The paper demonstrates that quadratic integrate-and-fire (QIF) neurons are superior to leaky integrate-and-fire (LIF) neurons for gradient descent training in spiking neural networks because their con…

The paper proposes a multi-dimensional evaluation framework to assess EEG foundation models under realistic low-resource conditions, finding that while these models excel in long-context tasks, their…

The paper proposes a lightweight Temporal Convolutional Network (TCN) that incorporates physical motion-aware attention mechanisms to efficiently and effectively perform WiFi CSI-based Human Activity…

The paper introduces Spike-PTSD, a novel, biologically inspired adversarial attack framework that successfully compromises the robustness of Spiking Neural Networks (SNNs) by modeling abnormal neural…

The paper proposes GUIDE, a physics-guided deep unfolding framework that enables practical, real-time cross-band channel prediction for AI-RAN by embedding wireless channel physics, significantly impr…

The paper systematically evaluates 27 Spiking Neural Network (SNN) configurations to determine the optimal combination of neuron model and spike encoding scheme for network intrusion detection, findin…

The paper evaluates 27 different Spiking Neural Network (SNN) configurations to determine the optimal design for network intrusion detection, finding that the LeakyParallel neuron combined with latenc…

The paper proposes a theoretically grounded adversarial multi-task learning framework (AMTIDIN) that significantly improves joint interference detection, modulation identification, and interference id…

The paper proposes DRIFT, a lightweight joint channel estimation and prediction framework, to significantly reduce pilot overhead and boost spectral efficiency in power-constrained LEO Non-Terrestrial…

The paper introduces FHE-DiCSNN, a novel framework that uses the TFHE scheme to enable secure and efficient computation on Spiking Neural Networks (SNNs), achieving high accuracy and fast inference ti…

The paper proposes a compact magnetic tunnel junction (MTJ) device with orthogonal easy axes to implement signed leaky integrate-and-fire (LIF) neurons, enabling bipolar spike generation for enhanced…

The paper introduces NaRA, a noise-aware LoRA technique that dynamically adapts fine-tuning parameters based on the noise level during diffusion, significantly improving the performance of Diffusion L…

The paper introduces RadioMaster, a novel multi-agent system that successfully translates high-level user intents into physically viable, real-world radio signals, significantly outperforming existing…

This paper proposes and evaluates the KAN-LSTM model, demonstrating that Kolmogorov-Arnold Networks (KANs) significantly outperform traditional deep learning models for accurate and parameter-efficien…

This paper investigates a novel vulnerability in tactile sensing by demonstrating that targeted Electromagnetic Interference (EMI) can induce strong, misleading 'phantom forces' in Hall-effect fingert…

The paper proposes a lightweight, self-adaptive framework using LoRA to efficiently extract and aggregate radio frequency fingerprints for robust open-set authentication in dynamic wireless environmen…