Learning (DL) by creating a robust and efficient Convolutional Neural Network (CNN) model.
This CNN model will be subjected to detecting and recognizing traffic signs in real-time. We …

Identifying real world 3D objects such as pedestrians, vehicles and traffic signs using 2D
images is a challenging task. There are multiple approaches to tackle this problem with …

An obstacles detection method based on the date clustering and information fusion of
millimetre-wave radar and the camera in real traffic scenarios is proposed in this paper. The …

Deep learning and signal processing are closely correlated in many IoT scenarios such as
anomaly detection to empower intelligence of things. Many IoT processors utilize digital …

Deep Neural Networks and its associate learning paradigm-Deep Learning-represents
today a breakthrough in the field of Artificial Intelligence due to the impressive results …

Convolutional neural networks (CNNs) have achieved great success in the field of computer
vision. Researchers are currently focusing on more complicated three-dimensional (3D) …

Mobile cloud computing offloads intensive code to remote servers to improve execution
performance and battery lifetime. Unfortunately, it is prone to data breaches and dependent …

We propose a method for minimizing global buffer access within a deep learning accelerator
for convolution operations by maximizing the data reuse through a local register file, thereby …

Object detection, as one of the most important components in image recognition, has always
been the focus and difficulty in the field of computer vision research. It has a very important …

Convolution Neural Networks (CNN) are rapidly deployed in ADAS and Autonomous driving
for object detection, recognition, and semantic segmentation. The prior art of supporting …