Collaborative Deep Neural Network Inference (CDNN) has emerged as one of the
significant strategies for efficient and lightweight computation on resource-constrained …

Edge computing is becoming very popular. Many researchers are using edge devices for
computing artificial intelligence, especially in real-time applications of computer vision by the …

To ensure that accuracy and latency are not compromised while deploying Deep Neural
Networks (DNNs) on edge devices, trained DNN models can be partitioned across many …

As edge computing and the Internet of Things (IoT) expand, horizontal collaboration (HC)
emerges as a distributed data processing solution for resource-constrained devices. In …

Machine learning (ML) algorithms have been widely used in many critical automated
systems, including as a technique in Dynamic Data Driven Applications Systems (DDDAS) …

Use of edge computing in application of Computer Vision (CV) is an active field of research.
Today, most CV applications make use of Convolutional Neural Networks (CNNs) to …

Autonomous vehicles (AVs) are a conglomeration of various electronic components in a
tightly bound communication network. This list of electronic components includes an …

Although Collaborative Deep Neural Network (CDNN) promises to be an alternative
mechanism to mitigate the effects of the untrusted cloud, this approach is susceptible to …

Abstract Deployment of Convolutional Neural Networks for Computer Vision (CV) tasks is an
integral part of many modern applications in edge computing (eg self-driving cars). Xilinx …

This dissertation investigates the use of waste vegetable oil (WVO) for production of
biodiesel. This study explores the improvement of biodiesel production to achieve high …