电气工程与系统科学

Continuous oxygen saturation (SPO2) monitoring from photoplethysmography (PPG) is important for wearable health sensing, but wrist-based SPO2 estimation remains challenging due to subtle wrist micro-perturbations and inter-subject…

Continuous oxygen saturation (SpO$_2$) estimation from wearable photoplethysmography (PPG) is important for long-term health monitoring, but low-quality red and infrared PPG segments can distort waveform morphology and degrade SpO$_2$…

The realization of the full potential of Reconfigurable Intelligent Surfaces (RIS) in a wireless system is tied to their strategic spatial deployment. While existing literature primarily focuses on enabling fairness by maximizing coverage…

Grid codes increasingly require grid-forming (GFM) inverters to demonstrate prescribed active-power response to phase-angle jumps at the point of interconnection (POI). This paper shows that such requirements embed an implicit…

Hamilton-Jacobi (HJ) reachability provides rigorous safety and reachability guarantees for continuous-time dynamical systems, but its numerical solution suffers from the curse of dimensionality. Deep reinforcement learning (DRL), by…

State estimates used in sampled monitoring and automation need bounds that remain valid between measurements. We develop a finite-horizon input-to-state-stability tube and observer co-design framework for continuous-time observers driven by…

Automated segmentation of cervical-spine MRI is increasingly used in clinical workflows, yet no fairness audit exists for this anatomy. We show that auditing these segmentation tasks is complicated by a common property of modern…

This paper presents a Multi-Map Dynamic-Entropy Intrusion-Aware Chaotic Modulation (MU-DE-IAEACM-MM) framework for adaptive physical-layer security in multi-user wireless systems. Unlike conventional chaos-based schemes that rely on static…

This paper investigates semantic-aware neural joint source-channel coding (JSCC) for robust video transmission over block erasure channels. We propose a neural video compression framework exploring both spatial-domain and feature-domain…

Energy Matching has emerged as a powerful generative framework that combines flow model efficiency with the explicit likelihood of Energy-Based Models (EBMs) via a single, time-independent scalar potential. However, directly training this…

Recent advances in multi-band wireless communication systems have driven the increasing need for dual-band bandpass filters. These types of filters are capable of isolating a small section of the frequency spectrum within a broader…

Accurate state-of-charge (SOC) estimation remains a fundamental challenge in lithium-ion battery management systems because battery dynamics are highly nonlinear, operating-condition dependent, and sensitive to parameter variations caused…

Neural audio codecs (NACs) enable efficient audio compression and have achieved success in downstream tasks such as speech synthesis. However, their discrete representations consistently underperform traditional spectral features in…

The whole-system impedance model has proven a powerful tool for assessing the small-signal stability of multi-inverter power systems; however, its application is limited to a small range around a steady-state operating point due to the…

Virtualized radio access networks (vRAN) run the compute-intensive multiple-input multiple-output (MIMO) baseband as software on shared servers, which makes energy efficiency (EE) a primary design objective. Distributed MIMO vRAN consumes…

Generating signals on graphs requires permutation-equivariant models that exhibit stability with respect to relative structural perturbations. While favorable stability properties of Graph Neural Networks (GNNs) have been well documented,…

5G New Radio (NR) Non-Terrestrial Networks (NTNs) extend cellular connectivity through Low Earth Orbit (LEO) and Medium Earth Orbit (MEO) satellite constellations while enabling the reuse of downlink NR Positioning Reference Signals (PRS)…

Semantic and terahertz (THz)-band communications are algorithmic and spectral enablers of future wireless networks. This work investigates deep learning-based semantic communication (DeepSC) over THz channels. We show that DeepSC models…

Sparse vector transmission (SVT) has emerged as a promising technique for ultra-reliable low-latency short-packet communications. However, existing SVT schemes typically assume negligible phase noise (PN), an assumption that rarely holds in…

Wireless powered communication networks (WPCNs) are a key enabler for sustainable Internet of Things (IoT) systems, yet their practical performance is constrained by inefficient wireless energy transfer, limited spatial adaptability, and…