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.

Upgrade of the ALICE Experiment Letter Of Intent

Abstract: ALICE (A Large Ion Collider Experiment) is studying the physics of strongly interacting matter, and in particular the properties of the Quark–Gluon Plasma (QGP), using proton–proton, proton–nucleus and nucleus–nucleus collisions at the CERN LHC (Large Hadron Collider). The ALICE Collaboration is preparing a major upgrade of the experimental apparatus, planned for installation in the second long LHC shutdown in the years 2018–2019. These plans are presented in the ALICE Upgrade Letter of Intent, submitted to the LHCC (LHC experiments Committee) in September 2012. In order to fully exploit the physics reach of the LHC in this field, high-precision measurements of the heavy-flavour production, quarkonia, direct real and virtual photons, and jets are necessary. This will be achieved by an increase of the LHC Pb–Pb instant luminosity up to 6×1027 cm−2s−1 and running the ALICE detector with the continuous readout at the 50 kHz event rate. The physics performance accessible with the upgraded detector, together with the main detector modifications, are presented.

Cross-Subject Emotion Recognition Using Flexible Analytic Wavelet Transform From EEG Signals

Abstract: Human emotion is a physical or psychological process which is triggered either consciously or unconsciously due to perception of any object or situation. The electroencephalogram (EEG) signals can be used to record ongoing neuronal activities in the brain to get the information about the human emotional state. These complicated neuronal activities in the brain cause non-stationary behavior of the EEG signals. Thus, emotion recognition using EEG signals is a challenging study and it requires advanced signal processing techniques to extract the hidden information of emotions from EEG signals. Due to poor generalizability of features from EEG signals across subjects, recognizing cross-subject emotion has been difficult. Thus, our aim is to comprehensively investigate the channel specific nature of EEG signals and to provide an effective method based on flexible analytic wavelet transform (FAWT) for recognition of emotion. FAWT decomposes the EEG signal into different sub-band signals. Furthermore, we applied information potential to extract the features from the decomposed sub-band signals of EEG signal. The extracted feature values were smoothed and fed to the random forest and support vector machine classifiers that classified the emotions. The proposed method is applied to two different publicly available databases which are SJTU emotion EEG dataset and database for emotion analysis using physiological signal. The proposed method has shown better performance for human emotion classification as compared to the existing method. Moreover, it yields channel specific subject classification of emotion EEG signals when exposed to the same stimuli.

Centrality Dependence of the Charged-Particle Multiplicity Density at Midrapidity in Pb-Pb Collisions at sNN =5.02 TeV

Abstract: The pseudorapidity density of charged particles, dNch/dη, at midrapidity in Pb-Pb collisions has been measured at a center-of-mass energy per nucleon pair of √sNN=5.02 TeV. For the 5% most central collisions, we measure a value of 1943 ± 54. The rise in dNch/dη as a function of √sNN p is steeper than that observed in proton-proton collisions and follows the trend established by measurements at lower energy. The increase of dNch/dη as a function of the average number of participant nucleons, ⟨Npart⟩, calculated in a Glauber model, is compared with the previous measurement at √sNN=2.76 TeV. A constant factor of about 1.2 describes the increase in dNch/dη from √sNN=2.76 to 5.02 TeV for all centrality classes, within the measured range of 0%–80% centrality. The results are also compared to models based on different mechanisms for particle production in nuclear collisions.