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E-print Articles: Recent submissions

  • Deep learning based systems for crater detection: a review 
    Tewari, Atal; Prateek, K.; Singh, Amrita; Khanna, Nitin (Cornell University Library, 2023-09)
    Craters are one of the most prominent features on planetary surfaces, used in applications such as age estimation, hazard detection, and spacecraft navigation. Crater detection is a challenging problem due to various ...
  • A graph neural network approach for temporal mesh blending and correspondence 
    Gangopadhyay, Aalok; Harish, Abhinav Narayan; Singh, Prajwal; Raman, Shanmuganathan (Cornell University Library, 2023-06)
    We have proposed a self-supervised deep learning framework for solving the mesh blending problem in scenarios where the meshes are not in correspondence. To solve this problem, we have developed Red-Blue MPNN, a novel graph ...
  • Process voltage temperature variability estimation of tunneling current for band-to-band-tunneling based neuron 
    Patil, Shubham; Sharma, Anand; R., Gaurav; Kadam, Abhishek; Singh, Ajay Kumar; Lashkare, Sandip; Mohapatra, Nihar Ranjan; Ganguly, Udayan (Cornell University Library, 2023-06)
    Compact and energy-efficient Synapse and Neurons are essential to realize the full potential of neuromorphic computing. In addition, a low variability is indeed needed for neurons in Deep neural networks for higher accuracy. ...
  • Minimum intensity error intuitionistic fuzzy contrast enhancement transformation for magnetic resonance images 
    Joseph, Justin; V. R., Simi (Research Square Company, 2023-02)
    Background: Low contrast in magnetic resonance (MR) images adversely affects the performance of software tools used for its automated analysis. Compared to the traditional crisp transformation functions often used for ...
  • BEAMERS: brain-engaged, active music-based emotion regulation system 
    Li, Jiyang; Wang, Wei; Bhagtani, Kratika; Jin, Yincheng; Jin, Zhanpeng (Cornell University Library, 2022-11)
    With the increasing demands of emotion comprehension and regulation in our daily life, a customized music-based emotion regulation system is introduced by employing current EEG information and song features, which predicts ...
  • Deep gaussian processes for air quality inference 
    Desai, Aadesh; Gujarathi, Eshan; Parikh, Saagar; Yadav, Sachin; Patel, Zeel B.; Batra, Nipun (Cornell University Library, 2022-11)
    Air pollution kills around 7 million people annually, and approximately 2.4 billion people are exposed to hazardous air pollution. Accurate, fine-grained air quality (AQ) monitoring is essential to control and reduce ...
  • PointResNet: residual network for 3D point cloud segmentation and classification 
    Desai, Aadesh; Parikh, Saagar; Kumari, Seema; Raman, Shanmuganathan (Cornell University Library, 2022-11)
    Point cloud segmentation and classification are some of the primary tasks in 3D computer vision with applications ranging from augmented reality to robotics. However, processing point clouds using deep learning-based ...
  • Challenges in Gaussian processes for non intrusive load monitoring 
    Desai, Aadesh; Vashishtha, Gautam; Patel, Zeel B.; Batra, Nipun (Cornell University Library, 2022-11)
    Non-intrusive load monitoring (NILM) or energy disaggregation aims to break down total household energy consumption into constituent appliances. Prior work has shown that providing an energy breakdown can help people save ...
  • Automatic crater shape retrieval using unsupervised and semi-supervised systems 
    Tewari, Atal; Jain, Vikrant; Khanna, Nitin (Cornell University Library, 2022-11)
    Impact craters are formed due to continuous impacts on the surface of planetary bodies. Most recent deep learning-based crater detection methods treat craters as circular shapes, and less attention is paid to extracting ...
  • Revisiting heterophily in graph convolution networks by learning representations across topological and feature spaces 
    Tiwari, Ashish; Tosniwal, Sresth; Raman, Shanmuganathan (Cornell University Library, 2022-11)
    Graph convolution networks (GCNs) have been enormously successful in learning representations over several graph-based machine learning tasks. Specific to learning rich node representations, most of the methods have solely ...
  • Dynamics of van der waals charge qubit in 2D bilayers: ab initio quantum transport and qubit measurement 
    Cao, Jiang; Gandus, Guido; Agarwal, Tarun; Luisier, Mathieu; Lee, Youseung (Cornell University Library, 2022-10)
    A van der Waals (vdW) charge qubit, electrostatically confined within two-dimensional (2D) vdW materials, is proposed as building block of future quantum computers. Its characteristics are systematically evaluated with ...
  • DQ-axis modelling and field oriented control of hybrid flux motor 
    Boomiraja, Balaganesh; Kanagaraj, Ragavan (Research Square Company, 2022-09)
    This paper presents a prototype of three phase hybrid flux motor (HFM) with a power rating of 1.2 kW that validates the concept of hybrid flux topology (combining longitudinal and transverse flux topologies). Conventional ...
  • Geometrical homogeneous clustering for image data reduction 
    Mody, Shril; Thakkar, Janvi; Joshi, Devvrat; Soni, Siddharth; Patil, Rohan; Batra, Nipun (Cornell University Library, 2022-08)
    In this paper, we present novel variations of an earlier approach called homogeneous clustering algorithm for reducing dataset size. The intuition behind the approaches proposed in this paper is to partition the dataset ...
  • Modeling of high and low resistant states in single defect atomristors 
    Misra, Yuvraj; Agarwal, Tarun Kumar (Cornell University Library, 2022-06)
    Resistance-change random access memory (RRAM) devicesare nanoscale metal-insulator-metal structures that can store informationin their resistance states, namely the high resistance (HRS) and lowresistance (LRS) states. ...
  • Fast and real-time end to end control in autonomous racing cars through representation learning 
    Venkatesh, Praveen; Rana, Rwik; Palanthandalam-Madapusi; Harish J. (Cornell University Library, 2021-11)
    The challenges presented in an autonomous racing situation are distinct from those faced in regular autonomous driving and require faster end-to-end algorithms and consideration of a longer horizon in determining optimal ...
  • Learning by cheating: an end-to-end zero shot framework for autonomous drone navigation 
    Venkatesh, Praveen; Shah, Viraj; Shah, Vrutik; Kamble, Yash; Mekie, Joycee (Cornell University Library, 2021-11)
    This paper proposes a novel framework for autonomous drone navigation through a cluttered environment. Control policies are learnt in a low-level environment during training and are applied to a complex environment during ...
  • Addressing practical challenges in active learning via a hybrid query strategy 
    Agarwal, Deepesh; Srivastava, Pravesh; Martin-del-Campo, Sergio; Natarajan, Balasubramaniam; Srinivasan, Babji (Cornell University Library, 2021-10)
    Active Learning (AL) is a powerful tool to address modern machine learning problems with significantly fewer labeled training instances. However, implementation of traditional AL methodologies in practical scenarios is ...
  • In-orbit lunar satellite image super resolution for selective data transmission 
    Tewari, Atal; Prateek, Chennuri; Khanna, Nitin (Cornell University Library, 2021-10)
    Rapid technological advancements have tremendously increased the data acquisition capabilities of remote sensing satellites. However, the data utilization efficiency in satellite missions is very low. This growing data ...
  • HyGain: high performance, energy-efficient hybrid gain cell based cache hierarchy 
    Singh, Sarabjeet; Surana, Neelam; Jain, Pranjali; Mekie, Joycee; Awasthi, Manu (Cornell University Library, 2021-10)
    In this paper, we propose a 'full-stack' solution to designing high capacity and low latency on-chip cache hierarchies by starting at the circuit level of the hardware design stack. First, we propose a novel Gain Cell (GC) ...
  • HDR-cGAN: single LDR to HDR image translation using conditional GAN 
    Raipurkar, Prarabdh; Pal, Rohil; Raman, Shanmuganathan (Cornell University Library, 2021-10)
    The prime goal of digital imaging techniques is to reproduce the realistic appearance of a scene. Low Dynamic Range (LDR) cameras are incapable of representing the wide dynamic range of the real-world scene. The captured ...

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