Suprakash Datta

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A fundamental problem in wireless sensor networks is localization -- the determination of the geographical locations of sensors. Most existing localization algorithms were designed to work well either in networks of static sensors or networks in which all sensors are mobile. In this paper, we propose two localization algorithms, MSL and MSL*, that work well(More)
The paper provides theoretical justification for the “3-periodicity property” observed in protein coding regions within genomic DNA sequences. We propose a new classification criteria improving upon traditional frequency based approaches for identification of coding regions. Experimental studies indicate superior performance compared with other algorithms(More)
We present a model-based clustering method, SWIFT (Scalable Weighted Iterative Flow-clustering Technique), for digesting high-dimensional large-sized datasets obtained via modern flow cytometry into more compact representations that are well-suited for further automated or manual analysis. Key attributes of the method include the following: (a) the analysis(More)
Existing discrete Fourier transform (DFT)-based algorithms for identifying protein coding regions in DNA sequences (S. Tiwari et al., 1997, D. Anastassiou, 2001, D. Kotlar et al., 2003) exploit the empirical observation that the spectrum of protein coding regions of length N nucleotides has a peak at frequency k=N/3. In this paper, we prove the(More)
While the well-known Transport Control Protocol (TCP) is a <i>de facto</i> standard for reliable communication on the Internet, and performs well in practice, the question "how good is the TCP/IP congestion control algorithm?" is not completely resolved. In this paper, we provide some answers to this question using the competitive analysis framework. First,(More)
A multistage clustering and data processing method, SWIFT (detailed in a companion manuscript), has been developed to detect rare subpopulations in large, high-dimensional flow cytometry datasets. An iterative sampling procedure initially fits the data to multidimensional Gaussian distributions, then splitting and merging stages use a criterion of(More)
Flow cytometry (FC) is a powerful technology for rapid multivariate analysis and functional discrimination of cells. Current FC platforms generate large, high-dimensional datasets which pose a significant challenge for traditional manual bivariate analysis. Automated multivariate clustering, though highly desirable, is also stymied by the critical(More)
Side effect machines produce features for classifiers that distinguish different types of DNA sequences. They have the, as yet unexploited, potential to give insight into biological features of the sequences. We introduce several innovations to the production and use of side effect machine sequence features. We compare the results of using consensus(More)
The information available to connectivity-based positioning algorithms is the radio range of sensor devices and the position estimates of neighbors and neighbors of neighbors. This information creates special graph theoretic structures which impose new constraints on the positions of sensor devices. The new constraints sometimes lead to a feasible set of(More)