September 2026

Stereonetworks trained on synthetic data often degrade in real-world scenes. Within an IGEV-style pipeline, domain-sensitive features may disturb correspondence cues, average matching-dimension poolin

This letter addresses target detection for intra-pulse polarization-coding radar, where conventional fixed-polarization detectors cannot fully exploit the polarization variation within a pulse. A pola

Perceptual image hashing aims to generate compact representations that are robust to content-preserving distortions. This letter presents a deep learning-based framework that learns invariant hash cod

Target tracking in multistatic radar systems is fundamental to wide-area surveillance, yet it faces two major challenges. First, the measurement geometry is highly nonlinear and varies with target ran

When deployed on unreliable hardware, deep neural networks (DNNs) encounter weight perturbations that affect model outputs, increasing uncertainty and posing significant risks to the application of de

Multimodal sentiment analysis is vulnerable to unstable local cues and temporal asynchrony among textual, visual, and acoustic signals. Existing fusion methods extensively model cross-modal interactio

This letter investigates the problem of robust target localization in distributed multiple-input multiple-output (MIMO) radar systems under space-time imperfections, i.e., antenna position errors and

Low-light object detection remains challenging because conventional image signal processing (ISP) pipelines may suppress weak sensor-level cues before detection. Existing enhancement methods usually o

Multistatic ranging with mobile receivers enables good tracking performance due to the combined adaptive sensor geometry. However, environments that contain signal obstructing obstacles require receiv

High-dimensional and correlated reference signals make multi-reference active noise control (ANC) computationally demanding, prone to slow convergence, and difficult to deploy in real-time application