Indian Institute of Technology Bhubaneswar

About: Indian Institute of Technology Bhubaneswar is a education organization based out in Bhubaneswar, India. It is known for research contribution in the topics: Large Hadron Collider & Higgs boson. The organization has 1185 authors who have published 3132 publications receiving 48832 citations.

First Observation of the Doubly Cabibbo-Suppressed Decay of a Charmed Baryon: Λc+→pK+π-

Abstract: We report the first observation of the decay Lambda(+)(c) -> pK(+)pi(-) using a 980 fb(-1) data sample collected by the Belle detector at the KEKB asymmetric-energy e(+)e(-) collider. This is the first observation of a doubly Cabibbo-suppressed decay of a charmed baryon. We measure the branching ratio of this decay with respect to its Cabibbo-favored counterpart to be B(Lambda(+)(c) -> pK(+)pi(-))/B(Lambda(+)(c) -> pK(-)pi(+)) = (2.35 +/- 0.27 +/- 0.21) x 10(-3), where the uncertainties are statistical and systematic, respectively.

A Multiobjective Optimization Based Fast and Robust Design Methodology for Low Power and Low Phase Noise Current Starved VCO

Abstract: This paper presents a novel design methodology for design of optimal and robust current starved voltage controlled oscillator (CSVCO) circuit. A recently developed multiobjective optimization technique infeasibility driven evolutionary algorithm is used to minimize the power and the phase noise of the circuit at its schematic and physical level. The multiobjective optimization is carried out by taking into account the extracted parasitics that would be present in the physical integrated circuit and the random variations of parameters during fabrication in foundry. This method helps the designer in semiconductor industry by effectively reducing several time consuming design iterations to a single iteration ensuring the near optimal performance of the CSVCO. The performance of the circuit is validated by carrying out simulations for transient and noise analysis in Cadence tools using 90 nm 1P9M CMOS process.

EEG-Based Age and Gender Prediction Using Deep BLSTM-LSTM Network Model

Abstract: With the rapid development of brain–computer interfaces (BCI), the number of applications that use BCI technology is increasingly thick and fast. Prediction of age and gender of a person through EEG analysis is a new application of BCI that has been proposed in this paper. An industry standard EEG recording device has been used to record cerebral activities of 60 subjects (both male and female) in relaxed position with closed eyes. Deep BLSTM-LSTM network has been used to construct a hybrid learning framework for the aforementioned analysis. Accuracy of 93.7% and 97.5% have been recorded for age and gender classification problems respectively. These values are better than the state-of-the-art methods. Our analysis also reveals that the beta band frequencies are better in predicting the age and gender as compared to other frequency bands of the EEG signals. The proposed method has several applications, including biometric, health-care, entertainment, and targeted advertisements.

Micrometer Wire Assisted Inline Mach–Zehnder Interferometric Curvature Sensor

Abstract: A highly sensitive curvature sensor based on the inline Mach–Zehnder interferometer (MZI) adjoined with a micrometer wire taper is realized using all single mode fiber (SMF). Experimental results suggest that the addition of the SMF micrometer wire with MZI reduces the period of the interference pattern from 39.27 to 1.76 nm and increases the amplitude sensitivity by twofold. The sensitivity of the proposed sensor is −11.92 dB/ $\text{m}^{-1}$ and has a resolution of $8.4\times 10^{-5}~\text{m}^{\mathrm {-1}^{\vphantom {a}}}$ in the curvature range of 0–1 $\text{m}^{-1}$ . The sensing setup is highly stable and shows negligible hysteresis. The proposed sensor shows the negligible temperature sensitivity less than 0.0008 dB/°C. All the SMF structure and intensity-based interrogation method makes the sensor an economical alternative to the existing curvature sensors.