Faculty Publications
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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 Critical review of PV grid-tied inverters(MDPI AG indexing@mdpi.com Postfach Basel CH-4005, 2019) Kavya Santhoshi, B.K.; Mohanasundaram, K.M.; Padmanaban, S.; Holm-Nielsen, J.B.; Koothu Kesavan, K.K.Solar Photovoltaic (PV) systems have been in use predominantly since the last decade. Inverter fed PV grid topologies are being used prominently to meet power requirements and to insert renewable forms of energy into power grids. At present, coping with growing electricity demands is a major challenge. This paper presents a detailed review of topological advancements in PV-Grid Tied Inverters along with the advantages, disadvantages and main features of each. The different types of inverters used in the literature in this context are presented. Reactive power is one of the ancillary services provided by PV. It is recommended that reactive power from the inverter to grid be injected for reactive power compensation in localized networks. This practice is being implemented in many countries, and researchers have been trying to find an optimal way of injecting reactive power into grids considering grid codes and requirements. Keeping in mind the importance of grid codes and standards, a review of grid integration, the popular configurations available in literature, Synchronization methods and standards is presented, citing the key features of each kind. For successful integration with a grid, coordination between the support devices used for reactive power compensation and their optimal reactive power capacity is important for stability in grid power. Hence, the most important and recommended intelligent algorithms for the optimization and proper coordination are peer reviewed and presented. Thus, an overview of Solar PV energy-fed inverters connected to the grid is presented in this paper, which can serve as a guide for researchers and policymakers. © 2019 by the authors.Item Enhanced mobility aware routing protocol for Low Power and Lossy Networks(Springer New York LLC barbara.b.bertram@gsk.com, 2019) Sanshi, S.; Jaidhar, C.D.Due to the technological advancement in Low Power and Lossy Networks (LLNs), sensor node mobility becomes a basic requirement for many extensive applications. Routing protocol designed for LLNs must ensure real-time data transmission with minimum power consumption. However, the existing mobility support protocols cannot work efficiently in LLNs as they are unable to adapt to the change in the network topology quickly. Therefore, we propose an Enhanced Routing Protocol for LLNs (ERPL), which updates the Preferred Parent (PP) of the Mobile Node (MN) quickly whenever the MN moves away from the already selected PP. Further, a new objective function that takes the mobility of the node into an account while selecting a PP is proposed. Performance of the ERPL has been evaluated with the varying system and traffic parameters under different topologies similar to most of the real-life networks. The simulation results showed that the proposed ERPL reduced the power consumption, packet overhead, latency and increased the packet delivery ratio as compared to other existing works. © 2017, Springer Science+Business Media, LLC, part of Springer Nature.Item A 1.8 V 8.62 µW Inverter-based Gain-boosted OTA with 109.3 dB dc Gain for SC Circuits(Taylor and Francis Ltd, 2019) Kaliyath, Y.; Laxminidhi, T.This paper presents a low-power inverter-based gain-boosted operational transconductance amplifier (OTA) for switched capacitor (SC) circuits operating at higher supply voltage (>1 V). The proposed OTA is implemented using UMC 180 nm CMOS technology with a supply voltage of 1.8 V and it offers a high dc gain with a unity gain bandwidth (UGB) suitable for audio applications. All the transistors of the proposed OTA are operated in sub-threshold region to minimize the power consumption. Gain-boosting technique is employed to achieve a higher dc gain. The post-layout simulations demonstrate the robust performance of the proposed OTA, which delivers a high dc gain of 109.3 dB and a UGB of 5.29 MHz at 81° phase margin (PM) with a capacitive load of 2.5 pF for a typical process corner at room temperature (27°C). The proposed OTA draws a quiescent current ((Formula presented.)) of 4.79 µA, resulting in a power consumption of 8.62 µW. © 2019, © 2019 IETE.Item Stacking Deep learning and Machine learning models for short-term energy consumption forecasting(Elsevier Ltd, 2022) Sujan Reddy, A.; Akashdeep, S.; Harshvardhan, R.; Kamath S․, S.Accurate prediction of electricity consumption is essential for providing actionable insights to decision-makers for managing volume and potential trends in future energy consumption for efficient resource management. A single model might not be sufficient to solve the challenges that result from linear and non-linear problems that occur in electricity consumption prediction. Moreover, these models cannot be applied in practice because they are either not interpretable or poorly generalized. In this paper, a stacking ensemble model for short-term electricity consumption is proposed. We experimented with machine learning and deep models like Random Forests, Long Short Term Memory, Deep Neural Networks, and Evolutionary Trees as our base models. Based on the experimental observations, two different ensemble models are proposed, where the predictions of the base models are combined using Gradient Boosting and Extreme Gradient Boosting (XGB). The proposed ensemble models were tested on a standard dataset that contains around 500,000 electricity consumption values, measured at periodic intervals, over the span of 9 years. Experimental validation revealed that the proposed ensemble model built on XGB reduces the training time of the second layer of the ensemble by a factor of close to 10 compared to the state-of-the-art, and also is more accurate. An average reduction of approximately 39% was observed in the Root mean square error. © 2022 Elsevier LtdItem 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 Geo-Distributed Multi-Tier Workload Migration Over Multi-Timescale Electricity Markets(Institute of Electrical and Electronics Engineers Inc., 2023) Addya, S.K.; Satpathy, A.; Ghosh, B.C.; Chakraborty, S.; Ghosh, S.K.; Das, S.K.Virtual machine (VM) migration enables cloud service providers (CSPs) to balance workload, perform zero-downtime maintenance, and reduce applications' power consumption and response time. Migrating a VM consumes energy at the source, destination, and backbone networks, i.e., intermediate routers and switches, especially in a Geo-distributed setting. In this context, we propose a VM migration model called Low Energy Application Workload Migration (LEAWM) aimed at reducing the per-bit migration cost in migrating VMs over Geo-distributed clouds. With a Geo-distributed cloud connected through multiple Internet Service Providers (ISPs), we develop an approach to find out the migration path across ISPs leading to the most feasible destination. For this, we use the variation in the electricity price at the ISPs to decide the migration paths. However, reduced power consumption at the expense of higher migration time is intolerable for real-time applications. As finding an optimal relocation is $\mathcal {NP}$NP-Hard, we propose an Ant Colony Optimization (ACO) based bi-objective optimization technique to strike a balance between migration delay and migration power. A thorough simulation analysis of the proposed approach shows that the proposed model can reduce the migration time by 25%-30% and electricity cost by approximately 25% compared to the baseline. © 2008-2012 IEEE.Item A 28 nm CMOS low-noise amplifier with novel redundant noise cancellation technique beyond ultra-wideband for 6G-based wireless systems(Elsevier GmbH, 2024) Naik, D.N.; Gorre, P.; Prasad Gupta, M.; Kumar, S.; Al-Shidaifat, A.; Song, H.In the current scenario, almost 5G-based wireless systems have been deployed everywhere but still performance trade-offs of RF amplifiers in the sub-nanometer regime are challenging. In this work, a high-performance low-noise amplifier (LNA) is realized in a 28 nm CMOS process with a novel redundant noise cancellation technique (RnC). The proposed technique improves the noise figure (NF) beyond the ultra-wideband of a low-noise amplifier (LNA) and minimizes the trade-off in the 28 nm process. An ultra-low NF is achieved in two approaches; Firstly, a current mirror network is employed in the primary path to cancel the thermal noise of the dominant transistor of a common gate-common source (CG-CS) without an extra power supply. Secondly, an auxiliary amplifier stage is introduced here to reduce the noise which contributes to the current mirror circuit and cancels the distortion in CG-CS topology without violating the traditional noise cancellation condition. In addition, an analytical approach is followed to optimize the input impedance, gain bandwidth and noise figure. Hence, the proposed RnC LNA benefits in achieving good tradeoffs among gain, bandwidth, NF, and power consumption in 28 nm technology node. The proposed RnC LNA is analyzed and fabricated using CMOS 28 nm technology, occupying an area of 0.011 mm2. The proposed design achieves an optimum performance: nearly flat gain of 15.3 dB, minimum NF of 1.7 dB over 1.7 to 12.52 GHz, and an IIP3 of − 2.6 dBm at 6.5 GHz. The proposed LNA consumes ultra-low power consumption of 1.8 mW under the power supply of 1 V. © 2023Item Enhancing Anomaly Detection in Critical Systems Using Household Appliance Power Consumption Data(Institute of Electrical and Electronics Engineers Inc., 2024) Nayak, R.; Jaidhar, C.D.It is crucial to detect anomalous use of electrical power in critical systems to prevent malfunctions or hazards, ensure operational security, and optimize the energy economy. Since anomalies in critical systems can serve as early warning systems for potential issues or threats that could lead to severe failures, it becomes strategically crucial to discover them as soon as possible. This study proposes and suggests a novel technique for anomaly identification in industrial critical systems using a household appliance's electrical power consumption dataset in the absence of a dedicated critical system or industrial equipment dataset. The study looks at the ability of a deep learning (DL) model trained on household data to identify anomalous patterns in large-scale industrial equipment's power use. Convolutional neural network (CNN) is used in this work to analyze anomalous electrical power use based on micro-moments. In this work, an appliance-level dataset is employed for experimentation. 10 × 10 appliance-wise grayscale images are generated from numeric dataset with and without the instance-wise N-gram approach. The effectiveness of the proposed approach is evaluated and compared it with other ML and DL models used earlier. The experimental findings showed that the proposed approach worked better than other models. Compared to images created without the instance-wise N-gram approach, the performance of the proposed approach with images created with N-gram is superior. © 2001-2012 IEEE.Item Infrared Perspectives: Computing laptop energy dissipation via thermal imaging and the Stefan-Boltzmann equation(Elsevier Ltd, 2024) Anbalagan, A.; Arumuga Perumal, D.; Persiya, J.Energy conservation is crucial for reducing greenhouse gas emissions and addressing climate change. Laptops contribute significantly to energy consumption, emphasizing the need for improved energy efficiency. This paper explores the application of thermal imaging technology to enhance energy conservation in laptops. Thermal imaging provides valuable insights into heat distribution on the laptop's surface, aiding in identifying areas of excessive energy consumption. By identifying areas of a laptop that generate excessive heat and implementing energy-efficient measures, energy consumption can be reduced, and the device's lifespan can be extended. The study leverages computer vision and artificial intelligence techniques to analyze thermal images. We collected the thermal images for the dataset using the FLIR E75 Thermal camera. Two methods of Region of Interest (ROI) extraction, contour-based thresholding, and Detectron2-based extraction are employed. Feature extraction includes statistical, texture, spatial, and energy features, and Principal Component Analysis (PCA) is used to reduce dimensionality. K-means clustering categorizes data points based on reduced features, and performance metrics validate the effectiveness of the clustering methods. The study also computes energy dissipation from thermal images using the Stefan-Boltzmann Law. Results indicate that thermal imaging, coupled with advanced analysis techniques, holds promise for improving energy conservation in laptops. © 2024 Elsevier Ltd