The objective of the current work is to show the effectiveness of using wavelet transform for detection and localization of small damages. The spatial data used here are the rotational mode shapes of the damaged and undamaged plate-like structures. The continuous wavelet transform using complex Gaussian wavelet is used to get the spatially distributed wavelet coefficients so as to identify the damage position on a square plate. The rotational mode shape data of the square plate with damage of different sizes are obtained using ANSYS 9.0. Damage identification for different boundary conditions is studied.

An algorithm has been developed for the simultaneous measurement of the fetal and maternal heart rates from the maternal abdominal electrocardiogram during pregnancy and labor. The algorithm is based on digital filtering, adaptive thresholding, statistical properties in the time domain and differencing of local maxima and minima. The technique has been developed through a combination and modification of earlier techniques making it suitable for ambulatory monitoring. Eighteen volunteers at various weeks of gestation were studied for the fetal heart rate detection. The computation complexity is such that the developed algorithm can extract both maternal and fetal heart rates in real-time utilizing a single-lead configuration.

Most useful information in different kind of Signals is usually carried by such singularities as the edges and peaks. Examples of such Signals include Radar Signals, the Signals generated by the digital communication systems and biological Signals (such as ECG, EEG and even medical images). Therefore, extraction and locating these singularities is a main and an initial common step in most of the Signal and image Processing application. In this paper a new multi-resolution based method for automatically extraction of singular points within the Signals is presented where the information at various Signal resolutions is combined together in a novel fuzzy manner. In the proposed algorithm, first, the multiresolution description of the Signal is obtained using the wavelet transform. The information at each wavelet transform scale is next transformed into a fuzzy subset of the Signal by means of appropriate fuzzy flying functions for each kind of the singularities (edges or peaks). The resulting fuzzy subsets describe to what degree any sample point from the Signal domain can represent a singularity at that particular scale. Finally, combining the information at various fuzzy subsets of the Signal by means of the fuzzy operators, the sample points with the highest possibility of coincidence with a singularity are identified. The superiority of the proposed algorithm in comparison to the commonly used techniques is shown using various synthetic and real Signals.

Language identification is a critical step prior to any natural language Processing. In this paper, a Signal Processing method for Language Identification is proposed. Sequence of characters in a word and the order of words in stream identify the language. The sequence of characters in a stream provides a signature to recognize the language without understanding its meaning. The signature can be extracted using Signal Processing techniques via converting texts into time series. Although several research and commercial software have been developed to identify text language, they need a standard dictionary for each language. We proposed a dictionary independent method consisting of three main steps, I) preProcessing, II) clustering and finally III) classification. First, the texts are converted to time series using UTF-8 codes. Second, to group similar languages, the obtained series are clustered. Third, each cluster is decomposed into 32 sub-bands using a Wavelet packet, and 32 features are extracted from each sub-band. Also, a multilayer perceptron neural network is used to classify the extracted features. The proposed method was tested on our dataset with 31000 texts from 31 different languages. The proposed method achieved 72.20% accuracy for language identification.