326 papers
This paper proposes an energy efficiency maximization framework for continuous aperture array (CAPA)-based multi-group multicast communications by developing an optimal block coordinate descent algorithm and a low-complexity zero-forcing approach, demonstrating CAPA's superior performance over discrete arrays while revealing nuanced impacts of aperture size and user distribution.
This paper proposes a low-complexity, compressive sensing-based 2D super-resolution technique to accurately estimate target signatures in XL-MIMO FMCW radars by effectively addressing the spatial wideband effect that couples range and angle domains.
This study demonstrates that strong correlations exist between subjective perceptual ratings and objective acoustic measures in head and neck cancer patients, suggesting that a single intelligibility measure may be sufficient for clinical monitoring of speech following chemoradiation treatment.
This paper proposes a novel, information-centric framework for deriving generic Cramér-Rao Lower Bounds (CRLBs) for rigid body localization that explicitly accounts for both translation and rotation errors under arbitrary measurement types and constraints, providing a versatile tool to evaluate and improve state-of-the-art estimation algorithms.
This paper proposes a hybrid control framework that enhances quadrotor resilience in underground environments by using a normalizing flow-based prior as a runtime monitor to dynamically switch between a learning-based controller for efficiency and a safety controller for collision avoidance when encountering out-of-distribution scenarios.
This study presents a novel computer vision framework that utilizes external observation techniques, including YOLO-based object detection and lane monitoring, to classify distracted and impaired driver behaviors in non-connected vehicles without relying on inter-vehicular communication.
This paper proposes a knowledge distillation framework for mmWave beam tracking that leverages a large attention-enhanced neural network as a teacher to train a lightweight student model, achieving state-of-the-art long-term prediction accuracy with significantly reduced parameters, latency, and power consumption.
This paper proposes a novel cascaded accumulator method that efficiently computes time-index powered weighted sums in real-time by reducing multiplicative complexity from to constant multiplications while eliminating the need for storing entire data blocks.
This paper presents an energy-efficient Adiabatic Capacitive Neuron (ACN) implemented in 0.18μm CMOS technology that achieves over 90% synapse energy savings compared to non-adiabatic benchmarks while offering improved accuracy, robustness, and scalability through a novel low-offset Threshold Logic activation function.
This paper proposes Open-RFNet, a multi-domain supervised contrastive learning framework enhanced by an improved generative OpenMax algorithm, to achieve high-accuracy open-set recognition of unauthorized UAVs in 5G-Advanced networks, outperforming existing benchmarks with 95.12% closed-set and 96.08% open-set accuracy across 25 UAV types.
The paper proposes ACCOR, an attention-enhanced complex-valued contrastive learning framework that achieves state-of-the-art accuracy in occluded object classification using mmWave radar IQ signals across 64 GHz and 67 GHz frequencies.
This paper bridges the gap between discrete-time models and practical implementation of Affine Frequency Division Multiplexing (AFDM) by establishing a continuous-time analytical framework that derives fundamental bounds, analyzes spectral characteristics under realistic pulse-shaping, and evaluates performance sensitivity to hardware impairments.
This paper presents the DISPLACE-M challenge, a benchmark for conversational AI in noisy, multi-speaker medical settings, by releasing a dataset of frontline health worker interactions and evaluating baseline systems across speaker diarization, speech recognition, topic identification, and dialogue summarization tasks.
This paper introduces the PARLO Dementia Corpus (PDC), the first publicly available, multi-center German dataset featuring speech recordings, transcriptions, and clinical metadata from Alzheimer's patients and healthy controls, which enables scalable, non-invasive speech-based detection and assessment of cognitive impairment.
This paper proposes a tri-hybrid beamforming architecture for uplink multi-user MIMO systems using segmented waveguide-enabled pinching antennas (SWANs), optimizing digital, analog, and pinching components in both fully-connected and interleaved partially-connected structures to achieve superior performance and energy efficiency compared to conventional hybrid and pinching-antenna systems.
This paper establishes a unified framework linking block-level energy statistics to low-frequency intensity-fluctuation spectra, deriving a semi-analytical model and design rules to optimize constellation shaping parameters for mitigating nonlinear interference in high-capacity coherent fiber links.
This paper proposes a Deep Learning-based Conformal Bayes Filter (DCBF) that integrates conformal quantile regression with Bayesian filtering to overcome the model mismatch of traditional Kalman filters and the overconfidence of deep learning methods, thereby delivering reliable MIMO channel predictions with calibrated uncertainty.
This study proposes and validates a maximum-based Delay-Doppler Map noise floor strategy with a two-tier verification framework for detecting GNSS interference using CYGNSS LEO satellite data, demonstrating superior sensitivity and reliability compared to existing methods in both controlled jamming tests and persistent interference regions.
This paper proposes a method to reduce the pass-band droop's dependency on the decimation factor in -th order comb decimators by cascading a symmetric 3-tap FIR filter with coefficients determined solely by the filter order .
This paper identifies and formalizes a new "label hijacking" vulnerability in track-consensus-based distributed multi-target tracking systems, where adversaries can exploit spoofed tracks to corrupt target identities across the network, thereby highlighting the critical need for enhanced robustness at the consensus layer.