Department of Electrical Engineering

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IIT Jodhpur

This paper presents an analysis of jitter due to various deterministic noise sources in a CMOS inverter. The results obtained from the semi-analytical jitter estimation approach presented in the paper are compared with the results obtained from full SPICE based simulations. For the estimation of jitter, EMPSIJ method [1] is used in the paper. The paper also discusses the sensitivity of various noise paths on jitter. © 2019 IEEE.

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2019 6th International Conference on Signal Processing and Integrated Networks, SPIN 2019

Analysing the Impact of Various Deterministic Noise Sources on Jitter in a CMOS Inverter

In this letter, we propose two novel methods for four-class motor imagery (MI) classification using electroencephalography (EEG). Also, we developed a real-time health 4.0 (H4.0) architecture for brain-controlled internet of things (IoT) enabled environments (BCE), which uses the classified MI task to assist disabled persons in controlling IoT-enabled environments such as lighting and heating, ventilation, and air-conditioning (HVAC). The first method for classification involves a simple and low-complex classification framework using a combination of regularized Riemannian mean (RRM) and linear SVM. Although this method performs better compared to state-of-the-art techniques, it still suffers from a nonnegligible misclassification rate. Hence, to overcome this, the second method offers a persistent decision engine (PDE) for the MI classification, which improves classification accuracy (CA) significantly. The proposed methods are validated using an in-house recorded four-class MI data set (data set I, collected over 14 subjects), and a four-class MI data set 2a of BCI competition IV (data set II, collected over 9 subjects). The proposed RRM architecture obtained average CAs of 74.30% and 67.60% when validated using datasets I and II, respectively. When analyzed along with the proposed PDE classification framework, an average CA of 92.25% on 12 subjects of data set I and 82.54% on 7 subjects of data set II is obtained. The results show that the PDE algorithm is more reliable for the classification of four-class MI and is also feasible for BCE applications. The proposed low-complex BCE architecture is implemented in real time using Raspberry Pi 3 Model B+ along with the Virgo EEG data acquisition system. The hardware implementation results show that the proposed system architecture is well suited for body-wearable devices in the scenario of Health 4.0. We strongly feel that this study can aid in driving the future scope of BCE research. © 2019 Massachusetts Institute of Technology.

Fulltext

Neural Computation

A real-time health 4.0 framework with novel feature extraction and classification for brain-controlled iot-enabled environments

Metal assisted chemical etched Si nanostructures have been transferred onflexible sheets (adhesive tape) followed by deposition of Au/Ag nanoparticles to make SERS active platform. It shows significant enhancement of Raman signal of glucose in the vicinity of metal nanoparticles due to its coupling with the surface plasmon of nanostructures. SERS detection of glucose is extremely difficult because of its weak Raman cross section and poor adsorption to metal NPs. Fabricated SERS platform was utilized to detect glucose without any label at low concentration 10 micro molar (μM). The developed cost-effective SERS platform has potential applications as a platform device for non-invasive, label free detection of glucose in human fluid for point of care applications. © 2019 IEEE.

2019 IEEE 16th India Council International Conference, INDICON 2019 - Symposium Proceedings

Label free identification of glucose using flexible SERS platforms

The importance of replacing dialysis is growing day to day because of its disadvantages. Kidney-on-Chip is the right candidate to save kidney patients in an efficient way. The major parts of KOC are ultrafilter and bio-reactor unlike dialysis possessing of only ultrafiltration. This paper deals with designing of bioreactor for regenerating the reabsorption function based on size dependent selective function of kidney. The structure is designed with a main tubule carrying lumen fluid surrounded by blood tubules on the either side. The main tubule and blood tubules are connected through tiny transporting channels, which reabsorb the required solutes based on size. The flow velocity in the channels is found to be 8.82×10-5 m/s and total flow rate is 0.6 ×10-16 m3/s. Results show that the structure is capable of reabsorbing 52% of solutes. The present work has the potential to be applied in the design of Kidney-on-Chip. © 2019 IEEE.

Design and analysis novel structure for replication of reabsorption function for artificial kidney applications

Human breath contains various biomarkers which possibly can give us information about various diseases. Acetone is one such biomarker giving the information of blood glucose levels in the body. Breath acetone content for a healthy person is in the range of 0.5 to 2 ppm. Acetone concentration above this concentration may be an indication of person being diabetic. Hence it is imperative to detect this gas at low concentrations. This paper presents the development of iron oxide thin film for the highly sensitive detection of Acetone (CH3COCH3). Developed iron oxide thin film is characterized using FESEM and micro-Raman. It was demonstrated that the film exhibited a response of 65% for the acetone concentration as low as 1 ppm. The limit of detection for the film is ~50ppb. © 2019 IEEE.

Development of highly sensitive iron (III) oxide thin film for acetone sensing

A microfluidic device capable of sensing even fraction of pico Farad capacitive change is proposed. Purpose of the developed device is fluid concentration identification and characterization on the basis of the dielectric permittivity. A microchannel, realized on Silicon layer, below a sensing capacitor formed by electrodes sputter printed on a glass substrate. Fluids inside the microchannel affects the capacitance that is measured by a standard electronic interface system. Experimental results obtained for different fluids injected in the microchannel demonstrate the ability of the system to differentiate the fluids and estimate their dielectric permittivity both as pure samples and as mixtures at varying concentration of solute fractions. This makes the device a promising building block for fluid mixing monitoring in microfluidic systems. © 2019 IEEE.

Design and Development of Microfluidic Based Dielectric Sensors for Biomedical Applications

In this work, process development for the realization of Microchannels is carried out using standard micro fabrication techniques with polysilicon as sacrificial material. Polysilicon (PolySi) material is deposited using LPCVD process. Thermal silicon oxide layer work as a capping layer after the polysilicon layer is released using wet chemical etching using Tetra Methyl Ammonium Hydroxide (TMAH). Fabricated microchannels are characterized using 3D optical imaging and scanning electron microscopy. Fabrication of the Microchannel with an open cross-section of 4 μm X 0.85 μm and 50 μm length is demonstrated. © 2019 IEEE.

Microflui dic channel farbication using Poly-Si as a sacrificial layer

Security is a prime issue whenever biometric templates are stored in centralized databases. Templates are highly susceptible to varied security and privacy attacks. Unlike passwords, biometric traits are permanently unrecoverable if lost once. In this paper efforts have been made to generate cancelable knuckle print templates. To the best of our knowledge, this is the first attempt for generating secure template for this biometric-trait. Here for learning feature representation of a biometric sample, local binary pattern based CNN is used. The experimental results are evaluated on PolyU FKP knuckle database and demonstrate high performance. The proposed protected template is resilient to various privacy attacks as well as it satisfies one important criteria of cancelable biometrics i.e. revocability.

Cancelable Knuckle Template Generation Based on LBP-CNN

Growth enhances the complexity in control of distribution networks which are progressing towards the vision of smart grids. Inclusion of non-conventional resource power generators affects in magnitude and flow directions of faults in the distribution system there by posing challenges towards protection of distribution system.. A wavelet and alienation coefficients based algorithm described in this paper for electrical distribution network protection which is also connected with wind farm. The algorithm takes the current samples at selected nodes of the wind farm connected distribution system. From the collected samples of the current signals approximation coefficients are extracted using wavelet technique. A moving half cycle window is used to obtain alienation coefficients of the current signals which were approximated by wavelet technique. Using the WT (Wavelet transform) decomposition on the currents measured at selected nodes approximation coefficients are computed, which are used for acquiring the alienation coefficients to fault classification and identification. MATLAB Simulink is used for simulation of modified IEEE 13 bus test feeder system.Effectiveness of proposed technique examine for all possible shunt faults at every bus of test feeder system. Also the effect of wind speed variations and location of wind farm connection conditions are tested to evaluate the reliability of the technique. © BEIESP.

International Journal of Engineering and Advanced Technology

Wavelet-alienation coefficient based protection of distribution system

This paper presents a wavelet transform based alienation technique for the protection of a radial 5 bus distribution system integrated with four wind type doubly fed Induction generators (DFIG).This technique is used to detect islanding condition and faults, classification of faults and their discrimination. Islanding is simulated at point of common coupling (PCC) and faults are simulated at each DG bus of the network. Daubechies wavelet transform has been used to decompose the current signals to get approximate coefficients. The Alienation coefficients of these approximate decompositions are termed as islanding and fault indexes. These indexes have been compared with predetermined threshold to detect islanding and faults. The same threshold value is utilized to discriminate transients associated with islanding and fault. Alienation coefficients at each bus over a half cycle window clearly detect both islanding and fault. Testing of the proposed algorithm has been carried out for various angles of incidence. Hence, the proposed algorithm is more effective and successful for finding the islanding condition as well as faults in distribution system with distributed generation. © 2019 Institute of Advanced Engineering and Science. All rights reserved.