Faculty Publications
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Publications by NITK Faculty
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Item Energy storage and management in supercapacitors for application in piezoelectric energy harvesting systems(Sphinx Knowledge House info@sphinxsai.com, 2015) Sripad, S.; Kumar, S.; Jain, A.Electrical double layer capacitors (supercapacitors) were fabricated using activated carbon as the active material and polyvinylidine fluoride (PVDF) as a binder with a suitable conductive additive (MWCNTs) together in an optimized ratio. The supercapacitor cells were assembled using an aqueous solution of 0.5M Na2SO4 as the electrolyte. These cells had an average capacitance of 1.7F each as measured by the constant current charging method. The two electrode symmetric cell had a specific capacitance of 23.05 F/g. The fabrication methodology has been discussed as well as the potential applications of the supercapacitor in piezoelectric element based energy harvesting systems have been elucidated. © 2015, International Journal of ChemTech Research. All rights reserved.Item Artificial neural network based modeling to evaluate methane yield from biogas in a laboratory-scale anaerobic bioreactor(Elsevier Ltd, 2016) Nair, V.V.; Dhar, H.; Kumar, S.; Thalla, A.K.; Mukherjee, S.; Wong, J.W.C.The performance of a laboratory-scale anaerobic bioreactor was investigated in the present study to determine methane (CH4) content in biogas yield from digestion of organic fraction of municipal solid waste (OFMSW). OFMSW consists of food waste, vegetable waste and yard trimming. An organic loading between 40 and 120 kg VS/m3 was applied in different runs of the bioreactor. The study was aimed to focus on the effects of various factors, such as pH, moisture content (MC), total volatile solids (TVS), volatile fatty acids (VFAs), and CH4 fraction on biogas production. OFMSW witnessed high CH4 yield as 346.65 L CH4/kg VS added. A target of 60–70% of CH4 fraction in biogas was set as an optimized condition. The experimental results were statistically optimized by application of ANN model using free forward back propagation in MATLAB environment. © 2016 Elsevier LtdItem Optoelectronic exploration of novel non-symmetrical star-shaped discotic liquid crystals based on cyanopyridine(Royal Society of Chemistry, 2018) Vinayakumara, D.R.; Swamynathan, K.; Kumar, S.; Vasudeva Adhikari, A.A novel family of non-symmetrical star-shaped cyanopyridine based discotic liquid crystals (CPBz6, CPBz8 and CPBz12) was designed and synthesized, as potential luminescent materials for optoelectronic applications. The length of one of the arms in the design was systematically varied to determine the structure-property relationships. Evidently, all of the compounds exhibited a highly prospective ordered columnar mesomorphism stabilized at ambient temperature with advantageous very low isotropization temperatures. Furthermore, their photophysical characteristics were investigated in depth, both in solution and in the liquid crystal (LC) state. The discotics were found to be intense blue emitters with reasonably good quantum efficiency. Their dynamic intramolecular charge-transfer (ICT) behaviour was confirmed by steady-state absorption and fluorescence spectral analysis in varied solvent polarity. Furthermore, their electrochemical properties were studied from the combination of an experimental method and the theoretical simulations, which elucidated the low laying frontier molecular orbitals (FMOs) with a narrow energy band gap of ?2.0 eV. The resulted visually perceivable emission with favourable energy levels showcases their possible application in electronic display devices. © 2018 The Royal Society of Chemistry and the Centre National de la Recherche Scientifique.Item An Integrated Approach of CNT Front-end Amplifier towards Spikes Monitoring for Neuro-prosthetic Diagnosis(SpringerOpen, 2018) Kumar, S.; Kim, B.-S.; Song, H.The future neuro-prosthetic devices would be required spikes data monitoring through sub-nanoscale transistors that enables to neuroscientists and clinicals for scalable, wireless and implantable applications. This research investigates the spikes monitoring through integrated CNT front-end amplifier for neuro-prosthetic diagnosis. The proposed carbon nanotube-based architecture consists of front-end amplifier (FEA), integrate fire neuron and pseudo resistor technique that observed high electrical performance through neural activity. A pseudo resistor technique ensures large input impedance for integrated FEA by compensating the input leakage current. While carbon nanotube based FEA provides low-voltage operation with directly impacts on the power consumption and also give detector size that demonstrates fidelity of the neural signals. The observed neural activity shows amplitude of spiking in terms of action potential up to 80 ?V while local field potentials up to 40 mV by using proposed architecture. This fully integrated architecture is implemented in Analog cadence virtuoso using design kit of CNT process. The fabricated chip consumes less power consumption of 2 ?W under the supply voltage of 0.7 V. The experimental and simulated results of the integrated FEA achieves 60 G? of input impedance and input referred noise of 8.5 nv/Hzover the wide bandwidth. Moreover, measured gain of the amplifier achieves 75 dB midband from range of 1 KHz to 35 KHz. The proposed research provides refreshing neural recording data through nanotube integrated circuit and which could be beneficial for the next generation neuroscientists. © 2018, The Korean BioChip Society and Springer-Verlag GmbH Germany, part of Springer Nature.Item Life cycle assessment of municipal solid waste management options for India(Elsevier Ltd, 2019) Khandelwal, H.; Thalla, A.K.; Kumar, S.; Kumar, R.Life Cycle Assessment (LCA) tool can be used for environmental assessment of Municipal Solid Waste Management (MSWM) system. The present study aims to evaluate the impact of MSWM system in Nagpur city, India under four different scenarios. i.e., composting combined with landfilling (S1), material recovery facility (MRF) & composting combined with landfilling (S2), MRF & anaerobic digestion (AD) combined with landfilling (S3) and MRF, AD & composting combined with landfilling (S4) using LCA tool. The sensitivity analysis was also performed for evaluating the influence of recycling rate of valuable resources in all the considered scenarios. The scenarios were compared using Gabi 8.5.0.79 model and CML-1A impact characterization method. S2 was found to have the least environmental impacts on global warming, human toxicity, eutrophication, and photochemical ozone creation potential categories. The sensitivity analysis indicated an inversely proportional relation between change in recycling rate and total environmental burdens. © 2019 Elsevier LtdItem Multi-Res-Attention UNet: A CNN Model for the Segmentation of Focal Cortical Dysplasia Lesions from Magnetic Resonance Images(Institute of Electrical and Electronics Engineers Inc., 2021) Thomas, E.; Pawan, S.J.; Kumar, S.; Horo, A.; Niyas, S.; Vinayagamani, S.; Kesavadas, C.; Rajan, J.In this work, we have focused on the segmentation of Focal Cortical Dysplasia (FCD) regions from MRI images. FCD is a congenital malformation of brain development that is considered as the most common causative of intractable epilepsy in adults and children. To our knowledge, the latest work concerning the automatic segmentation of FCD was proposed using a fully convolutional neural network (FCN) model based on UNet. While there is no doubt that the model outperformed conventional image processing techniques by a considerable margin, it suffers from several pitfalls. First, it does not account for the large semantic gap of feature maps passed from the encoder to the decoder layer through the long skip connections. Second, it fails to leverage the salient features that represent complex FCD lesions and suppress most of the irrelevant features in the input sample. We propose Multi-Res-Attention UNet; a novel hybrid skip connection-based FCN architecture that addresses these drawbacks. Moreover, we have trained it from scratch for the detection of FCD from 3 T MRI 3D FLAIR images and conducted 5-fold cross-validation to evaluate the model. FCD detection rate (Recall) of 92% was achieved for patient wise analysis. © 2013 IEEE.Item High-Performance Graphene FET Integrated Front-End Amplifier Using Pseudo-resistor Technique for Neuro-prosthetic Diagnosis(SpringerOpen, 2022) Naik, J.D.; Gorre, P.; Akuri, N.G.; Kumar, S.; Al-Shidaifat, A.D.; Song, H.A complex analysis of spike monitoring in neuro-prosthetic diagnosis demands a high-speed sub-nanoscale transistors with an advanced device technologies. This work reports the high performance of Graphene field-effect transistor (GFET) based front-end amplifier (FEA) design for the neuro-prosthetic application. The 9 nm Graphene FET device is optimized by characterization of transconductance and drain current towards high sensitivity and small factor. The proposed GFET-based FEA with pseudo-resistor technique demonstrates very high-input impedance in Tera-ohms that nullify the input leakage current. Here, gain-bandwidth product and noise optimization of GFET FEA enhances the overall gain with negligible noise. The proposed design operates at low voltage, further reduces the power consumption, and achieves less chip area in sub-nano size so it could be more suitable for implantable devices. The GFET-based FEA architecture achieves an action potential spike of 1.4 µV while the local field potentials spike of 1.8 mV. The proposed architecture is implemented in Advanced Design System using the design kit of the GFET process. Power consumption of 3.14 µW is observed with a supply voltage of 0.9 V. The simulated and experimental results of the proposed design achieve an input impedance of 2 TΩ with excellent noise performance over a wideband of 13.85 MHz. The proposed work demonstrates better neural activity sensing when compared to the state-of-the-artwork, which could be highly beneficial for future neuroscientists. © 2022, The Korean BioChip Society.Item A comprehensive characterization of 3D printable poly ether ketone ketone(Elsevier Ltd, 2024) Ojha, N.; Kumar, S.; Ramesh, M.R.; Balan, A.A.S.; Doddamani, M.The current work focuses on the comprehensive characterization of a 3D printable biomaterial, polyether ketone ketone (PEKK). The PEKK granules are first characterized and then utilized for extrusion of the PEKK filaments. The extruded PEKK filaments are characterized for crystallinity, quality, and printability, wherein they exhibit amorphous nature, good quality, and appropriate printability. Utilizing the filaments, the samples are printed with the appropriate printing parameters, which are further characterized for layer adhesion, voids, and crystallinity, wherein they showed seamless layer adhesion, improper beads consolidation, and the amorphous nature. The as printed samples are further annealed at different temperatures (200 and 250 °C). The scanning electron microscopy (SEM) of the annealed samples (A-200 and A-250) revealed better void consolidation, while the X-ray diffraction (XRD) revealed better crystallinity compared to the un-annealed sample. The printed samples are also investigated for dynamic mechanical analysis (DMA), shape memory, and tensile properties. The storage moduli of the annealed samples are observed to be better than the un-annealed sample. The annealed samples exhibited better shape memory properties: shape fixity and shape recovery ratio of A-200 and A-250 samples, 90.28 and 90.75%, and 99.16 and 94.73%, respectively, compared to the un-annealed samples. The highest shape fixity ratio and the shape recovery ratio are noted for A-250 (90.75%) and A-200 (∼ 100%). The A-200 and A-250 samples showed enhanced tensile modulus and strength, 4.16 and 49.67%, and 36.61 and 35.06%, respectively compared to the un-annealed sample. The highest modulus is noted for A-250, while the strength is comparable (36.61 and 35.06%) for A-200 and A-250. © 2023 Elsevier LtdItem EffiCOVID-net: A highly efficient convolutional neural network for COVID-19 diagnosis using chest X-ray imaging(Academic Press Inc., 2025) Kumar, S.; Bhowmik, B.The global COVID-19 pandemic has drastically affected daily life, emphasizing the urgent need for early and accurate detection to provide adequate medical treatment, especially with limited antiviral options. Chest X-ray imaging has proven crucial for distinguishing COVID-19 from other respiratory conditions, providing an essential diagnostic tool. Deep learning (DL)-based models have proven highly effective in image diagnostics in recent years. Many of these models are computationally intensive and prone to overfitting, especially when trained on limited datasets. Additionally, conventional models often fail to capture multi-scale features, reducing diagnostic accuracy. This paper proposed a highly efficient convolutional neural network (CNN) called EffiCOVID-Net, incorporating diverse feature learning units. The proposed model consists of a bunch of EffiCOVID blocks that incorporate several layers of convolution containing (3×3) filters and recurrent connections to extract complex features while preserving spatial integrity. The performance of EffiCOVID-Net is rigorously evaluated using standard performance metrics on two publicly available COVID-19 chest X-ray datasets. Experimental results demonstrate that EffiCOVID-Net outperforms existing models, achieving 98.68% accuracy on the COVID-19 radiography dataset (D1), 98.55% on the curated chest X-ray dataset (D2), and 98.87% on the mixed dataset (DMix) in multi-class classification (COVID-19 vs. Normal vs. Pneumonia). For binary classification (COVID-19 vs. Normal), the model attains 99.06%, 99.78%, and 99.07% accuracy, respectively. Integrating Grad-CAM-based visualizations further enhances interpretability by highlighting critical regions influencing model predictions. EffiCOVID-Net's lightweight architecture ensures low computational overhead, making it suitable for deployment in resource-constrained clinical settings. A comparative analysis with existing methods highlights its superior accuracy, efficiency, and robustness performance. However, while the model enhances diagnostic workflows, it is best utilized as an assistive tool rather than a standalone diagnostic method. © 2025 Elsevier Inc.