# Neural Architectural Nonlinear Pre-Processing for mmWave Radar-based Human Gesture Perception

arXiv: https://arxiv.org/abs/2211.03502 · HTML: https://mlsanity.com/paper/2211.03502/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 7 Nov 2022.

## Builds on (most cited first, 4 of 4)

- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications](https://mlsanity.com/paper/2008.02608/) (2020)
- [Randomized Adversarial Imitation Learning for Autonomous Driving](https://mlsanity.com/paper/1905.05637/) (2019)
- [Joint Scalable Coding and Routing for 60 GHz Real-Time Live HD Video Streaming Applications](https://mlsanity.com/paper/1302.1663/) (2013)

## Abstract (arXiv)

In modern on-driving computing environments, many sensors are used for context-aware applications. This paper utilizes two deep learning models, U-Net and EfficientNet, which consist of a convolutional neural network (CNN), to detect hand gestures and remove noise in the Range Doppler Map image that was measured through a millimeter-wave (mmWave) radar. To improve the performance of classification, accurate pre-processing algorithms are essential. Therefore, a novel pre-processing approach to denoise images before entering the first deep learning model stage increases the accuracy of classification. Thus, this paper proposes a deep neural network based high-performance nonlinear pre-processing method.