Conference Papers (Jan – April 2021)


KIIT School of Electrical Engineering

1. Mahato, G. C.,  Choudhury, T. R., Nayak, B., Debnath, D., Santra, S. B. and Misra, B. (2021), A Review on High PV Penetration on Smart Grid: Challenges and Its Mitigation Using FPPT, 1st International Conference on Power Electronics and Energy (ICPEE), Bhubaneswar, India, pp. 1–6, doi: 10.1109/ICPEE50452.2021.9358474.

With the increasing penetration level, the intermittent nature of solar energy poses new challenges for the conventional grid. The major challenges are overvoltage, voltage fluctuations, frequency fluctuations, reverse power flow etc. These challenges suggest updating the conventional grid code. The updated grid code can incorporate FPPT instead of MPPT to meet the challenges arising due to large PV penetration. The main plot of the paper is on the review of techniques to mitigate such challenges during higher PV penetration on Smart Grid.

2. Mahato, G. C., Choudhury, T. R. and Nayak, B. (2020), Study of MPPT and FPPT: A Brief Comparison,” 2020 IEEE 17th India Council International Conference (INDICON), New Delhi, India, pp. 1–7, doi: 10.1109/INDICON49873.2020.9342374.

Maximum Power Point Tracking (MPPT) is required by the Photovoltaic (PV) systems for maximum harvesting of solar energy. Solar energy is intermittent. There is a peak power generation period when a large amount of energy is generated and a large amount of energy is feed-in the grid. Thus, the grid system must be retrofitted to accommodate the large power. However, upgrading the existing power system is not economically viable. An economical solution to this issue is by implementing Flexible Power Point Tracking (FPPT).

3. Tesfay, M. W., Roy, T., Swain, S. K. and Nanda, L. (2021), A Novel Step-up 7L Switched-Capacitor Multilevel Inverter and Its Extended Structure, 2021 1st International Conference on Power Electronics and Energy (ICPEE), pp. 1–6, doi: 10.1109/ICPEE50452.2021.9358651.

In this paper, a novel 7 level switched capacitor based multilevel inverter structure is proposed first. After that, the extended version of the proposed structure has been developed which is based on a single dc source. A comprehensive comparison study with respect to recently published single source based SCMLI structures shows that the proposed structure requires a reduced number of components for producing the same number of output voltage levels. Extensive simulation studies on 7 level SCMLI structure considering different load conditions show the effectiveness of the structure

KIIT School of Electronics Engineering

  1. Bakshi, A. Mishra, S. N., and Dash, S. K. (2021), Design of Current Mode MOS Logic for Low-Power Digital Applications, International Conference on Communication, Circuits, and Systems (IC3S 2020), Lecture Notes in Electrical Engineering, vol. 728, pp 493-500, Springer Singapore.

Abstract: MOS current mode logic (MCML) is graced with certain advantages which include low level of noise generation, static power dissipation independent of switching activity, low voltage swing, a weak dependence of propagation delay on fan-out load capacitance, lower power dissipation at higher frequencies, etc. In this paper, we present an in-depth study of MCML base approach in which the analysis of low-power applications is performed at the target data rate of 1 Gbps. Work has been done on a standard CMOS technology of 0.18 μm.

Keywords: Current mode, High performance, Low power, MCML, Digital IC

  1. Shrivastava, V. K., Pradhan, M. K., and Thakur, M. P. (2021), Application of Pre-Trained Deep Convolutional Neural Networks for Rice Plant Disease Classification, International Conference on Artificial Intelligence and Smart Systems (ICAIS), pp. 1023–1030, doi: 10.1109/ICAIS50930.2021.9395813

ABSTRACT: The early detection of rice plant disease is a much-needed task to prevent spreading of diseases. This paper has explored the performance of various pre-trained deep CNN models such as: (i) AlexNet; (ii) Vgg16; (iii) ResNet152V2; (iv) InceptionV3; (V) InceptionResNetV2; (vi) Xception; (vii) MobileNet; (viii) DenseNet169; (ix) NasNetMobile; and (x) NasNetLarge for image based rice plant disease classification. The dataset used in this paper consist of 1216 rice plant diseased images and these have been collected from the real agricultural field having seven classes. The Vgg16 model resulted in the highest classification accuracy of 93.11%.

Keywords: Classification, Convolution Neural Networks, Pre-trained Models, Rice Plant Diseases, Transfer Learning

  1. Samanta, P. K., Rout N. K., (2021), Skin Lesion Classification Using Deep Convolutional Neural Network and Transfer Learning Approach, Advances in Smart Communication Technology and Information Processing (OPTRONIX 2020), pp. 327–335.

Abstract: This paper has proposed an automatic classification system of images containing a skin lesion as malignant or benign. In this method the transfer learning and a pre-trained deep learning network are implemented. In this proposed work transfer, learning is applied to VGGNet architecture by replacing the last layer by a softmax layer for the classification of two different lesions (malignant and benign). Fine-tuning, data augmentations, and cross-validations are also added to the method. After evaluating the performances of the proposed method on the testing set of the ISIC dataset, the method has achieved a significantly higher classification accuracy rate of 98.02%, the sensitivity of 98.10%, and Specificity of 97.05%.

Keywords: Skin lesion, biomedical image processing, Convolutional neural network, DCNN, Transfer learning

  1. Samanta, P. K., Mukherjee, S., and Rout, N.K., (2021),Susceptibility Analysis of Novel Corona Virus Using Hadoop Distributed File System,  Advances in Smart Communication Technology and Information Processing (OPTRONIX 2020), pp. 337–346.

Abstract: The COVID-19, which happens because of the virus Corona, showcases symptoms which are mild and at times doesn’t even reflect any. In this proposed research, a huge amount of semi structured healthcare data is stored and processed which are collected from healthcare dataset. Hence, in the proposed work ‘Hadoop’ is utilized for processing the data gathered. The input data is processed using MapReduce and finally the result is loaded into the Hadoop Distributed File System (HDFS). After details analysis and justification with the help of ‘Hadoop’ proposed work has predicted many possible susceptible cases which can help to reduce the number of fatalities and also save human lives.

Keywords: Healthcare, Hadoop, MapReduce, HDFS, COVID-19

  1. Sen, A. P. and Rout, N. K., (2021), Implementation of Transfer Learning Technique for the Detection of COVID-19, International Conference on Communication, Circuits, and Systems (IC3S 2020), Lecture Notes in Electrical Engineering 728, Springer, Singapore, pp.135-140,

Abstract: With the view to minimize the spread of the deadly COVID-19 disease, testing and analysis of tremendous amounts of suspected cases for isolation of such individual and further treatment is a need. Pathogenic lab testing is the analytic best quality level however, it is tedious with critical false negative outcomes. Fast and exact characteristic techniques are fundamentally expected to fight the deadly disease. Considering COVID-19 radio graphical changes in CT images the paper center to develop a deep learning model that could isolate COVID-19 cases in order to give a clinical end before the pathogenic test.

Keywords: COVID-19, deep CNN, transfer learning, medical images, infection detection

  1. Rout, R., N. K., Das, D. P., (2021), Wavelet Transform for Signal Compression in Sparse Algorithms, International Conference on Communication, Circuits, and Systems (IC3S 2020), Lecture Notes in Electrical Engineering 728, Springer Singapore pp. 355-362,

Abstract: The increase in amount of data present in a wireless network is the problem faced during signal transmission in internet of things (IoT) systems and 5G networks. The requirement of such extensive connectivity in the network leads to a large amount of data to be produced. Therefore, signal compression is required before application. One such application is to identify the sparse physical system after compressing the input signal by a wavelet transform. The proportionate normalized least mean square (PNLMS) algorithm and its variants are simulated for different types of input signals. The input signals are a Gaussian random signal, band-limited signal; a speech signal, a color signal, and a uniform random signal are analyzed.

Keywords: Sparse, Compression, Gaussian, Wavelet

  1. Kar, S. P., Ghosh, M., and Rout, N. K., (2021), Augmented Reality as a Supported Educational Method for Embedded Devices and Technology, International Conference on Communication, Circuits, and Systems (IC3S 2020), Lecture Notes in Electrical Engineering  728, Springer Singapore, pp. 501-508,

ABSTRACT: One of the recent developments in the field of technology is augmented reality (AR). Eighty percent of youth currently own smart phones but very fewer proportions of young adults use smart phones for learning purposes. AR technologies will make classrooms more entertaining and more immersive to knowledge. The concept is to introduce AR as a mobile application to enable educators, and learners to understand the critical concept of various embedded hardware, its main features, and programming. The application generates a virtual label and parts of the product and displays such demonstrations in a step-by-step manner with interactive three-dimensional animations to connect different peripherals with other dedicated devices.

Keywords: Augmented reality, Embedded hardware, Immersive learning

  1. Sahoo, S., Nanda, S., and Sahoo, H. K. (2021) MIMO-OFDM Outdoor Channel Estimation Using Sparse Momentum Fractional Adaptive Filter. An International Conference on Communication, Circuits, and Systems (IC3S 2020), Lecture Notes in Electrical Engineering 728, Springer Singapore.

Abstract:- To reduce the sparsity of the practical wireless channel a sparse penalty term is added in the adaptive algorithms to estimate channel parameters which plays a vital role for recovering the original data at the receiver end of MIMO-OFDM system. As the complexity of the MIMO system increases due to the spatial multiplexing, so to reduce the complexity of the system and to achieve fast convergence of the system a momentum based fractional order LMS with sparse penalty term named as sparse momentum fractional LMS (SmFLMS) is used in this paper with a Jake’s outdoor channel model

Keywords:- QAM, Sparsity norm, FLMS, MIMO, Channel Estimation, MSE

  1. Sarkar, M., Sahoo, S., and Nanda, S. (2021), Sparse Based Mixed-Norm Channel Estimation in MIMO OFDM System, International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE), pp. 472-475, doi: 10.1109/WIECON-ECE52138.2020.9398009.

ABSTRACT: These days with the growth of wireless communication strategies focus on enhanced data rates, system capacity and service quality have also gained a lot of interest. To encounter these issues, proper channel modeling and precise assessment of state information of the channel are imperative for the design of the communication system. Hence, this paper presents an efficient channel estimator for a MIMO-OFDM system based on sparse modeling for estimation of wireless channel parameters. It uses a sigmoid-based least-mean mixed norm approach to estimate Jake’s outdoor channel model. The performance of the system is evaluated by the Mean Square Error (MSE) and Bit Error Rate (BER) level.

Keywords: Jake’s fading channel, MIMO-OFDM system channel estimation, LMMN, SLMMN,ZA-SLMMN,RZA-SLMMN

  1. Patnaik, A., Nanda, S., (2021) Reweighted Zero-Attracting Modified Variable Step-Size Continuous Mixed p-Norm Algorithm for Identification of Sparse System Against Impulsive Noise, International Conference on Communication, Circuits, and Systems (IC3S 2020), Lecture Notes in Electrical Engineering, Springer Singapore, pp 509.

ABSTRACT. The performance of traditional adaptive filtering algorithms for sparse system identification might not be good compared to the identification of non-sparse systems. The modified variable step-size continuous mixed p-norm (MVSS-CMPN) algorithm for identifying non-sparse systems was developed in the existence of impulsive noise. In this paper, a reweighted zero attracting MVSS-CMPN algorithm is developed by inducing a sparse penalty function into the MVSS-CMPN algorithm to exploit the sparsity of the system. From the simulations, it is found that the proposed algorithm provides superior performance to other algorithms for sparse system identification under the effect of impulsive noise.

Keywords: Sparse system identification, continuous mixed p-norm algorithm, reweighted zero attracting, impulsive noise

  1.  Dutta, K., Pal, R., Prasad, R., (2021),      EBN-Net: A Thermodynamical Approach to Power Estimation Using Energy-Based Multi-layer Perceptron Networks, International Conference on Communication, Circuits, and Systems (IC3S 2020), Lecture Notes in Electrical Engineering 728, Springer Singapore, 407-417.

ABSTRACT: In this paper, the power estimation at the combined cycle power plant has been done taking into consideration the various factors affecting the process with the help of multi- layer perceptron networks. The proposed algorithm helps to improve the forecasting with Multi- Layer Perceptron Networks (MLPN) essentially to predict power generation output using the minimum number of input variables. The results, which can be considered highly satisfactory, demonstrate the MLPN’s prediction accuracy with a normalized root mean square error for all conditions of less than 5% and with practically no deviation. We demonstrate how beneficial matching of two already proven techniques can bring about comprehensive results in energy generation prediction.

Keywords: Energy-based model , Multi-layer perceptron network, Neural networks ,Power estimation , Combined cycle power plants, Artificial neural networks

  1. Rout, D. K., Mohapatra, A. R., Bhardwaj, R., Kumar, R., and Singh,V. R. (2021),Signal Propagation Modelling on the Inner Surface of Human Arm, International Conference on Communication, Circuits, and Systems (IC3S 2020), Lecture Notes in Electrical Engineering 728, Springer Singapore.

ABSTRACT: Body area networks are a novel way of tracking vital health parameters of elderly and patients with chronic health conditions. The signals collected by sensors are transmitted to remote servers for real-time monitoring. Transceivers have an important role in the transmission and reception of critical health parameters in body area networks (BANs). Signal propagation modelling helps to design highly power efficient transceivers. Thus, the paper proposes a channel model derived from at least 500 real-time received power measurements in the vicinity of the inner surface of human arm. 

Keywords: Body Area Networks, Signal Propagation Modelling, Channel model, Sensors