237 papers
This paper presents a novel co-design approach that simultaneously optimizes the physical arrangement and control strategies of multiple rigidly connected quadcopters transporting a payload, utilizing a new H2-inspired robustness metric to maximize disturbance rejection capabilities, which is experimentally validated with diverse multi-UAV fleets and payload shapes.
This paper proposes Multi-View Tensor Fusion (MVTF), a novel framework that leverages synchronized multi-perspective lip videos during training to learn cross-view correlations, thereby significantly enhancing target speaker extraction performance and robustness for both single-view and multi-view inference scenarios.
This paper introduces two novel inverse optimal control frameworks utilizing "contractor and expander functions" to achieve stabilization for general control-affine nonlinear systems constrained to the positive orthant with positive controls, addressing the limitations of conventional methods through asymmetric cost designs and providing explicit universal formulae for applications such as predator-prey models.
GOUHFI 2.0 is an updated deep-learning toolbox that enables robust, contrast-agnostic whole-brain segmentation, cortical parcellation, and volumetry for Ultra-High Field MRI by utilizing a domain-randomization strategy and two specialized 3D U-Net networks trained on diverse datasets.
This paper proposes a novel multi-product market mechanism for local energy markets that combines combinatorial clock exchange with machine learning to simplify preference elicitation for prosumers, enabling them to express complex, interdependent preferences through an intuitive package-reporting format while achieving rapid price convergence and transparent linear pricing.
This paper establishes a system-theoretic framework for dynamic Generalized Nash Equilibria by demonstrating the equivalence between strict dissipativity and the turnpike phenomenon, deriving conditions for optimal steady-state operation, and designing linear terminal penalties to ensure the convergence and stability of game-theoretic Model Predictive Control.
This paper proposes a predictive, privacy-preserving control scheme for low-voltage distribution systems that aggregates residential flexibility into real-time admissible power charts through offline multiparametric optimization, enabling efficient and cost-effective network management validated on a 43-bus test case.
The paper proposes HyWA, a novel Personalized Voice Activity Detection (PVAD) approach that utilizes a hypernetwork to generate personalized weights for selected layers of a standard VAD model, demonstrating consistent performance improvements and enhanced deployment flexibility compared to existing speaker-conditioning methods.
This paper proposes a distributed formation control strategy for multi-agent systems that utilizes a rotation symmetry-based potential function to drive agents into a desired planar symmetric configuration with minimal connectivity ( edges) and extends the approach to enable coordinated maneuvering including translation, rotation, and scaling.
This paper proposes a rollout-based event-based control framework that utilizes sparsity-promoting regularization to balance control performance and actuation frequency, providing theoretical guarantees on stability and performance relative to periodic control.
This paper proposes a low-complexity manifold optimization framework that enforces reciprocity constraints on Beyond-Diagonal Reconfigurable Intelligent Surfaces (BD-RIS) to maximize sum-rate performance, demonstrating superior results compared to state-of-the-art approaches.
This paper proposes a robust Audio-Visual Target Speaker Extraction framework that leverages emotion-aware multiple enrollment fusion, demonstrating that training with high modality missing rates significantly enhances performance stability against real-world signal loss while achieving optimal results by fusing single-frame facial images with frame-level lip features.
This paper proposes a learned optimization framework that combines deep unfolding with low-complexity approximated computations and data-driven hyperparameter learning to achieve state-of-the-art performance in hybrid beamforming and robust PCA while reducing computational complexity by over three orders of magnitude.
This paper proposes an analytical framework and receiver design for Frequency Modulated OFDM (FM-OFDM) as a constant-envelope ISAC solution that maximizes power amplifier efficiency by operating in saturation while maintaining robust sensing and communication performance under doubly dispersive channels.
This paper proposes a platform-aware framework that models 3D wireless links as a novel mutual antenna pattern, demonstrating that while individual platform effects are unidentifiable, the coupled pattern can be effectively estimated from noisy measurements to reduce path loss errors by up to 10 dB compared to traditional models.
This paper proposes a modular supervisory control approach for discrete event systems to reduce computational complexity and enhance the robustness of predicting and mitigating power system cascading failures, demonstrating its effectiveness through simulations on IEEE 30-bus, 118-bus, and 300-bus test systems.
This paper presents a systematic review of recent advancements in signal processing and learning over time-varying graphs, comparing methodologies across graph time-spectral filtering, multivariate time-series forecasting, and spatiotemporal neural networks while identifying current limitations and outlining future research directions.
This paper proposes a low-complexity phase selection method for multi-frequency multi-user RIS systems that divides the surface into user-specific subsurfaces, deriving exact closed-form mean SNR expressions for correlated Ricean and Rayleigh channels and demonstrating that iterative and converged versions of this approach outperform existing methods in robustness and efficiency despite reduced bandwidth per user.
The paper introduces 3D-PIUNet, a novel hybrid deep learning framework that enhances EEG brain source reconstruction by initializing a 3D U-Net with physics-based pseudo-inverse estimates and refining them through data-driven learning, thereby achieving superior spatial accuracy and practical applicability compared to traditional and end-to-end methods.
This paper introduces DMaC, a novel distributed algorithm that guarantees finite-time max-consensus in directed graphs with unreliable communication links by leveraging narrowband error-free feedback for acknowledgments and a fully distributed termination mechanism to ensure exact convergence under arbitrary packet loss.