230 papers
This paper presents a hardware-based validation of angular droop control for grid-forming DC/AC converters, demonstrating its ability to unify primary and secondary control for exact frequency regulation while addressing implementation challenges like discretization and clock drift through single and multi-converter experiments.
This paper introduces Human-CLAP, a human-perception-based contrastive language-audio model trained on subjective evaluation scores to significantly improve the correlation between automated CLAP scores and human judgments, addressing the previously low alignment of standard CLAP metrics with human perception.
This paper proposes and analyzes a finite-horizon approximation method for computing feedback Nash equilibria in infinite-horizon discrete-time linear-quadratic games, establishing conditions for equilibrium uniqueness, providing an efficient solution algorithm, and proving the convergence of costs with explicit error bounds.
This paper proposes TI-DANSE+, an improved distributed signal estimation algorithm for topology-unconstrained wireless acoustic sensor networks that accelerates convergence by utilizing partial in-network sums and a tree-pruning strategy, while maintaining robustness to link failures and reducing communication bandwidth compared to existing methods.
This paper proposes a hybrid framework for autonomous rendezvous that utilizes an active learning-based input design to enhance observability for angle-only initial relative orbit determination, subsequently transitioning to an Extended Kalman Filter and Model Predictive Controller to achieve reliable end-to-end mission execution.
This paper proposes S2S-ZEST, a textless and non-parallel speech-to-speech framework that achieves zero-shot emotion style transfer by extracting semantic, speaker, and emotion representations to synthesize speech that preserves the source's content and identity while adopting the reference's emotional style.
This paper proposes a distributed model predictive control framework that enables heterogeneous, nonlinear multi-agent systems to achieve dynamic cooperation and satisfy individual and coupling constraints by optimizing artificial references, thereby ensuring recursive feasibility, asymptotic stability, and emergent task solutions without predetermined trajectories.
This paper introduces Filtered Control Barrier Functions (FCBFs), a framework that integrates an input regularization filter with High-Order CBFs within a unified quadratic program to simultaneously guarantee system safety, control bounds, and Lipschitz continuity of control inputs, thereby preventing abrupt changes that could degrade performance or violate actuator limits.
This paper proposes a novel algorithm for verifying the safety of nonlinear neural feedback systems by computing tight polyhedral enclosures of their transition functions and encoding them into a mixed-integer linear program (MILP), achieving an order-of-magnitude improvement over existing state-of-the-art methods.
This paper proposes an entropy-and-channel-aware adaptive-rate semantic communication framework for MIMO Rayleigh fading channels that dynamically selects feature maps and symbols based on channel conditions and content complexity, while leveraging a fine-tuned multimodal large language model (MLLM) at the receiver to compensate for discarded information and optimize task performance across varying signal-to-noise ratios.
This paper proposes a robust control and gain scheduling approach that mitigates distributional shifts in approximant model parameters caused by system nonlinearity by constraining the closed-loop system to remain consistent with learning data, a problem formulated as a computationally efficient convex semi-definite program.
This paper proposes a model-based image compression technique for tetherless underwater remotely operated vehicles that leverages novel view synthesis priors and gradient descent optimization to achieve superior compression ratios and image quality, particularly in scenarios involving new objects within the scene.
This paper provides a comprehensive statistical analysis of the "Einstein from noise" phenomenon, demonstrating how template bias causes pure noise observations to converge to the template's structural features in both Fourier phases and magnitudes, thereby revealing critical pitfalls in template matching techniques across multiple scientific disciplines.
This paper introduces NanoBench, an open-source multi-task benchmark dataset collected on a 27g Crazyflie 2.1 nano-quadrotor that provides synchronized actuator-level signals, controller internals, and millimeter-accurate ground truth to address the unique modeling and control challenges of nano-scale aerial robotics.
This study evaluates a low-cost FMCW MIMO radar for non-contact vital sign monitoring, revealing that while it achieves high accuracy for average respiratory and heart rates at an optimal distance of 70 cm with sufficient chirp counts, it faces significant limitations in capturing high-resolution beat-to-beat and breath-to-breath variability.
This paper presents a lightweight 3D LiDAR-based UAV tracking system for small drones that utilizes an Adaptive Extended Kalman Filter to achieve robust, high-accuracy relative positioning in GPS-denied environments by dynamically adjusting to sparse and noisy point cloud data.
This paper introduces PanoAffordanceNet, a novel framework and the first high-quality dataset (360-AGD) designed to enable holistic affordance grounding in 360-degree indoor environments by addressing challenges like geometric distortion and semantic dispersion through distortion-aware calibration and multi-level constraints.
The paper introduces -Occ, a robust 3D semantic occupancy prediction framework that leverages a Multi-view Masked Reconstruction module and a Feature Memory Module to maintain geometric and semantic coherence under incomplete multi-camera inputs, significantly outperforming existing methods in scenarios with missing views.
This paper introduces a physically consistent, IEEE-inspired benchmark dataset generator for evaluating passive reconnaissance in smart-grid communications, which models adversary-induced propagation effects across tiered network topologies to enable standardized, label-free assessment of graph-temporal and federated detection methods.
This paper introduces the Randomized Distributed Function Computation (RDFC) framework, a semantic communication approach that achieves local differential privacy and significantly reduces transmission rates compared to lossless methods, even in scenarios without shared randomness, by leveraging strong coordination metrics and randomized function generation.