Sridharakumar Narasimhan

Bio: Sridharakumar Narasimhan is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: System identification & Optimization problem. The author has an hindex of 13, co-authored 62 publications receiving 591 citations. Previous affiliations of Sridharakumar Narasimhan include Norwegian University of Science and Technology & Clarkson University.

Unsupervised Segmentation of Cervical Cell Images Using Gaussian Mixture Model

Abstract: Cervical cancer is one of the leading causes of cancer death in women. Screening at early stages using the popular Pap smear test has been demonstrated to reduce fatalities significantly. Cost effective, automated screening methods can significantly improve the adoption of these tests worldwide. Automated screening involves image analysis of cervical cells. Gaussian Mixture Models (GMM) are widely used in image processing for segmentation which is a crucial step in image analysis. In our proposed method, GMM is implemented to segment cell regions to identify cellular features such as nucleus, cytoplasm while addressing shortcomings of existing methods. This method is combined with shape based identification of nucleus to increase the accuracy of nucleus segmentation. This enables the algorithm to accurately trace the cells and nucleus contours from the pap smear images that contain cell clusters. The method also accounts for inconsistent staining, if any. The results that are presented shows that our proposed method performs well even in challenging conditions.

Control structure design for optimal operation of heat exchanger networks

Abstract: When only single bypasses and utility duties are used as manipulations, optimal operation of heat exchanger networks (HENs) can be categorized as an active constraint control problem. This work suggests a simple split-range control scheme to implement the optimal operation. For a given HEN and information about the disturbances, the corresponding control structure can be found by solving an integer-linear programming (ILP) problem with two objective functions providing optimal split-range pairs (for tracking active constraints during the operation) and appropriate control pairings (for fast control action). A HEN case study is used to demonstrate the application of the proposed design technique. Dynamic simulation shows the ability to provide the optimal operation of the obtained control structure. 2007 American Institute of Chemical Engineers AIChE J, 54: 150–162, 2008

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Singular Value Decomposition for Genome-Wide Expression Data Processing and Modeling

Abstract: ‡We describe the use of singular value decomposition in transforming genome-wide expression data from genes 3 arrays space to reduced diagonalized ‘‘eigengenes’’ 3 ‘‘eigenarrays’’ space, where the eigengenes (or eigenarrays) are unique orthonormal superpositions of the genes (or arrays). Normalizing the data by filtering out the eigengenes (and eigenarrays) that are inferred to represent noise or experimental artifacts enables meaningful comparison of the expression of different genes across different arrays in different experiments. Sorting the data according to the eigengenes and eigenarrays gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype, respectively. After normalization and sorting, the significant eigengenes and eigenarrays can be associated with observed genome-wide effects of regulators, or with measured samples, in which these regulators are overactive or underactive, respectively.

Numerical solution of initial value problems in differential - algebraic equations

Abstract: During the last decade, a big progress has been achieved in the analysis and numerical treatment of Initial Value Problems (IVPs) in Differential Algebraic Equations (DAEs) and Ordinary Differential Equations (ODEs). In spite of the rich variety of results available in the literature, there are still many specific problems that require special attention. Two of such, which are considered in this work, are the optimization of order of accuracy and reduction of cost of functional evaluations of Explicit Runge - Kutta (ERK) methods. Traditionally, the maximum attainable order p of an s-stage ERK method for advancing the solution of an IVP is such that p(s) 4 In 1999, Goeken presented an s-stage ERK Method of order p(s)=s +1,s>2. However, this work focuses on the design of a new approximation algorithm that reduces the cost of functional evaluations and yet increases the attainable order higher U n and Jonhson [94]; and the classical ERK methods. The order p of the new scheme called Multiderivative Explicit Runge-Kutta (MERK) Methods is such that p(s) 2. The stability, convergence and implementation for the optimization of IVPs in DAEs and ODEs systems are also considered.