Faculty Publications
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Item Time–Frequency–Phase analysis for automatic detection of ocular artifact in EEG signal using S-transform(Springer Verlag service@springer.de, 2019) Senapati, K.; Kamath, P.R.Artifacts are unwanted components in the EEG signals which may affect the EEG signal reading, thereby not allowing the signal to be interpreted properly. One of the most common artifacts is the ocular artifact. This artifact arises due to the movement of the eye including eye blink. In most cases, detection of ocular artifacts in EEG signals is done by skilled professionals who are small in number. This paper proposes a new approach of automatic detection of ocular artifacts using the phase information present in the S-transform (ST) of EEG signal. S-transform of a signal provides absolutely referenced phase information of the signal in addition to time–frequency information. A time delay exists between the signals recorded by electrodes placed at different distances from the point of origin of the artifact. This time delay translates to phase delay in the frequency domain. The phase information of the EEG signal recorded from different electrodes placed in the frontal region is used to detect the artifacts which are generated near the region where the eye is located. © Springer Nature Singapore Pte Ltd 2019.Item Iteration of certain exponential-like meromorphic functions(Springer, 2018) Chakra, T.K.; Nayak, T.; Senapati, K.The dynamics of functions f?(z)=?ezz+1forz?C,?>0 is studied showing that there exists ??> 0 such that the Julia set of f? is disconnected for 0 < ?< ?? whereas it is the whole Riemann sphere for ?> ??. Further, for 0 < ?< ??, the Julia set is a disjoint union of two topologically and dynamically distinct completely invariant subsets, one of which is totally disconnected. The union of the escaping set and the backward orbit of ? is shown to be disconnected for 0 < ?< ?? whereas it is connected for ?> ??. For complex ?, it is proved that either all multiply connected Fatou components ultimately land on an attracting or parabolic domain containing the omitted value of the function or the Julia set is connected. In the latter case, the Fatou set can be empty or consists of Siegel disks. All these possibilities are shown to occur for suitable parameters. Meromorphic functions En(z)=ez(1+z+z22!+?+znn!)-1, which we call exponential-like, are studied as a generalization of f(z)=ezz+1 which is nothing but E1(z). This name is justified by showing that En has an omitted value 0 and there are no other finite singular value. In fact, it is shown that there is only one singularity over 0 as well as over ? and both are direct. Non-existence of Herman rings are proved for ?En. © 2018, Indian Academy of Sciences.Item Extending the applicability of the inexact Newton-HSS method for solving large systems of nonlinear equations(Springer, 2020) Argyros, I.K.; George, S.; Senapati, K.We revisit the study of the semi-local convergence of the inexact Newton-HSS method (INHSS) introduced by Amiri et al. (2018), for solving large systems of nonlinear equations. In particular, first we present the correct convergence criterion, since the one in the preceding reference is incorrect. Secondly, we present an even weaker convergence criterion using our idea of recurrent functions. Moreover, the bound functions are compared. Finally, numerical examples are provided to show that the earlier convergence criteria are not satisfied but the new ones are satisfied. Hence, the applicability of the INHSS method is extended and under the same information as in the earlier studies. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.Item Shrinking generators based on ?-LFSRs(Elsevier B.V., 2020) Bishoi, S.K.; Senapati, K.; Shankar, B.R.The word-based LFSRs called ?-LFSRs are very attractive as they take advantage of the modern word-based processor and thus increase the throughput. Secondly, the bitstream produced by ?-LFSR has excellent statistical properties with a high period except for low linear complexity. In order to increase the linear complexity, the concept of both bit-oriented shrinking and self-shrinking generators is introduced in case of ?-LFSRs. In both the cases, the lower bound for the period as well as for the linear complexity of the bitstream are shown to be exponential. Further, we have experimented and investigated more results on the periodicity and statistical properties of the bitstream in self-shrinking ?-LFSRs. This helps to find and prove the exact period of the bitstream produced by self-shrinking generators. © 2020 Elsevier B.V.Item Extended local convergence for Newton-type solver under weak conditions(Babes-Bolyai University, 2021) Argyros, I.K.; George, S.; Senapati, K.We present the local convergence of a Newton-type solver for equations involving Banach space valued operators. The eighth order of convergence wasshown earlier in the special case of the k-dimensional Euclidean space, usinghypotheses up to the eighth derivative although these derivatives do not appearin the method. We show convergence using only the first derivative. This way weextend the applicability of the methods. Numerical examples are used to showthe convergence conditions. Finally, the basins of attraction of the method, onsome test problems are presented © 2021, Studia Universitatis Babes-Bolyai Mathematica. All Rights Reserved.Item Short-term wind speed forecasting using S-transform with compactly supported kernel(John Wiley and Sons Ltd, 2021) Kamath, P.R.; Senapati, K.This paper presents a modified S-transform (ST) based on a compactly supported kernel. A version of Cheriet-Belochrani (CB) kernel is chosen for this purpose. It is shown that the proposed modified S-transform (CBST) offers better frequency resolution than the traditional ST. It is used to decompose the wind speed time series into frequency-based subseries. Further, artificial neural network (ANN) is applied to each of the subseries for an hour ahead prediction. Finally, forecast for the original wind speed series is obtained by combining the prediction result of all the subseries. Initially, increasing the number of subseries results in a decrease in prediction error. However, when the number of subseries is sufficiently large, no significant change in prediction error is observed if the number is further increased. It is also observed that, for a model based on neural-network, involving decomposition of wind speed time series, the proposed model offers low prediction error. A comparative study with the methods based on wavelet transform (WT) and empirical mode decomposition (EMD) demonstrates the effectiveness of the proposed method. For this study, we have used simulated wind speed data generated by nonhydrostatic mesoscale model and data recorded using anemometer and LiDAR instrument at different heights to evaluate the short-term forecasting results. © 2020 The Authors. Wind Energy published by John Wiley & Sons LtdItem Despeckling of SAR Images Using Shrinkage of Two-Dimensional Discrete Orthonormal S-Transform(World Scientific, 2021) Kamath, P.R.; Senapati, K.; Padikkal, P.Speckles are inherent to SAR. They hide and undermine several relevant information contained in the SAR images. In this paper, a despeckling algorithm using the shrinkage of two-dimensional discrete orthonormal S-transform (2D-DOST) coefficients in the transform domain along with shock filter is proposed. Also, an attempt has been made as a post-processing step to preserve the edges and other details while removing the speckle. The proposed strategy involves decomposing the SAR image into low and high-frequency components and processing them separately. A shock filter is used to smooth out the small variations in low-frequency components, and the high-frequency components are treated with a shrinkage of 2D-DOST coefficients. The edges, for enhancement, are detected using a ratio-based edge detection algorithm. The proposed method is tested, verified, and compared with some well-known models on C-band and X-band SAR images. A detailed experimental analysis is illustrated. © 2021 World Scientific Publishing Company.Item Local convergence analysis of two iterative methods(Springer Science and Business Media B.V., 2022) George, S.; Argyros, I.K.; Senapati, K.; Kanagaraj, K.In this paper we consider two three-step iterative methods with common first two steps. The convergence order five and six, respectively of these methods are proved using assumptions on the first derivative of the operator involved. We also provide dynamics of these methods © 2022, The Author(s), under exclusive licence to The Forum D’Analystes.Item A Novel Decision Level Class-Wise Ensemble Method in Deep Learning for Automatic Multi-Class Classification of HER2 Breast Cancer Hematoxylin-Eosin Images(Institute of Electrical and Electronics Engineers Inc., 2024) Pateel, G.P.; Senapati, K.; Pandey, A.K.The Human Epidermal Growth Factor Receptor 2 (HER2) is one of the aggressive subtypes of breast cancer. The HER2 status decides the requirement of breast cancer patients to receive HER2-targeted therapy. The HER2 testing involves combining Immunohistochemistry (IHC) screening, followed by fluorescence in situ hybridization (FISH) for cases where IHC results are equivocal. These tests may involve multiple trials, are time intensive, and tend to be more expensive for certain classes of people. Hematoxylin and Eosin (HE) staining is employed for visualizing general tissue morphology and is a routine, cost-effective method. In this study, we introduce a novel automated class-wise weighted average ensemble deep learning algorithm at the decision level. The proposed algorithm fuses three pre-trained deep-learning models at the decision level by assigning a weight to each class based on their performance of the model to classify the HE-stained breast histopathology images into multi-class HER2 statuses as HER2-0+, HER2-1+, HER2-2+, and HER2-3+. The class-wise weight allocation to the base classifiers is one of the key features of the proposed algorithm. The presented framework surpasses all the existing methods currently employed on the Breast Cancer Immunohistochemistry (BCI) dataset, establishing itself as a dependable approach for detecting HER2 status from HE-stained images. This study highlights the robustness of the proposed algorithm as well as the sufficient information encapsulated within HE-stained images for the effective detection of the HER2 protein present in breast cancer. Therefore, the proposed method possesses the potential to sideline the need for IHC laboratory tests, which hoard time and money. © 2013 IEEE.Item Extended convergence for two-step methods with non-differentiable parts in Banach spaces(Springer Science and Business Media B.V., 2024) Argyros, I.K.; George, S.; Senapati, K.In this study, we have extended the applicability of two-step methods with non-differentiable parts for solving nonlinear equations defined in Banach spaces. The convergence analysis uses conditions weaker than the ones in earlier studies. Other advantages include computable a priori error distances based on generalized conditions, an extended region of convergence as well as a better knowledge of the isolation for the solutions. By setting the divided differences equal to zero the results can be used to solve equations with differentiable part too. © The Author(s), under exclusive licence to The Forum D’Analystes 2023.