimensional wavelet transform in near-shore X-band radar images

Among existing remote sensing applications, land-based X-band radar is an effective technique to monitor the wave fields, and spatial wave information could be obtained from the radar images. Two-dimensional Fourier Transform (2-D FT) is the common algorithm to derive the spectra of radar images. However, the wave field in the nearshore area is highly non-homogeneous due to wave refraction, shoaling, and other coastal mechanisms. When applied in nearshore radar images, 2-D FT would lead to ambiguity of wave characteristics in wave number domain. In this article, we introduce two-dimensional Wavelet Transform (2-D WT) to capture the non-homogeneity of wave fields from nearshore radar images. The results show that wave number spectra by 2-D WT at six parallel space locations in the given image clearly present the shoaling of nearshore waves. Wave number of the peak wave energy is increasing along the inshore direction, and dominant direction of the spectra changes from South South West (SSW) to West South West (WSW). To verify the results of 2-D WT, wave shoaling in radar images is calculated based on dispersion relation. The theoretical calculation results agree with the results of 2-D WT on the whole. The encouraging performance of 2-D WT indicates its strong capability of revealing the non-homogeneity of wave fields in nearshore X-band radar images.

Objective classification of fabric pilling based on the two-dimensional discrete wavelet transform

A number of methods for automated objective ratings of fabric pilling based on image analysis are described in the literature. The periodic structure of fabrics makes them suitable candidates for frequency domain analysis. We propose a new method of frequency domain analysis based on the two-dimensional discrete wavelet transform to objectively measure pilling intensity in sample images. We present a preliminary evaluation of the proposed method based on analysis of two series of standard pilling evaluation test images. The initial results suggest that the proposed method is feasible, and that the ability of the method to discriminate between levels of pilling intensity depends on the wavelet analysis scale being closely matched to the fabric interyarn pitch. We also present a heuristic method for optimal selection of an analysis wavelet and associated analysis scale.

Evaluating the robustness of objective pilling classification with the two-dimensional discrete wavelet transform

Previously, we proposed a new method of frequency domain analysis based on the two-dimensional discrete wavelet transform to objectively measure pilling intensity in sample fabric images. We have further evaluated this method, and our results indicate that it is robust to small horizontal and/or vertical translations and to significant variations in the brightness of the image under analysis, and is sensitive to rotation and to dilation of the image. These results suggest that as long as precautions are taken to ensure fabric test samples are imaged under consistent conditions of weave/knit pattern alignment (rotation) and apparent interyarn pitch (dilation), the method will yield repeatable results.

Image matching using TI multi-wavelet transform

A multi-resolution image matching technique based on multiwavelets followed by a coarse to fine strategy is presented. The technique addresses the estimation of optimal corresponding points and the corresponding disparity maps in the presence of occlusion, ambiguity and illuminative variations in the two perspective views taken by two different cameras or at different lighting conditions. The problem of occlusion and ambiguity is addressed by a geometric topological refining approach along with the uniqueness constraint whereas the illuminative variation is dealt by using windowed normalized correlation.

Disparity estimation using TI multi-wavelet transform

A multi-resolution image matching technique based on translation invariant discrete multi-wavelet transform followed by a coarse to fine matching strategy is presented. The technique addresses the estimation of optimal corresponding points and the corresponding disparity maps in the presence of occlusion, ambiguity and illuminative variations in the two perspective views taken by two different cameras or at different lighting conditions. The problem of occlusion and ambiguity is addressed explicitly by a geometric optimization approach along with the uniqueness constraint whereas the illuminative variation is dealt with by using windowed normalized correlation on the discrete multi-wavelet coefficients.

Objective assessment of pilling of nonwoven fabrics using the two dimensional discrete wavelet transform

Fabric pilling is a serious problem for the apparel industry, causing an unsightly appearance and premature wear. Woolen products are particularly prone to pilling. Recently, a process for production of woolen nonwoven apparel fabrics has been commercialized in Australia, and may lead to new markets for Australian wool. However, the success of such nonwoven fabrics will partly rely on their propensity to pill. A key element in the control of fabric pilling is the evaluation of resistance to pilling by testing. Resistance to pilling is normally tested in the laboratory by processes that simulate accelerated wear, followed by a manual assessment of the degree of pilling by an expert based on a visual comparison of the sample to a set of test images. To bring more objectivity into the pilling rating process, a number of automated systems based on image analysis have been developed. The authors previously proposed a new method of image analysis based on the two-dimensional discrete wavelet transform to objectively measure the pilling intensity for woven fabrics. This paper presents preliminary work in extending this method to nonwoven fabrics.

Review: The preliminary study for the interpretation of electrochemical noise with continuous wavelet transform

Random fluctuations of the electrical quantities (electrode potential and cell current) in electrochemical systems commonly are referred to as electrochemical noise (ECN). The ECN signal for the corrosion of mild steel in reinforced concrete specimen was analyzed with the Continuous Wavelet Transform (CWT). The original signal was transformed into a time-frequency phase plane with colors representing the coefficients of the CWT. The signal shows a self-similarity structure in the phase plane. Through this way, the chaotic nature of corrosion process is manifested.

Feature extraction and classification of metal detector signals using the wavelet transform and the fuzzy ARTMAP neural network

In this paper, the Fuzzy ARTMAP (FAM) neural network is used to classify metal detector signals into different categories for automated target discrimination. Feature extraction of the metal detector signals is conducted using a wavelet transform technique. The FAM neural network is then employed to classify the extracted features into different target groups. A series of experiments using individual FAM networks and a voting FAM network is conducted. Promising classification accuracy rates are obtained from using individual and voting FAM networks, respectively. The experimental outcomes positively demonstrate the effectiveness of the generated features, and of the FAM network in classifying metal detector signals for automated target discrimination tasks.

Common architecture for discrete wavelet transform analysis and synthesis with sequential and constant processing elements

This paper adresses the problem on processing biological data such as cardiac beats, audio and ultrasonic range, calculating wavelet coefficients in real time, with processor clock running at frequency of present ASIC's and FPGA. The Paralell Filter Architecture for DWT has been improved, calculating wavelet coefficients in real time with hardware reduced to 60%. The new architecture, which also processes IDWT, is implemented with the Radix-2 or the Booth-Wallace Constant multipliers. Including series memory register banks, one integrated circuit Signal Analyzer, ultrasonic range, is presented.