Faculty Publications
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Publications by NITK Faculty
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Item A feed-forward/feedback run-to-run control of a mixed product process: Simulation and experimental studies(2007) Wu, M.-F.; Lin, W.-K.; Ho, C.-L.; Wong, D.S.-H.; Jang, S.-S.; Zheng, Y.; Jain, A.Run-to-run (RtR) control is an important quality assurance method for batch-based manufacturing process. Usually, products of different grades are produced on a tool that will experience gradual drift between maintenance cycles. A feed-forward/feedback RtR control strategy that compensates this drift for all products manufactured on this tool was proposed. This and other RtR control schemes were analyzed and validated by simulation and experimentally using a bench scale reactor that produces silica particles with different diameters by a sol-gel process. A simple EWMA (exponentially weighted moving average) RtR control scheme based on products of the same grade was found to be stable but inefficient for infrequent products. A simple EWMA RtR control scheme that attributed disturbance entirely as the effect of tool drift was found to be unstable. The feed-forward/feedback RtR control proposed was able to maintain stable quality by effectively utilizing information about tool changes to adjust recipes of infrequent products. © 2007 American Chemical Society.Item Educational computing for the blind in India: Design, development and learning impact(2012) Gupta, N.; Raghavan, A.; Shanbhogue, M.; Jain, A.The aim of this paper is to present software engineering methodologies that were employed in developing educational solutions for the visually impaired. Empirical studies and experiments were conducted to measure the impact of the educational tools on the learning and cognitive abilities of the target user group. This study highlights the various technological and design challenges that were faced while developing and deploying these customized learning solutions. Observations and results indicate that there is significant merit in developing and utilizing such applications for the educational empowerment of the blind. © 2012 IEEE.Item Energy efficient computing- Green cloud computing(2013) Jain, A.; MÃshra, M.; Peddoju, S.K.; Jain, N.Moving towards Cloud Computing, high performance computing usage of huge data center (DC) and huge cluster is increasing day by day and energy consumption by these DC and energy dissipation in environment by these DC is also rising day by day. The large amount of CO2 dissipation in environment has generated the necessity of Green computing (saving energy by recycling it and reusing it over a period of time and minimizing the wastage in terms of usage of resources). More processor chips generates more heat, more heat requires more cooling and cooling again generates heats and thus we come to a stage where we want to balance the system by getting the same computing speed at decreased energy consumption. In this paper we proposed different ideas towards green cloud computing approach. © 2013 IEEE.Item Fast Verification of Digital Signatures in IoT(Springer Verlag service@springer.de, 2017) Kittur, A.S.; Jain, A.; Pais, A.R.Internet of Things (IoT) is the recent advancement in Wireless technology where multiple embedded devices are connected through internet for exchange of information. Since the information exchanged is private and at times confidential, state of the art focusses at providing proper security to the system. To avoid illegal users from getting access to information system, authentication through Digital Signatures becomes integral part of IoT. Verifying individual signatures is a time consuming process, hence it is not advisable in IoT systems. Using Batch verification of Digital signatures, reduction in verification time is achievable. Hence in this paper, we have studied different RSA based batch verification techniques and their analysis is provided. Batch verification of digital signatures in IoT devices is a promising area for further research. © 2017, Springer Nature Singapore Pte Ltd.Item A Framework To Study Heuristic TSP Algorithms With Google Maps API(Institute of Electrical and Electronics Engineers Inc., 2019) Ajumal, P.A.; Ananthakrishnan, S.; Jain, A.; Athreya, H.N.; Chandrasekaran, K.Millions of people depend on the navigation facilities available in smart-phones and web browsers for their daily commutes, planning long trips ahead of time, looking up places etc. Integration of GPS and compass made navigating anywhere in the world a trivial task. Today, there are several applications available that fit the purpose of navigation such as Waze, HereWeGo (previously known as Here Maps by Nokia), Google Maps, etc. When Google Maps was used to embark on a tour that will take us to chosen places by covering the least distance possible, it is observed that none of the aforementioned applications provide such a feature. In this paper, a framework is developed with Google Maps APIs to create such a feature. This problem is mapped to the Traveling salesman problem and tried to solve it using algorithms known for approximating TSP such as Artificial Bee Colony Algorithm, Particle Swarm Optimization and Two-opt Algorithm. The framework is tested with these algorithms and found that, Particle Swarm Optimization gives the best possible route. © 2019 IEEE.Item Resource Buffers in Construction Projects(Springer Science and Business Media Deutschland GmbH, 2021) Hegde, A.L.; Jain, A.; Das, B.B.Critical Chain Project Management (CCPM) is created on techniques and procedures taken from the Theory of Constraints (TOC). CCPM was presented in late 90s (1997) in a book named Critical Chain from several studies by Standish Group and others for customary project management methods, only half of the projects normally finish on time, projects generally take twice the duration originally planned, twice of the original planned cost, around 70% of projects fall short of their planned scope, and round about 30% of the projects are shut down in midway. CCPM can be used to avoid these customary statistics. Usually, CCPM case studies report greater than 95% on-time and on-budget completion when CCPM is applied in the approved manner. Initially, the CCPM guidelines and prescriptions and discovers its differences with customary project scheduling methods like Program Evaluation and Review Technique (PERT) and Critical Path Method (CPM). Then the Critical Chain Project Management solution has been outlined, which covers all the proposed steps—the elements of the Critical Chain Project Management solution. Thereafter, the methodology used for data collection and analysis has been explained. Further, it explains the application of CCPM in a Transmission Line Project undertaken by Larsen and Toubro Power Transmission and Distribution. Further, the results of the analysis and discussions about the same have been written. © 2021, Springer Nature Singapore Pte Ltd.Item Epidemic Outbreak Prediction with Ensemble of Deep Learning Models(Institute of Electrical and Electronics Engineers Inc., 2022) Vasudev, R.; Dahikar, P.; Jain, A.; Patil, N.The ability of today's technology has proved it's significance and dire need in the world yet again, with COVID-19 being a global pandemic. Various techniques are being incorporated and researches being conducted everyday in order to mitigate this pandemic. Forecasting of COVID-19 cases is one such task in machine learning which is being researched intensively to develop reliable forecasting models.In the proposed work, we have forecasted the number of COVID-19 confirmed,recovered and death cases globally using time series data with machine learning and deep learning ensemble models. The purpose of this study is to prove that ensemble of several week learners that we have developed can result in a better performing model. Deep learning models always tend to perform better than machine learning and traditional linear models due to their non-linearity. Our study concludes that deep learning ensemble model achieves better performance than the machine learning ensemble (Random forest) and the individual base learners used in ensemble model itself in COVID-19 forecasting. © 2022 IEEE.Item Lightweight and Homomorphic Security Protocols for IoT(Springer, 2023) Singh, I.; Jain, A.; Dhody, I.S.; Chandavarkar, B.R.The rise in usage of IoT devices for data collection in various fields has been astronomical in recent times. There has been an increased requirement to process the collected data on various cloud providers as IoT devices are compute-constrained. However, online data processing presents a substantial security challenge, especially in sensitive data, such as finance and medicine. The motivation behind this chapter comes from the observation that the encryption algorithms used for IoT devices need to be lightweight because IoT devices are not capable of heavy computation and the algorithm must be homomorphic. This is important because when the encrypted data moves from the device’s private environment to the public network, the data integrity is a major factor for such sensors and measurement devices. In this way, the data never needs to be in its decrypted form outside the organization’s ecosystem. This chapter aims to first present the limitations of IoT devices in the context of IoT networks. Then, the chapter analyses some of the most popular security protocols for IoT networks and subsequently understands the need for lightweight and homomorphic encryption. Then, the chapter presents and compares the most widely used lightweight and homomorphic algorithms/schemes, finally presenting the observations and conclusions based on the study. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item Sentiment Analysis on Worldwide COVID-19 Outbreak(Springer Science and Business Media Deutschland GmbH, 2024) Vasudev, R.; Dahikar, P.; Jain, A.; Patil, N.Sentiment analysis has proved to be an effective way to easily mine public opinions on issues, products, policies, etc. One of the ways this is achieved is by extracting social media content. Data extracted from the social media has proven time and again to be the most powerful source material for sentiment analysis tasks. Twitter, which is widely used by the general public to express their concerns over daily affairs, can be the strongest tool to provide data for such analysis. In this paper, we intend to use the tweets posted regarding the COVID-19 pandemic for a sentiment analysis study and sentiment classification using BERT model. Due to its transformer architecture and bidirectional approach, this deep learning model can be easily preferred as the best choice for our study. As expected, the model performed very well in all the considered classification metrics and achieved an overall accuracy of 92%. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Scalable and Efficient orchestration of machine learning workloads on DSPs with multi-level memory architecture(Society for Imaging Science and Technology, 2024) Sequeira, A.; Sam, F.; Jain, A.; Swami, P.Deep learning has enabled rapid advancements in the field of image processing. Learning based approaches have achieved stunning success over their traditional signal processing-based counterparts for a variety of applications such as object detection, semantic segmentation etc. This has resulted in the parallel development of hardware architectures capable of optimizing the inferencing of deep learning algorithms in real time. Embedded devices tend to have hard constraints on internal memory space and must rely on larger (but relatively very slow) DDR memory to store vast data generated while processing the deep learning algorithms. Thus, associated systems have to be evolved to make use of the optimized hardware balancing compute times with data operations. We propose such a generalized framework that can, given a set of compute elements and memory arrangement, devise an efficient method for processing of multidimensional data to optimize inference time of deep learning algorithms for vision applications. © 2024, Society for Imaging Science and Technology.