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The book is intended to be used in an introductory and intermediate level course on computer networks, both whitin Computer Science and Electric Engineering scopes. One of its characteristics is that the entire research ...

Dynamic gesture recognition is a core technology for human–computer interaction, yet existing methods face prominent limitations in hand spatial topology modeling, long–short-term temporal feature fusion, and general...

In this research, we address a critical problem for strawberry growers in Lima: the management of phytosanitary diseases. Traditionally, these farmers relied on time-consuming, imprecise, and error-prone visual observati...

This paper explores the application of convolutional neural networks (CNNs) in predictive modelling for seismic events and flood risks, with a particular focus on forecasting extreme quantile events that exceed historica...

Mobile Ad-Hoc Networks (MANET) is a type of ad-hoc networks which use less infrastructure, that means the nodes in this network forward the massages without the need of infrastructure such as routers, switches etc. One o...

ABSTRACT This paper presents HealthNet, a novel framework for the dynamic optimisation of healthcare transportation networks using multi‐agent reinforcement learning. HealthNet leverages a spatiotemporal dependency mod...

Abstract River water quality forecasting on directed river networks requires jointly modeling temporal dynamics and directional spatial dependencies among monitoring sites. Existing spatio-temporal models often emphasize...

Recently, a three-terminal interface dipole modulation field-effect transistor (IDM FET) memory device has been proposed that leverages electric-field-induced dipole modulation at oxide/oxide interfaces. This device has ...

Abstract The rapid adoption of encrypted communication protocols has raised privacy levels but has also dealt a significant blow to traditional intrusion detection systems that rely on payload inspection for anomaly dete...

Abstract Underwater imaging suffers from various challenges such as color distortion, low contrast, and reduced visibility caused by light attenuation. Addressing these issues is crucial for improving the performance of ...

Recent advances in computer vision and deep learning, particularly Convolutional Neural Networks (CNNs), have significantly increased road safety. CNNs were used in this work to automatically detect and categorise traffi...

INTRODUCTION: High renewable energy penetration introduces significant uncertainties in distribution networks, posing challenges for source-load power forecasting and voltage management. OBJECTIVES: This study aims to e...

Abstract The Error Diffusion Learning Algorithm (EDLA) is a learning scheme that performs synaptically local weight updates driven by a single, globally defined error signal. Although originally proposed as an alternativ...

IntroductionReliable classification of lung nodules from computed tomography (CT) images remains a challenging problem due to variations in image intensity, noise, and unstable feature representations during deep network...

Abstract Detecting epileptic seizures from electroencephalogram (EEG) signals presents a significant challenge due to the nonlinear and non-stationary nature of brain activity. In this study, we propose a hybrid classifi...

Abstract The increasing number of Internet of Things (IoT) devices demands a rigid and adaptive routing methodologies to ensure the reliable communication especially for the low-power and lossy networks. The Routing Prot...

Abstract Quantum networks (QNs) have been predominantly driven by discrete-variable (DV) architectures. Yet, optical platforms naturally generate Gaussian states—the common states of continuous-variable (CV) systems, m...

Abstract We introduce quantum Kolmogorov-Arnold networks (QKAN), a quantum algorithmic framework inspired by the recently proposed Kolmogorov-Arnold Networks (KAN). QKAN inherits the compositional structure of KAN and is...

Abstract Smart home networks are a rapidly evolving part of the Internet of Things (IoT), where connected devices exchange information to automate energy use, enhance comfort, and improve user safety. Despite their benef...

Abstract Training deep learning models requires substantial financial and human resources, so once deployed in untrusted environments, these models immediately attract the attention of attackers who seek to steal and mis...

This paper addresses the problem of ensuring predictable Quality of Service (QoS) in hybrid campus networks that integrate wired Ethernet segments and wireless Wi-Fi segments into a unified infrastructure. It is shown th...

 INTRODUCTION: The frequent occurrence of extreme natural disasters poses severe threats to power grids, necessitating robust emergency communication. Low Earth Orbit (LEO) broadband satellite networks offer a promising...

Abstract Traffic flow prediction is a fundamental building block for Intelligent Transportation Systems (ITS), enabling proactive congestion mitigation, adaptive traffic signal control, and reliable route guidance. Howev...

ABSTRACT Adversarial jamming attacks have increased on communication systems, causing distortion and threatening transmissions. Typical attacks rely on traditional, well‐defined cryptographic protocols and frequency‐...