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.

Measurement of D-meson production at mid-rapidity in pp collisions at √s=7 TeV

Abstract: The production cross sections for prompt charmed mesons $$\mathrm{D^0}$$ , $$\mathrm{D^+}$$ , $$\mathrm{D^{*+}}$$ and $$\mathrm{D_s^+}$$ were measured at mid-rapidity in proton–proton collisions at a centre-of-mass energy $$\sqrt{s}=7~{\mathrm {TeV}}$$ with the ALICE detector at the Large Hadron Collider (LHC). D mesons were reconstructed from their decays $$\mathrm{D}^0 \rightarrow \mathrm{K}^-\pi ^+$$ , $$\mathrm{D}^+\rightarrow \mathrm{K}^-\pi ^+\pi ^+$$ , $$\mathrm{D}^{*+} \rightarrow \mathrm{D}^0 \pi ^+$$ , $$\mathrm{D_s^{+}\rightarrow \phi \pi ^+\rightarrow K^-K^+\pi ^+}$$ , and their charge conjugates.With respect to previous measurements in the same rapidity region, the coverage in transverse momentum ( $$p_\mathrm{T}$$ ) is extended and the uncertainties are reduced by a factor of about two. The accuracy on the estimated total $$\mathrm{c}{\overline{\mathrm{c}}}$$ production cross section is likewise improved. The measured $$p_\mathrm{T}$$ -differential cross sections are compared with the results of three perturbative QCD calculations.

Classification of cardiac sound signals using constrained tunable-Q wavelet transform

Abstract: The features extracted from the cardiac sound signals are commonly used for detection and identification of heart valve disorders. In this paper, we present a new method for classification of cardiac sound signals using constrained tunable-Q wavelet transform (TQWT). The proposed method begins with a constrained TQWT based segmentation of cardiac sound signals into heart beat cycles. The features obtained from heart beat cycles of separately reconstructed heart sounds and murmur can better represent the various types of cardiac sound signals than that from containing both. Therefore, heart sounds and murmur have been separated using constrained TQWT. Then the proposed novel raw feature set has been created by the parameters that have been optimized while constraining the output of TQWT together with that of extracted by using time-domain representation and Fourier-Bessel (FB) expansion of separated heart sounds and murmur. However, the adaptively selected features have been used to obtain the final feature set for subsequent classification of cardiac sound signals using least squares support vector machine (LS-SVM) with various kernel functions. The performance of the proposed method has been validated with publicly available datasets and the results have been compared with the existing short-time Fourier transform (STFT) based method. The proposed method shows higher percentage classification accuracy of 94.01 as compared to 93.53 of STFT based method. In comparison with STFT based method, it is noteworthy that the proposed method uses well defined and lower dimensionality of feature vector that can reduce the computational complexity.