Faculty Publications
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Item Effect of exhaust gas recirculation (EGR) on diesel engine using Simarouba glauca biodiesel blends(Regional Energy Resources Information Center (RERIC) enreric@ait.ac.th, 2015) Bedar, P.; Pandey, J.K.; Kumar, G.N.This article deals with the usage of non-edible Simarouba glauca (paradise) oil as a biodiesel for single cylinder diesel engine with application of exhaust gas recirculation (EGR) rates. Biodiesel blends B10, B20 with EGR rates of 10%, 15%, and 20% are used for different load conditions. Parameters like brake thermal efficiency (BTE), nitrogen oxides (NOx), carbon monoxide (CO), hydrocarbons (HC) and smoke opacity were evaluated from the experimental study. The results show that Simarouba glauca biodiesel usage decreases HC, CO and smoke emissions with slight increase of NOx, also an improvement in the performance was observed for B10 blend. EGR rates 10% and 15% are beneficiated in terms of performance and emission but negative trend is observed for 20% EGR rate. On the whole it is concluded that a better trade-off between NOx and other emissions is attained with simultaneous application of EGR (15%) and biodiesel blend (B10) without compromising engine performance.Item Control Strategy to Maximize Power Extraction in Wind Turbine(Taylor and Francis Inc. 325 Chestnut St, Suite 800 Philadelphia PA 19106, 2016) RAJENDRAN, R.; Jena, D.This article deals with nonlinear control of variable speed wind turbine (VSWT), where the dynamics of the wind turbine (WT) is obtained from a single mass model. The main objective of this work is to maximize the energy capture form the wind with reduced oscillation on the drive train. The generator torque is considered as the control input to the WT. In general the conventional control techniques such as Aerodynamic Torque Feed-Forward (ATF) and Indirect Speed Control (ISC) are unable to track the dynamic aspect of the WT. To overcome the above drawbacks the nonlinear controllers such Sliding Mode Controller (SMC) and SMC with integral action (ISMC) with the estimation of effective wind speed are proposed. The Modified Newton Raphson (MNR) is used to estimate the effective wind speed from aero dynamic torque and rotor speed. The proposed controller is tested with different wind profiles with the presence of disturbances and model uncertainty. From the results the proposed controller was found to be suitable in maintaining a trade-off between the maximum energy capture and reduced transient on the drive train. Finally both the controllers are validated by using FAST (Fatigue, Aerodynamics, Structures, and Turbulence) WT simulator. © Association of Energy Engineers (AEE).Item Influence of Granulated Blast Furnace Slag and Cement on the Strength Properties of Lithomargic Clay(Springer India sanjiv.goswami@springer.co.in, 2017) C. Sekhar, D.C.; Nayak, S.; Preetham, H.K.Utilizing industrial byproducts in soil stabilization benefits the economic, environmental and social benefits. Granulated blast furnace slag is a byproduct of iron and steel industry having oxides similar to that of cement but in different proportions. This study describes experimental results achieved by the use of granulated blast furnace slag (GBFS) and cement in stabilizing lithomargic clay for geotechnical applications. Soil was replaced by GBFS in percentages of 10, 15, 20, 25, 30, 35, 40, 45, 50% and cement of 2, 4, 6, and 8% by dry weight of soil is added. Various experimental studies like specific gravity, Atterberg limits, compaction, UCS, CBR and triaxial compression test, were performed on samples to understand the effect of these mixes on their few index and strength properties. The study also includes an investigation on a combination of optimum percentage of GBFS with varying percentage of cement and lime on their shear parameters. The study result shows significant improvement in the strength properties of the mixes. Hence it can be concluded that lithomargic clay stabilized with GBFS and cement/lime satisfy the strength requisite to be employed in the numerous geotechnical applications. © 2017, Indian Geotechnical Society.Item Signal constellations employing multiplicative groups of Gaussian and Eisenstein integers for Enhanced Spatial Modulation(Elsevier B.V., 2017) G.D., G.S.; Raghavendra, R.; Koila, K.; Shripathi Acharya, U.In this paper, we propose two new signal constellation designs employing Gaussian and Eisenstein Integers for Enhanced Spatial Modulation (ESM). ESM is a novel technique which was propounded by Cheng et al. The advantage of ESM over other Spatial Modulation (SM) schemes lies in its ability to enhance spectral efficiency while keeping the energy efficiency intact. This is done by activating either one or two antennas judiciously depending upon the required trade-off. In ESM, information radiated from the antennas depends upon index of the active transmit antenna combination(s) and also on the set of constellation points chosen, which may include points from multiple constellations. In this paper, we propose signal constellations based on multiplicative groups of Gaussian and Eisenstein integers. The set comprising of Gaussian and Eisenstein integers serves as primary and secondary constellation points for Gaussian Enhanced Spatial Modulation (GESM) scheme. The secondary constellation points are deduced from a single geometric interpolation from the primary constellation points. The Monte Carlo simulation results indicate that the proposed nonuniform constellations achieve impressive SNR gains compared to conventional constellation points used in the design of ESM. This new design has been described for MIMO employing 4 × 4 and 8 × 8 antenna configurations with only two active antennas. Furthermore, a closed form expression for the pairwise error probability (PEP) for the GESM scheme has been deduced. The PEP is utilized to determine the upper bound on the average bit error probability (ABEP). Our simulations indicate that the proposed GESM from Gaussian and Eisenstein integers scheme outperforms all the other variants of SM including conventional ESM by at least 2.5 dB at an average bit error ratio (ABER) of 10?5. Close correspondence between the theoretical analysis and the Monte Carlo simulation results are observed. © 2017 Elsevier B.V.Item Influence maximisation in social networks(Inderscience Publishers, 2019) Tejaswi, V.; Bindu, P.V.; Santhi Thilagam, P.S.Influence maximisation is one of the significant research areas in social network analysis. It helps in identifying influential entities from social networks that can be used in marketing, election campaigns, outbreak detection and so on. Influence maximisation deals with the problem of finding a subset of nodes called seeds in the social network such that these nodes will eventually spread maximum influence in the network. This is an NP-hard problem. The aim of this paper is to provide a complete understanding of the influence maximisation problem. This paper focuses on providing an overview on the influence maximisation problem, and covers three major aspects: 1) different types of inputs required; 2) influence propagation models that map the spread of influence in the network; 3) the approximation algorithms proposed for seed set selection. In addition, we provide the state of the art and describe the open problems in this domain. © 2019 Inderscience Enterprises Ltd.Item Energy poverty and economic development: Household-level evidence from India(Elsevier Ltd, 2019) Rajesh Acharya, R.H.; Sadath, A.C.In this paper, we investigate the relationship between energy poverty and economic development in India and its trend over a decade. For this purpose, we estimate a Multidimensional Energy Poverty Index (MEPI) and an index of development at the district level using household level data. Empirical results show that energy poverty is quite extensive in India with substantial variations across the states and districts. Over the years, energy poverty shows a declining trend at all-India level, but with the exception of few bigger and less developed states. Further, the study records a negative relationship between economic development and energy poverty, the strength of relationship has increased during the study period. Among the components of economic development, education has a greater impact on reducing energy poverty compared with income. The study observes that energy poverty and socio-economic backwardness in India are highly correlated; Dalits and Adivasis have higher energy poverty and a lower rate in the reduction of energy poverty in comparison with the national average. Energy poverty is lower in urban India in comparison with rural India. © 2018Item Minstrel PIE: Curtailing queue delay in unresponsive traffic environments(Elsevier B.V., 2019) Patil, S.D.; Tahiliani, M.P.Active Queue Management (AQM) algorithms aim to maintain a proper trade-off between queue delay and bottleneck link utilization. However, it is often noticed that this trade-off is not achieved convincingly when unresponsive UDP flows coexist with responsive TCP flows. This paper proposes an extension to Proportional Integral controller Enhanced (PIE) algorithm called Minstrel PIE, which adapts the reference queue delay to improve the trade-off between queue delay and link utilization when unresponsive flows share the same bottleneck queue as responsive flows. Extensive evaluations through simulations and real time experiments demonstrate that Minstrel PIE improves the performance of PIE in the presence of unresponsive flows, and delivers similar performance otherwise. Moreover, the Minstrel PIE algorithm does not introduce new knobs to improve the performance of PIE and hence, can be easily deployed without any additional complexity. © 2019 Elsevier B.V.Item Extending BookSim2.0 and HotSpot6.0 for power, performance and thermal evaluation of 3D NoC architectures(Elsevier B.V., 2019) Halavar, B.; Pasupulety, U.; Talawar, B.With the increase in number and complexity of cores and components in Chip-Multiprocessors (CMP) and Systems-on-Chip (SoCs), a highly structured and efficient on-chip communication network is required to achieve high-performance and scalability. Network-on-Chip (NoC) has emerged as a reliable communication framework in CMPs and SoCs. Many 2-D NoC architectures have been proposed for efficient on-chip communication. Cycle accurate simulators model the functionality and behaviour of NoCs by considering micro-architectural parameters of the underlying components to estimate performance, power and energy characteristics. Employing NoCs in three-dimensional integrated circuits (3D-ICs) can further improve performance, energy efficiency, and scalability characteristics of 3D SoCs and CMPs. Minimal error estimation of energy and performance of NoC components is crucial in architecture trade-off studies. Accurate modeling of re:Horizontal and vertical links by considering micro-architectural and physical characteristics reduces the error in power and performance estimation of 3D NoCs. Additionally, mapping the temperature distribution in a 3D NoC reduces estimation error. This paper presents the 3D NoC modelling capabilities extended in two existing state-of-the-art simulators, viz., the 2D NoC Simulator - BookSim2.0 and the thermal behaviour simulator - HotSpot6.0. With the extended 3D NoC modules, the simulators can be used for power, performance and thermal measurements through micro-architectural and physical parameters. The major extensions incorporated in BookSim2.0 are: Through Silicon Via power and performance models, 3D topology construction modules, 3D Mesh topology construction using variable X, Y, Z radix, tailored routing modules for 3D NoCs. The major extensions incorporated in HotSpot6.0 are: parameterized 2D router floorplan, 3D router floorplan including Through Silicon Vias (TSVs), power and thermal distribution models of 2D and 3D routers. Using the extended 3D modules, performance (average network latency), and energy efficiency metrics (Energy-Delay Product) of variants of 3D Mesh and 3D Butterfly Fat Tree topologies have been evaluated using synthetic traffic patterns. Results show that the 4-layer 3D Mesh is 2.2 × better than 2-layer 3D Mesh and 4.5 × better than 3D BFT variants in terms of network latency. 3D Mesh variants have the lowest Energy Delay Product (EDP) compared to 3D BFT variants as there is an 80% reduction in link lengths and up to 3 × more TSVs. Another observation is that the EDP of the 4-layer 3D BFT (with transpose traffic) is 1.5 × the EDP of the 4-layer 3D Mesh (with transpose traffic). Further optimizations towards a tailored 3D BFT for transpose traffic could reduce this EDP gap with the 4-layer 3D Mesh. From the 3D NoC heat maps, it was found that the edge routers in the floorplan of the tested 3D Mesh and 3D BFT topologies have the least ambient temperature. © 2019Item An enhanced protein secondary structure prediction using deep learning framework on hybrid profile based features(Elsevier Ltd, 2020) Kumar, P.; Bankapur, S.; Patil, N.Accurate protein secondary structure prediction (PSSP) is essential to identify structural classes, protein folds, and its tertiary structure. To identify the secondary structure, experimental methods exhibit higher precision with the trade-off of high cost and time. In this study, we propose an effective prediction model which consists of hybrid features of 42-dimensions with the combination of convolutional neural network (CNN) and bidirectional recurrent neural network (BRNN). The proposed model is accessed on four benchmark datasets such as CB6133, CB513, CASP10, and CAP11 using Q3, Q8, and segment overlap (Sov) metrics. The proposed model reported Q3 accuracy of 85.4%, 85.4%, 83.7%, 81.5%, and Q8 accuracy 75.8%, 73.5%, 72.2%, and 70% on CB6133, CB513, CASP10, and CAP11 datasets respectively. The results of the proposed model are improved by a minimum factor of 2.5% and 2.1% in Q3 and Q8 accuracy respectively, as compared to the popular existing models on CB513 dataset. Further, the quality of the Q3 results is validated by structural class prediction and compared with PSI-PRED. The experiment showed that the quality of the Q3 results of the proposed model is higher than that of PSI-PRED. © 2019 Elsevier B.V.Item ELBA-NoC: Ensemble learning-based accelerator for 2D and 3D network-on-chip architectures(Inderscience Publishers, 2020) Kumar, A.; Talawar, B.Network-on-chips (NoCs) have emerged as a scalable alternative to traditional bus and point-to-point architectures, it has become highly sensitive as the number of cores increases. Simulation is one of the main tools used in NoC for analysing and testing new architectures. To achieve the best performance vs. cost trade-off, simulators have become an essential tool. Software simulators are too slow for evaluating large scale NoCs. This paper presents a framework which can be used to analyse overall performance of 2D and 3D NoC architectures which is fast and accurate. This framework is named as ensemble learning-based accelerator (ELBA-NoC) which is built using random forest regression algorithm to predict parameters of NoCs. On 2D, 3D NoC architectures, ELBA-NoC was tested and the results obtained were compared with extensively used Booksim NoC simulator. The framework showed an error rate of less than 5% and an overall speedup of up to 16 K×. © © 2020 Inderscience Enterprises Ltd.