MM

As three-dimensional (3D) vision data can provide abundant spatial information, it is being widely used in many areas, including autonomous driving and mobile robots. We aim to develop efficient and accurate 3D data processing techniques for classification, segmentation, pose estimation, etc.

To maximize the performance and efficiency of deep learning based data processing, we are currently exploring sensor platform design optimized for deep learning by leveraging the interactions between a sensor platform and a DNN.

Our research focuses on front-end audio processing techniques for speech processing, such as voice activity detection (VAD), noise suppression, direction of arrival (DoA) estimation, and classification.

Machine learning and deep learning have made rapid progress in many computer vision applications over a short period. We focus on diverse techniques for image and video processing powered by machine/deep learning.

One of the challenges of deploying deep neural networks in sensor platforms is the variations in the input images; structural noise (adversarial images), inherent random noise (image perturbation), input scene variability, and weight value errors.

A critical goal in the image sensor node design is to deliver high-quality visual information under stringent energy and bandwidth constraints. This goal becomes more challenging under dynamic conditions such as environmental noise and variations in a wireless channel condition.