2. Conference Papers
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Item CFD investigation of unsteady three-dimensional savonius hydrokinetic turbine in irrigation channel with varying positions for hydro power application(2021) Shashikumar C.M.; Hindasageri V.; Madav V.Savonius turbines are a drag driven device, and it has high starting torque. It is a vertical axis turbine and installed in small irrigation channels to utilize the hydrokinetic energy available. Since the density of water is more and the flow of water in the channel is constrained to one direction is the advantage for a vertical axis turbine as it reduces the yaw control mechanism. In the present work, a three-dimensional conventional Savonius turbine modeled and meshed in ANSYS Fluent and unsteady transient simulations are carried out using a sliding mesh technique. The computational simulations were carried out at three different positions to analyze the effect of placing a turbine blade in the high depth of water using a conventional Savonius turbine blade with an aspect ratio of 0.7 and 0.0 overlap ratio. The turbulence model used for CFD simulation is the k-ω SST model, and the results found that the maximum coefficient of torque and coefficient of power of 0.22 and 0.17 at a tip speed ratio of 0.7 and 0.9 respectively. © 2021 Author(s).Item Child online safety in indian context(2020) Andrews D.; Alathur S.; Chetty N.; Kumar V.Children initiates the usage of Internet during young age and spend more time online. Apart from the benefits like improved education, entertainment, news and gaming, Internet poses severe threats to the children online. Ensuring online safety is a global challenge. The purpose of this paper is to examine online social media responses and awareness posts on children online safety. In this relation, Twitter social media responses after freeing the accusers of children sexual harassment and Facebook pages of some prominent personalities in India for online safety are analyzed. The results reveal that though the people are angry and fearful, they believe judiciary and police system and expecting safety from the same. The analysis of Facebook posts depicts that the concerned authorities are active towards child online safety and providing awareness through their representatives. People demand legal actions against the perpetrators of the crime to punish them. The necessary actions should be taken for cyber-crime awareness information to reach all social media users. © 2020 IEEE.Item Benchmarking semantic, centroid, and graph-based approaches for multi-document summarization(2021) Agrawal A.; George R.A.; Ravi S.S.; Kamath S.S.Multi-document summarization (MDS) is a pre-programmed process to excerpt data from various documents regarding similar topics. We aim to employ three techniques for generating summaries from various document collections on the same topic. The first approach is to calculate the importance score for each sentence using features including TF-IDF matrix as well as semantic and syntax similarity. We build our algorithm to sort the sentences by importance and add it to the summary. In the second approach, we use the k-means clustering algorithm for generating the summary. The third approach makes use of the Page Ranking algorithm wherein edges of the graph are formed between sentences that are syntactically similar but are not semantically similar. All these techniques have been used to generate 100–200 word summaries for the DUC 2004 dataset. We use ROUGE scores to evaluate the system-generated summaries with respect to the manually generated summaries. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.Item Cell Segmentation by Modified U-Net Architecture for Biomedical Images(2020) Kumar C.A.; Kumar M.T.N.; Narasimhadhan A.V.Biomedical image segmentation is one of the main and fast growing field in medical image processing domain. Deep neural networks is one of the popular field used for image segmentation. Convolutional neural networks(CNNs) in deep neural networks have shown good performance for biomedical image segmentation. However, a strong notion exists that large number of annotated images are required for training of CNNs. Therefore, in this paper we have come up with a modified U-Net architecture for limited number of annotated data with an intersection over union score of 92.54%. The architecture uses rectified-adam optimizer(advanced version of adam) for minimizing the loss function which helps us to come close to global optima. We have also compared the performance of various optimizers on the proposed network. © 2020 IEEE.Item Brushless DC hub motor drive control for electric vehicle applications(2020) Vishnu Sidharthan P.; Kashyap Y.Global warming, air pollution and sound pollution are major environmental problems faced by today's world. Environmental pollution and increasing cost of fuels had led to the importance of using alternate sources for transportation since Internal combustion (IC) engine vehicles carries more than 40% of the pollution. Battery electric vehicles (BEV) is an alternative which received high attention in transportation industry. This paper focus on the control of Brushless DC (BLDC) motor which is used to drive the Electric two-wheeler. Hardware implementation of the motor is developed and an Atmega 328 microcontroller is utilized to control the motor which is provides a speed control with current commutation. A 60V, 1000W BLDC hub Motor is used to meet the drive requirements. The mathematical modeling of BLDC motor is discussed with the mathematical equations. MATLAB Simulation is developed, and the closed loop speed control is done in Simulink. PWM pulses generated from the MCU as per the commutation sequence and loaded to control the switches of 3-phase inverter and in response to the driver inputs (start/stop, accelerator, brake) the motor speeds are dynamically varied. © 2020 IEEE.Item Brain tumor segmentation based on 3D residual U-Net(2020) Bhalerao M.; Thakur S.We propose a deep learning based approach for automatic brain tumor segmentation utilizing a three-dimensional U-Net extended by residual connections. In this work, we did not incorporate architectural modifications to the existing 3D U-Net, but rather evaluated different training strategies for potential improvement of performance. Our model was trained on the dataset of the International Brain Tumor Segmentation (BraTS) challenge 2019 that comprise multi-parametric magnetic resonance imaging (mpMRI) scans from 335 patients diagnosed with a glial tumor. Furthermore, our model was evaluated on the BraTS 2019 independent validation data that consisted of another 125 brain tumor mpMRI scans. The results that our 3D Residual U-Net obtained on the BraTS 2019 test data are Mean Dice scores of 0.697, 0.828, 0.772 and Hausdorff95 distances of 25.56, 14.64, 26.69 for enhancing tumor, whole tumor, and tumor core, respectively. © Springer Nature Switzerland AG 2020.Item BBRvl vs BBRv2: Examining Performance Differences through Experimental Evaluation(2020) Nandagiri A.; Tahiliani M.P.; Misra V.; Ramakrishnan K.K.BBR, a congestion control algorithm proposed by Google, regulates the source sending rate by deriving an estimate of the bottleneck's available bandwidth and RTTof the path. The initial version of BBR, called BBRvl, was found to be unfair, getting higher than the fair share of bandwidth when co-existing on bottleneck links with other congestion control algorithms. It also does not perform as well with networks having routers with shallow buffers. To overcome these concerns, a newer version, called BBRv2, has been proposed. Our goal in this paper is to understand the differences between the two versions and examine the primary reasons behind the improvement in performance of BBRv2. We present an experimental evaluation of BBRvl and BBRv2, evaluating their fairness across connections using the same protocol (intra-protocol fairness) and using different protocols (inter-protocol fairness) as well as delay and link utilization. From experiments with shallow and deep buffers, BBRv2 is most effective when it uses Explicit Congestion Notification (ECN), but fairness issues continue to exist in BBRv2 when ECN is disabled. A concern for BBRv2 is that it is somewhat complex to deploy in Wide Area Networks (WAN) because of the dependency with the DCTCP-style reduction of the congestion window, which is primarily usable in low-feedback delay Data Center Networks. © 2020 IEEE.Item Broker-based mechanism for cloud provider selection(2020) Achar R.; Thilagam P.S.; Acharya S.Cloud computing has recently emerged as a new computing paradigm for delivering on demand virtualised computing resources over the internet on a pay-as-use basis. Applications hosted in cloud have different requirements which include both low level (resource) requirements and high level (performance) requirements. However, most of the cloud providers satisfy SLAs based on resource requirements rather than providing performance guarantees to applications. This gap creates a need for selecting a more suitable cloud provider who can satisfy performance requirements of applications along with resource requirements. This work aims at proposing a broker-based approach to rank cloud providers based on QoS requirements of customers. It helps the SaaS providers to save cost and complexity in choosing a suitable cloud provider for hosting applications. The experimental results show that proposed approach selects the suitable cloud provider for hosting various types of applications satisfying the needs of different cloud customers. © 2020 Inderscience Enterprises Ltd.Item A Computationally Efficient sEMG based Silent Speech Interface using Channel Reduction and Decision Tree based Classification(2020) Abdullah A.; Chemmangat K.Silent Speech Interface is one of the promising areas of Human-Computer Interaction research. The surface electromyography based silent speech interface is a technique where the electric activity of facial muscles are used to detect speech. The existing sEMG based SSI techniques use complex machine learning algorithms and too many number of electrodes on the subject's face. It creates inconvenience to the user who might have undergone laryngectomy. More number of electrodes becomes highly invasive to the user, while complex classification algorithms increase the computational cost and prevents real time implementation of sEMG based SSI. Thus the objective of this research work was to develop a less complex and computationally less expensive model to classify words. To achieve this goal channel reduction technique and the use of Decision Tree based classification algorithm was employed. Only the time domain features are used as input to the classification algorithm. The motive was to exploit the advantage of computational ease in extracting the time domain features as compared to the frequency domain features. The sEMG data of the words used in this work are obtained from the complete utterance of the sentences and not by individual utterances of the word. Our algorithm was able to achieve a word accuracy of 95.17% even after applying a channel reduction, thereby allowing us to use only the data of 5 channels, in place of a conventional seven channel setup. © 2020 The Authors. Published by Elsevier B.V.Item Basics on Categorizing Travel-Time-Based Degrees of Satisfaction Using Triangular Fuzzy-Membership Functions(2020) Anand A.; George V.; Kanthi R.; Tagore M.; Padmashree M.S.The travel desires of trip-makers in urban activity centres depend mainly on the location of residential areas, proximity to various activity centres, household characteristics, and socio-economic factors that influence the choice of travel modes. Decision-making with regard to the choice of a particular mode of travel is fuzzy in nature, and seldom follows a rigid rule-based approach. In this context, the fuzzy-logic approach was considered since it could handle inherent randomness in decision-making related to mode-choice. The present study focuses on the application of this technique making use of revealed preference survey data collected through CES and MVA Systra, later compiled and corrected in various stages at NITK. The difference between the actual travel time by a particular mode, and the theoretical travel time based on average vehicular speeds was used as an important indicator in determining the degrees of satisfaction of the trip-maker. This indicator was computed, and fitted using a normal distribution. It was assumed that indicator values between μ-3σ and μ could be considered for the category of satisfied trip-makers according to the three sigma rule where μ is the mean indicator value, and σ represents the standard deviation. The computed values of the indicators were used in classifying the data into 6 categories of degrees of satisfaction that formed the basic framework for modelling using fuzzy-logic technique. This paper aims at understanding the basic mathematical computations involved in defuzzification using the centroid method for triangular membership functions, and provides a comparison with results obtained using MATLAB. © 2020 The Authors. Published by Elsevier B.V.