A diffusion-based super resolution model for enhancing sonar images.

Improved hardware and processing techniques such as synthetic aperture sonar have led to imaging sonar with centimeter resolution. However, practical limitations and old systems limit the resolution in modern and legacy datasets. This study proposes using single image super resolution based on a conditioned diffusion model to map between images at different resolutions.

Instant Detection of Synthetic Cannabinoids on Physical Matrices, Implemented on a Low-Cost, Ultraportable Device.

Synthetic cannabinoids (SCs) make up a class of novel psychoactive substances (NPS), used predominantly in prisons and homeless communities in the U.K. SCs can have severe side effects, including psychosis, stroke, and seizures, with numerous reported deaths associated with their use.

Synthetic cannabinoid receptor agonists are monoamine oxidase-A selective inhibitors.

Synthetic cannabinoid receptor agonists (SCRAs) are one of the fastest growing classes of recreational drugs. Despite their growth in use, their vast chemical diversity and rapidly changing landscape of structures make understanding their effects challenging. In particular, the side effects for SCRA use are extremely diverse, but notably include severe outcomes such as cardiac arrest.

Photochemical Fingerprinting Is a Sensitive Probe for the Detection of Synthetic Cannabinoid Receptor Agonists; toward Robust Point-of-Care Detection.

With synthetic cannabinoid receptor agonist (SCRA) use still prevalent across Europe and structurally advanced generations emerging, it is imperative that drug detection methods advance in parallel. SCRAs are a chemically diverse and evolving group, which makes rapid detection challenging. We have previously shown that fluorescence spectral fingerprinting (FSF) has the potential to provide rapid assessment of SCRA presence directly from street material with minimal processing and in saliva.

Background Subtraction with DirichletProcess Mixture Models.

Video analysis often begins with background subtraction. This problem is often approached in two steps-a background model followed by a regularisation scheme. A model of the background allows it to be distinguished on a per-pixel basis from the foreground, whilst the regularisation combines information from adjacent pixels.