Earticle

Two-dimensional polyacrylamide gel electrophoresis of proteins is a robust and reproducible technique. It is the most widely used separation tool in proteomics. Current efforts in the field are directed at development of tools for expanding the range of proteins accessible with two-dimensional gels. Proteomics was built around the two-dimensional gel. The idea that multiple proteins can be analyzed in parallel grew from two-dimensional gel maps. Proteomics researchers needed to identify interested protein spots by examining the gel. This is time consuming, labor extensive and error prone. It is desired that the computer can analyze the proteins automatically by first detecting then quantifying the protein spots in the 2-D gel images. In our previous work, we presented a new technique for segmentation of 2-D gel images using the fuzzy c-means algorithm using the notion of fuzzy relations. In this paper, we will describe the new relational fuzzy c-means algorithm (RFCM) and use it for automatic protein spots quantification. We will also use two methods to evaluate its performance: the unsupervised evaluation method and comparison with the expert spots quantification.

In this paper, a wavelet-based scheme for digital image watermarking is presented. This proposed scheme is blind, which means that it requires neither the original image nor any side information in watermark recovery. It is based on inserting the watermark bits into the coarsest scale wavelet coefficients. Three-level wavelet decomposition and a watermark equal in size to the detail sub-bands in the coarsest scale are used. Only, perceptually significant wavelet coefficients are used to embed the watermark bits. The proposed scheme differs from the traditional wavelet-based schemes in the use of quantization and non-additive watermark embedding. It produces watermarked images with less degradation than the traditional wavelet-based schemes.

Blind deconvolution of Green’s Function (GF) as well as the Source Time Function (STF) is a fundamental problem in seismic signal processing. This paper presents a comparative study of two blind deconvolution methods for determining the Green’s function as well as the source time function from the recorded seismic data. We have tested these algorithms on the December 26, 2004 Sumatra earthquake (Mw=9.3). An improvised Least Mean Square (LMS) algorithm has been used to improve the multi-pulse method proposed earlier by Cookey et al. [1]. We have also evaluated the performance of this improvised multi-pulse method against the Frequency-Domain Blind Deconvolution (FDBD) method. The FDBD method is based on Mutual Information Rate (MIR) proposed by Larue et al. [3]. These methods have also been tested for some of the records of the earthquake at different stations worldwide available on IRIS-dataset. We have found the azimuthal dependency of the source time function on these records. We have also compared both the methods using synthetic as well as real data. The STFs recovered on different stations using multi-pulse model and FDBD method show similarity in shape and duration as well. However the proposed improvisation on the multipulse method is computationally efficient.

Multimedia is one of the most popular data shared in the Web, and the protection of it via encryption techniques is of vast interest. In this paper, an Optimized Multiple Huffman Tables (OMHT) technique is proposed to face some compression and security problems found in Multiple Huffman Tables (MHT) technique. OMHT depends on using statistical-modelbased compression method to generate different tables from the same data type of images or videos to be encrypted leading to increase compression efficiency and security of the used tables. A systematic study on how to strategically integrate different atomic operations to build a multimedia compression-encryption system is presented. The resulting system can provide superior performance over both generic encryption and its simple adaptation to multimedia in terms of a joint consideration of security, and bitrate overhead. The effectiveness of this scheme is verified through a series of experiments, and the robustness of our approach is demonstrated by comparing it against a standard compression technique, JPEG on which the MHT technique is built.

In this paper, a safe and powerful method is presented which can detect and identify Farsi license plate irrespective of distance (how far a vehicle is), rotation (angle between camera and vehicle), and contrast (being dirty, reflected, or deformed). In addition, more than one car can be existed in an image. The proposed method extracts edges and then determines the candidate regions by using adaptive image enhancement and applying window movement. Finally by region elements analysis, the license plates are detected. The region elements analysis is working according to the plate geometric structure, continuity and parallelism. After detecting license plates, we estimate rotation angle and try to compensate it. In order to identify a detected plate, every character should be recognized. For this purpose, we present 53 features and use them as input to artificial neural network classifier.

Compound characters which are one of the features of Marathi script, derived from Devanagari, occur frequently in the script. Recognition of these characters poses challenges to the researchers due to their complex structure. This paper presents a novel approach for recognition of unconstrained handwritten Marathi compound characters. The recognition is carried out using multistage feature extraction and classification scheme. The initial stages of feature extraction are based upon the structural features and the classification of the characters is done according to the structural parameters into 24 classes. The final stage of feature extraction employs wavelet transform. Single level wavelet decomposition is used to generate the approximation coefficients which are used as features. These coefficients are further modified and then used as another set of features. Both the wavelet approximation features as well as the modified wavelet features are applied to neural network for recognition. A separate neural network block is built for each of the 24 classes. The average recognition rate is found to be 96.14% and 94.22% respectively for training and testing samples with wavelet approximation features and 98.68% and 96.23% respectively for training and testing samples with modified wavelet features.

Smart antennas possess the capability of suppressing jamming signal, so they can improve signal to interference plus noise ratio (SINR). Array processing utilizes information regarding locations of signal to aid in interference suppression and signal enhancement and is considered promising technology for anti-jamming. In this paper we studied three beamforming algorithms, Least Mean Square (LMS) algorithm, Optimized-LMS algorithm and Recursive Least Squares (RLS) algorithm. Simulation results are presented to compare the ability of these three algorithms to form beam in the direction of desired signal and place null in the direction of interference signal. Dependency of these algorithm on SNR and SIR is also analyzed. It has been found that RLS algorithm is best suited for anti-jamming applications.