ON THE INVESTIGATION OF VIBRATION SIGNALS USING JOINT TIME FREQUENCY ANALYSIS

This paper addresses the problem of coupled blade bending and shaft torsional vibration signals using the Joint Time Frequency Analysis (JTFA). Simulation results for the blade bending and shaft torsional vibration are studied using the Fast Fourier Transform (FFT) and the JTFA. The FFT spectra showed little information on the nonlinear dynamic interaction between the blade bending and the shaft torsional vibration; and thus cannot be used as a tool for monitoring the blade vibration by looking into the shaft torsional vibration signal. In contrast, the JTFA in the form of Wigner Ville Distribution (WVD) has given more useful information and reflected the dynamic interaction between different vibration modes on one side and between the blade vibration and the shaft torsional vibration on the other side. The obtained WVD representations of the shaft torsional vibration showed frequency layers that represent blade vibration activity

Towards a Universal Multiresolution-Based Perceptual Model

Following a recently introduced perceptual model for balanced multiwavelets, we outline, in this paper, an extension of our previous work and propose a new perceptual model for scalar wavelets. The proposed model is derived using multiresolution domain extensions of our previous scheme. Unlike existing models, the proposed one depends only on the image activity and not the filter sets used by the transform. The perceptual redundancy, present in the image, is efficiently quantified through a just-noticeable distortion (JND) profile. In this model, a visibility threshold of distortion is assigned to each wavelet subband coefficient. Therefore, perceptually insignificant subband components can be clearly discriminated from perceptually significant ones. For instance, this discrimination can be constructively used to achieve the imperceptibility requirement often encountered in watermarking and data hiding applications. Furthermore, we illustrate, through simulation, the ability of the proposed model to efficiently capture the salient features of the underlying image regardless of the wavelet filters being used

Higher-order statistics (HOS)-based deconvolution for ultrasonic nondestructive evaluation (NDE) of materials.

High resolution signal processing techniques involving higher-order statistics (HOS) and artificial neural networks (ANN) which re useful in ultrasonic nondestructive evaluation (NDE) of materials systems subject to additive white Gaussian noise (AWGN) and masking effects of measurement systems used and propagation paths, are investigated in this Thesis. The proposed techniques are: i) a batch-type deconvolution method using the complex bicepstrum algorithm, and ii) automatic ultrasonic defect classification system using a modular learning strategy. Performance evaluation of the proposed methods and comparisons with existing methods are made by means of Monte-Carlo simulations, experimental data and analysis. The first scheme makes use of the complex cepstrum of the third-order cumulants (complex bicepstrum) of the ultrasonic signals. The second scheme based on a modular learning stretegy consisting of three functional blocks, takes into account the nonstationary character of the ultrasonic NDE system and makes use of the " information preserving rule" which allows accurate and reliable classification procedure. It is demonstrated that the proposed techniques perform very efficiently in both white or colored Gaussian and symmetrically-distributed noise-classes at moderate and low signal-to-noise ratios (SNR). Comparisons with existing methods demonstrate improved performance characterized by high resolution properties, robustness and low sensitivity to additive Gaussian noise. However, the improved performance is achieved at the expense of higher computational complexity and data requirements

A Robust Perceptual Audio Hashing Using Balanced Multiwavelets

Digital multimedia content (especially audio) is becoming a major part of the average computer user experience. Large digital audio collections of music, audio and sound effects are also used by the entertainment, music, movie and animation industries. Therefore, the need for identification and management of audio content grows proportionally to the increasing widespread availability of such media virtually ”any time and any where” over the Internet. In this paper, we propose a novel framework for robust perceptual hashing of audio content using balanced multiwavelets (BMW). The framework for generating robust perceptual hash values (or fingerprints) is described. The generated hash values are used for identifying, searching, and retrieving audio content from large audio databases. Furthermore, we illustrate, through extensive computer simulation, the robustness of the proposed framework to efficiently represent audio content and withstand several signal processing attacks and manipulations

Classification of Shoeprint Images Using Directional Filterbanks

With the abundance of evidence data collected from scenes of crime (SoC), shoeprint (or soleprint) traces usually constitute one of the hardest types of evidence for a criminal to remove before leaving the SoC. Traditional approaches to shoeprint representations attempt to classify shoeprint images based on a number of possible patterns. Such approaches are difficult to implement in an automatic fashion without the intervention of a forensic specialist. In this paper, we propose a fully-automated system to assist forensic specialists in rapidly classifying a shoeprint image found in a scene of crime (SoC). The proposed multiresolution-based system uses directional filterbanks (DFBs) to capture both local and global details in a shoeprint in a compact representation called ShoeHash. Experimental results based on forensic shoeprint databases of more than 1000 images are presented to validate the effectiveness of the proposed system in extracting shoeprint features and achieving good performance

A Robust Perceptual Audio Hashing Using Balanced Multiwavelets

Digital multimedia content (especially audio) is becoming a major part of the average computer user experience. Large digital audio collections of music, audio and sound effects are also used by the entertainment, music, movie and animation industries. Therefore, the need for identification and management of audio content grows proportionally to the increasing widespread availability of such media virtually ”any time and any where” over the Internet. In this paper, we propose a novel framework for robust perceptual hashing of audio content using balanced multiwavelets (BMW). The framework for generating robust perceptual hash values (or fingerprints) is described. The generated hash values are used for identifying, searching, and retrieving audio content from large audio databases. Furthermore, we illustrate, through extensive computer simulation, the robustness of the proposed framework to efficiently represent audio content and withstand several signal processing attacks and manipulations

Data-Hiding Capacities of Non-Redundant Complex Wavelets

In this paper, we apply an information-theoretic model, developed for digital image watermarking, to derive the data hiding capacities of image sources in the mapping-domain of non-redundant complex wavelet transforms (NCWTs). Results, based on the same model, have been recently reported for balanced multiwavelet (BMW) transforms. In this model, the underlying statistical model defines the hiding capacity in terms of the distortion constraints imposed on the watermark embedder and the attacker, and the information available to the watermark embedder, to the attacker, and to the watermark decoder. The motivations behind the use of NCWTs is the directionality and phase information provided by such representations

UTILIZING EXTENSION CHARACTER ‘KASHIDA’ WITH POINTED LETTERS FOR ARABIC TEXT DIGITAL WATERMARKING

This paper exploits the existence of the redundant Arabic extension character, i.e. Kashida. We propose to use pointed letters in Arabic text with a Kashida to hold the secret bit ‘one’ and the un-pointed letters with a Kashida to hold ‘zero’. The method can be classified under secrecy feature coding methods where it hides secret information bits within the letters benefiting from their inherited points. This watermarking technique is found attractive too to other languages having similar texts to Arabic such as Persian and Urdu

A Fingerprinting System for Musical Content

Driven by the recent advances in digital entertainment technologies, digital multimedia content (such as music and movies) is becoming a major part of the average computer user experience. Through daily interaction with digital multimedia content, large digital collections of music, audio and sound effects have emerged. Furthermore, these collections are produced/consumed by different groups of users such as the entertainment, music, movie and animation industries. Therefore, the need for identification and management of such content grows proportionally to the increasing widespread availability of such media virtually ”any time and any where” over the Internet. In this paper, we propose a novel algorithm for robust perceptual hashing of musical content using balanced multiwavelets (BMW). The procedure for generating robust perceptual hash values (or fingerprints) is described in details. The generated hash values are used for identifying, searching, and retrieving musical content from large musical databases. Furthermore, we illustrate, through extensive computer simulation, the robustness of the proposed framework to efficiently represent audio content and withstand several signal processing attacks and manipulations

MULTISCALE EDGE DETECTION USING WAVELET MAXIMA FOR IRIS LOCALIZATION

Automated personal identification based on biometrics has been receiving extensive attention over the past decade. Iris recognition, as an emerging biometric recognition approach, is becoming a very active topic in both research and practical applications and is regarded as the most reliable and accurate biometric identification system available. Common problems include variations in lighting, poor image quality, noise and interference caused by eyelashes while feature extraction and classification steps rely heavily on the rich textural details of the iris to provide a unique digital signature for an individual. As a result, the stability and integrity of a system depends on effective localization of the iris to generate the iris-code. A new localization method is presented in this paper to undertake these problems. Multiscale edge detection using wavelet maxima is discussed as a preprocessing technique that detects a precise and effective edge for localization and which greatly reduces the search space for the Hough transform, thus improving the overall performance. Linear Hough transform has been used for eyelids isolating, and an adaptive thresholding has been used for eyelashes isolating. A large number of experiments on the CASIA iris database demonstrate the validity and the effectiveness of the proposed approach