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.

Superior non-enzymatic glucose sensing properties of Ag-/Au-NiCo2O4 nanosheets with insight from electronic structure simulations

Abstract: Ag-/Au-NiCo2O4 nanosheets were synthesized by a facile electrodeposition approach on conducting Ni foam, and their non-enzymatic glucose sensing performance was investigated. The hybrid nanosheets of NiCo2O4 and noble-metal nanoparticles supported on conductive Ni foam possessed high active surface area along with intrinsic electrocatalytic and biocatalytic properties and promoted electronic/ionic transport in the electrodes, leading to improved glucose sensing properties. The sensitivity of the bare NiCo2O4 nanosheets for electrochemical non-enzymatic glucose sensing was 20.8 μA μM-1 cm-2, whereas the NiCo2O4-Ag and NiCo2O4-Au nanosheet electrodes exhibited enhanced sensitivities of 29.86 and 44.86 μA μM-1 cm-2, respectively, with lower response times in the same linear range of 5-45 μM. We also performed density functional theory simulations to corroborate our experimental observations by investigating the interactions and charge-transfer mechanism of glucose on Ag- and Au-doped NiCo2O4. As Au is bound more strongly to the NiCo2O4 surface compared to Ag, the binding energy of glucose is greater on the Au-doped NiCo2O4 surface than on the Ag-doped NiCo2O4 surface, and Au doping makes NiCo2O4 more conductive compared to Ag doping. Thus, it can be theoretically inferred that Au-doped NiCo2O4 has better glucose sensing performance, which supports our experimental data.

Numerical simulation on the performance analysis of a graphene-coated optical fiber plasmonic sensor at anti-crossing.

Abstract: A graphene-based surface plasmon resonance sensor using D-shaped fiber in anti-crossing has been designed. Silver as a plasmon active metal is followed by graphene, which helps in preventing oxidation and shows better adsorption efficiency to biomolecules. A wavelength interrogation technique based on the finite element method has been used to evaluate performance parameters. Design parameters such as thickness of silver, residual cladding, and GeO2 dopant concentration have been optimized. The wavelength sensitivity is found to be 6800 nm/RIU and resolution of 8.05×10−5 RIU. We believe that usage of graphene on silver may open a new window for study of online biomolecular interaction.

Performance study of cyclostationary based digital modulation classification schemes

Abstract: Automatic Modulation Classification (AMC) is a essential component in Cognitive Radio (CR) for recognizing the modulation scheme. Many modulated signals manifest the property of cyclostationarity as a feature so it can be exploited for classification. In this paper, we study the performance of digital modulation classification technique based on the cy-clostationary features and different classifiers such as Neural Network, Support Vector Machine, k-Nearest Neighbor, Naive Bayes, Linear Discriminant Analysis and Neuro-Fuzzy classifier. In this study we considered modulations i.e. BPSK, QPSK, FSK and MSK for classification. All classification methods studied using performance matrix including classification accuracy and computational complexity (time). The robustness of these methods are studied with SNR ranging from 0 to 20dB. Based upon the result we found that combining cyclostationary features with Naive Bayes and Linear Discriminant Analysis classifiers leads to provide better classification accuracy with less computational complexity.

Automatic Identification of S1 and S2 Heart Sounds Using Simultaneous PCG and PPG Recordings

Abstract: Accurate and reliable identification of the first (S1) and second (S2) sounds of the phonocardiogram (PCG) is still a challenging task due to the presence of the S3 and S4, murmurs, high-pitched sounds, physiological interference, and other environmental noises. This paper presents an automated method for identification of the S1 and S2 sounds by simultaneously recording, processing, and fusing the extracted fiducial points of the PCG and photoplethysmogram (PPG) signals. The method consists of four main stages: the PCG and PPG signal sensing with Arduino interfacing, the heart sound delineation (HSD) and pulse waveform delineation (PWD) using variational mode decomposition (VMD), and the S1/S2 identification based on the estimated timings of the pulse onset, peak, upward slope zerocrossing, and downward slope zerocrossing of the pulsatile waveform. The accuracy and robustness of the HSD and PWD algorithms are evaluated using both the benchmark databases and simultaneous PCG and PPG recordings. Then, the proposed S1/S2 identification method is evaluated using the simultaneous PCG and PPG recordings. The VMD-based HSD algorithm achieves an average sensitivity (SE) = 99.80%, positive predictivity (PP) = 99.0%, and accuracy (ACC) = 98.81% whereas the Gaussian derivative filter (GDF)-based HSD and bandpass filter (BPF)-based HSD algorithms achieve the average SE = 99.75%, PP = 93.59%, and ACC = 93.37% and the average SE = 99.67%, PP = 84.97%, and ACC = 84.73%, respectively, for the recorded PCG signals with SNR value of 5 dB. The proposed S1/S2 identification method achieves a correct identification rate of 99.75% which outperforms the GDF+systole interval(SI)/diastole interval(DI) and the BPF+SI/DI feature-based methods having 66% and 59.25%, respectively, in the case of noisy recordings with a SNR value of 5 dB. The proposed method has great potential in improving the identification accuracy and robustness in the presence of other sounds and murmurs, and different kinds of environmental noises.