Although there are well established object detection methods based on static images, their
application to video data on a frame by frame basis faces two shortcomings:(i) lack of …

Connected and Autonomous Vehicles (CAVs) combine technologies of autonomous
vehicles (AVs) and connected vehicles (CVs) to develop quicker, more reliable, and safer …

The paper discusses algorithmic priority inversion in mission-critical machine inference
pipelines used in modern neural-network-based cyber-physical applications, and develops …

Hardware accelerators, such as those based on GPUs and FPGAs, offer an excellent
opportunity to efficiently parallelize functionalities. Recently, modern embedded platforms …

The emergence of the Internet-of-Things (IoT) sheds light on applying the machine teaching
(MT) algorithms for online personalized education on home devices. This direction becomes …

Autonomous micromobility systems (AMS) such as low-speed minicabs and robots are
thriving. In AMS, multiple Deep Neural Networks execute in parallel on heterogeneous AI …

This work focuses on the time-predictable execution of Deep Neural Networks (DNNs)
accelerated on FPGA System-on-Chips (SoCs). The modern DPU accelerator by **linx is …

Since autonomous driving (AD) is one of the most critical problems in the automobile
industry, it has garnered the interest of many academics in recent years. An AD framework or …

Sustaining high fidelity and high throughput of perception tasks over vision sensor streams
on edge devices remains a formidable challenge, especially given the continuing increase …

Identifying the type of vehicle on the road is a challenging task, especially in the natural
environment with all its complexities, such that the traditional architecture for object detection …