Faculty Publications
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Item Energy efficient and reliable network design to improve lifetime of low power IoT networks(Institute of Electrical and Electronics Engineers Inc., 2017) Sarwesh, P.; Shekar, N.; Shet, V.; Chandrasekaran, K.Internet of Things is smart technology that is used in wide range of applications, IoT converges physical devices with cyber systems to facilitate global information sharing. In IoT network, devices are constrained by energy (limited by battery power). Thus, efficient energy utilization is the major challenge in low power IoT networks. In this paper, energy efficient and reliable network architecture is proposed to improve the lifetime of IoT networks. In proposed network architecture, routing technique and node placement technique are effectively integrated to address energy and reliability related issues. In node placement technique, density of sensor nodes are hierarchically varied to balance the energy consumption and reliability related parameters are included in routing mechanism. Hence, effective combination of these two techniques in single network architecture prolongs the lifetime of the network. In proposed work, sensor nodes and relay nodes, sensors do sensing and relay nodes handles path computation and data transmission. We included IEEE 802.15.4 PHY/MAC radio and IPv6 module in proposed work to adopt IoT Scenario. From our results, it is observed that proposed architecture prolongs the lifetime of low power IoT network. © 2017 IEEE.Item Automatic identification of diabetic maculopathy stages using fundus images(2009) Nayak, J.; Subbanna Bhat, P.S.; Acharya, R.Diabetes mellitus is a major cause of visual impairment and blindness. Twenty years after the onset of diabetes, almost all patients with type 1 diabetes and over 60% of patients with type 2 diabetes will have some degree of retinopathy. Prolonged diabetes retinopathy leads to maculopathy, which impairs the normal vision depending on the severity of damage of the macula. This paper presents a computer-based intelligent system for the identification of clinically significant maculopathy, non-clinically significant maculopathy and normal fundus eye images. Features are extracted from these raw fundus images which are then fed to the classifier. Our protocol uses feed-forward architecture in an artificial neural network classifier for classification of different stages. Three different kinds of eye disease conditions were tested in 350 subjects. We demonstrated a sensitivity of more than 95% for these classifiers with a specificity of 100%, and results are very promising. Our systems are ready to run clinically on large amounts of datasets. © 2009 Informa Healthcare USA, Inc.Item Probabilistic query generation and fuzzy c -means clustering for energy-efficient operation in wireless sensor networks(John Wiley and Sons Ltd vgorayska@wiley.com Southern Gate Chichester, West Sussex PO19 8SQ, 2016) Kumar, P.; Chaturvedi, A.Depending upon sensing attributes, wireless sensor networks (WSNs) are classified as event driven, time driven, and query driven. In a given surveillance area, approximation of query generation process using uniform probability mass function (PMF) model seems to be reasonable in aggregate terms based on observations extracted from lifetime span of WSNs. However, owing to random generation aspects of query and the associated temporal variations, the Poisson distribution-based model appears to be more appropriate to resemble the realistic query generation pattern. Invariably, in all the sensor network architectures, the energy management requires an important consideration owing to limited energy resources. For the optimal utilization of energy resources, we propose fuzzy c-means (FCM) algorithm to form clusters in a hierarchical network configuration. Network performance is measured in terms of key performance measures, namely, average residual energy status, critical residual energy status (CRES), and number of network nodes that attain the CRES mark. These performance measures are estimated and analyzed for three different PMF models of query generation namely Uniform, Gaussian and Poisson. The merit of deploying FCM algorithm in terms of maintaining much better energy profile of the entire network is discussed. © Copyright 2016 John Wiley & Sons, Ltd.Item Spatial–Temporal Aspects Integrated Probabilistic Intervals Models of Query Generation and Sink Attributes for Energy Efficient WSN(Springer New York LLC barbara.b.bertram@gsk.com, 2017) Kumar, P.; Chaturvedi, A.With advancement in device miniaturization and efficacy of network protocols, in a variety of civilian and military applications, wireless sensor networks (WSNs) architectures find room as viable network paradigm. Invariably, in all these WSN architectures, devising suitable algorithms for the efficient network resources utilization has been a challenging task. In certain events driven scenarios, random arrival pattern of queries generation; their geographical distribution (spatial aspect) and generation rate (temporal aspect) are hard to predict precisely. However, these phenomenons could be appropriately modelled using probabilistic framework while yielding adequate accuracy. Usually, in adopted probabilistic models, the associated control parameters are treated as crisp numbers, which fail to encompass uncertainties that are inevitably associated with the modeled parameters. To include impact of such uncertainties, we propose a modified Poisson PMF expressions in that dependency on spatial and temporal aspects is incorporated based on interval concepts. The paper also validates the dynamic fuzzy c-means algorithm as the most efficient clusters formation scheme. Sink node is an important entity/interface between end users and remotely located sensor nodes. To exploit implications of sink nodes attributes, three different case studies are presented. Wherein, we explore the network surveillance by a single stationary/portable sink and four stationary sinks. Obtained simulation results are analyzed for different scenarios which in principle governed by usage of four distinct clustering schemes and sink(s) attribute driven network surveillance. © 2017, Springer Science+Business Media New York.Item Effective integration of reliable routing mechanism and energy efficient node placement technique for low power IoT networks(IGI Global cust@igi-global.com, 2017) Sarwesh, P.; Shet, N.S.V.; Chandrasekaran, K.Internet of Things (IoT) is the emerging technology that links physical devices (sensor devices) with cyber systems and allows global sharing of information. In IoT applications, devices are operated by battery power and low power radio links, which are constrained by energy. In this paper, node placement technique and routing mechanism are effectively integrated in single network architecture to prolong the lifetime of IoT network. In proposed network architecture, sensor node and relay node are deployed, sensor nodes are responsible for collecting the environmental data and relay nodes are responsible for data aggregation and path computation. In node placement technique, densities of relay nodes are varied based on traffic area, to prevent energy hole problem. In routing technique, energy efficient and reliable path computation is done to reduce number of re transmissions. To adopt IoT scenario, we included IEEE 802.15.4 PHY/MAC radio and IPv6 packet structure in proposed network architecture. Proposed work result shows, proposed architecture prolongs network lifetime. © © 2017, IGI Global.Item Unobtrusive Behavioral Analysis of Students in Classroom Environment Using Non-Verbal Cues(Institute of Electrical and Electronics Engineers Inc., 2019) Ashwin, T.S.; Guddeti, G.R.Pervasive intelligent learning environments can be made more personalized by adapting the teaching strategies according to the students' emotional and behavioral engagements. The students' engagement analysis helps to foster those emotions and behavioral patterns that are beneficial to learning, thus improving the effectiveness of the teaching-learning process. Unobtrusive student engagement analysis is performed using the students' non-verbal cues such as facial expressions, hand gestures, and body postures. Though there exist several techniques for classifying the engagement of a single student present in a single image frame, there are limited works on the students' engagement analysis in a classroom environment. In this paper, we propose a convolutional neural network architecture for unobtrusive students' engagement analysis using non-verbal cues. The proposed architecture is trained and tested on faces, hand gestures and body postures in the wild of more than 350 students present in a classroom environment, with each test image containing multiple students in a single image frame. The data annotation is performed using the gold standard study, and the annotators reliably agree with Cohen's ? = 0.43. We obtained 71% accuracy for the students' engagement level classification. Further, a pre-test/post-test analysis was performed, and it was observed that there is a positive correlation between the students' engagement and their test performance. © 2013 IEEE.Item Dense refinement residual network for road extraction from aerial imagery data(Institute of Electrical and Electronics Engineers Inc., 2019) Eerapu, K.K.; Ashwath, B.; Lal, S.; Dell’Acqua, F.; Narasimha Dhan, A.V.Extraction of roads from high-resolution aerial images with a high degree of accuracy is a prerequisite in various applications. In aerial images, road pixels and background pixels are generally in the ratio of ones-to-tens, which implies a class imbalance problem. Existing semantic segmentation architectures generally do well in road-dominated cases but fail in background-dominated scenarios. This paper proposes a dense refinement residual network (DRR Net) for semantic segmentation of aerial imagery data. The proposed semantic segmentation architecture is composed of multiple DRR modules for the extraction of diversified roads alleviating the class imbalance problem. Each module of the proposed architecture utilizes dense convolutions at various scales only in the encoder for feature learning. Residual connections in each module of the proposed architecture provide the guided learning path by propagating the combined features to subsequent DRR modules. Segmentation maps undergo various levels of refinement based on the number of DRR modules utilized in the architecture. To emphasize more on small object instances, the proposed architecture has been trained with a composite loss function. The qualitative and quantitative results are reported by utilizing the Massachusetts roads dataset. The experimental results report that the proposed architecture provides better results as compared to other recent architectures. © 2019 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.Item Investigation of CMOS Based Integration Approach Using DAI Technique for Next Generation Wireless Networks(Springer New York LLC barbara.b.bertram@gsk.com, 2019) Roy, G.M.; Kanuajia, B.K.; Dwari, S.; Kumar, S.; Song, H.This research work investigates a CMOS based low noise amplifier (LNA) using differential active inductor with eight-shaped patch antenna for next generation wireless communication. The proposed work conceded into three different phases. The first phase proposes LNA architecture which includes multistage cascode amplifier with a gate inductor gain peaking technique. The ground approach for this architecture employs active inductor technique that includes two stages of differential amplifier. The proposed novel technique leads to give incremental in inductance by using of common mode feedback resistor and lowers the undesirable parasitic resistance effect. Additionally, this technique offers gain enhanced noise cancellation and achieves a frequency band of around 5.7 GHz. The proposed architecture includes single stage differential AI and enhances the bandwidth up to 6.8 GHz with peak gain of 21 dB at 7.8 GHz. The noise figure and stability factor are achieved which is reasonably good at 1 dB. The proposed architecture is design and optimized on advanced design RF simulator using 0.045 µm CMOS process technology. While in second phase, a narrow band eight-shaped patch antenna is designed which provides operating band range from 5.8 to 6.5 GHz with 6.2 GHz resonating frequency. Highest peak gain of 15 dB and maximum radiation power of 42.5 dBm is succeed by proposed antenna. The final phase provides integration strategy of LNA with antenna and achieves desired gain of nearly 21 dB with minimum NF of 1.2–1.5 dB in the same band. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.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 LBNoc: Design of low-latency router architecture with lookahead bypass for network-on-chip using FPGA(Association for Computing Machinery acmhelp@acm.org, 2020) Parane, K.; Prabhu Prasad, B.M.; Talawar, B.An FPGA-based Network-on-Chip (NoC) using a low-latency router with a look-ahead bypass (LBNoC) is discussed in this article. The proposed design targets the optimized area with improved network performance. The techniques such as single-cycle router bypass, adaptive routing module, parallel Virtual Channel (VC), and Switch allocation, combined virtual cut through and wormhole switching, have been employed in the design of the LBNoC router. The LBNoC router is parameterizable with the network topology, traffic patterns, routing algorithms, buffer depth, buffer width, number of VCs, and I/O ports being configurable. A table-based routing algorithm has been employed to support the design of custom topologies. The input buffer modules of NoC router have been mapped on the FPGA Block RAM hard blocks to utilize resources efficiently. The LBNoC architecture consumes 4.5% and 27.1% fewer hardware resources than the ProNoC and CONNECT NoC architectures. The average packet latency of the LBNoC NoC architecture is 30% and 15% lower than the CONNECT and ProNoC architectures. The LBNoC architecture is 1.15× and 1.18× faster than the ProNoC and CONNECT NoC frameworks. © 2020 Association for Computing Machinery.