This paper critically examines model compression techniques within the machine learning
(ML) domain, emphasizing their role in enhancing model efficiency for deployment in …

The real-time target detection of crop pests can help detect and control pests in time. In this
study, we built a lightweight agricultural pest identification method based on modified …

Motivated by the advances in deep learning techniques, the application of unmanned aerial
vehicle (UAV)-based object detection has proliferated across a range of fields, including …

The convergence of edge computing with Artificial Intelligence has accelerated growth of
Edge AI, a transformative approach that brings intelligence straight to the point of data …

With the rapid advances in deep learning and smart manufacturing in Industry 4.0, there is
an imperative for high-throughput, high-performance, and fully integrated visual inspection …

Over the years, edge devices have a large impact in terms of processes for artificial
intelligence. The advancement quickly created a more accessible technology for the …

The network of services, including delivery, farming, and environmental monitoring, has
experienced exponential expansion in the past decade with Unmanned Aerial Vehicles …

This study presents a comprehensive comparison of U-Net and Ghost U-Net for road crack
segmentation, emphasizing their performance and memory efficiency across various data …

The new intermittent computing paradigm allows for intermittent operation of energy-
harvesting devices, posing new challenges to edge intelligence in delivering high-quality …

In this work we systemically study performance of deep learning models for Natural
Language Processing (NLP) and Computer Vision (CV) tasks using two popular …