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299 results on '"Singh, Damanpreet"'

2. SubT-MRS Dataset: Pushing SLAM Towards All-weather Environments

Author

Zhao, Shibo, Gao, Yuanjun, Wu, Tianhao, Singh, Damanpreet, Jiang, Rushan, Sun, Haoxiang, Sarawata, Mansi, Qiu, Yuheng, Whittaker, Warren, Higgins, Ian, Du, Yi, Su, Shaoshu, Xu, Can, Keller, John, Karhade, Jay, Nogueira, Lucas, Saha, Sourojit, Zhang, Ji, Wang, Wenshan, Wang, Chen, and Scherer, Sebastian

Subjects
Computer Science - Robotics
Abstract

Simultaneous localization and mapping (SLAM) is a fundamental task for numerous applications such as autonomous navigation and exploration. Despite many SLAM datasets have been released, current SLAM solutions still struggle to have sustained and resilient performance. One major issue is the absence of high-quality datasets including diverse all-weather conditions and a reliable metric for assessing robustness. This limitation significantly restricts the scalability and generalizability of SLAM technologies, impacting their development, validation, and deployment. To address this problem, we present SubT-MRS, an extremely challenging real-world dataset designed to push SLAM towards all-weather environments to pursue the most robust SLAM performance. It contains multi-degraded environments including over 30 diverse scenes such as structureless corridors, varying lighting conditions, and perceptual obscurants like smoke and dust; multimodal sensors such as LiDAR, fisheye camera, IMU, and thermal camera; and multiple locomotions like aerial, legged, and wheeled robots. We develop accuracy and robustness evaluation tracks for SLAM and introduced novel robustness metrics. Comprehensive studies are performed, revealing new observations, challenges, and opportunities for future research.

Published
2023

3. Distributed Optimal Control Framework for High-Speed Convoys: Theory and Hardware Results

Author

Bagree, Namya, Noren, Charles, Singh, Damanpreet, Travers, Matthew, and Vundurthy, Bhaskar

Subjects
Computer Science - Robotics
Abstract

Practical deployments of coordinated fleets of mobile robots in different environments have revealed the benefits of maintaining small distances between robots, especially as they move at higher speeds. However, this is counter-intuitive in that as speed increases, reducing the amount of space between robots also reduces the time available to the robots to respond to sudden motion variations in surrounding robots. However, in certain examples, the benefits in performance due to traveling at closer distances can outweigh the potential instability issues, for instance, autonomous trucks on highways that optimize energy by vehicle ``drafting'' or smaller robots in cluttered environments that need to maintain close, line of sight communication, etc. To achieve this kind of closely coordinated fleet behavior, this work introduces a model predictive optimal control framework that directly takes non-linear dynamics of the vehicles in the fleet into account while planning motions for each robot. The robots are able to follow each other closely at high speeds by proactively making predictions and reactively biasing their responses based on state information from the adjacent robots. This control framework is naturally decentralized and, as such, is able to apply to an arbitrary number of robots without any additional computational burden. We show that our approach is able to achieve lower inter-robot distances at higher speeds compared to existing controllers. We demonstrate the success of our approach through simulated and hardware results on mobile ground robots., Comment: 8 pages, IFAC Conference

Published
2022

13. A machine learning-based approach to determine infection status in recipients of BBV152 (Covaxin) whole-virion inactivated SARS-CoV-2 vaccine for serological surveys

Author

Singh, Prateek, Ujjainiya, Rajat, Prakash, Satyartha, Naushin, Salwa, Sardana, Viren, Bhatheja, Nitin, Singh, Ajay Pratap, Barman, Joydeb, Kumar, Kartik, Gayali, Saurabh, Khan, Raju, Rawat, Birendra Singh, Tallapaka, Karthik Bharadwaj, Anumalla, Mahesh, Lahiri, Amit, Kar, Susanta, Bhosale, Vivek, Srivastava, Mrigank, Mugale, Madhav Nilakanth, Pandey, C.P., Khan, Shaziya, Katiyar, Shivani, Raj, Desh, Ishteyaque, Sharmeen, Khanka, Sonu, Rani, Ankita, Promila, Sharma, Jyotsna, Seth, Anuradha, Dutta, Mukul, Saurabh, Nishant, Veerapandian, Murugan, Venkatachalam, Ganesh, Bansal, Deepak, Gupta, Dinesh, Halami, Prakash M., Peddha, Muthukumar Serva, Veeranna, Ravindra P., Pal, Anirban, Singh, Ranvijay Kumar, Anandasadagopan, Suresh Kumar, Karuppanan, Parimala, Rahman, Syed Nasar, Selvakumar, Gopika, Venkatesan, Subramanian, Karmakar, Malay Kumar, Sardana, Harish Kumar, Kothari, Anamika, Parihar, Devendra Singh, Thakur, Anupma, Saifi, Anas, Gupta, Naman, Singh, Yogita, Reddu, Ritu, Gautam, Rizul, Mishra, Anuj, Mishra, Avinash, Gogeri, Iranna, Rayasam, Geethavani, Padwad, Yogendra, Patial, Vikram, Hallan, Vipin, Singh, Damanpreet, Tirpude, Narendra, Chakrabarti, Partha, Maity, Sujay Krishna, Ganguly, Dipyaman, Sistla, Ramakrishna, Balthu, Narender Kumar, A, Kiran Kumar, Ranjith, Siva, Kumar, B. Vijay, Jamwal, Piyush Singh, Wali, Anshu, Ahmed, Sajad, Chouhan, Rekha, Gandhi, Sumit G., Sharma, Nancy, Rai, Garima, Irshad, Faisal, Jamwal, Vijay Lakshmi, Paddar, Masroor Ahmad, Khan, Sameer Ullah, Malik, Fayaz, Ghosh, Debashish, Thakkar, Ghanshyam, Barik, S.K., Tripathi, Prabhanshu, Satija, Yatendra Kumar, Mohanty, Sneha, Khan, Md. Tauseef, Subudhi, Umakanta, Sen, Pradip, Kumar, Rashmi, Bhardwaj, Anshu, Gupta, Pawan, Sharma, Deepak, Tuli, Amit, Ray chaudhuri, Saumya, Krishnamurthi, Srinivasan, Prakash, L., Rao, Ch V., Singh, B.N., Chaurasiya, Arvindkumar, Chaurasiyar, Meera, Bhadange, Mayuri, Likhitkar, Bhagyashree, Mohite, Sharada, Patil, Yogita, Kulkarni, Mahesh, Joshi, Rakesh, Pandya, Vaibhav, Mahajan, Sachin, Patil, Amita, Samson, Rachel, Vare, Tejas, Dharne, Mahesh, Giri, Ashok, Paranjape, Shilpa, Sastry, G. Narahari, Kalita, Jatin, Phukan, Tridip, Manna, Prasenjit, Romi, Wahengbam, Bharali, Pankaj, Ozah, Dibyajyoti, Sahu, Ravi Kumar, Dutta, Prachurjya, Singh, Moirangthem Goutam, Gogoi, Gayatri, Tapadar, Yasmin Begam, Babu, Elapavalooru VSSK., Sukumaran, Rajeev K., Nair, Aishwarya R., Puthiyamadam, Anoop, Valappil, Prajeesh Kooloth, Pillai Prasannakumari, Adrash Velayudhan, Chodankar, Kalpana, Damare, Samir, Agrawal, Ved Varun, Chaudhary, Kumardeep, Agrawal, Anurag, Sengupta, Shantanu, and Dash, Debasis

Published
2022
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36. Breast mass segmentation using mammographic data: a systematic review.

Author

Singh, Harmandeep, Sharma, Vipul, and Singh, Damanpreet

Subjects
BREAST, COMPUTER-aided diagnosis, CANCER diagnosis, EVIDENCE gaps, MEDICAL research personnel, BREAST cancer
Abstract

Breast cancer continues to be the most prevalent cancer infecting humans worldwide. Timely detection can assist in the reduction of both mortality rates and treatment costs. Segmentation plays a crucial role in the timely detection of breast cancer. This study describes the methodology and findings of a systematic review (SR) that aimed to evaluate cutting-edge segmentation approaches in computer-aided diagnosis (CAD). The primary objective is to provide a general overview and analysis of each of these methods, as well as to address the limitations and difficulties associated with them. Based on the observed shortcomings, directions for future investigation have also been offered. In addition, this SR also provides an overview of numerous publicly available benchmark datasets used in the reviewed literature. Unsupervised algorithms have been found to be more commonly used than supervised and DL counterparts. DL techniques require powerful computers and enormous volumes of training data. In most of the existing segmentation approaches, digitised images from DDSM and mini-MIAS datasets have been employed for experimentation. Both medical professionals and researchers who are aiming to address research gaps and make significant contributions to breast cancer diagnoses may find this systematic review to be valuable. [ABSTRACT FROM AUTHOR]

Published
2023
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47. Dietary flavonoids-rich Citrus reticulata peel extract interacts with CREB signaling to suppress seizures and linked neurobehavioral impairments in a kindling mouse model.

Author

Sharma, Pallavi, Dhiman, Poonam, and Singh, Damanpreet

Subjects
MANDARIN orange, MICE, LABORATORY mice, ANIMAL disease models, EPILEPSY, CITRUS fruits
Abstract

Objectives: The citrus fruits peel contains a variety of bioactive metabolites that have shown multiple therapeutic effects. However, despite having substantial ethnomedicinal value, citrus peels remained underexplored and regarded as bio-waste. This present study was planned to investigate the effect of a characterized peel extract of Citrus reticulata c.v. (CRE) in pentylenetetrazole (PTZ)-induced kindling and associated cognitive and behavioral impairments in a mouse model. Methods: The kindled animals were treated daily with CRE (100 and 200 mg/kg) and challenged with a sub-effective dose of PTZ every 5th day to record the severity of seizures. In the end, different tests were performed to record behavioral and cognitive performance. Results: CRE-treated kindled animals showed a significant suppression in seizure severity following 20 days of the treatment. In the T-maze test, the extract treatment resulted in a marked increase in the spontaneous alternations, whereas it showed no change in anxiety behavior of kindled animals in the elevated plus-maze test. In both forced swim and tail suspension tests, CRE treatment demonstrated a considerable reduction in immobility time. However, no change in overall locomotion was observed in the open field test among all the groups. An increase in the hippocampal Creb and Bdnf expression and decreased glutamate-to-GABA ratio were observed in the CRE-treated kindled animals. Discussion: The results showed that CRE treatment suppresses epileptic seizures and associated cognitive deficits and depression-like behavior in kindled mice. The gene expression findings supported that the observed protective effects of CRE be due to its interaction with CREB signaling. [ABSTRACT FROM AUTHOR]

Published
2023
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