Faculty Publications
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Publications by NITK Faculty
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Item Ionic conductivity and dielectric studies of acid doped cellulose acetate propionate solid electrolyte for supercapacitor(John Wiley and Sons Inc, 2016) Sudhakar, Y.N.; Bhat, D.; Muthu, M.Phosphoric acid doped cellulose acetate propionate (CAP) consisting of poly(ethylene glycol) (PEG) as plasticizer was investigated. Ionic conductivities and dielectric studies were carried at different temperature with varying concentration of H3PO4 using AC impedance method. The highest conductivity was 8.1 × 10-4 S cm-1 at 343 K and a long tail was featured in dielectric studies indicating good capacitance nature of the electrolyte. Interactions between added constituents were observed in FTIR and differential scanning calorimetry studies. Thin and compact fabricated supercapacitor demonstrated specific capacitance of 64 F g-1 using cyclic voltammetry. Furthermore, the supercapacitor properties like AC impedance and charge-discharge were studied. Stability was up to 96% at 1000th cycle. POLYM. ENG. SCI., 56:196-203, 2016. © 2015 Society of Plastics Engineers.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 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 Finite control set model predictive control of three-port converter for interfacing a PV-battery energy storage system to a three-phase stand-alone AC system(Oxford University Press, 2024) Preeti, G.A.; Karthikeyan, A.This paper proposes a multiport bidirectional non-isolated converter topology that provides advantages in terms of simultaneous multiple operations, single-stage conversion, high power density and reduced power losses due to the lower number of switches. The proposed multiport converter uses a centralized non-linear controller known as a finite control set model predictive controller to manage the flow of power between different ports. It deals with the parallel operation of photovoltaic and battery energy storage systems for stand-alone alternating current (AC) systems. The converter connects the lower voltage battery to the photovoltaic port using a bidirectional buck/boost converter and the photovoltaic port is linked to the stand-alone AC load through a three-phase full-bridge inverter. Each leg of the three-phase converter will act as a bidirectional direct current (DC)/DC converter as well as an inverter simultaneously. Only six switches manage the power transfer between all the connected ports of photovoltaic-battery energy storage system linked to the stand-alone AC load. The proposed multiport converter is mathematically modelled and controlled by a finite control set model predictive controller. The system is validated in simulation (1-kW rating) and experimental environment (200-W rating). The hardware prototype is developed in the laboratory and the controller is implemented on the field-programmable gate array board. Two independent case studies are carried out to validate the efficacy of the system. The first scenario is for a change in solar irradiance, while the second scenario is for a change in the output load. © The Author(s) 2024. Published by Oxford University Press on behalf of National Institute of Clean-and-Low-Carbon Energy.Item An Ultra-low Noise, Highly Compact Implantable 28 nm CMOS Neural Recording Amplifier(Institute of Electronics Engineers of Korea, 2024) Akuri, N.G.; Naik, D.N.; Kumar, S.; Song, H.; Kar, A.An ultra-low noise, Tera-ohm input impedance two-stage front-end neural amplifier (FENA) in the 28 nm CMOS process is presented in this work. As per the author’s best knowledge, the proposed FENA is implemented on a 28 nm CMOS process for the first time. The proposed FENA consists of an operational transconductance amplifier integrated low-pass filter (LPF) technique. This technique effectively removes the noise current density by using the LPF transfer function and FENA circuit to achieve the best performances, such as ultra-low input-referred noise, ultra-high input impedance, and high gain. The proposed mathematical technique is employed to optimize the dimensions of the neural amplifier in the 28 nm lower node, which results in a noise-free biasing current and ultra-low input referred noise of 18 fV/√Hz at 10 KHz. The ultra-low input referred noise of FENA is achieved by reducing the gate-distributed resistance method. The FENA achieves an ultra-high input impedance of 0.2 Tera-ohm, while a splendid measured gain of 60 dB has succeeded. FENA occupies a chip area of 0.0023 mm2, which consumes a lower power consumption of 1 µW under supply voltage of 1.2 V. The FENA is found to be less prone to PVT variations as 1 mHz of high-pass corner frequency towards robust design. The best performance parameters of FENA could be beneficial for deep exploration neural recording in wireless neural monitoring systems. © 2024, Institute of Electronics Engineers of Korea. All rights reserved.