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 & Computer science. The organization has 1185 authors who have published 3132 publications receiving 48832 citations.

A simple method for detection and classification of ECG noises for wearable ECG monitoring devices

Abstract: An assessment of electrocardiogram (ECG) signal quality has become an unavoidable first step in most holter and ambulatory ECG signal analysis applications. In this paper, we present a simple method for automatically detection and classification of ECG noises. The proposed method consists of four major steps: moving average filter, blocking, feature extraction, and multistage decision-tree algorithm. In the proposed method, the dynamic amplitude range and autocorrelation maximum peak features are extracted for each block. In the first decision stage, a amplitude-dependent decision rule is used for detecting the presence of low-frequency (LF) noise (including, baseline wander (BW) and abrupt change (ABC) artifacts) and the high-frequency (HF) noise (including, power line interference (PLI) and muscle artifacts). In the second decision stage, the proposed method further classifies the LF noise into a BW noise or a ABC noise using the local dynamic amplitude range feature. The HF noise is classified into a PLI noise or a muscle noise using the local autocorrelation maximum peak feature. The proposed detection and classification method is tested and validated using a wide variety of clean and noisy ECG signals. Results show that the method can achieve an average sensitivity (Se) of 97.88%, positive productivity (+P) of 91.18% and accuracy of 89.06%.

Search for nonresonant Higgs boson pair production in the b b ¯ b b ¯ final state at √s = 13 TeV

Abstract: Results of a search for nonresonant production of Higgs boson pairs, with each Higgs boson decaying to a $ \mathrm{b}\overline{\mathrm{b}} $ pair, are presented. This search uses data from proton-proton collisions at a centre-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 35.9 fb$^{−1}$, collected by the CMS detector at the LHC. No signal is observed, and a 95% confidence level upper limit of 847 fb is set on the cross section for standard model nonresonant Higgs boson pair production times the squared branching fraction of the Higgs boson decay to a $ \mathrm{b}\overline{\mathrm{b}} $ pair. The same signature is studied, and upper limits are set, in the context of models of physics beyond the standard model that predict modified couplings of the Higgs boson.

A novel unified framework for noise-robust ECG-based biometric authentication

Abstract: This paper presents a simple unified ensemble averaged heartbeat extraction framework for noise-robust ECG-based biometric authentication using heartbeat morphology. The proposed method consists of three major steps: preprocessing, ensemble averaged beat construction, and similarity matching. At the preprocessing stage, the signal blocking and mean removal operations are performed. The ensemble averaging stage includes the steps of: discrete cosine transform (DCT) based filter for simultaneous removal of BW and PLI noises, straightforward Gaussian derivative filter (GDF)-based R-peak detector, period normalization and peak-centering, and ensemble averaging computation. At the similarity matching stage, we study the performance of both time-domain and wavelet-domain distance metrics for finding the similarity between a test ensemble ECG beat template and an enrolled ECG beat template. The performance of the proposed framework is tested and validated using different types of ECG signals taken from the standard ECG databases. Results show that the ECG-biometric method using normalized cross correlation (NCC) metric provides consistent verification results as compared to the other four methods under different noisy conditions and sampling rates. For predefined threshold of 0.97, the NCC-based ECG-biometric method achieves an average false rejection rate (FRR) of 11.6% and false acceptance rate (FAR) of 5.8% for 3969 imposters and 19404 test segments from enrolled subjects, respectively.

Comparison of Pole Placement and LQR Applied to Single Link Flexible Manipulator

Abstract: this work presents a comparative study of two different control strategies for a flexible single-link manipulator. The dynamic model of the flexible manipulator involves modeling the rotational base and the flexible link as rigid bodies using the Euler Lagrange's method. The resulting system has one Degree-Of-Freedom (one DOF) and it provide freedom to increase the degree as well. Two types of regulators are studied, the State-Regulator using Pole Placement, and the Linear-Quadratic regulator (LQR). The LQR is obtained by resolving the Ricatti equation, in this work, we apply and compare two strategies to control the tip of the flexible link: state-feedback and linear quadratic regulator. These regulators are designed to reduce tip vibrations and increase system stability due to the flexibility of the arm.

Computer vision-guided intelligent traffic signaling for isolated intersections

Abstract: Computer vision-guided traffic management is an emerging area of research. Intelligent traffic signal control using computer vision is a less explored area of research. In this paper, we propose a new approach of traffic flow-based intelligent signal timing by temporally clustering optical flow features of moving vehicles using Temporal Unknown Incremental Clustering (TUIC) model. First, we propose a new inference scheme that works approximately 5-times faster as compared to the one originally proposed in TUIC in a dense traffic intersection. The new inference scheme can trace clusters representing moving objects that may be occluded while being tracked. Cluster counts of approach roads have been used for signal timing for traffic intersections. It is done by detecting cluster motion inside the regions-of-interest (ROI) marked at the entry and exit locations of intersection approaches. Departure rates are learned using Gaussian regression to parameterize traffic variations. Using the learned parameters as a function of cluster count, an adaptive signal timing algorithm, namely Throughput and Average Waiting Time Optimization (TAWTO) has been proposed. Experimental results reveal that the proposed method can achieve better average waiting time and throughput as compared to the state-of-the-art signal timing algorithms. We intend to publish two datasets as part of this work for enabling the research community to explore computer vision aided solutions for typical problems such as intelligent traffic controlling, violation detection in chaotic road intersections, etc.