A security system based on human iris identification using wavelet transform

A security system based on the recognition of the iris of human eyes using the wavelet transform is presented. The zero-crossings of the wavelet transform are used to extract the unique features obtained from the grey-level profiles of the iris. The recognition process is performed in two stages. The first stage consists of building a one-dimensional representation of the grey-level profiles of the iris, followed by obtaining the wavelet transform zerocrossings of the resulting representation. The second stage is the matching procedure for iris recognition. The proposed approach uses only a few selected intermediate resolution levels for matching, thus making it computationally efficient as well as less sensitive to noise and quantisation errors. A normalisation process is implemented to compensate for size variations due to the possible changes in the camera-to-face distance. The technique has been tested on real images in both noise-free and noisy conditions. The technique is being investigated for real-time implementation, as a stand-alone system, for access control to high-security areas.

Decision support system for age-related macular degeneration using discrete wavelet transform

Age-related macular degeneration (AMD) affects the central vision and subsequently may lead to visual loss in people over 60 years of age. There is no permanent cure for AMD, but early detection and successive treatment may improve the visual acuity. AMD is mainly classified into dry and wet type; however, dry AMD is more common in aging population. AMD is characterized by drusen, yellow pigmentation, and neovascularization. These lesions are examined through visual inspection of retinal fundus images by ophthalmologists. It is laborious, time-consuming, and resource-intensive. Hence, in this study, we have proposed an automated AMD detection system using discrete wavelet transform (DWT) and feature ranking strategies. The first four-order statistical moments (mean, variance, skewness, and kurtosis), energy, entropy, and Gini index-based features are extracted from DWT coefficients. We have used five (t test, Kullback–Lieber Divergence (KLD), Chernoff Bound and Bhattacharyya Distance, receiver operating characteristics curve-based, and Wilcoxon) feature ranking strategies to identify optimal feature set. A set of supervised classifiers namely support vector machine (SVM), decision tree, k -nearest neighbor ( k -NN), Naive Bayes, and probabilistic neural network were used to evaluate the highest performance measure using minimum number of features in classifying normal and dry AMD classes. The proposed framework obtained an average accuracy of 93.70 %, sensitivity of 91.11 %, and specificity of 96.30 % using KLD ranking and SVM classifier. We have also formulated an AMD Risk Index using selected features to classify the normal and dry AMD classes using one number. The proposed system can be used to assist the clinicians and also for mass AMD screening programs.

Acoustic feature extraction using perceptual wavelet packet decomposition for frog call classification

Frog protection has become increasingly essential due to the rapid decline of its biodiversity. Therefore, it is valuable to develop new methods for studying this biodiversity. In this paper, a novel feature extraction method is proposed based on perceptual wavelet packet decomposition for classifying frog calls in noisy environments. Pre-processing and syllable segmentation are first applied to the frog call. Then, a spectral peak track is extracted from each syllable if possible. Track duration, dominant frequency and oscillation rate are directly extracted from the track. With k-means clustering algorithm, the calculated dominant frequency of all frog species is clustered into k parts, which produce a frequency scale for wavelet packet decomposition. Based on the adaptive frequency scale, wavelet packet decomposition is applied to the frog calls. Using the wavelet packet decomposition coefficients, a new feature set named perceptual wavelet packet decomposition sub-band cepstral coefficients is extracted. Finally, a k-nearest neighbour (k-NN) classifier is used for the classification. The experiment results show that the proposed features can achieve an average classification accuracy of 97.45% which outperforms syllable features (86.87%) and Mel-frequency cepstral coefficients (MFCCs) feature (90.80%).

Wavelet-based multiresolution analysis of Wivenhoe Dam water temperatures

Water temperature measurements from Wivenhoe Dam offer a unique opportunity for studying fluctuations of temperatures in a subtropical dam as a function of time and depth. Cursory examination of the data indicate a complicated structure across both time and depth. We propose simplifying the task of describing these data by breaking the time series at each depth into physically meaningful components that individually capture daily, subannual, and annual (DSA) variations. Precise definitions for each component are formulated in terms of a wavelet-based multiresolution analysis. The DSA components are approximately pairwise uncorrelated within a given depth and between different depths. They also satisfy an additive property in that their sum is exactly equal to the original time series. Each component is based upon a set of coefficients that decomposes the sample variance of each time series exactly across time and that can be used to study both time-varying variances of water temperature at each depth and time-varying correlations between temperatures at different depths. Each DSA component is amenable for studying a certain aspect of the relationship between the series at different depths. The daily component in general is weakly correlated between depths, including those that are adjacent to one another. The subannual component quantifies seasonal effects and in particular isolates phenomena associated with the thermocline, thus simplifying its study across time. The annual component can be used for a trend analysis. The descriptive analysis provided by the DSA decomposition is a useful precursor to a more formal statistical analysis.

Fast FIR Algorithms for the Continuous Wavelet Transform From Constrained Least Squares

New algorithms for the continuous wavelet transform are developed that are easy to apply, each consisting of a single-pass finite impulse response (FIR) filter, and several times faster than the fastest existing algorithms. The single-pass filter, named WT-FIR-1, is made possible by applying constraint equations to least-squares estimation of filter coefficients, which removes the need for separate low-pass and high-pass filters. Non-dyadic two-scale relations are developed and it is shown that filters based on them can work more efficiently than dyadic ones. Example applications to the Mexican hat wavelet are presented.

Adaptive frequency scaled wavelet packet decomposition for frog call classification

Environmental changes have put great pressure on biological systems leading to the rapid decline of biodiversity. To monitor this change and protect biodiversity, animal vocalizations have been widely explored by the aid of deploying acoustic sensors in the field. Consequently, large volumes of acoustic data are collected. However, traditional manual methods that require ecologists to physically visit sites to collect biodiversity data are both costly and time consuming. Therefore it is essential to develop new semi-automated and automated methods to identify species in automated audio recordings. In this study, a novel feature extraction method based on wavelet packet decomposition is proposed for frog call classification. After syllable segmentation, the advertisement call of each frog syllable is represented by a spectral peak track, from which track duration, dominant frequency and oscillation rate are calculated. Then, a k-means clustering algorithm is applied to the dominant frequency, and the centroids of clustering results are used to generate the frequency scale for wavelet packet decomposition (WPD). Next, a new feature set named adaptive frequency scaled wavelet packet decomposition sub-band cepstral coefficients is extracted by performing WPD on the windowed frog calls. Furthermore, the statistics of all feature vectors over each windowed signal are calculated for producing the final feature set. Finally, two well-known classifiers, a k-nearest neighbour classifier and a support vector machine classifier, are used for classification. In our experiments, we use two different datasets from Queensland, Australia (18 frog species from commercial recordings and field recordings of 8 frog species from James Cook University recordings). The weighted classification accuracy with our proposed method is 99.5% and 97.4% for 18 frog species and 8 frog species respectively, which outperforms all other comparable methods.

A discrete wavelet transform approach to multiresolution complex SAR image generation

We present a signal processing approach using discrete wavelet transform (DWT) for the generation of complex synthetic aperture radar (SAR) images at an arbitrary number of dyadic scales of resolution. The method is computationally efficient and is free from significant system-imposed limitations present in traditional subaperture-based multiresolution image formation. Problems due to aliasing associated with biorthogonal decomposition of the complex signals are addressed. The lifting scheme of DWT is adapted to handle complex signal approximations and employed to further enhance the computational efficiency. Multiresolution SAR images formed by the proposed method are presented.

Interdependence between Greece and other European stock markets: A comparison of wavelet and VMD copula, and the portfolio implications

The interdependence of Greece and other European stock markets and the subsequent portfolio implications are examined in wavelet and variational mode decomposition domain. In applying the decomposition techniques, we analyze the structural properties of data and distinguish between short and long term dynamics of stock market returns. First, the GARCH-type models are fitted to obtain the standardized residuals. Next, different copula functions are evaluated, and based on the conventional information criteria and time varying parameter, Joe-Clayton copula is chosen to model the tail dependence between the stock markets. The short-run lower tail dependence time paths show a sudden increase in comovement during the global financial crises. The results of the long-run dependence suggest that European stock markets have higher interdependence with Greece stock market. Individual country’s Value at Risk (VaR) separates the countries into two distinct groups. Finally, the two-asset portfolio VaR measures provide potential markets for Greece stock market investment diversification.

A Wavelet based Teager Energy Operator for Spike Detection in Microelectrode Array Recordings

Spike detection in neural recordings is the initial step in the creation of brain machine interfaces. The Teager energy operator (TEO) treats a spike as an increase in the `local' energy and detects this increase. The performance of TEO in detecting action potential spikes suffers due to its sensitivity to the frequency of spikes in the presence of noise which is present in microelectrode array (MEA) recordings. The multiresolution TEO (mTEO) method overcomes this shortcoming of the TEO by tuning the parameter k to an optimal value m so as to match to frequency of the spike. In this paper, we present an algorithm for the mTEO using the multiresolution structure of wavelets along with inbuilt lowpass filtering of the subband signals. The algorithm is efficient and can be implemented for real-time processing of neural signals for spike detection. The performance of the algorithm is tested on a simulated neural signal with 10 spike templates obtained from [14]. The background noise is modeled as a colored Gaussian random process. Using the noise standard deviation and autocorrelation functions obtained from recorded data, background noise was simulated by an autoregressive (AR(5)) filter. The simulations show a spike detection accuracy of 90%and above with less than 5% false positives at an SNR of 2.35 dB as compared to 80% accuracy and 10% false positives reported [6] on simulated neural signals.

Wavelet transform based error concealment approach for image denoising

Denoising of images in compressed wavelet domain has potential application in transmission technology such as mobile communication. In this paper, we present a new image denoising scheme based on restoration of bit-planes of wavelet coefficients in compressed domain. It exploits the fundamental property of wavelet transform - its ability to analyze the image at different resolution levels and the edge information associated with each band. The proposed scheme relies on the fact that noise commonly manifests itself as a fine-grained structure in image and wavelet transform allows the restoration strategy to adapt itself according to directional features of edges. The proposed approach shows promising results when compared with conventional unrestored scheme, in context of error reduction and has capability to adapt to situations where noise level in the image varies. The applicability of the proposed approach has implications in restoration of images due to noisy channels. This scheme, in addition, to being very flexible, tries to retain all the features, including edges of the image. The proposed scheme is computationally efficient.

Transient signal detection using wigner-ville distribution and wavelet denoising

The problem of detecting an unknown transient signal in noise is considered. The SNR of the observed data is first enhanced using wavelet domain filter The output of the wavelet domain filter is then transformed using a Wigner-Ville transform,which separates the spectrum of the observed signal into narrow frequency bands. Each subband signal at the output of the Wigner-ville block is subjected kto wavelet based level dependent denoising (WBLDD)to supress colored noise A weighted sum of the absolute value of outputs of WBLDD is passed through an energy detector, whose output is used as test statistic to take the final decision. By assigning weights proportional to the energy of the corresponding subband signals, the proposed detector approximates a frequency domain matched filter Simulation results are presented to show that the performance of the proposed detector is better than that of the wavelet packet transform based detector.

Wavelet transform based error concealment approach for image denoising

Denoising of images in compressed wavelet domain has potential application in transmission technology such as mobile communication. In this paper, we present a new image denoising scheme based on restoration of bit-planes of wavelet coefficients in compressed domain. It exploits the fundamental property of wavelet transform - its ability to analyze the image at different resolution levels and the edge information associated with each band. The proposed scheme relies on the fact that noise commonly manifests itself as a fine-grained structure in image and wavelet transform allows the restoration strategy to adapt itself according to directional features of edges. The proposed approach shows promising results when compared with conventional unrestored scheme, in context of error reduction and has capability to adapt to situations where noise level in the image varies. The applicability of the proposed approach has implications in restoration of images due to noisy channels. This scheme, in addition, to being very flexible, tries to retain all the features, including edges of the image. The proposed scheme is computationally efficient.

Wave propagation analysis in carbon nanotube embedded composite using wavelet based spectral finite elements

In this paper, elastic wave propagation is studied in a nanocomposite reinforced with multiwall carbon nanotubes (CNTs). Analysis is performed on a representative volume element of square cross section. The frequency content of the exciting signal is at the terahertz level. Here, the composite is modeled as a higher order shear deformable beam using layerwise theory, to account for partial shear stress transfer between the CNTs and the matrix. The walls of the multiwall CNTs are considered to be connected throughout their length by distributed springs, whose stiffness is governed by the van der Waals force acting between the walls of nanotubes. The analyses in both the frequency and time domains are done using the wavelet-based spectral finite element method (WSFEM). The method uses the Daubechies wavelet basis approximation in time to reduce the governing PDE to a set of ODEs. These transformed ODEs are solved using a finite element (FE) technique by deriving an exact interpolating function in the transformed domain to obtain the exact dynamic stiffness matrix. Numerical analyses are performed to study the spectrum and dispersion relations for different matrix materials and also for different beam models. The effects of partial shear stress transfer between CNTs and matrix on the frequency response function (FRF) and the time response due to broadband impulse loading are investigated for different matrix materials. The simultaneous existence of four coupled propagating modes in a double-walled CNT-composite is also captured using modulated sinusoidal excitation.