244 papers
This paper proposes a method for estimating failure rates in stationary periodic Rate Monotonic real-time systems by approximating response time distributions with an inverse Gaussian mixture model, whose parameters are derived using a re-parameterized Expectation-Maximization algorithm and validated through extensive simulations.
This paper presents a deep learning framework that utilizes a convolutional neural network (CNN) combined with a convolutional denoising autoencoder (CDAE) to accurately segment total retinal structures and pigment epithelial detachments in novel low-cost full-field OCT images, thereby enabling automated computer-aided diagnosis for home-based monitoring of age-related macular degeneration.
This paper presents a reference architecture and roadmap for Quantum-Centric Supercomputing (QCSC) systems that integrate quantum, GPU, and CPU resources to overcome current isolation challenges and enable seamless, high-performance hybrid workflows across three evolutionary phases.
This paper presents a novel robotic gripper featuring an active dented-roller fingertip and compliant fingers that reliably separates and opens sterile medical pouch flaps, offering a solution to automate a physically demanding and injury-prone manual task performed by hospital staff.
This paper proposes distributed dynamical algorithms that enable agents with only local data and no raw data sharing to collectively compute global system certificates, specifically solving the Lyapunov equation for stability verification and the algebraic Riccati equation for optimal LQR control, with guarantees of exact convergence and robustness.
This paper proposes a hierarchical Graph Attention Network (HR-GAT) model that leverages public deployment records to accurately estimate fine-grained spectrum demand across multiple cities, significantly outperforming existing baselines and providing regulators with actionable insights for efficient spectrum sharing and allocation.
This paper proposes an AI-driven framework that improves 5G/6G traffic demand prediction accuracy and spatial generalization by employing a context-aware two-stage splitting strategy and residual error correction to mitigate neighborhood leakage, as validated by experiments across five Canadian cities.
This paper proposes Sign-Prioritized FL (SP-FL), a novel wireless federated learning framework that enhances model training reliability under resource constraints by prioritizing the transmission of gradient signs through a hierarchical resource allocation scheme, achieving up to 9.96% higher accuracy than existing methods on the CIFAR-10 dataset.
This paper presents a fully GPU-native trajectory optimization framework that leverages sequential convex programming and consensus-based ADMM with temporal splitting to achieve real-time, high-throughput nonlinear optimal control for autonomous systems, demonstrating significant speedups and energy efficiency over CPU baselines while enabling scalable multi-trajectory and robust Model Predictive Control.
This paper presents an efficient pipelined FPGA architecture with optimized memory organization for the displacement vector search module of JPEG XS's Intra Pattern Copy tool, achieving a throughput of 38.3 Mpixels/s at 277 mW to enable practical hardware deployment.
This paper presents a dynamic model and control strategy for an underactuated bipedal hopping robot that utilizes an internal reaction wheel to stabilize body posture during ballistic flight under low-gravity conditions, successfully reducing mid-air angular deviation by over 65% and ensuring precise upright landings in lunar gravity simulations.
This paper establishes theoretical conditions under which Monotone Operator Equilibrium Networks maintain convergence and bounded error under weight quantization by analyzing spectral perturbations, and validates these findings through experiments showing that quantization-aware training can recover provable convergence at four-bit precision.
This paper introduces a novel language-guided framework that leverages pretrained vision-language models and a specialized adapter to achieve zero-shot, generative detection and localization of subsurface defects in carbon fiber-reinforced polymers using active infrared thermography, thereby eliminating the need for costly, task-specific training datasets while significantly improving signal-to-noise ratios and detection accuracy.
This paper proposes a world model framework for lithium-ion battery degradation prognosis that encodes cycle data into latent states and propagates them forward using learned dynamics, demonstrating that iterative rollout significantly reduces trajectory forecast error compared to direct regression while a Single Particle Model constraint specifically enhances prediction accuracy at the degradation knee.
This paper proposes a robust 3-D trajectory optimization framework for movable antenna systems that minimizes the worst-case mean square angular error in direction estimation by deriving a closed-form performance bound and solving a min-max problem via successive convex approximation, thereby achieving isotropic sensing superior to fixed-position and 2-D movable antenna schemes.
This paper introduces Concept-Gated Visual Distillation (CGVD), a training-free, model-agnostic inference framework that overcomes the "Precision-Reasoning Gap" in Vision-Language-Action models by parsing instructions to identify distractors and using Fourier-based inpainting to generate clean observations, thereby significantly improving robotic manipulation success rates in highly cluttered environments.
This paper introduces \texttt{nlm}, an open-source set of C++ Max externals that enables efficient, real-time non-linear modal synthesis for strings, membranes, and plates, thereby making advanced physical modeling accessible to composers and sound designers through interactive parameter control and multichannel output.
This paper proposes a phase-noise resilient framework for mmWave massive MIMO systems using generalized receiver spatial modulation, which combines compact MQAM symbol pool design, enhanced spatial mapping strategies, and a practical single-stage compensation architecture to significantly mitigate phase noise effects while maintaining robust spatial detection.
This paper argues that the aggregate distortion model is overly pessimistic for monostatic ISAC sensing because the transmitter can monitor its own waveform, and it derives new PA-aware and PN-aware Cramér–Rao bounds to demonstrate that this approach reveals an irreducible velocity-error floor while significantly overestimating sensing degradation compared to practical scenarios.
This paper introduces Diffusive INR (DINR), a novel framework that regularizes implicit neural representations with a diffusion prior trained on synthetic data to achieve high-quality, artifact-reduced 3D reconstructions of concrete microstructures from sparse-view neutron computed tomography, outperforming state-of-the-art methods under extreme data limitations.