M. R. Kaimal

An Improved Hybrid Model for Molecular Image Denoising

In this paper an improved hybrid method for removing noise from low SNR molecular images is introduced. The method provides an improvement over the one suggested by Jian Ling and Alan C. Bovik (IEEE Trans. Med. Imaging, 21(4), [2002]). The proposed model consists of two stages. The first stage consists of a fourth order PDE and the second stage is a relaxed median filter, which processes the output of fourth order PDE. The model enjoys the benefit of both nonlinear fourth order PDE and relaxed median filter. Apart from the method suggested by Ling and Bovik, the proposed method will not introduce any staircase effect and preserves fine details, sharp corners, curved structures and thin lines. Experiments were done on molecular images (fluorescence microscopic images) and standard test images and the results shows that the proposed model performs better even at higher levels of noise.

T-S fuzzy model with nonlinear consequence and PDC controller for a class of nonlinear control systems

In this paper a new Takagi-Sugeno (T-S) fuzzy model with nonlinear consequence (TSFMNC) is presented which can approximate a class of smooth nonlinear systems, nonlinear dynamical systems and nonlinear control systems. It is also proved that Takagi-Sugeno fuzzy controller with nonlinear consequence (TSFCNC) can be used to approximate a class of nonlinear state-feedback controllers using the so-called parallel distributed compensation (PDC) method. The inverted pendulum problem has been simulated with TSFCNC and compared with Takagi-Sugeno fuzzy controller with linear consequence (TSFCLC) and the results show that TSFCNC performs better than TSFCLC. A real-life example of dynamic positioning of ship is simulated and the results also show that TSFCNC performs better than TSFCLC.

Smoothening and Sharpening Effects of Theta in Complex Diffusion for Image Processing

In this paper we present a study on how the changing values of theta in complex diffusion affects the images. Normally it is considered that the low value of theta is suitable for image smoothening using complex diffusion, because at higher values of theta the imaginary part may feed back into the real part, creating wave-like ringing effects. Our study shows that as the value of theta increases, ringing effects starts appearing and reaches its peak at 1800 and then it starts disappearing, and the process continues in a 360 degree cycle, where the peak of the wave indicates image with maximum ringing effects (or the maximum sharpened image, property of inverse diffusion). Regarding non-linear complex diffusion we experimentally proved the smoothening is fast at higher values of theta, which can be used for image denoising purpose.

Speckle reduction in images with WEAD and WECD

In this paper we discuss the speckle reduction in images with the recently proposed Wavelet Embedded Anisotropic Diffusion (WEAD) and Wavelet Embedded Complex Diffusion (WECD). Both these methods are improvements over anisotropic and complex diffusion by adding wavelet based bayes shrink in its second stage. Both WEAD and WECD produces excellent results when compared with the existing speckle reduction filters. The comparative analysis with other methods were mainly done on the basis of Structural Similarity Index Matrix (SSIM) and Peak Signal to Noise Ratio (PSNR). The visual appearance of the image is also considered.

GAVLC: GA with Variable Length Chromosome for the simultaneous design and stability analysis of T-S fuzzy controllers

Most of the design techniques of T-S fuzzy controllers assumes that there exists an approximate T-S model of the system with fixed antecedent parts & rules and uses techniques like GA, LMI, etc for the optimal design of the gain values. This paper presents a novel integrated approach for the design and stability analysis of T-S fuzzy controllers using GA with variable length chromosomes (VLCs) and LMI. This approach helps to find out the optimal parameters of the antecedent parts of the rules along with rule optimization and also to optimize the consequent parts.

Sparsity-based representation for categorical data

Over the past few decades, many algorithms have been continuously evolving in the area of machine learning. This is an era of big data which is generated by different applications related to various fields like medicine, the World Wide Web, E-learning networking etc. So, we are still in need for more efficient algorithms which are computationally cost effective and thereby producing faster results. Sparse representation of data is one giant leap toward the search for a solution for big data analysis. The focus of our paper is on algorithms for sparsity-based representation of categorical data. For this, we adopt a concept from the image and signal processing domain called dictionary learning. We have successfully implemented its sparse coding stage which gives the sparse representation of data using Orthogonal Matching Pursuit (OMP) algorithms (both Batch and Cholesky based) and its dictionary update stage using the Singular Value Decomposition (SVD). We have also used a preprocessing stage where we represent the categorical dataset using a vector space model based on the TF-IDF weighting scheme. Our paper demonstrates how input data can be decomposed and approximated as a linear combination of minimum number of elementary columns of a dictionary which so formed will be a compact representation of data. Classification or clustering algorithms can now be easily performed based on the generated sparse coded coefficient matrix or based on the dictionary. We also give a comparison of the dictionary learning algorithm when applying different OMP algorithms. The algorithms are analysed and results are demonstrated by synthetic tests and on real data.

A fuzzy approach to the prisoner's dilemma game using fuzzy expected value models

A game is a decision-making situation with many players, having objectives that partly or completely conflict with each other. The attitude of the players such as moral or philosophical motives, which are important in taking the optimal decision, are better modeled using fuzzy set theory. Prisoners dilemma is important in the study of game theory because the payoff structure associated with it arises in various strategic situations in real life. Therefore it is important to consider prisoners dilemma under the framework of fuzzy models. In this paper the idea of the expected value of a fuzzy variable is used as a tool to analyze a fuzzy approach to the prisoners dilemma game and some useful conclusions are reached regarding the selection of optimal strategies.

Gender identification in face images using KPCA

The data in face images are distributed in a complex manner due to the variation of light intensity, facial expression and pose. In this paper the Kernel Principal Component Analysis (KPCA) is used to extract the feature set of male and female faces. A Gaussian model of skin segmentation method is applied here to exclude the global features such as beard, eyebrow, moustache, etc. both training and test images are randomly selected from four different data bases to improve the training. The experimental results show that the proposed framework is efficient for recognizing the gender of a face image even though it is an impersonation face.

Implementation of projected clustering based on SQL queries and UDFs in relational databases

Projected clustering is one of the clustering approaches that determine the clusters in the subspaces of high dimensional data. Although it is possible to efficiently cluster a very large data set outside a relational database, the time and effort to export and import it can be significant. In commercial RDBMSs, there is no SQL query available for any type of subspace clustering, which is more suitable for large databases with high dimensions and large number of records. Integrating clustering with a relational DBMS using SQL is an important and challenging problem in todays world of Big Data. Projected clustering has the ability to find the closely correlated dimensions and find clusters in the corresponding subspaces. We have designed an SQL version of projected clustering which helps to get the clusters of the records in the database using a single SQL statement which in itself calls other SQL functions defined by us. We have used PostgreSQL DBMS to validate our implementation and have done experimentation with synthetic as well as real data.

Application of chaotic noise reduction techniques to chaotic data trained by ANN

We propose a novel method of combining artificial neural networks (ANNs) with chaotic noise reduction techniques that captures the metric and dynamic invariants of a chaotic time series, e.g. a time series obtained by iterating the logistic map in chaotic regimes. Our results indicate that while the feedforward neural network is capable of capturing the dynamical and metric invariants of chaotic time series within an error of about 25%, ANNs along with chaotic noise reduction techniques, such as Hammel’s method or the local projective method, can significantly improve these results. This further suggests that the effort on the ANN to train data corresponding to complex structures can be significantly reduced. This technique can be applied in areas like signal processing, data communication, image processing etc.