ADCNet: Learning from Raw Radar Data via Distillation

As autonomous vehicles and advanced driving assistance systems have entered wider deployment, there is an increased interest in building robust perception systems using radars. Radar-based systems are lower cost and more robust to adverse weather conditions than their LiDAR-based counterparts; however the point clouds produced are typically noisy and sparse by comparison. In order to combat these challenges, recent research has focused on consuming the raw radar data, instead of the final radar point cloud. We build on this line of work and demonstrate that by bringing elements of the signal processing pipeline into our network and then pre-training on the signal processing task, we are able to achieve state of the art detection performance on the RADIal dataset. Our method uses expensive offline signal processing algorithms to pseudo-label data and trains a network to distill this information into a fast convolutional backbone, which can then be finetuned for perception tasks. Extensive experiment results corroborate the effectiveness of the proposed techniques.Comment: Update 12/13/2023: upgrade organization and presentation of the paper, adding appendi

Study of Role of Financial Institution in Financial Inclusion

India is one of the largest growing economies in the world. Financial inclusion is providing financial services at an affordable rate to all people. It comes into existence in the year 1950 establishment of Reserve Bank of India. There are various incentives which have been undertaken to increase financial inclusion in India. With the nationalization of commercial banks. And the formation of NABARD Self-help Groups and Kisan credit bank. After 2000, the schemes like Swavalamban swabhiman have been launched to increase its role. The schemes by government of India like PMJDY and Startup India schemes. Financial inclusion helps in forming cashless economy and increase capital formation and increase economic growth of the country. It provides business and growth opportunities to the Intermediaries. This system also provides affordable services to the poor and played a vital role in improving country financial services

Thermal stability and optoelectronic behavior of polyaniline/GNP (graphene nanoplatelets) nanocomposites

Polyaniline and graphene nanoplatelets (PANI-GNP) nanocomposites are synthesized by in situ oxidative polymerization of polyaniline using an oxidizing agent, ammonium peroxy disulphate (APS). The mass of GNP in the nanocomposites varied by 5, 10, and 15 wt% compared to PANI. The synthesized polyaniline coated graphene nanoplatelets (PANI-GNP) nanocomposites are chemically characterized and using Fourier Transform Infrared Spectroscopy (FTIR), Raman spectroscopy, Scanning electron microscopy (SEM), UV-Vis spectroscopy, and X-ray diffraction analysis (XRD). FTIR and Raman spectroscopy analysis confirmed the uniform coating of polyaniline on GNP. The SEM micrograph and XRD pattern demonstrate the polymerization quality and crystallization degree of samples. UV-Vis analysis showed a decrease in the bandgap of polyaniline, which confirms that nanocomposites are more suitable for optoelectronic application because of variation in the bandgap. TGA analysis showed the thermal stability of PANI is increased with the increased mass of GNP. This study suggests the potential of GNP as a filler for efficient modification in the morphological, electrical, optical, and thermal properties of PANI.Comment: 13 pages, with 8 figures and one tabl

ZeroFlow: Scalable Scene Flow via Distillation

Scene flow estimation is the task of describing the 3D motion field between temporally successive point clouds. State-of-the-art methods use strong priors and test-time optimization techniques, but require on the order of tens of seconds to process full-size point clouds, making them unusable as computer vision primitives for real-time applications such as open world object detection. Feedforward methods are considerably faster, running on the order of tens to hundreds of milliseconds for full-size point clouds, but require expensive human supervision. To address both limitations, we propose Scene Flow via Distillation, a simple, scalable distillation framework that uses a label-free optimization method to produce pseudo-labels to supervise a feedforward model. Our instantiation of this framework, ZeroFlow, achieves state-of-the-art performance on the Argoverse 2 Self-Supervised Scene Flow Challenge while using zero human labels by simply training on large-scale, diverse unlabeled data. At test-time, ZeroFlow is over 1000x faster than label-free state-of-the-art optimization-based methods on full-size point clouds (34 FPS vs 0.028 FPS) and over 1000x cheaper to train on unlabeled data compared to the cost of human annotation (\$394 vs ~\$750,000). To facilitate further research, we will release our code, trained model weights, and high quality pseudo-labels for the Argoverse 2 and Waymo Open datasets.Comment: 9 pages, 4 pages of citations, 6 pages of Supplemental. Project page with data releases is at http://vedder.io/zeroflow.htm