A novel biorthogonal wavelet network system for off-angle iris recognition

One important category of non-ideal conditions for iris recognition is off-angle iris images. Practically it is very difficult for images to be captured with no offset. It then becomes necessary to account for off angle information in order to maintain robust performance. A biorthogonal wavelet based iris recognition system, previously designed at our lab, is modified and demonstrated to perform off-angle iris recognition. Biorthogonal wavelet network (BWN) are developed and trained for each class. The non-ideal factors are adjusted by repositioning the BWN. To test, along with the real data, synthetic iris images are generated by using affine and geometric transforms of 0°, 10° and 20^° experimentally collected images. The tests were carried out on the experimentally collected off-angle data and synthetically generated data for angles from 0° to 60^° with a resolution of 5^°. This approach is shown to have less constraints than a transformation based iris recognition approach. Iris images off-angle by up to 42^° for synthetic data and up to 45^° for experimental data are successfully recognized.     

Effective fault detection and isolation using bond graph-based domain decomposition

The problem of fault detection and isolation in complex plants can be effectively addressed by a hierarchical strategy involving successive narrowing of the search space of potential faults. A bond graph network is one means of achieving a hierarchical strategy based on the physical domains present in the plant. First, the multivariate statistical method of principal component analysis is used to reduce the data dimension. Second, a discrete wavelet transform is applied to abstract the dynamics at different scales. Thirdly, the Mahalanobis distance is applied to calculate the confidence level. Following a conclusion of the existence of a fault, isolation is achieved by comparing the time scale at which the violation occurred to the time scale associated with a physical domain. In the final step, a Bayesian network is employed to describe the conditional dependence between faulty domains and fault signatures. Two examples are presented to demonstrate these concepts.     

Study of the copper anode passivation by electrochemical noise analysis using spectral and wavelet transforms

The passivation of copper anode in sulphuric acid industrial electrolyte at 65 °C was investigated by electrochemical noise (EN) technique using three imposed anodic currents 153, 180 and 210 mA cm⁻² (galvanostatic or chronopotentiometric technique). The wavelet transforms and power spectral density (PSD) analyses have been employed to analyse the EN data. It has been found that during active dissolution the electrode surface is dominated by long time scale process and the change of the position of the maximum relative energy from D7 to D8 could be an indication of future passivation. This is associated in fast Fourier transform analyses by the disappearance of the low frequency plateau (white noise). During anodic dissolution the electrode surface was mainly undertaking general corrosion and the relative energy distribution was concentrated on large timescale crystals (D7-D8) with range of scale between 25.6 and 12.6 s. During passivation the electrode surface was dominated by short time scale process (D3), and during trans-passivation by medium (D4) followed by short time scale process (D2) with range of scale between 0.8 and 0.4 s, 1.6 and 0.8 s and 0.4 and 0.2 s, respectively. Crystals D2 and D3 could be related to metastable pitting and crystal D4 to localized corrosion.     

Comparison study of the biorthogonal spline wavelet filtering for areal rough surfaces

Research into techniques for areal surface analysis has recently included wavelet filtering. The requirements for filtering surface analysis include: linear phase shift; finite pulse response and perfect reconstruction — only biorthogonal wavelets satisfy these conditions. The biorthogonal spline wavelet is considered for areal surface characterisation due to the curvature of the surfaces considered as examples. In this paper, the models and algorithms of biorthogonal B-spline and cubic spline wavelets are introduced. The lifting algorithm is used as a simple tool for design of spline wavelets. Bioengineering surfaces are used to test and compare the two kinds of wavelet filters to obtain the rough surfaces.     

Analysis of plate wave propagation in anisotropic laminates using a wavelet transform

A new approach is presented for the analysis of transient waves propagating in anisotropic composite laminates. The wavelet transform (WT) using the Gabor wavelet is applied to the time-frequency analysis of dispersive flexural waves in these plates. It can be shown that the peaks of the magnitude of WT in a time-frequency domain is related to the arrival times of the group velocity. Experiments were performed using a lead break as the simulated acoustic emission source on the surface of unidirectional and quasi-isotropic laminates. A method was developed to obtain the group velocity of the flexural mode as a function of frequency. Theoretical predictions were made using the Mindlin plate theory, which includes the effects of shear deformation and rotatory inertia. Our predictions on the dispersion of the flexural mode showed good agreement with the experimental results.     

An inverse shock response spectrum

The shock response spectrum (SRS) is a tool commonly used by application engineers that characterizes the severity of a transient acceleration. Due to the definition of the SRS, neither an analytical nor a unique inverse exists for an arbitrary function. An SRS presented without any temporal information makes creating a corresponding acceleration time history for an experimental or numerical study prohibitively difficult without a rigorous method to determine an inverse of the SRS (a corresponding time history). The present work develops a method to calculate an inverse of an arbitrary SRS using three sets of well characterized basis functions: an impulse function, a sine function/damped sine function, and a modified Morlet wavelet. These three basis functions are specifically chosen for the properties of their transformations: the impulse introduces a constant increase to the SRS above a given frequency, the sine wave introduces a narrow peak at a given frequency, and the Morlet wavelet introduces a plateau with an adjustable width and relative height. Using the definition of the SRS, the transformations of the basis functions are calculated and these expressions are used to derive a methodology for calculating an inverse SRS. The effectiveness of the method is demonstrated by several examples. The quality of an inverse SRS is evaluated by comparing the SRS of the inverse to the target SRS. This method is developed in order to provide a quick estimate of a corresponding time history; in applications where a higher fidelity representation of the SRS is needed than can be provided by the method developed, a genetic algorithm is used to optimize the coefficients of the basis functions. Given a sufficient number of basis functions for the optimization, the resulting SRS can almost exactly match a randomly generated target SRS that is nonzero over the frequency range considered. For applications in which the permissable basis functions are limited (such as for an experimental test apparatus), an extension of the genetic algorithm method is discussed.     

Tool wear detection with fuzzy classification and wavelet fuzzy neural network

In the paper, a new method of tool wear detection with cutting conditions and detected signals is presented, which includes the model of wavelet fuzzy neural network with acoustic emission (AE) and the model of fuzzy classification with motor current. The results of tool wear estimated by cutting conditions and detected signals (spindle motor current, feed motor current and AE) are fused by fuzzy inference. Experimental results show that the method of tool wear detection is reliable and practical.     

Experimental evaluation of fault diagnosis in a skew-configured UAV sensor system

This paper presents an experimental evaluation of a hybrid fault detection and isolation scheme against three successive faults in skew-configured inertial sensors of an unmanned aerial vehicle (UAV). An additional small and low-cost inertial measurement unit is installed with a skewed angle to a primary inertial measurement unit. A parity space method and an in-lane monitoring method are combined to increase system tolerance to the occurrence of multiple successive faults during flight. The first and second faults are detected and isolated by the parity space method. The third fault is detected by the parity space method and isolated by the in-lane monitoring method based on the discrete wavelet transform. Hardware in-the-loop tests and flight experiments with a fixed-wing UAV are performed to verify the performance of the proposed fault diagnosis scheme.     

A new method for modification of ground motions using wavelet transform and enhanced colliding bodies optimization

In this paper a simple and robust approach is presented for spectral matching of ground motions utilizing the wavelet transform and an improved metaheuristic optimization technique. For this purpose, wavelet transform is used to decompose the original ground motions to several levels, where each level covers a special range of frequency, and then each level is multiplied by a variable. Subsequently, the enhanced colliding bodies optimization technique is employed to calculate the variables such that the error between the response and target spectra is minimized. The application of the proposed method is illustrated through modifying 12 sets of ground motions. The results achieved by this method demonstrate its capability in solving the problem. The outcomes of the enhanced colliding bodies optimization (ECBO) are compared to those of the standard colliding bodies optimization (CBO) to illustrate the importance of the enhancement of the algorithm.     

Algorithm design and implementation of finite wavelet grow tree

Based on grow tree composite model, Finite Field Wavelet Grow Tree (FW-GT) was proposed in this paper. FW-GT is a novel framework to be used in data encryption enhancing data security. It is implemented by replacement operator and wavelet operator. Forward integration and inverse decomposition of FW-GT are performed by replacement, inverse wavelets and its corresponding replacement, wavelet transforms. Replacement operator joined nonlinear factor, wavelet operator completed data transformation between lower dimensional space and higher dimensional space. FW-GT security relies on the difficulty of solving nonlinear equations over finite fields. By using FW-GT, high security of data could be obtained at the cost of low computational complexity. It proved FW-GT algorithm’s correctness in this paper. The experimental result and theory analysis shows the excellent performance of the algorithm.