230 papers
This paper empirically demonstrates that site-specific finetuning of neural receivers using real-world 5G NR measurements significantly improves error-rate performance across diverse environments and hardware, validating previous synthetic findings with practical benchmarks.
Speech-Omni-Lite is a cost-efficient framework that extends frozen visual-language backbones with lightweight, trainable speech modules and a novel data construction strategy to achieve high-performance spoken QA comparable to massive omni-models, using only thousands of hours of training data.
This paper proposes an infinite-horizon Model Predictive Control framework that achieves constrained finite-time stabilization for discrete-time linear and feedback-linearizable nonlinear systems by replacing short-horizon terminal costs with an infinite sum of stage costs, thereby significantly enlarging the initial feasibility region while ensuring computational tractability and eliminating the need for terminal equality constraints or switching strategies.
This paper proposes a low-rank cyclostationarity predictive routing method that achieves near-real-time performance with less than 1.5 minutes of average excess travel time, offering a highly effective offline alternative for transportation planning tasks.
This paper introduces a formalism for interpolating Stochastic Differential Equations (iSDEs) and proposes a novel fast solver that reduces the computational cost of speech restoration models like SGMSE+ to as few as 10 neural network evaluations by adapting fast sampling techniques to the unique interpolation-based diffusion process.
This paper proposes an end-to-end multi-channel keyword spotting framework that integrates a spatial encoder and directional priors to achieve superior noise robustness and performance compared to conventional single-channel or cascaded systems in complex acoustic environments.
This paper proposes a vector-field guided constraint-following control approach that solves the dynamics-level geometric path-following problem for uncertain mechanical systems, effectively handling both fully-actuated and underactuated configurations with heterogeneous, fast time-varying uncertainties of unknown bounds and self-intersecting desired paths.
This paper proposes a unified, system-wide dynamic performance metric for inverter-based resource (IBR) networks that combines weighted local voltage phasor variations with complex power injections to decompose grid dynamics into device-driven and network-driven components, validated through sensitivity analysis on a modified IEEE 39-bus system.
This paper proposes a Two-Stage Distributionally Robust Model Predictive Control (TSDR-MPC) framework that utilizes a Wasserstein ambiguity set and a cutting-plane algorithm to achieve adaptive constraint tightening and guaranteed closed-loop stability for systems subject to disturbances with unknown, time-varying distributions.
This paper addresses the functional state estimation problem for linear time-delay systems with distinct state and measurement delays by proposing three observer structures, establishing algebraic existence conditions, and introducing a functional augmentation framework to facilitate the systematic design of observers with varying orders.
This paper introduces a method for computing less conservative -gain bounds for nonlinear Lur'e systems over restricted frequency and amplitude ranges by combining Scaled Relative Graphs with Sobolev theory, resulting in a three-dimensional nonlinear generalization of the Bode diagram that recovers both the standard LTI Bode plot and the global -gain as limiting cases.
This paper proposes a two-stage hybrid transceiver design for partially connected multi-user THz MIMO systems that utilizes low-resolution ADCs and a novel beamforming technique with few true time delay lines to mitigate the dual-wideband effect, achieving a 13% improvement in spectral efficiency over existing methods.
This paper proposes a novel Location-Agnostic Dynamic Channel Knowledge Map (LAD-CKM) framework that utilizes dynamic RF radiance field rendering, a dedicated RARE-Net, and an adaptive deformation module to predict channel state information from instantaneous uplink and partial downlink data, thereby significantly improving effective data rates in dynamic 6G scenes without requiring precise user location information.
This study demonstrates that reducing the penetration speed of nails in large-format lithium-ion pouch cells prevents thermal runaway, causing the cells to self-discharge instead, thereby highlighting penetration velocity as a critical factor for improving battery safety protocols.
The paper introduces StuPASE, a generative speech enhancement model that achieves studio-quality output with low hallucination by fine-tuning PASE with dry targets and replacing its GAN module with flow matching to handle strong additive noise.
This thesis advances emotion understanding and synthesis in spoken language by proposing methods to jointly model acoustic and semantic information through pre-training strategies, hierarchical conversational architectures, and a textless speech-to-speech framework for controllable emotion style transfer.
This paper proposes a sequential convex programming-based Model Predictive Control framework for station-keeping spacecraft formations on libration point orbits that ensures recursive feasibility and prevents inter-sample constraint violations while maintaining propellant efficiency comparable to existing methods.
This paper proposes an Emotion-Aware Prefix for a two-stage voice conversion backbone that significantly improves emotion conversion accuracy from 42.40% to 85.50% while preserving speaker identity, linguistic integrity, and speech quality through joint control of sequence modulation and acoustic realization.
The paper presents MetaSpectra+, a compact broadband metasurface camera capable of snapshot hyperspectral imaging combined with either HDR or polarization data, achieving superior reconstruction accuracy and a shorter total track length than prior narrowband multifunctional imagers.
This paper presents a comprehensive dynamic modeling and stability analysis of a grid-connected Integrated Energy System combining Small Modular Reactors and battery storage to power data centers, demonstrating through IEEE 118-bus simulations that this configuration significantly enhances voltage and frequency stability compared to conventional grid connections.