230 papers
This paper proposes the RE-RPfair model, an extension of the Renewable Energy Robust Optimization Problem, which incorporates the Gini Index to ensure fair allocation of renewable energy suppression among PV sources while addressing uncertainties in unit commitment.
This paper introduces GF-BiLSTM, a two-stream gated fusion network that systematically demonstrates the critical role of incorporating Wi-Fi Channel State Information phase data alongside amplitude to significantly improve the accuracy and cross-speed robustness of robotic activity recognition.
This paper introduces the first exemplar-free continual learning benchmark for Audio-Visual Segmentation (AVS) and proposes the ATLAS baseline, which utilizes audio-guided pre-fusion conditioning and Low-Rank Anchoring to effectively mitigate catastrophic forgetting in dynamic, evolving environments.
This paper proposes a distributed, formation-aware adaptive conformal prediction framework that generates risk-sensitive uncertainty bounds to enforce visibility constraints and ensure safe, high-performance leader-follower multi-robot operations under heteroscedastic perception errors.
This paper presents a Safety Enhanced Passivity-Based Nonlinear Model Predictive Control (SEP-NMPC) framework that unifies strict passivity-based stability and high-order control barrier function safety guarantees to enable real-time, collision-free transport of slung payloads by quadrotors in cluttered environments.
This paper presents a novel hierarchical control framework that integrates an indirect adaptive law with model predictive control to enable quadrupedal robots to robustly transport heavy static and dynamic payloads across diverse terrains by estimating unknown parameters and ensuring stability through a convex stability criterion.
This paper introduces the NP-complete Mobile Base Station Optimal Tour (MOT) problem for efficient data collection in wide-area IoT sensor networks and proposes a polynomial-time greedy heuristic that outperforms state-of-the-art approaches by balancing travel cost, coverage gain, and execution time while adhering to energy and restricted-area constraints.
This paper reviews the evolving design, integration, and safety challenges of Emergency Locator Transmitters (ELTs) within More Electric Aircraft (MEA) environments, addressing critical constraints in power, thermal management, and electromagnetic compatibility while outlining future trends for enhanced reliability and certification.
This paper proposes a privacy-preserving dynamic average consensus algorithm that utilizes randomly generated sinusoidal masking signals to protect reference data from eavesdroppers while maintaining convergence, and demonstrates its practical effectiveness through state-of-charge balancing in battery energy storage systems.
This survey presents a structured taxonomy and critical analysis of cloud-based 6G deployments, examining current solutions from major cloud providers, key technical challenges like security and scalability, and future directions such as AI-driven orchestration to guide the transition from hardware-bound to cloud-native cellular networks.
This paper proposes a linear bandit-based beam training framework for near-field multi-path channels that utilizes Thompson Sampling with a correlated Gaussian prior and three distinct search strategies to significantly reduce pilot overhead while maximizing beamforming gain and approaching full-CSI performance.
This paper proposes a novel field-free 3-bit spintronic flash ADC architecture utilizing perpendicular SOT MTJs with VCMA and STT switching, which achieves a 304.1 MHz conversion rate and 476 µW power consumption by employing a dual-set dummy/conversion scheme to eliminate the reset step.
CycleULM is a novel, label-free deep learning framework that leverages CycleGAN to bridge the simulation-to-reality gap in ultrasound localisation microscopy, significantly enhancing microbubble localisation accuracy, image resolution, and processing speed for real-time clinical application without requiring paired ground truth data.
This paper proposes a learning-augmented primal-dual control framework for secondary frequency regulation that guarantees asymptotic stability and steady-state optimality while using neural networks to optimize transient performance metrics such as frequency nadir and control effort.
This paper proposes a hybrid model-assisted approach that enhances the classic close-in free-space reference distance model with an environment-adaptive compensation term and optimized image organization schemes to achieve superior path loss prediction accuracy in complex suburban scenarios.
This technical report presents a novel, interpretable, and computationally efficient NI-based strategy for estimating initial parameters of trigonometric models from unevenly sampled data, enabling robust convergence to the global minimum even under conditions of strong noise, limited periods, or partial coverage with a signal-to-noise ratio as low as 1.4 dB.
This paper proposes the distributed multichannel Wiener filter (dMWF), a non-iterative algorithm for wireless acoustic sensor networks that achieves optimal, centralized-level speech estimation performance with reduced communication bandwidth, even when nodes observe different sets of sources, thereby outperforming existing iterative solutions like DANSE.
This paper introduces DRES, a 1.5-hour semi-spontaneous Dutch speech dataset recorded in noisy public indoor environments, and evaluates its utility by demonstrating that while several state-of-the-art ASR models achieve competitive performance, modern single-channel speech enhancement algorithms fail to improve recognition accuracy in these realistic conditions.
This paper proposes a novel method for generating Room Impulse Responses by fine-tuning a pre-trained text-to-audio model, utilizing vision-language models to create text-RIR pairs and in-context learning to handle free-form prompts, thereby producing plausible acoustic simulations for applications like speech data augmentation.
This paper investigates the implementation of embedded Model Predictive Control for high-speed EMS-type maglev vehicles, demonstrating its ability to robustly stabilize the highly nonlinear system at speeds exceeding 600 km/h while validating the algorithm's performance on resource-constrained microcontrollers through processor-in-the-loop studies.