edge computing

Flat optics for analog computing: From fundamental mechanisms to advanced meta-processors

As the explosive growth of visual data increasingly strains the latency and energy limits of conventional electronic computing, optical analog computing has re-emerged as a disruptive paradigm for zero-power, speed-of-light information processing. Propelled by the unprecedented wave-manipulation capabilities of optical...

Zak phase and bulk-boundary correspondence in a generalized Dirac-Kronig-Penney model

Abstract We investigate the topological properties of a generalized Dirac-Kronig-Penney model, a continuum one-dimensional model for a relativistic quantum chain. By tuning the coupling parameters this model can accommodate five Altland-Zirnbauer-Cartan symmetry classes, three of which (AIII, BDI and D) support non-tri...

AI-Enabled Wearable Sensor Technologies for Athlete Performance Monitoring: A Critical Review of Signal Processing, Machine Learning, and Real-Time Embedded Systems

Abstract Wearable sensing and artificial intelligence (AI) are increasingly being used to quantify athlete movement, physiological response, training load, and recovery in field and laboratory settings. However, the practical value of these systems depends not only on the predictive capability of an algorithm but also...

Securing Intelligent Edge Ecosystems

With the advent of Internet of Things technologies and intelligent edge computing systems, the surface of cyber-attacks has been increasingly growing, which results in the problem of network security in distributed systems. This chapter will provide a review of intelligent intrusion detection methods using AI for secur...

Future Directions in Tiny Machine Learning

Tiny machine learning (TinyML) represents a transformative frontier in the evolution of artificial intelligence (AI), merging machine learning capabilities with ultra-low-power embedded systems to enable real-time inference at the edge. As data generation accelerates across billions of Internet of Things (IoT) devices,...

Hands‐On Project and Case Studies

TinyML is a technology that incorporates artificial intelligence into edge machines, which allows machine learning models to be executed on very low-power microcontrollers with small memory and processing units. In contrast to cloud-based AI-based solutions, TinyML does not require the cloud to run inference on an embe...

Graph Neural Networks (GNNs) for Complex System Anomaly Detection and Cybersecurity Telemetry: Topologies, Temporal Dynamics, and Enterprise SOC Benchmarks

Abstract—Modern enterprise computing fabrics, cloud infrastructures, and Security Operations Centers (SOCs) are inundated by billions of security events daily. Conventional Security Information and Event Management (SIEM) engines and intrusion detection systems (IDS) analyze telemetry as isolated, flat tabular logs (e....

ALGORITHMIC PHYSICAL INTELLIGENCE: ENGINEERING BIO-DIGITAL HYBRID COMPUTING FROM FIRST PRINCIPLES

Algorithmic Physical Intelligence (API) introduces a paradigm-shifting framework for bio-digital hybrid computing from first principles, addressing the fundamental thermodynamic barrier of Landauer's Limit in silicon-based systems. The El-Rakhawi Hybrid Architecture integrates three novel modules: AI-guided molecular l...

Machine Learning Techniques for Malware Detection

Malware is one of the major threats to modern computer systems, mobile devices, cloud platforms, and networks. Traditional signature-based antivirus systems are effective against known malware but often have difficulty detecting new, polymorphic, obfuscated, and zero-day variants. Machine Learning (ML) provides a data-...

A privacy-preserving task offloading method in collaborative mobile edge and cloud computing based on deep reinforcement learning

THE EL-RAKHAWI FRAMEWORK FOR COGNITIVE TOPOLOGICAL METAMATERIALS: AI-DRIVEN PROGRAMMABLE MATTER FOR ZERO-LOSS ENERGY AUTONOMY AND EXTREME ENVIRONMENT ADAPTATION

The El-Rakhawi Framework for Cognitive Topological Metamaterials (CTM) introduces a revolutionary paradigm transforming passive matter into intelligent, adaptive systems. Addressing the fundamental limitation of static materials in extreme environments, CTM integrates higher-order topological insulators (HOTIs) with ph...

Robust Multidomain Joint Optimization of Trajectory, Offloading, and Resources in UAV-MEC Networks

Uncrewed aerial vehicle (UAV)-assisted mobile edge computing (MEC) is essential for supporting large-scale Internet of Things (IoT) connectivity; however, its performance is fundamentally constrained by fragmented optimization across the electromagnetic, network, and user behavior domains. This article proposes a holis...