Swagata Devi
@nits.ac.in
Electronics and Communication Engineering
National Institute of Technology Silchar
EDUCATION
Ph.D. in Electronics and Communication Engineering from National Institute of Technology Silchar (2022)
M.Tech in Electronics Design and Technology from Tezpur University (2016)
B.E in Electronics and Tele-Communication Engineering from Gauhati University (2013)
12th from State Board (2009)
10th from State Board (2007)
RESEARCH INTERESTS
Analog VLSI Circuit Design, Biomedical Circuits and Optimization Methods
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
K Manojkumar and S Devi
IEEE
The congestion control is defined as a contrivance that is used to control and monitor the entry of data packets into the network. This helps to enhance and develop a better infrastructure in the communication network. The wireless sensor network is a communication network with infrastructure that are accompanied with less wireless network that is composed of innumerable nodes that are transportable through several instructions in the system. The nodes are interconnected in the communication system with each other in random fashion. The congestion is denoted as a decrease in the quality of service (QOS) in network that results in packet loss and new connections in the network may get blocked. The congestion can be prevented by implementing open loop congestion control policies. This can be controlled by the source or through the destination. This happens when there is a shortage in the buffer space. The soft computing based congestion control in routing the data is done through the following techniques that are classified as fuzzy logic system, artificial neural network, game theory based and particle swarm optimization techniques.
Swagata Devi, Koushik Guha, Olga Jakšić, Krishna Lal Baishnab, and Zoran Jakšić
MDPI AG
This work is dedicated to parameter optimization for a self-biased amplifier to be used in preamplifiers for the diagnosis of seizures in neuro-diseases such as epilepsy. For the sake of maximum compactness, which is obligatory for all implantable devices, power is to be supplied by a piezoelectric nanogenerator (PENG). Several meta-heuristic optimization algorithms and an ANN (artificial neural network)-assisted goal attainment method were applied to the circuit, aiming to provide us with the set of optimal design parameters which ensure the minimal overall area of the preamplifier. These parameters are the slew rate, load capacitor, gain–bandwidth product, maximal input voltage, minimal input voltage, input voltage, reference voltage, and dissipation power. The results are re-evaluated and compared in the Cadence 180 nm SCL environment. It has been observed that, among the metaheuristic algorithms, the whale optimization technique reached the best values at low computational cost, decreased complexity, and the highest convergence speed. However, all metaheuristic algorithms were outperformed by the ANN-assisted goal attainment method, which produced a roughly 50% smaller overall area of the preamplifier. All the techniques described here are applicable to the design and optimization of wearable or implantable circuits.
Swagata Devi, Koushik Guha, Krishna Lal Baishnab, Jacopo Iannacci, and Narayan Krishnaswamy
Springer Science and Business Media LLC
Swagata Devi, Koushik Guha, and Krishna Lal Baishnab
CRC Press
Swagata Devi, Koushik Guha, and Krishna Lal Baishnab
Inderscience Publishers
N Prakash, Syed Asif Basha, Subrata Chowdhury, B Reshmi, Dhiraj Kapila, and S. Devi
IEEE
Edge Detection is one of the discontinuity based image segmentation technique perform optimized function for detection, counting of people and other objects like vehicle, animals, etc in an image sequence. Edge detection is the technique bidding to designate the intensity changes concerned to the physical processes that have established them. Prewitt is one of the famous edge detection algorithms which use the derivative approximation to find the edges. This paper implements the Prewitt edge detection operator using VLSI design technique. Coding is written in VHDLand simulated using QUARTUS. The Simulation results are also compared with that of MATLAB outputs. The experimental results show that the proposed algorithm can be effectively used to detect objects in difficult videos of crowds with many occlusions, local noise and local scale variations.
T. Aditya Sai Srinivas, B Ravindra Babu, Miskir Solomon Tsige, R. Rajagopal, S. Devi, and Subrata Chowdhury
IEEE
This article presents the activities carried out in research for the development of a prototype digital stethoscope using a smartphone. First the importance of biomedical engineering in medical decisions and the key concepts of listening to body sound are discussed, with a focus on understanding how signals from the heart, lungs and tract gastrointestinal are received. Using the digital signal processing approach, the main methods of filtering digital signals, which are the subject of this work, are discussed. It also shows the evolution of mobile computing, modern technologies for operating systems and the share of medical solutions. With these concepts in mind, we have developed an external device and app for the Android operating system that can receive and filter body signals to create a digital stethoscope. The process of developing a data acquisition system is also presented, together with the results of the tests and simulations carried out, offering a final discussion on the importance of the characteristics of the smartphone in medical practice, possible improvements in the process digital filtering and effective digital stethoscoperealization.
S. Kannan, G. Premalatha, M. Jamuna Rani, D. Jayakumar, P. Senthil, S. Palanivelrajan, S. Devi, and Kibebe Sahile
Hindawi Limited
This work is implemented for the management of patients with epilepsy, and methods based on electroencephalography (EEG) analysis have been proposed for the timely prediction of its occurrence. The proposed system is used for crisis detection and prediction system; it is useful for both patients and medical staff to know their status easily and more accurately. In the treatment of Parkinson’s disease, the affected patients with Parkinson’s disease can assess the prognostic risk factors, and the symptoms are evaluated to predict rapid progression in the early stages after diagnosis. The presented seizure prediction system introduces deep learning algorithms into EEG score analysis. This proposed work long short-term memory (LSTM) network model is mainly implemented for the identification and classification of qualitative patterns in the EEG of patients. While compared with other techniques like deep learning models such as convolutional neural networks (CNNs) and traditional machine learning algorithms, the proposed LSTM model plays a significant role in predicting impending crises over 4 different qualifying intervals from 10 minutes to 1.5 hours with very few wrong predictions.
Sourav Nath, Swagata Devi, Merin Loukrakpam, Koushik Guha, and Krishna Lal Baishnab
Springer Singapore
Sourav Nath, N. M. Laskar, Swagata Devi, Koushik Guha, K. L. Baishnab, and Jacopo Iannacci
Springer Science and Business Media LLC
Swagata Devi, Koushik Guha, Naushad Manzoor Laskar, Sourav Nath, Krishna Lal Baishnab, Jacopo Iannacci, and Narayan Krishnaswamy
Springer Science and Business Media LLC
Swagata Devi, Koushik Guha, and Krishna Lal Baishnab
IEEE
This paper deals with metaheuristic-based approach for optimal solution of improvised amplifier for low power application keeping in special focus on area minimization. It implements a new technique in the circuit design to optimize the amplifier intended for low power low voltage biomedical applications. The methodology establishes the optimum aspect ratios and the biasing currents of the transistors, with reference to the analytical model of the amplifier so as to consider its area as an objective function. Various high-performance meta-heuristic optimization algorithms have been employed to determine the best possible aspect ratios at low computational cost and reduced complexity. A proper evaluation of all these algorithms discloses that the whale optimization algorithm is apt amongst all of them. The optimized parameters are estimated for the optimum area at high convergence speed. These results are cross-verified in contradiction to the simulation results in Cadence SCL 180nm environment. The algorithm sets up the aspect ratios and the biasing parameters of the transistors. This automated method of fixing up the parameters could possibly minimize the computational complexity of the problem and offer an accurate design solution for the amplifiers. These types of circuits are vastly explored in the design of preamplifier circuits of the neural amplifier to treat neuro-diseases like epilepsy, Alzheimer's disease, and many more.
J. Angelin Jeba and S. Nirmala Devi
IEEE
Automatic object recognition with shape descriptors help to interact with the real-time environment. This paper describes the shape analysis of radioactivity Fluoro Deoxy Glucose (FDG) uptakes present in pre and postoperative stages of Fused PET/CT images using shape feature extraction approach. Shape features are invariant to various affine transformations such as translation, rotational, flipped, scale, etc., are more robust for shape analysis. Geometrical properties of the FDG uptakes are extracted at various levels of spatial resolution by hierarchical Kdabstraction from the segmented PET/CT images. Rotational invariants considered are the Zernike moments magnitudes and the shape signatures of Fourier descriptors are investigated. The discrimination power of features between pre and post-operative images is examined and evaluated.
Swagata Devi, Koushik Guha, Naushad Manzoor Laskar, Sourav Nath, and Krishna Lal Baishnab
Springer Singapore
J. Angelin Jeba and S. Nirmala Devi
Elsevier BV
Ananthakrishnan, Anaswar Ajit, P.V. Arathi, Kiran Haridas, Niraj Mohan Nambiar, and S. Devi
IEEE
Adders are an almost requisite element of every modern integrated circuit. As in the current era of increasing digitalization, where everyone works towards miniaturization, three important aspects of design, i.e. area, power and delay, need to be optimally balanced. Since adders are used in many complex digital circuits as a basic component, enhancing digital adder performance would greatly accelerate binary operations within such complex circuits. In binary adders, the speed of activity is constrained when taken in propagating the carry through the adder. The adder topology used in our work includes the comparison between Carry Select adder, Carry Look ahead adder, Carry Increment adder, Ripple carry adder and Brent-kung adder. The work is being carried out using Hardware Description Language (HDL) in the platform Xilinx ISE 14.7.
Greeshma Nair and S Devi
IEEE
The demand for Vital sign detection in Military Surveillance purposes and Healthcare monitoring purposes has been rising largely thereby making enhancements in RADAR performances imperative. Flicker noise tends to influence the performance of Zero-IF architectures highly. Use of Pre-Defined Filters for Flicker noise elimination causes degradation in radar performance due to elimination of useful low frequencies along with the noise frequencies. The degradation can be improved by introducing an error compensation algorithm which accounts for the eliminated frequencies. In this paper we propose an efficient synthesizable hardware implementation of the compensation algorithm based on a practical Pre-Defined Filter. The Pre-Defined Filter along with the Compensation Hardware can replace the traditional LPFs thereby enhancing RADAR performances.
Lekshmi Vimalan and S Devi
IEEE
One of the basic components in any radio frequency (RF) communication systems is Low Noise Amplifier (LNA). This paper compares various Common Source Low Noise Amplifier (CS-LNA) topologies based on their performance analysis. The three main common source LNA topologies that has been taken for the performance analyses are an inductively loaded Common Source LNA, a CS-LNA with feedback resistor and a CS-LNA cascode stage with inductive source degeneration. In this paper, all the three LNA's are designed for 1.57 GHz frequency which is generally used for Global Positioning System (GPS) applications. The performance of these LNA topologies are analyzed by comparing various aspects such as gain, Noise Figure (NF), S parameters, stability and linearity of each circuit. The simulations are done at 90nm CMOS technology by using Cadence Virtuoso Spectre RF. Out of the three topologies analyzed in this paper, the cascode CS-LNA with inductive source degeneration provides high gain and low noise figure at 1.57 GHz frequency and with 1.2 V power supply.
M. Suresh Kumar and S. Nirmala Devi
IEEE
Early identification of cardiovascular disease helps in treatment of heart related disease. Due to non-stationary nature of ECG and abundant recording, analysis of long term ECG recording by manual method posing great challenge. In this paper, an automatic diagnosis system is developed for detection and classification of cardiovascular disease through invariant feature extraction method. Often, ECG recording is contaminated by high frequency powerline interference and low frequency baseline wandering. Therefore, at first the recording must undergo noise removal treatment. In this study, an Adaptive denoising procedure is proposed which combines the synchrosqueezing transform and feature of wiener filter to achieve noise free recording. Then QRS complex detection followed by beat segmentation algorithm is applied for QRS beat template creation. Extraction of invariant feature from QRS beat template is proposed and such features used as input to train multiclass support vector machine for disease classification. The present study suggests that synchrosqueezing transform based diagnostic system achieves high classification accuracy.
G. Saranya and S. Nirmala Devi
Inderscience Publishers
V.M. Selvalakshmi and S. Nirmala Devi
Inderscience Publishers
Swagata Devi and Soumik Roy
Elsevier BV
Divya R. Nair, Devi S., Manjula G. Nair, and K. Ilango
IEEE
Integration of microgrids to grid with the help of modern power electronic devices disturbs the grid stability. For improved integration and power sharing, a knowledge based algorithm using the concept of smart parks is introduced in this paper. Smart parks are electric vehicle charging centers which have a great potential to support the grid. Smart parks uses modified Icosφ control algorithm and by proper control of this algorithm, microgrids can provide active power support, reactive power compensation and harmonic elimination. Fuzzy logic controller is used for controlling the operation of microgrids depending upon certain factors like SOC level, source current and tariff. The simulation of tariff based fuzzy logic controller is performed in MATLAB/Simulink and it shows that it can provide improved integration of microgrids with less power quality issues.