Semantic Communication (SemCom) systems, empowered by deep learning (DL), represent
a paradigm shift in data transmission. These systems prioritize the significance of content …

State-of-the-art convolutional neural networks are designed to identify numerous object
classes. Inference using such complex networks is fairly resource intensive, prohibiting their …

Modern convolutional neural networks (CNNs) in computer vision are trained on a large
number of images from numerous categories to form rich discriminative feature extractors …

This paper presents a cost-effective, low-power approach to unintentional fall detection
using knowledge distillation-based LSTM (Long Short-Term Memory) models to significantly …

Recently, adopting UAVs equipped with the edge computing platform to provide computing
service has been considered as a promising approach for resource-limited devices in …

Abstract Deep Convolutional Neural Networks (CNNs) exhibit remarkable performance in
many pattern recognition, segmentation, classification, and comprehension tasks that were …

A Methodology for Synthesis of Resource-Scalable CNNs for Inference at the Edge State-of-
the-art image recognition systems use sophisticated Convolutional Neural Networks (CNNs) …