Indian Institute of Technology Guwahati

About: Indian Institute of Technology Guwahati is a education organization based out in Guwahati, Assam, India. It is known for research contribution in the topics: Catalysis & Computer science. The organization has 6933 authors who have published 17102 publications receiving 257351 citations.

Using Deep and Convolutional Neural Networks for Accurate Emotion Classification on DEAP Dataset.

Abstract: Emotion recognition is an important field of research in Brain Computer Interactions. As technology and the understanding of emotions are advancing, there are growing opportunities for automatic emotion recognition systems. Neural networks are a family of statistical learning models inspired by biological neural networks and are used to estimate functions that can depend on a large number of inputs that are generally unknown. In this paper we seek to use this effectiveness of Neural Networks to classify user emotions using EEG signals from the DEAP (Koelstra et al (2012)) dataset which represents the benchmark for Emotion classification research. We explore 2 different Neural Models, a simple Deep Neural Network and a Convolutional Neural Network for classification. Our model provides the state-of-the-art classification accuracy, obtaining 4.51 and 4.96 percentage point improvements over (Rozgic et al (2013)) classification of Valence and Arousal into 2 classes (High and Low) and 13.39 and 6.58 percentage point improvements over (Chung and Yoon(2012)) classification of Valence and Arousal into 3 classes (High, Normal and Low). Moreover our research is a testament that Neural Networks could be robust classifiers for brain signals, even outperforming traditional learning techniques.

A formal framework for on-line software version change

Abstract: The usual way of installing a new version of a software system is to shut down the running program and then install the new version. This necessitates a sometimes unacceptable delay during which service is denied to the users of the software. An online software replacement system replaces parts of the software while it is in execution, thus eliminating the shutdown. While a number of implementations of online version change systems have been described in the literature, little investigation has been done on its theoretical aspects. We describe a formal framework for studying online software version change. We give a general definition of validity of an online change, show that it is in general undecidable and then develop sufficient conditions for ensuring validity for a procedural language.

Preferential and Enhanced Adsorption of Different Dyes on Iron Oxide Nanoparticles: A Comparative Study

Abstract: The iron oxide nanoparticles, having an average size of 20−40 nm with a surface area of ∼70 m2 g−1, have been synthesized and used for selective adsorption of various dyes (selectively containing hydroxyl groups) from aqueous solution. The nanoparticles are ferromagnetic in nature at both room and low temperature and can be separated by an external magnetic field. Herein, we report a preferable and enhanced adsorption phenomena of the dyes containing hydroxyl (−OH) groups on iron oxide nanoparticles. The group of erichrome black-T, bromophenol blue, bromocresol green, and fluorescein was adsorbed more on the iron oxide surface as compared to methyl red, methylene blue, and methyl orange, which does not have any hydroxyl (−OH) groups. The association−OH of the dye in preferential adsorption phenomena has also been supported with FT-IR analysis. The adsorption process was studied by varying different regulating parameters like solution pH, initial dye, and iron oxide concentration and analyzed in terms of k...

Multiscale Energy and Eigenspace Approach to Detection and Localization of Myocardial Infarction

Abstract: In this paper, a novel technique on a multiscale energy and eigenspace (MEES) approach is proposed for the detection and localization of myocardial infarction (MI) from multilead electrocardiogram (ECG). Wavelet decomposition of multilead ECG signals grossly segments the clinical components at different subbands. In MI, pathological characteristics such as hypercute T-wave, inversion of T-wave, changes in ST elevation, or pathological Q-wave are seen in ECG signals. This pathological information alters the covariance structures of multiscale multivariate matrices at different scales and the corresponding eigenvalues. The clinically relevant components can be captured by eigenvalues. In this study, multiscale wavelet energies and eigenvalues of multiscale covariance matrices are used as diagnostic features. Support vector machines (SVMs) with both linear and radial basis function (RBF) kernel and K-nearest neighbor are used as classifiers. Datasets, which include healthy control, and various types of MI, such as anterior, anteriolateral, anterioseptal, inferior, inferiolateral, and inferioposterio-lateral, from the PTB diagnostic ECG database are used for evaluation. The results show that the proposed technique can successfully detect the MI pathologies. The MEES approach also helps localize different types of MIs. For MI detection, the accuracy, the sensitivity, and the specificity values are 96%, 93%, and 99% respectively. The localization accuracy is 99.58%, using a multiclass SVM classifier with RBF kernel.