Indian Institute of Technology Indore

About: Indian Institute of Technology Indore is a education organization based out in Indore, Madhya Pradesh, India. It is known for research contribution in the topics: Fading & Support vector machine. The organization has 1606 authors who have published 4803 publications receiving 66500 citations.

Zinc oxide-graphitic carbon nitride nanohybrid as an efficient electrochemical sensor and photocatalyst

Abstract: In this study, zinc oxide-graphitic carbon nitride (ZnO-CN) nanohybrid has been synthesized via a facile in-situ one pot solid-state thermal decomposition method, here [Zn(hmp-H)2(H2O)(μ-Cl)Zn(μ-Cl)(Cl)3] was used as single-source molecular precursor (SSMP) for ZnO and urea was taken as a source for graphitic carbon nitride (CN). Synthesized ZnO-CN nanohybrid was used as a modifier towards the fabrication of a binder free glassy carbon electrode surface (ZnO-CN/GCE) for detection of –NO2 containing aromatic compounds. The developed sensor shows the remarkable sensitive lower detection limit responses of 100 nM, 110 nM, 202 nM towards the 4-nitrotoluene (4-NT); 2,4-dinitrotuluene (2,4-DNT); 2,4,6-trinitrophenol (2,4,6-TNP), respectively. Further, a superior and rapid photo-catalytic degradation of Chicago Sky Blue (CSB), Congo Red (CR) and Methylene Blue (MB) was also achieved by employing ZnO-CN as a photo-catalyst with the percentage degradation of ∼85-99.6%. The alluring performance of the ZnO-CN nanohybrid towards the sensing of -NO2 containing aromatics and degradation of organic pollutants was ascribed to high surface area of as synthesized nanohybrid and heterojunction formed between the interfaces of ZnO and graphitic carbon nitride. These properties may facilitate the electron transfer process due to the higher electron conductivity and the separation of photo-induced electron−hole pairs.

Fuzzy Based Scalable Clustering Algorithms for Handling Big Data Using Apache Spark

Abstract: A huge amount of digital data containing useful information, called Big Data, is generated everyday To mine such useful information, clustering is widely used data analysis technique A large number of Big Data analytics frameworks have been developed to scale the clustering algorithms for big data analysis One such framework called Apache Spark works really well for iterative algorithms by supporting in-memory computations, scalability etc We focus on the design and implementation of partitional based clustering algorithms on Apache Spark, which are suited for clustering large datasets due to their low computational requirements In this paper, we propose Scalable Random Sampling with Iterative Optimization Fuzzy c-Means algorithm (SRSIO-FCM) implemented on an Apache Spark Cluster to handle the challenges associated with big data clustering Experimental studies on various big datasets have been conducted The performance of SRSIO-FCM is judged in comparison with the proposed scalable version of the Literal Fuzzy c-Means (LFCM) and Random Sampling plus Extension Fuzzy c-Means (rseFCM) implemented on the Apache Spark cluster The comparative results are reported in terms of time and space complexity, run time and measure of clustering quality, showing that SRSIO-FCM is able to run in much less time without compromising the clustering quality

Weak multiplexing in neural networks: Switching between chimera and solitary states.

Abstract: We investigate spatio-temporal patterns occurring in a two-layer multiplex network of oscillatory FitzHugh-Nagumo neurons, where each layer is represented by a nonlocally coupled ring We show that weak multiplexing, ie, when the coupling between the layers is smaller than that within the layers, can have a significant impact on the dynamics of the neural network We develop control strategies based on weak multiplexing and demonstrate how the desired state in one layer can be achieved without manipulating its parameters, but only by adjusting the other layer We find that for coupling range mismatch, weak multiplexing leads to the appearance of chimera states with different shapes of the mean velocity profile for parameter ranges where they do not exist in isolation Moreover, we show that introducing a coupling strength mismatch between the layers can suppress chimera states with one incoherent domain (one-headed chimeras) and induce various other regimes such as in-phase synchronization or two-headed chimeras Interestingly, small intra-layer coupling strength mismatch allows to achieve solitary states throughout the whole network