Beamforming is one of the most important ARRAY SIGNAL PROCESSING blocks in sonar systems which due to the nature of the environment and conditions of operating, requires using of the robust and adaptive methods to provide the feasible specifications in outputs. In the present paper, the latest methods for the adaptive robust beamforming such as enhanced and modified covariance matrix methods are investigated and finally, by using of simulation in different scenarios and conditions such as steering vector error, sensors gain and phase perturbation and high power noise and strong interference, their capabilities and abilities are presented and method are evaluated. The results show that the methods of Diagonal Loading, LCMV and LCMV mod in different states are not feasible and CMR and ESB methods in the presence of error of steering vector, strong interference and high power noise and gain and phase distortion are more suitable.

In this paper, we propose a new model for additive noise based on GARCH time-series in ARRAY SIGNAL PROCESSING. Due to the some reasons such as complex implementation and computational problems, probability distribution function of additive noise is assumed Gaussian. In the different applications, scrutiny and measurement of noise shows that noise can sometimes significantly non-Gaussian and thus the methods based on Gaussian noise will degrade in an actual conditions. Heavy-tail probability density function (PDF) and time-varying statistical characteristics (e.g.; variance) are the most features of the additive noise process. On the other hand, GARCH process has important properties such as heavy-tail PDF (as excess kurtosis) and volatility modeling through feedback mechanism onto conditional variance so that it seems the GARCH model is a good candidate for the additive noise model in the ARRAY PROCESSING applications. In this paper, we propose a new method based on GARCH using the maximum likelihood approach in ARRAY PROCESSING and verify the performance of this approach in the estimation of the Direction-of-Arrivals of sources against the other methods and using the Cramer-Rao Bound.

Background and Objectives: One major problem in the minimum power distortionless response (MPDR) beamformer is the SIGNAL cancellation problem, i. e., the desired SIGNAL is canceled by the reflected SIGNAL, even though the distortionless response constraint is satisfied. Solving this problem is the objective of this paper. Methods: It is well known that the SIGNAL cancellation problem can be avoided by minimizing the cross-spectrum matrix of noise, i. e., using the minimum variance distortionless response (MVDR) beamformer. But, in the case of disturbance SIGNALs which have correlation with the desired SIGNAL, estimation of this matrix is a challenging problem. In this paper we propose an approach for estimating the cross-spectrum matrix of noise SIGNAL from which we can solve the SIGNAL cancellation problem. Results: Simulation examples show that using the proposed method we can bypass the SIGNAL cancellation problem completely. Conclusion: A common belief is that in the case of a disturbance that is a reflected version of the desired SIGNAL, due to cohesive appearance and disappearance of both the disturbance and the desired SIGNAL, the estimation of cross-spectrum matrix of noise SIGNAL is typically not possible in practice. So, based on this common belief, we can’ t use the MVDR beamformer in this case. In this paper we show that this common belief is a fault. We propose a general approach for estimating the cross-spectrum matrix of noise SIGNAL that is applicable even in the case of correlated disturbances.

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.

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.

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.

Wavelet Transform is a new tool for SIGNAL analysis which can perform a simultaneous SIGNAL time and frequency representations. Under Multi Resolution Analysis (MRA), one can quickly determine details for SIGNALs and their properties using Fast Wavelet Transform (FWT) Algorithms.In this paper, for a better physical understanding of a SIGNAL and its basic algorithms, Multi Resolution Analysis together with wavelet transforms in a form of Digital SIGNAL PROCESSING (DSP) will be discussed. For a Seismic SIGNAL PROCESSING (SSP), sets of Orthonormal Daubechies Wavelets (ODW) are suggested. When dealing with the application of wavelets in SSP, one may discuss about denoising from the SIGNAL and Data Compression existed in the SIGNAL, which is important in seismic SIGNAL data PROCESSING. Using these techniques, El-Centro and Nagan SIGNALs were remodeled with a 25% of total points, resulted in a satisfactory results with an acceptable error drift. Thus a total of 1559 and 2500 points for El Centro and Nagan seismic curves each, were reduced to 389 and 625 points respectively, with a very reasonable error drift, details of which are recorded in the paper. Finally, the future progress in SIGNAL PROCESSING, based on wavelet theory will be appointed