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: Computer science & Chemistry. The organization has 1606 authors who have published 4803 publications receiving 66500 citations.

Anion-Anion Mediated Coupling in Layered Perovskite La2Ti2O7 for Visible Light Photocatalysis

Abstract: Anionic–anionic (N–N, P–P, N–P, and C–S) mediated coupling can be introduced in the layered perovskite La2Ti2O7 structure for visible light photocatalysis. The anionic–anionic codoped La2Ti2O7 systems lower the band gap much more than their respective monodoping systems. Moreover, the electronic band positions of the doped systems with respect to the water oxidation/reduction potentials show that codoped (N–N, N–P, and C–S) systems are more promising candidates for visible-light photocatalysis. The calculated defect formation energy shows that the codoped systems are more stable than their respective monodoped systems.

Multiple failure behaviors identification and remaining useful life prediction of ball bearings

Abstract: Accurate remaining useful life (RUL) prediction is the key for successful implementation of condition based maintenance program in any industry. Data driven prognostics approaches are generally used to predict the RUL of the components. Presence of noise in the data reduces the accuracy of RUL prediction. Mechanical components are prone to failures due to several failure modes; resulting into multiple failure behaviors or patterns in life test data obtained from various units. If such failure patterns or behaviors are not identified and treated appropriately, the same may act as one of the sources for data noise. In the present research, clustering and change point detection algorithm (CPDA) is used for identification of the presence of multiple failure behaviors in the data. Silhouette width value is used to find out the optimum number of clusters. Combined output of clustering and CPDA is used for developing RUL prediction models. Separate models for single and multiple failure behaviors are constructed using General Log-Linear Weibull (GLL-Weibull) distribution. Results show that identification of failure behavior helps in accurate prediction of RUL. The approach is validated using roller ball bearing life test data.

Higher harmonic flow coefficients of identified hadrons in Pb-Pb collisions at √sNN=2.76 TeV

Abstract: The elliptic, triangular, quadrangular and pentagonal anisotropic flow coefficients for π±, K± and p+p− in Pb-Pb collisions at √sNN=2.76 TeV were measured with the ALICE detector at the Large Hadron Collider. The results were obtained with the Scalar Product method, correlating the identified hadrons with reference particles from a different pseudorapidity region. Effects not related to the common event symmetry planes (non- flow) were estimated using correlations in pp collisions and were subtracted from the measurement. The obtained flow coefficients exhibit a clear mass ordering for transverse momentum (pT) values below ≈ 3 GeV/c. In the intermediate pT region (3< pT< 6 GeV/c), particles group at an approximate level according to the number of constituent quarks, suggesting that coalescence might be the relevant particle production mechanism in this region. The results for pT< 3 GeV/c are described fairly well by a hydrodynamical model (iEBE-VISHNU) that uses initial conditions generated by A Multi-Phase Transport model (AMPT) and describes the expansion of the fireball using a value of 0.08 for the ratio of shear viscosity to entropy density (η/s), coupled to a hadronic cascade model (UrQMD). Finally, expectations from AMPT alone fail to quantitatively describe the measurements for all harmonics throughout the measured transverse momentum region. However, the comparison to the AMPT model highlights the importance of the late hadronic rescattering stage to the development of the observed mass ordering at low values of pT and of coalescence as a particle production mechanism for the particle type grouping at intermediate values of pT for all harmonics.

A multivariate empirical mode decomposition based filtering for subject independent BCI

Abstract: Goal: A brain-computer interface (BCI) provides a way to translate the motion intentions of human using brain signals such as electroencephalogram (EEG) into control commands. EEG signals are highly subject specific and non-stationary. One of the most challenging tasks is to classify motion intentions since the recorded EEG signals have inherent non-stationarities which are due to changes in the signal properties over time within as well as across sessions. Thus it becomes difficult to achieve a stable operation of BCI. Method: We present a novel filtering method based on the multivariate empirical mode decomposition (MEMD) using subject independent BCI (MEMD-SI-BCI) for classification of motor imagery (MI) based EEG signals to achieve enhanced BCI. A subject independent BCI can be used immediately by the new user without using the user's training data. The MEMD method helps to utilize the cross channel information and enhance localization properties. It decomposes multichannel EEG signals into a set of multivariate intrinsic mode functions (MIMFs). These MIMFs can be considered narrow-band, amplitude and frequency modulated (AM-FM) signals. The statistical property, namely, mean frequency measure of these MIMFs has been used to combine these MIMFs to compute the enhanced EEG signals which have major contributions due to μ and β rhythms over the motor cortex region. The objective of the proposed method is to filter EEG signals before feature extraction and classification to enhance the features separability and ultimately the BCI task classification performance. The common spatial pattern (CSP) feature has been computed from the enhanced EEG signals and has been used as a feature set for classification of left hand and right hand MIs using a linear discriminant analysis (LDA) based classification method. Results: We have achieved an improvement of >11% in the evaluation stage using the MEMD-SI-BCI method when compared with SI-BCI. Significance: This study helps to develop BCI systems with intuitive motor imaginations, thus facilitates broad use of noninvasive BCIs. We have evaluated our method on publicly available BCI competition IV dataset 2A and have obtained improved performance.

Performance Evaluation and Optimization for Two-Way Relaying With Multi-antenna Sources

Abstract: In this paper, we investigate the performance of an analog network coding (ANC)-based two-way relay system that employs beamforming at the multi-antenna sources. Specifically, we analyze the overall system performance by deriving an exact expression for the overall symbol error rate (OSER) and an upper bound expression for the ergodic sum-rate (ESR) over independent and non-identically distributed Nakagami- m fading channels. We also derive closed-form representations of these expressions in the asymptotic high signal-to-noise ratio (SNR) regime to provide useful insights into the system behavior, and the optimal power allocation and relay location. Moreover, we address the joint optimization problem of power allocation and relay location to minimize the OSER and to maximize the ESR. Our results highlight the influence of key system/channel parameters on the overall system performance.