Faculty Publications
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Publications by NITK Faculty
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Item Automated Parking System in Smart Campus Using Computer Vision Technique(Institute of Electrical and Electronics Engineers Inc., 2019) Banerjee, S.; Ashwin, T.S.; Guddeti, R.M.R.In today's world we need to maintain safety and security of the people around us. So we need to have a well connected surveillance system for keeping active information of various locations according to our needs. A real-time object detection is very important for many applications such as traffic monitoring, classroom monitoring, security rescue, and parking system. From past decade, Convolutional Neural Networks is evolved as a powerful models for recognizing images and videos and it is widely used in the computer vision related work for the best and most used approach for different problem scenario related to object detection and localization. In this work, we have proposed a deep convolutional network architecture to automate the parking system in smart campus with modified Single-shot Multibox Detector (SSD) approach. Further, we created our dataset to train and test the proposed computer vision technique. The experimental results demonstrated an accuracy of 71.2% for the created dataset. © 2019 IEEE.Item Findings of the Shared Task on Machine Translation in Dravidian languages(Association for Computational Linguistics (ACL), 2021) Chakravarthi, B.R.; Priyadharshini, R.; Banerjee, S.; Saldanha, R.; Mccrae, J.P.; Anand Kumar, M.; Krishnamurthy, P.; Johnson, M.This paper presents an overview of the shared task on machine translation of Dravidian languages. We presented the shared task results at the EACL 2021 workshop on Speech and Language Technologies for Dravidian Languages. This paper describes the datasets used, the methodology used for the evaluation of participants, and the experiments’ overall results. As a part of this shared task, we organized four sub-tasks corresponding to machine translation of the following language pairs: English to Tamil, English to Malayalam, English to Telugu and Tamil to Telugu which are available at https://competitions.codalab.org/competitions/27650. We provided the participants with training and development datasets to perform experiments, and the results were evaluated on unseen test data. In total, 46 research groups participated in the shared task and 7 experimental runs were submitted for evaluation. We used BLEU scores for assessment of the translations. ©2021 Association for Computational LinguisticsItem Overview of the Shared Task on Machine Translation in Dravidian Languages(Association for Computational Linguistics (ACL), 2022) Anand Kumar, A.M.; Hegde, A.; Banerjee, S.; Chakravarthi, B.R.; Priyadarshini, R.; Shashirekha, H.L.; Mccrae, J.P.This paper presents an outline of the shared task on translation of under-resourced Dravidian languages at DravidianLangTech-2022 workshop to be held jointly with ACL 2022. A description of the datasets used, approach taken for analysis of submissions and the results have been illustrated in this paper. Five sub-tasks organized as a part of the shared task include the following translation pairs: Kannada to Tamil, Kannada to Telugu, Kannada to Sanskrit, Kannada to Malayalam and Kannada to Tulu. Training, development and test datasets were provided to all participants and results were evaluated on the gold standard datasets. A total of 16 research groups participated in the shared task and a total of 12 submission runs were made for evaluation. Bilingual Evaluation Understudy (BLEU) score was used for evaluation of the translations. © 2022 Association for Computational Linguistics.Item Minimum distance of the boundary of the set of PPT states from the maximally mixed state using the geometry of the positive semidefinite cone(Springer New York LLC barbara.b.bertram@gsk.com, 2019) Banerjee, S.; Patel, A.A.; Panigrahi, P.K.Using a geometric measure of entanglement quantification based on Euclidean distance of the Hermitian matrices (Patel and Panigrahi in Geometric measure of entanglement based on local measurement, 2016. arXiv:1608.06145), we obtain the minimum distance between the set of bipartite n-qudit density matrices with a positive partial transpose and the maximally mixed state. This minimum distance is obtained as 1dn(dn-1), which is also the minimum distance within which all quantum states are separable. An idea of the interior of the set of all positive semidefinite matrices has also been provided. A particular class of Werner states has been identified for which the PPT criterion is necessary and sufficient for separability in dimensions greater than six. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.Item Multimodal behavior analysis in computer-enabled laboratories using nonverbal cues(Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2020) Banerjee, S.; Ashwin, T.S.; Guddeti, R.M.R.In the modern era, there is a growing need for surveillance to ensure the safety and security of the people. Real-time object detection is crucial for many applications such as traffic monitoring, security, search and rescue, vehicle counting, and classroom monitoring. Computer-enabled laboratories are generally equipped with video surveillance cameras in the smart campus. But, from the existing literature, it is observed that the use of video surveillance data obtained from smart campus for any unobtrusive behavioral analysis is seldom performed. Though there are several works on the students’ and teachers’ behavior recognition from devices such as Kinect and handy cameras, there exists no such work which extracts the video surveillance data and predicts the behavioral patterns of both the students and the teachers in real time. Hence, in this study, we unobtrusively analyze the students’ and teachers’ behavioral patterns inside a teaching laboratory (which is considered as an indoor scenario of a smart campus). Here, we propose a deep convolution network architecture to classify and recognize an object in the indoor scenario, i.e., the teaching laboratory environment of the smart campus with modified Single-Shot MultiBox Detector approach. We used six different class labels for predicting the behavioral patterns of both the students and the teachers. We created our dataset with six different class labels for training deep learning architecture. The performance evaluation demonstrates that the proposed method performs better with an accuracy of 0.765 for classification and localization. © 2020, Springer-Verlag London Ltd., part of Springer Nature.Item The γ-Valerolactone (GVL) as Innoxious Reaction Media for the Synthesis of 2-Aryl-2H-Indazoles via C-N and N-N Bond Formation under Cu(I)-Catalyzed Ligand and Base Free Conditions(Taylor and Francis Ltd., 2024) Singh, L.S.; Kant, K.; Banerjee, S.; Sengupta, R.; AlObaid, A.A.; Pal, M.; Dutta, S.; Aljaar, N.; Malakar, C.C.An efficient method for N-arylation and N-N bond formation has been developed using an innoxious reaction medium, γ-valerolactone (GVL), as both a solvent and a ligand. The strategy involves utilizing CuI as a catalyst under conditions free of external ligands and bases. Various aldehyde and amine derivatives with different functional groups were investigated, resulting in the production of 2-aryl-2H-indazole compounds with yields ranging from 75% to 93%. This study highlights the effectiveness of GVL, a solvent derived from biomass, as a reaction medium and ligand in a multicomponent reaction. © 2023 Taylor & Francis Group, LLC.