Showing papers by "Raghunathan Rengaswamy published in 2020"
TL;DR: In this article, a cylindrical PEM fuel cell was designed to achieve high power and low open circuit voltage with high ohmic resistance, achieving a current density of 400mA/cm2 at 0.6V and peak power of 2W.
22 citations
TL;DR: In this paper, the authors proposed an approach for generating the system reliability using the sum of disjoint product method, which serves as the objective function to be maximized in various constrained optimization formulations for sensor network design.
10 citations
TL;DR: A modified definition of system reliability for sensor network design for two applications: reliable estimation of variables in a steady state linear flow process, and reliable fault detection and diagnosis for any process is presented.
10 citations
TL;DR: The approach surmounts the issue of low recall and bias towards genes with high mutation rates and predicts potential novel driver genes for further experimental screening and shows that certain mutation types such as nonsense mutations are more important for classification.
Abstract: An emergent area of cancer genomics has been the identification of driver genes. Driver genes confer a selective growth advantage to the cell and push it towards tumorigenesis. Functionally, driver genes can be divided into two categories, tumour suppressor genes (TSGs) and oncogenes (OGs), which have distinct mutation type profiles. While several driver genes have been discovered, many remain undiscovered, especially those that are mutated at a low frequency across samples. The current methods are not sufficient to predict all driver genes because the underlying characteristics of these genes are not yet well understood. Thus, to predict novel genes, we need to define new features and models that are not biased and identify genes that might otherwise be overshadowed by mutation profiles of recurrent driver genes. In this study, we define new features and build a model to identify novel driver genes. We overcome overfitting and show that certain mutation types such as nonsense mutations are more important for classification. Some known cancer driver genes, which are predicted by the model as TSGs with high probability are ARID1A, TP53, and RB1. In addition to these known genes, potential driver genes predicted are CD36, ZNF750 and ARHGAP35 as TSGs and TAB3 as an oncogene. Overall, our approach surmounts the issue of low recall and bias towards genes with high mutation rates and predicts potential novel driver genes for further experimental screening.
7 citations
TL;DR: In this article, a novel technique that makes use of two high bandwidth and short duration signals having dissimilar phases to estimate impedance is introduced, which can pave the way for a quick and cost effective alternative to EIS and allow for impedance measurement during operation.
6 citations
Posted Content•
TL;DR: This article proposes Structural Principal Component Analysis (SPCA), a wise modification of PCA to utilize structural information which improvises over existing methods like PCA by utilizing the essential structural information about the model.
Abstract: Model identification is a crucial problem in chemical industries. In recent years, there has been increasing interest in learning data-driven models utilizing partial knowledge about the system of interest. Most techniques for model identification do not provide the freedom to incorporate any partial information such as the structure of the model. In this article, we propose model identification techniques that could leverage such partial information to produce better estimates. Specifically, we propose Structural Principal Component Analysis (SPCA) which improvises over existing methods like PCA by utilizing the essential structural information about the model. Most of the existing methods or closely related methods use sparsity constraints which could be computationally expensive. Our proposed method is a wise modification of PCA to utilize structural information. The efficacy of the proposed approach is demonstrated using synthetic and industrial case-studies.
2 citations
TL;DR: An Indian population-specific GA dating formula developed from the GARBH-Ini cohort performs at par with the existing formulae but estimates the lowest PTB rate with better precision than otherformulae, reinforcing the fact that CRL-based USG method is best for estimation of GA in the first trimester.
Abstract: Background Different formulae have been developed globally to estimate gestational age (GA) by ultrasonography in the first trimester of pregnancy. In this study, we develop an Indian population-specific dating formula and compare its performance with published formulae. Finally, we evaluate the implications of the choice of dating method on preterm birth (PTB) rate. This study’s data was from GARBH-Ini, an ongoing pregnancy cohort of North Indian women to study PTB. Methods Comparisons between ultrasonography-Hadlock and last menstrual period (LMP) based dating methods were made by studying the distribution of their differences by Bland-Altman analysis. Using data-driven approaches, we removed data outliers more efficiently than by applying clinical parameters. We applied advanced machine learning algorithms to identify relevant features for GA estimation and developed an Indian population-specific formula (Garbhini-GA1) for the first trimester. PTB rates of Garbhini-GA1 and other formulae were compared by estimating sensitivity and accuracy. Results Performance of Garbhini-GA1 formula, a non-linear function of crown-rump length (CRL), was equivalent to published formulae for estimation of first trimester GA (LoA, - 0.46,0.96 weeks). We found that CRL was the most crucial parameter in estimating GA and no other clinical or socioeconomic covariates contributed to GA estimation. The estimated PTB rate across all the formulae including LMP ranged 11.27 – 16.50% with Garbhini-GA1 estimating the least rate with highest sensitivity and accuracy. While the LMP-based method overestimated GA by three days compared to USG-Hadlock formula; at an individual level, these methods had less than 50% agreement in the classification of PTB. Conclusions An accurate estimation of GA is crucial for the management of PTB. Garbhini-GA1, the first such formula developed in an Indian setting, estimates PTB rates with higher accuracy, especially when compared to commonly used Hadlock formula. Our results reinforce the need to develop population-specific gestational age formulae.
2 citations
Posted Content•
26 Aug 2020
TL;DR: A previously unknown time-frequency duality for linear systems when probed through a specific signal is described, which means that orders of magnitude reduction in experimentation time over standard EIS techniques is possible.
Abstract: Frequency response analysis (FRA) of systems is a well-researched area. Frequency response of electrochemical systems are identified using the electrochemical impedance spectroscopy (EIS) technique. EIS is unarguably the most used technique for diagnostic applications in several electrochemical systems that have relevance in renewable energy, corrosion resistance, sensors, and environmental applications. For years, EIS has been performed using input signals, which are a series of sinusoids or a sum of sinusoids. This results in large experimentation time, particularly when the system has to be probed at lower frequencies. In this work, we describe a previously unknown time-frequency duality for linear systems when probed through a specific signal. It is surprising that this result had not been uncovered given that FRA has been used in multiple disciplines for more than hundred years. The implication of this result is that orders of magnitude reduction in experimentation time over standard EIS techniques is possible. Theoretical and simulation studies support our claims.
Posted Content•
26 Aug 2020
TL;DR: In this paper, the authors report a hitherto unknown result that can be leveraged to mitigate the difficulties associated with isolating the system behavior precisely at individual frequencies, resulting in a tremendous reduction in the experimentation time.
Abstract: Hundreds of applications utilize frequency response characterization of a system. Identification of frequency response requires long experimentation time, use of transformation techniques and other difficulties associated with isolating the system behavior precisely at individual frequencies. In this work, we report a hitherto unknown result that can be leveraged to mitigate these difficulties resulting in a tremendous reduction in the experimentation time. This result has the possibility of revolutionizing how frequency response studies are performed.