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Neeraj Pandey

Publications and source records attributed to Neeraj Pandey.

2 recordsLinked to original sources

A Mathematical Model to Capture Urbanization Trajectory Induced by Economic Inequality

Analysis of the urban population fraction data for sixteen populous countries over the last fifty years reveals a universal increase in urbanization, exhibiting four qualitatively distinct temporal patterns: (i) continuously accelerating growth, (ii) continuously decelerating growth, (iii) two-phase growth transitioning from acceleration to deceleration, and (iv) two-phase growth transitioning from deceleration to acceleration. To understand the origin of these diverse urbanization trajectories, we develop a simple coarse-grained model in which a country is segregated into two regions, a rural and an urban region. Population in each region evolves due to natural (sexual) growth and migration from rural to urban areas, with the migration rate governed by economic inequality, quantified through the difference in GDP per capita between the two regions. The GDP per capita of both regions is assumed to grow exponentially with distinct rates. We demonstrate that this minimal model, involving four dynamical variables and a small number of demographic and economic parameters, is capable of reproducing all four empirically observed urbanization patterns. Assuming demographic and economic parameters remain approximately constant over a 50-year timescale, we estimate coarse-grained parameters for the United States using empirical data and obtain optimized values that accurately reproduce its observed urbanization trajectory. Our results highlight how simple demographic-economic interactions can generate rich and diverse urbanization dynamics.

physics.soc-ph

Classification Of Automotive Targets Using Inverse Synthetic Aperture Radar Images

We present a framework for simulating realistic inverse synthetic aperture radar images of automotive targets at millimeter wave frequencies. The model incorporates radar scattering phenomenology of commonly found vehicles along with range-Doppler based clutter and receiver noise. These images provide insights into the physical dimensions of the target, the number of wheels and the trajectory undertaken by the target. The model is experimentally validated with measurement data gathered from an automotive radar. The images from the simulation database are subsequently classified using both traditional machine learning techniques as well as deep neural networks based on transfer learning. We show that the ISAR images offer a classification accuracy above 90% and are robust to both noise and clutter.

eess.SP