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Gaurab Banerjee

Publications and source records attributed to Gaurab Banerjee.

3 recordsLinked to original sources

A 2-6 GHz Ultra-Wideband CMOS Transceiver for Radar Applications

This paper presents a low power, low cost transceiver architecture to implement radar-on-a-chip. The transceiver comprises of a full ultra-wideband (UWB) transmitter and a full UWB band receiver. A design methodology to maximize the tuning range of the voltage-controlled oscillator (VCO) is presented. At the transmitter side, a sub-harmonic mixer is used for signal up-conversion. The receiver low noise amplifier (LNA) has a 2 to 6 GHz input matching bandwidth with a power gain of 9 dB and a noise figure of 2.5 dB. The transceiver is implemented in Cadence EDA tools using 65nm CMOS technology. The system achieves a total dc power consumption of 50 mW. Good noise figure performance; good wide-band matching; gain; high level of integration; low power; low cost of the proposed UWB radar transceiver front-end make it a highly competitive SoC solution for low power UWB transceivers.

eess.SY

A Compact Quasi-Yagi Antenna for FMCW Radar-on-Chip based Through-Wall Imaging

A compact quasi-Yagi antenna with a modified ground plane is designed for a through-wall radar (TWR) on-chip. A slot-based ground plane modification in the proposed antenna results in significant miniaturization with an increase in the impedance bandwidth by 44.62%. The antenna has a high directivity of 9.02 dBi and a front-to-back ratio of 25.76 dB at 2.4 GHz. Based on experiments in real-world deployment scenarios, the performance of the proposed quasi-Yagi antenna is found to be comparable to that of a Vivaldi antenna and a commercial-off-the-shelf (COTS) horn antenna. Spectrogram-based signatures of a moving person behind a wooden partition and a 40 cm thick masonry wall are successfully obtained using the designed antenna, demonstrating the suitability of the quasi-Yagi antenna for portable applications using a radar-on-chip.

eess.SY

"You eat with your eyes first": Optimizing Yelp Image Advertising

A business's online, photographic representation can play a crucial role in its success or failure. We use Yelp's image dataset and star-based review system as a measurement of an image's effectiveness in promoting a business. After preprocessing the Yelp dataset, we use transfer learning to train a classifier which accepts Yelp images and predicts star-ratings. Additionally, we then train a GAN to qualitatively investigate the common properties of highly effective images. We achieve 90-98% accuracy in classifying simplified star ratings for various image categories and observe that images containing blue skies, open surroundings, and many windows are correlated with higher Yelp reviews.

cs.CV