Simulated annealing based automatic fuzzy clustering combined with ANN classification for analyzing microarray data

Microarray technology has made it possible to monitor the expression levels of many genes simultaneously across a number of experimental conditions. Fuzzy clustering is an important tool for analyzing microarray gene expression data. In this article, a real-coded Simulated Annealing (VSA) based fuzzy clustering method with variable length configuration is developed and combined with popular Artificial Neural Network (ANN) based classifier. The idea is to refine the clustering produced by VSA using ANN classifier to obtain improved clustering performance. The proposed technique is used to cluster three publicly available real life microarray data sets. The superior performance of the proposed technique has been demonstrated by comparing with some widely used existing clustering algorithms. Also statistical significance test has been conducted to establish the statistical significance of the superior performance of the proposed clustering algorithm. Finally biological relevance of the clustering solutions are established.     

Fairness of Transitions in Diagnosability of Discrete Event Systems

Failure diagnosability has been widely studied for discrete event system (DES) models because of modeling simplicity and computational efficiency due to abstraction. In the literature it is often held that for diagnosability, such models can be used not only for systems that fall naturally in the class of DES but also for the ones traditionally treated as continuous variable dynamic systems. A class of algorithms for failure diagnosability of DES models has been successfully developed for systems where fairness is not a part of the model. These algorithms are based on detecting cycles in the normal and the failure model that look identical. However, there exist systems with all transitions fair where the diagnosability condition that hinges upon this feature renders many failures non-diagnosable although they may actually be diagnosable by transitions out of a cycle. Hence, the diagnosability conditions based on cycle detection need to be modified to hold for many real-world systems where all transitions are fair. In this work, however, it is shown by means of an example that a system may have some transitions fair and some unfair. A new failure diagnosability mechanism is proposed for DES models with both fair and unfair transitions. Time complexity for deciding diagnosability of DES models with fair and unfair transitions is analyzed and compared with the time complexities of other DES diagnosability analysis methods reported in the literature.     

A general framework for efficient geographic routing in wireless networks

We propose a new link metric called normalized advance (NADV) for geographic routing in multihop wireless networks. NADV selects neighbors with the optimal trade-off between proximity and link cost. Coupled with the local next hop decision in geographic routing, NADV enables an adaptive and efficient cost-aware routing strategy. Depending on the objective or message priority, applications can use the NADV framework to minimize various types of link cost.We present efficient methods for link cost estimation and perform detailed experiments in simulated environments. Our results show that NADV outperforms current schemes in many aspects: for example, in high noise environments with frequent packet losses, the use of NADV leads to 81% higher delivery ratio. When compared to centralized routing under certain settings, geographic routing using NADV finds paths whose cost is close to the optimum. We also conducted experiments in Emulab testbed and the results demonstrate that our proposed approach performs well in practice.     

Learning Sparse Feature Representations Using Probabilistic Quadtrees and Deep Belief Nets

Learning sparse feature representations is a useful instrument for solving an unsupervised learning problem. In this paper, we present three labeled handwritten digit datasets, collectively called n-MNIST by adding noise to the MNIST dataset, and three labeled datasets formed by adding noise to the offline Bangla numeral database. Then we propose a novel framework for the classification of handwritten digits that learns sparse representations using probabilistic quadtrees and Deep Belief Nets. On the MNIST, n-MNIST and noisy Bangla datasets, our framework shows promising results and outperforms traditional Deep Belief Networks.     

Lattice Boltzmann method simulation of electroosmotic stirring in a microscale cavity

The suitable surface modification of microfluidic channels can enable a neutral electrolyte solution to develop an electric double layer (EDL). The ions contained within the EDL can be moved by applying an external electric field, inducing electroosmotic flows (EOFs) that results in associated stirring. This provides a solution for the rapid mixing required for many microfluidic applications. We have investigated EOFs generated by applying a steady electric field across a square cavity that has homogenous electric potentials along its walls. The flowfield is simulated using the lattice Boltzmann method. The extent of mixing is characterized for different electrode configurations and electric field strengths. We find that rapid mixing can be achieved by using this simple configuration which increases with increasing electric field strength. The mixing time for water-soluble organic molecules can be decreased by four orders of magnitude by suitable choice of wall zeta potential and electric field. We dedicate this paper to the memory of our colleagues Professors Kevin Granata and Liviu Librescu who fell tragically on April 16, 2007 while answering their call to serve higher education. They continue to inspire us. AM gratefully acknowledges support from Jadavpur University under the World Bank funded Technical Education Quality Improvement Programme of the Government of India and the hospitality of the Virginia Tech ESM Department where he conducted a portion of this work.     

In-house contamination of potable water in urban slum of Kolkata, India: a possible transmission route of diarrhea

We have investigated and determined the potentiality of different water sources, both for drinking and domestic purposes, in diarrheal disease transmission in diarrhea endemic foci of urban slums in Kolkata, India in a one and half year prospective study. Out of 517 water samples, collected from different sources, stored water (washing) showed higher prevalence of fecal coliforms (58%) (p < 0.0001) in comparison with stored (drinking) samples (28%) and tap/tubewell water (8%) respectively. Among different sources, stored water (washing) samples had the highest non-permissible range of physico-chemical parameters. Fecal coliform levels in household water containers (washing) were comparatively high and almost 2/3 of these samples failed to reach the satisfactory level of residual chlorine. Interestingly, 7% stored water (washing) samples were found to be harboring Vibrio cholerae Improper usage of stored water and unsafe/poor sanitation practices such as hand washing etc. are highlighted as contributory factors for sustained diarrheal episodes. Vulnerability of stored water for domestic usage, a hitherto unexplored source, at domiciliary level in an urban slum where enteric infections are endemic, is reported for the first time. This attempt highlights the impact of quality of stored water at domiciliary level for fecal-oral contamination vis-à-vis disease transmission.     

Boundedness analysis of finitely recursive processes. I. Concurrentprocesses

In the finitely recursive process (FRP) model of discrete event systems (DES), concepts about processes and process operators have been introduced. An infinite set of process expressions or functions can be built recursively through function composition using a few elementary operators. Given any process realization, it is important to find out whether the process is bounded, i.e., whether it has a finite state realization. In the FRP setting this translates to the problem of finding out whether the set of post-process expressions is finite or not. In Cieslak and Varaiya (1990) it has been shown that the boundedness problem is undecidable for general FRPs. This paper investigates the decidability of the problem for subclasses of FRP. In Inan and Varaiya (1988), it was conjectured that the set of functions that can be recursively generated using the parallel composition operator and different change operators (i.e. without using the sequential composition operator) will be finite and FRPs constructed over this set of functions will naturally be bounded. In the present work a counterexample has been provided to disprove the conjecture about the finiteness of the above set of functions. However, using a suitable post-process computation procedure, it has been shown here that the FRPs, built recursively over this set of functions, are bounded     

Effect of shielding gas mixture on gas metal arc welding of HSLA steel using solid and flux-cored wires

In this work, gas metal arc welding of high strength-low alloy (HSLA) steel with solid- and flux-cored arc welding wires using different shielding gas compositions was performed. The composition of filler wire and shielding gas in gas metal arc welds of HSLA steel determines the inclusion characteristics, microstructure and mechanical properties. Thus, acceptable weld metal properties in HSLA steel using gas metal arc welding (GMAW) process could be achieved with the proper combination of filler wire and shielding gas composition.