Indian Institute of Technology Indore

Abstract: ALICE is the heavy-ion experiment at the CERN Large Hadron Collider. The experiment continuously took data during the first physics campaign of the machine from fall 2009 until early 2013, using proton and lead-ion beams. In this paper we describe the running environment and the data handling procedures, and discuss the performance of the ALICE detectors and analysis methods for various physics observables.

Abstract: In this paper measurements are presented of pi(+/-), K-+/-, p, and (p) over bar production at midrapidity (vertical bar y vertical bar < 0.5), in Pb-Pb collisions at root s(NN) = 2.76 TeV as a function of centrality. The measurement covers the transverse-momentum (p(T)) range from 100, 200, and 300 MeV/c up to 3, 3, and 4.6 GeV/c for pi, K, and p, respectively. The measured p(T) distributions and yields are compared to expectations based on hydrodynamic, thermal and recombination models. The spectral shapes of central collisions show a stronger radial flow than measured at lower energies, which can be described in hydrodynamic models. In peripheral collisions, the p(T) distributions are not well reproduced by hydrodynamic models. Ratios of integrated particle yields are found to be nearly independent of centrality. The yield of protons normalized to pions is a factor similar to 1.5 lower than the expectation from thermal models.

Abstract: This paper presents a comprehensive study on clustering: exiting methods and developments made at various times. Clustering is defined as an unsupervised learning where the objects are grouped on the basis of some similarity inherent among them. There are different methods for clustering the objects such as hierarchical, partitional, grid, density based and model based. The approaches used in these methods are discussed with their respective states of art and applicability. The measures of similarity as well as the evaluation criteria, which are the central components of clustering, are also presented in the paper. The applications of clustering in some fields like image segmentation, object and character recognition and data mining are highlighted.

Abstract: In this paper, we present a new method for classification of electroencephalogram (EEG) signals using empirical mode decomposition (EMD) method. The intrinsic mode functions (IMFs) generated by EMD method can be considered as a set of amplitude and frequency modulated (AM-FM) signals. The Hilbert transformation of IMFs provides an analytic signal representation of the IMFs. The two bandwidths, namely amplitude modulation bandwidth (BAM) and frequency modulation bandwidth (BFM), computed from the analytic IMFs, have been used as an input to least squares support vector machine (LS-SVM) for classifying seizure and nonseizure EEG signals. The proposed method for classification of EEG signals based on the bandwidth features (BAM and BFM) and the LS-SVM has provided better classification accuracy than the method adopted by Liang and coworkers in their study published in 2010. The experimental results with the recorded EEG signals from a published dataset are included to show the effectiveness of the proposed method for EEG signal classification.

Abstract: This publication describes the methods used to measure the centrality of inelastic Pb-Pb collisions at a center-of-mass energy of 2.76 TeV per colliding nucleon pair with ALICE. The centrality is a key parameter in the study of the properties of QCD matter at extreme temperature and energy density, because it is directly related to the initial overlap region of the colliding nuclei. Geometrical properties of the collision, such as the number of participating nucleons and the number of binary nucleon-nucleon collisions, are deduced from a Glauber model with a sharp impact parameter selection and shown to be consistent with those extracted from the data. The centrality determination provides a tool to compare ALICE measurements with those of other experiments and with theoretical calculations.