Abstract Detecting low, slow and small maritime targets remains difficult because sea clutter is non-uniform, non-stationary, non-Gaussian and often observed at extremely low signal-to-clutter ratios. Here we present a switchable Copula-guided Transformer denoising diffusion probabilistic model (SCT-DDPM) for unsupervised radar target detection. The framework replaces the conventional i.i.d. Gaussian forward prior with Copula-structured noise, uses a copula-aware weighted MSE objective to align denoising with the observed dependence structure, and combines diffusion-based anomaly attention with Transformer encoding to score target-like deviations from normal clutter. Across 13 Copula families on the IPIX radar dataset, SCT-DDPM maintained high detection performance. These results show that dependence-aware diffusion priors can improve target-clutter separability and provide a statistically grounded route for configuring diffusion models in complex maritime environments.
As one of China’s pivotal cash crops, cotton’s leaf health directly impacts the textile industry and agricultural economic growth, with leaf diseases emerging as a critical constraint on cotton yield. Traditional manual identification of cotton leaf diseases, plagued by high subj…
Abstract Energy retention from losses is the primary goal of fault detection methodology for photovoltaic (PV) solar systems. A fault detection model should be designed effectively to minimize power and cost waste. We propose a novel fault detection and localization method that l…
Parametric models of the time–frequency-dependent power spectral density (TFPSD) function of seismic ground motion records can be developed from historical records, though model selection involves some subjectivity. The TFPSD function varies complexly in time–frequency domain, wi…
Urban visual pollution is an increasing concern in rapidly growing cities, affecting environmental quality, public perception, and urban sustainability. Existing visual pollution assessment approaches often rely on manual inspection, conventional image processing, or generic deep…
Vehicular Ad-Hoc Networks (VANETs) are vital for intelligent transport systems, yet they are extremely vulnerable to cyberattacks like Sybil, Blackhole, Denial-of-Service, Position Falsification, Message Suppression, and Replay. Existing intrusion detection systems use rigid metr…
The precision and early detection of subtypes of acute lymphoblastic leukaemia (ALL) in peripheral blood smear images are crucial for efficient clinical practice. Traditional deep learning methods tend to be challenging in terms of model interpretation and are often reliant on la…