147 papers
This paper proposes a novel phase-amplitude coupling (PAC) detection framework based on nonlinear system identification and dynamical systems theory that overcomes limitations of existing methods by accurately characterizing coupling dynamics while remaining robust to noise, filter bandwidth variations, and harmonic-induced spurious couplings.
This paper proposes a novel bottleneck transformer architecture that integrates convolutional blocks for frame-level feature extraction and multi-head self-attention for information aggregation to achieve improved non-intrusive prediction of the Short-Time Objective Intelligibility (STOI) metric, outperforming state-of-the-art self-supervised learning models in both seen and unseen scenarios.
This study demonstrates that while the multichannel linear descriptors-based block field method (MLD-BFM) achieves the highest accuracy in decoding five finger-joint degrees of freedom from HD sEMG, its performance is not statistically superior to conventional time-domain features, though it significantly outperforms dimensionality reduction methods, highlighting the critical importance of preserving spatial resolution in high-density recordings.
This paper proposes SPDIM, a parameter-efficient geometric deep learning framework that leverages information maximization on the symmetric positive definite (SPD) manifold to effectively address source-free unsupervised domain adaptation in EEG data, specifically overcoming the generalization limitations of prior methods when faced with label shifts.
This paper proposes a data-driven framework that harmonizes heterogeneous driving cycle data and employs statistical and deep learning models to enable efficient, probabilistic prediction of voltage hysteresis factors in silicon-graphite anode batteries, thereby improving state-of-charge estimation and generalizability across different vehicle models.
This paper develops a wideband Gaussian noise model for semiconductor optical amplifiers that yields a simple, closed-form expression for nonlinear noise-to-signal ratio, demonstrating that accounting for gain compression significantly enhances noise predictions and achieving high accuracy (error < 0.1 dB) when the product of bandwidth and gain recovery time exceeds 100.
This paper presents a 1.6-fJ/spike subthreshold analog Leaky Integrate-and-Fire neuron fabricated in 28 nm CMOS, which achieves a 300 kHz spiking frequency at 250 mV and demonstrates 82.5% accuracy on the MNIST dataset when used in a quantized Spiking Neural Network, validating its potential for energy-efficient embedded machine learning.
This paper presents a curved monopole antenna design optimized for HF skywave radar that, through parametric analysis of curvature and straight-section length, achieves significantly enhanced gain and bandwidth compared to conventional monopoles, and demonstrates further performance improvements when scaled into a 12-element linear array.
This paper introduces two novel model-driven evolutionary frameworks, NEMO-DE and NEEF-DE, that leverage differential evolution to perform near-field multi-source localization on continuous spherical-wave models with arbitrary array geometries, effectively overcoming the limitations of traditional grid-based subspace methods and data-dependent deep learning approaches without requiring labeled data or discretized grids.
This paper proposes an interpretable 3D Concept Bottleneck Model that integrates morphological and hemodynamic clinical features to classify intracranial aneurysms with high accuracy (up to 93.33%) while ensuring clinical transparency and regulatory compliance.
This paper proposes a lightweight, digital-twin-based framework for edge-assisted vehicle tracking and collision prediction that achieves approximately 88% accuracy by relying on object detection and K-D tree-indexed path maps instead of computationally intensive trajectory prediction models.
This paper proposes a bi-directional digital twin prototype anchoring framework enhanced with multi-periodicity learning to achieve robust few-shot fault diagnosis by leveraging meta-training in a virtual simulation space and test-time adaptation in the physical domain, thereby overcoming the limitations of traditional methods that require abundant labeled or unlabeled target data.
This paper introduces the concept of generalized pinching-antenna systems as a transformative, flexible architecture for next-generation wireless networks, providing a comprehensive tutorial on their physical principles, diverse realizations, design strategies, integration with emerging technologies, and future research directions.
This paper introduces AirCNN, a novel paradigm that leverages reconfigurable intelligent surfaces (RISs) to perform over-the-air analog convolution operations, demonstrating through joint optimization and simulations that multi-RIS MISO architectures effectively emulate both standard and depthwise separable CNN layers with high classification performance.
This paper proposes optimization-based and deep reinforcement learning (specifically DDQN) methods to achieve positive-rate keyless covert communications in Rayleigh block-fading channels by solving power and rate allocation problems under both non-causal and causal channel state information scenarios.
This paper proposes a novel generative diffusion model-based framework for high-dimensional MIMO channel estimation that leverages deep generative priors and unsupervised learning to achieve high-fidelity recovery with significantly reduced latency and pilot overhead, even under low-resolution quantization and without requiring ground truth data.
This paper proposes Hadamard-NOMA, a robust downlink non-orthogonal multiple access scheme that leverages the Hadamard Transform at the source level to mitigate fading and channel state information imperfections, achieving significant bit error rate improvements over existing T-NOMA and Usman-NOMA schemes.
The paper presents NANOMIND, a hardware-software co-design framework that decomposes Large Multimodal Models into modular components and dynamically schedules them across heterogeneous accelerators on unified-memory SoCs, enabling a battery-powered device to run LMMs entirely on-device with significantly improved energy efficiency and throughput.
This paper presents an adaptive entropy-driven sensor selection policy within a camera-LiDAR particle filter that dynamically switches between modalities to optimize tracking accuracy and continuity for single-vessel surveillance, validated through real-world maritime deployment.
This paper demonstrates that while wavelet features excel in binary ECG classification, a transformer-based model utilizing Koopman operator features derived from an optimized Extended Dynamic Mode Decomposition (EDMD) with a radial basis function dictionary achieves superior performance in multi-class ECG classification, outperforming both wavelet-only and hybrid approaches.