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There is a renewed interest in radar sensors in the autonomous driving industry. As a relatively mature technology, radars have seen steady improvement over the last few years, making them an appealing alternative or complement to the commonly used LiDARs. An emerging trend is to leverage rich, low-level radar data for perception. In this work we push this trend to the extremewe propose a method to perform end-to-end learning on the raw radar analog-to-digital (ADC) data. Specifically, we design a learnable signal processing module inside the neural network, and a pre-training method guided by traditional signal processing algorithms. Experiment results corroborate the overall efficacy of the end-to-end learning method, while an ablation study validates the effectiveness of our individual innovations.

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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.