Faculty Publications
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Publications by NITK Faculty
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Item Data synchronization on Android clients(Institute of Electrical and Electronics Engineers Inc., 2015) Kedia, A.; Prakash, A.Past decade has witnessed meteoric advances in the field of mobile computing owing to the development of affordable hardware technologies as well as user-friendly software platforms. Android, the platform marketed by Google has boomed in sales over the past few years making it one of the major mobile platforms in the market. The steady growth of wireless information and communication technology in convergence with rise in the penetration of Internet has led to the evolution of a wide range of mobile applications like news, multi-player games, social networking, messaging, etc. that need to access remote data. For the optimal functioning of all these applications an efficient synchronization mechanism is vital. However smart-phones have limited computational resources, power restrictions and intermittent Internet connections which pose a challenge for smooth synchronization. This paper proposes a two-way data synchronization mechanism between multiple Android clients and a central server to address these challenges. We employ a batching logic to ensure efficient data transfer in poor network environments and a server-side conflict resolution mechanism to reduce overhead on the clients, which ensures optimal processing and battery power consumption by the clients. © 2015 IEEE.Item Kinect Based Real Time Gesture Recognition Tool for Air Marshallers and Traffic Policemen(Institute of Electrical and Electronics Engineers Inc., 2017) Prakash, A.; Swathi, R.; Kumar, S.; Ashwin, T.S.; Guddeti, G.R.M.The Microsoft Kinect which is a motion sensing input device presents a very straightforward and affordable approach to facilitate real-time user interaction. Although a lot of research has been conducted on the application of Kinect to gaming and virtual reality environments, its relevance to real-world scenarios has not been explored much. The features provided by the driver platforms such as OpenNI and Microsoft Kinect Software Development Kit (SDK) for development using Kinect coupled with the motion sensing ability of Kinect, presents a unique opportunity for extending the scope of the Kinect sensor. This paper proposes a system for automatically recognizing the road traffic control gestures of police officers and air marshalling commands by ground personnels. This system is aimed for selflearning, training and testing these officers to equip them with the skills to tackle real-world situations. Since these applications are very crucial and performing accurate gestures are of at most importance, this system will prove to be very essential. Experimental results also demonstrate that our system is robust and effective and is suitable for real-time application. © 2016 IEEE.Item Optimizing Split Algorithm Performance: A Heuristic Method for Enhanced Tensor Product Matrix Computations(Institute of Electrical and Electronics Engineers Inc., 2024) Bhowmik, B.; Kumar, S.; Raju, S.R.; Prakash, A.; Mense, O.Optimizing tensor product matrix computations is critical for enhancing computational efficiency in high-performance applications. Traditional algorithms, like the Split algorithm, often struggle due to the unique properties of each matrix involved. This paper presents a novel heuristic method that optimizes the selection of cutting points and matrix ar-rangement, significantly reducing redundant calculations and minimizing memory usage. The proposed approach adapts to the varying characteristics of tensor products, improving performance across different computational scenarios. Enhancing floating-point operation efficiency and CPU utilization delivers substantial speed and efficiency gains, particularly in large-scale tensor product matrix operations, offering a robust solution for complex computational tasks. © 2024 IEEE.Item Forecasting COVID-19 Transmission Patterns with Hidden Markov Model(Institute of Electrical and Electronics Engineers Inc., 2024) Prakash, A.; Tomar, K.; Poptani, P.; Kumar, P.; Das, M.; Mohan, B.R.Global health and healthcare system have faced major hurdles as a result of coronavirus. Pandemic still had impacts on community in every part of the world even with the efforts made to curb the disease transmission. We have sought to address some of these issues by utilising data sourced from JHU's CSSE [1]. This article concentrated on U.S. COVID-19 statistics concerning the number of infections and deaths in major towns. Only the relevant infection rates, death rates, and time columns were left in the pre-processing dataset. The above finding proves that the pandemic is evolving and began as a low rate of infections and deaths which increase with every passing moment. Secondly, we look at how death rates correlates with the highest infection rate. In an attempt to improve the forecast of COVID-19 spread for health care, manufacturers, economies and academic institutions this research is developed. © 2024 IEEE.Item Dialect Identification Using Spectral and Prosodic Features on Single and Ensemble Classifiers(Springer Verlag, 2018) Chittaragi, N.B.; Prakash, A.; Koolagudi, S.G.In this paper, investigation of the significance of spectral and prosodic behaviors of speech signal has been carried out for dialect identification. Spectral features such as cepstral coefficients, spectral flux, and entropy are extracted from shorter frames. Prosodic attributes such as pitch, energy, and duration are derived from longer frames. IViE (Intonational Variations in English) speech corpus covering nine dialectal regions of British Isles has been considered, to evaluate the proposed approach. Since corpus is available in both read and semi-spontaneous modes, the influence of spectral and prosodic behavior over these datasets is distinguishably articulated. Further, two distinct classification algorithms, namely support vector machine (SVM) and an ensemble of decision trees along with the SVM are used for identification of nine dialects. Dialect discriminating information captured from both features are used for constructing feature vectors. Experiments have been conducted on individual and combinations of features. A better dialect recognition performance is observed with ensemble methods over a single independent SVM. © 2017, King Fahd University of Petroleum & Minerals.Item Multilevel Multimodal Framework for Automatic Collateral Scoring in Brain Stroke(Institute of Electrical and Electronics Engineers Inc., 2024) Raj, R.; Dayananda, D.; Gupta, A.; Mathew, J.; Kannath, S.K.; Prakash, A.; Rajan, J.In patients with ischemic brain stroke, collateral circulation plays a crucial role in selecting patients suitable for endovascular therapy. The presence of well-developed collaterals improves the patient's chances of recovery. In clinical practice, the presence of collaterals is diagnosed on a Computed Tomography Angiography scan. The radiologist grades it on the basis of subjective visual assessment, which is prone to interobserver and intraobserver variability. Computer-based methods of collateral assessment face the challenge of non-uniform scan volume, leading to manual selection of slices, meaning that the most imperative slices have to be manually selected by the radiologist. This paper proposes a multilevel multimodal hierarchical framework for automated collateral scoring. Specifically, we propose deploying a Convolutional Neural Network for image selection based on the visibility of collaterals and a multimodal model for comparing the occluded and contralateral sides of the brain for collateral scoring. We also generate a patient-level prediction by integrating automated machine learning in the proposed framework. While the proposed multimodal predictor contributes to Artificial Intelligence, the proposed end-to-end framework is an application in engineering. The proposed framework has been trained and tested on 116 patients, with five-fold cross-validation, achieving an accuracy of 91.17% for multi-class collateral scores and 94.118% for binary class collateral scores. The proposed multimodal predictor achieved a weighted F1 score of 0.86 and 0.95 on multi-class and binary-class collateral scores, respectively. The proposed framework is fast, efficient, and scalable for real-world deployments. Automated evaluation of collaterals with attention maps for explainability would complement radiologists' efforts. Code for the proposed framework is available at: https://github.com/rishiraj-cs/collaterals_ML_MM. © 2013 IEEE.Item Formulation and optimization of Ni-MOF/CuSe nanocomposite ink for high-performance flexible microsupercapacitor(Elsevier Ltd, 2024) Saquib, M.; Muthu, M.; Nayak, R.; Prakash, A.; Sudhakar, Y.N.; SenthilKumar, S.; Bhat, D.K.The growth of flexible and wearable electronics drives progress in printed, flexible micro-supercapacitors for energy storage. This study fabricates flexible and foldable micro-supercapacitors using a nanocomposite of Ni-based Metal-Organic Framework (Ni-MOF) and copper selenide (CuSe). The conductive ink, blending Ni-MOF and CuSe, ensures thorough mixing for screen-printing. The resulting devices exhibit impressive electrochemical performance, with the NC-5 FAS device showing high areal capacitance, promising energy density and (3.65 mWhcm?2 and power density (73.8 mWcm?2). Integration into a 3D enclosure configuration enhances performance, with improved capacitance, energy density (47.08 mWhcm?2) and power density and outstanding power density (985.8 mWcm?2), maintaining capacitance retention of the 93.9 % and with highly robust mechanical durability during flexibility tests. This study highlights tailored nanocomposite's potential to revolutionize flexible and foldable energy storage, advancing high-performance, portable electronics. © 2024