September 2026

The increasing realism of image manipulations poses significant challenges for forgery localization. However, existing methods are hindered by the limited adaptability of constrained frequency filters

Graph partitioning is a fundamental task in image segmentation and data clustering. However, identifying partitions with clear image and community boundaries remains challenging because convex relaxat

The normalized least-mean-square (NLMS) algorithm is widely used in adaptive filtering due to its simplicity and robustness. Conventional convergence analyses of NLMS focus on one-step update behavior

Recovering atmospheric profiles from multichannel microwave radiometer observations is a challenging inverse problem with limited training data. The K-band and V-band observations are short vectors of

In this letter, we address the problem of developing quantum state tomography (QST) methods that remain valid at any time during a sequence of measurements. Specifically, the aim is to provide a rigor

This letter proposes novel spectrum-null-constrained (SNC) sequence sets for non-contiguous spectrum communication. A class of SNC zero-correlation-zone sequence sets (SNC-ZSSs) with even lengths and

Deep-feature-based perceptual similarity models have demonstrated strong alignment with human visual perception in Image Quality Assessment (IQA). However, most existing methods operate at a single sp

This letter develops a noise unknown expectation- maximization based transfer identification method for dynamic systems with limited target data. Different from existing transfer identification method

This letter studies a Fourier-probe obstruction test for structured residue-class derivative sampling of analytic functions. At each of two sampling points, only derivatives whose orders lie in prescr

Due to the large approximation errors, current omega-k algorithms for multireceiver synthetic aperture sonar (MSAS) fail to achieve high-quality results. To address this issue, this letter presents a