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The detector of TRopIcal DEep-sea Neutrino Telescope (TRIDENT) will use large-area silicon photomultiplier (SiPM) arrays combined with photomultiplier tubes to boost photon detection efficiency and pointing capability. An…
Understanding the mechanisms underlying deep neural networks remains a fundamental challenge in machine learning and computer vision. One promising, yet only preliminarily explored approach, is feature inversion, which attempts to…
Compact gamma cameras with a square-shaped monolithic scintillator crystal and an array of silicon photomultipliers (SiPMs) are actively being developed for applications in areas such as small animal imaging, cancer diagnostics and…
Purpose: To propose a flexible and scalable imaging transformer (IT) architecture with three attention modules for multi-dimensional imaging data and apply it to MRI denoising with very low input SNR. Methods: Three independent attention…
Silicon Photo-Multipliers (SiPMs) are semiconductor-based photo-detectors with performances similar to the traditional Photo-Multiplier Tubes (PMTs). An increasing number of experiments dedicated to particle detection in colliders,…
The temperature of a nonneutral plasma confined in a Penning-Malmberg trap can be determined by slowly lowering one side of the trap's electrostatic axial confinement barrier; the temperature is inferred from the rate at which particles…
Multiple independent radio frequency (RF) beams find applications in communications, radio astronomy, radar, and microwave imaging. An $N$-point FFT applied spatially across an array of receiver antennas provides $N$-independent RF beams at…
We design, fabricate, and characterize fishbone grating couplers for triangular cross-section photonics in silicon carbide compatible with color center integration. The periodic and aperiodic grating coupler designs are optimized to…
One of the major challenges in design and developing of PET, scanners are the presence of inactive areas between the detector blocks which degrade the image spatial resolution and leads to streaking artifacts especially when we employ…
We present a Siamese-like Dual-branch network based on solely Transformers for tracking. Given a template and a search image, we divide them into non-overlapping patches and extract a feature vector for each patch based on its matching…
We demonstrate chip-scale flat-top filters at near-infrared wavelengths using negative index photonic crystal based Mach Zehnder interferometers. Supported by full three-dimensional numerical simulations, we experimentally demonstrate a new…
Digital cameras transform sensor RAW readings into RGB images by means of their Image Signal Processor (ISP). Computational photography tasks such as image denoising and colour constancy are commonly performed in the RAW domain, in part due…
Accurate blur estimation is essential for high-performance imaging across various applications. Blur is typically represented by the point spread function (PSF). In this paper, we propose a physics-informed PSF learning framework for…
Nanophotonics has emerged as a powerful tool for manipulating light on chips. Almost all of today's devices, however, have been designed using slow and ineffective brute-force search methods, leading in many cases to limited device…
Neutral hydrogen (HI) intensity mapping (IM) presents great promise for future cosmological large-scale structure surveys. However, a major challenge for HIIM cosmological studies is to accurately subtract the foreground contamination. An…
The diffraction contrast modalities accessible by X-ray grating interferometers are not imaged directly but have to be inferred from sine like signal variations occurring in a series of images acquired at varying relative positions of the…
Optical microresonators have proven powerful in a wide range of applications, including cavity quantum electrodynamics, biosensing, microfludics, and cavity optomechanics. Their performance depends critically on the exact distribution of…
A high-performance Scintillator Ion Beam Monitor (SIBM) provides diagnostics across a range of isotopes, energies, and intensities employing a machine-vision camera with two novel scintillator targets movable into/out of the beam without…
Feature-Imitating-Networks (FINs) are neural networks that are first trained to approximate closed-form statistical features (e.g. Entropy), and then embedded into other networks to enhance their performance. In this work, we perform the…
Deep learning for image processing typically treats input imagery as pixels in some color space. This paper proposes instead to learn from program traces of procedural fragment shaders -- programs that generate images. At each pixel, we…