Earticle

In this paper a fuzzy logic approach is introduced to embed the encrypted watermark in the wavelet domain. The multi-resolution representation based on DWT incorporates a model of Human Visual System (HVS). Encryption and digital watermarking techniques need to be incorporated in digital right management. It is clear that these two technologies are complimenting each other, and the complete security of the digital contains depends on both. Encryption transforms the original contain into unreadable format and watermarking leaves the digital object intact and recognizable. The objective is to develop simple, real time and robust secure watermarking system, which can be achieved through the hardware implementation. To meet a real time constrain we have proposed parallel computing architecture for wavelet transform. The experimental results demonstrate the high robustness of the proposed algorithm to various attacks like noise additions etc

Arabic, the world’s second most spoken language in terms of number of speakers, has not received much attention from the traditional speech processing research community. This study is specifically concerned with the analysis of vowels in modern standard Arabic dialect. The first and second formant values in these vowels are investigated and the differences and similarities between the vowels explored using consonant-vowels-consonant (CVC) utterances. For this purpose, a Hidden Markov Model (HMM) based recognizer is built to classify the vowels and the performance of the recognizer analyzed to help understand the similarities and dissimilarities between the phonetic features of vowels. The vowels are also analyzed in both time and frequency domains, and the consistent findings of the analysis are expected to enable future Arabic speech processing tasks such as vowel and speech recognition and classification.

In this paper, a new algorithm to efficiently compute the two-dimensional wavelet transform is presented. This algorithm aims at low memory consumption and reduced complexity, meeting these requirements by means of line-by-line processing. In this proposal, we use recursion to automatically place the order in which the wavelet transform is computed. This way, we solve some synchronization problems that have not been tackled by previous proposals. Furthermore, unlike other similar proposals, our proposal can be straightforwardly implemented from the algorithm description. To this end, a general algorithm is given which is further detailed to allow its implementation with a simple filter bank or using the more efficient lifting scheme. We also include a new fast run-length encoder to be used along with the proposed wavelet transform for fast image compression and reduced memory consumption.

In this paper, a technique is proposed for the automatic detection of the spikes in long term 18 channel human electroencephalograms (EEG) with less number of data set. The scheme for detecting epileptic and non epileptic spikes in EEG is based on a multi resolution, multi-level analysis and Artificial Neural Network(ANN) approach. Wavelet Transform (WT) is a powerful tool for signal compression, recognition, restoration and multi-resolution analysis of non-stationary signal. The signal on each EEG channel is decomposed into six sub bands using a non-decimated WT. Each sub band is analyzed by using a non-linear energy operator, in order to detect spikes. A parameter extraction stage extracts the parameters of the detected spikes that can be given as the input to ANN classifier. A robust system that combines multiple signal-processing methods in a multistage scheme, integrating wavelet transform and artificial neural network is proposed here. This system is experimented on a simulated EEG pattern waveform as well as with real patient data. The system is evaluated on testing data from 81 patients, totaling more than 800 hours of recordings. 90.0% of the epileptic events were correctly detected and the detection rate of non epileptic events was 98.0%. We conclude that the proposed system has good performance in detecting epileptic form activities; further the multistage multiresolution approach is an appropriate way of automatic classification problems in EEG.

This paper presents a novel method for the objective quality evaluation of letterings at the example of laser markings. The proposed method enables an automated quality evaluation during random inspection of small quantities of frequently changing items. The markings are segmented from the image within a first step and compared against a master piece in the second step. Each character and contour is evaluated with regard to completeness, contrast, edge width and homogeneity according to quality criteria based on the human visual perception. Thus our approach provides an user-independent evaluation method with high repeatability and reproducibility for its industrial application.

Proper coloring of the vertices of a graph with minimum number of colors has always been of great interest of researchers in the field of soft computing. Genetic Algorithm (GA) and its application as the solution method to the Graph Coloring problem have been appreciated and worked upon by the scientists almost for the last two decades. Various genetic operators such as crossover and mutation have been used in the GA probabilistically in the previous works, which distributes the promising solutions in the search space at each generation. This paper introduces a new operator, called double point Guided Mutation operator with a special feature. An evolutionary algorithm with double point Guided Mutation for the Graph Coloring problem is proposed here, which could advance the performance level of simple GA dramatically. The algorithm has been tested upon a large-scale test graphs and has shown better output than the earlier works on the same problem. This paper describes the advancement of performance of simple GA applied upon the problem of graph coloring using a operator called double point Guided Mutation in association of the general genetic operators Crossover and Mutation used probabilistically. Our work is still going on for designing better algorithms.

In this paper, we present a new blind digital image watermarking method. We introduce interval wavelet decomposition, which is a combination of a discrete wavelet transform and interval arithmetic, and we examine its properties. According to our experimental results, this combination is a good way to produce a kind of redundancy from the original image and to develop new watermarking methods. Thanks to this property, we can obtain specific frequency components where the watermark is embedded. We describe the procedure of our method in detail and its relations with the human visual system (HVS). We also give some experimental results demonstrating that our method gives watermarked images of better quality and is robust against attacks such as clipping, marking, and JPEG and JPEG2000 compressions.