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Position-sensitive detection of ultracold neutrons (UCNs) is demonstrated using an imaging charge-coupled device (CCD) camera. A spatial resolution less than 15 $\mu$m has been achieved, which is equivalent to an UCN energy resolution below…

Computer Aided Diagnosis has emerged as an indispensible technique for validating the opinion of radiologists in CT interpretation. This paper presents a deep 3D Convolutional Neural Network (CNN) architecture for automated CT scan-based…

We present a characterization of a small (9-liter) and mobile 0.1% 6Li-doped pulse-shape-sensitive plastic scintillator antineutrino detector called SANDD (Segmented AntiNeutrino Directional Detector), constructed for the purpose of…

Two-dimensional (2D) materials have attracted extensive attention due to their unique characteristics and application potentials. Raman spectroscopy, as a rapid and non-destructive probe, exhibits distinct features and holds notable…

Experiments searching for rare processes like neutrinoless double beta decay heavily rely on the identification of background events to reduce their background level and increase their sensitivity. We present a novel machine learning based…

Recent advancements in gravitational wave astronomy have seen the application of convolutional neural networks (CNNs) in signal detection from compact binary coalescences. This study presents a comparative analysis of two CNN architectures:…

We present a lossy image compression method based on deep convolutional neural networks (CNNs), which outperforms the existing BPG, WebP, JPEG2000 and JPEG as measured via multi-scale structural similarity (MS-SSIM), at the same bit rate.…

Characterization of lung nodules as benign or malignant is one of the most important tasks in lung cancer diagnosis, staging and treatment planning. While the variation in the appearance of the nodules remains large, there is a need for a…

We address the problem of contour detection via per-pixel classifications of edge point. To facilitate the process, the proposed approach leverages with DenseNet, an efficient implementation of multiscale convolutional neural networks…

Quantitative ultrasound (QUS) can reveal crucial information on tissue properties such as scatterer density. If the scatterer density per resolution cell is above or below 10, the tissue is considered as fully developed speckle (FDS) or…

Due to cellular heterogeneity, cell nuclei classification, segmentation, and detection from pathological images are challenging tasks. In the last few years, Deep Convolutional Neural Networks (DCNN) approaches have been shown…

The scintillation mechanism in NaI:Tl crystals produces different pulse shapes that are dependent on the incoming particle type. The time distribution of scintillation light from nuclear recoil events decays faster than for electron recoil…

It has been shown that for automated PAP-smear image classification, nucleus features can be very informative. Therefore, the primary step for automated screening can be cell-nuclei detection followed by segmentation of nuclei in the…

We present a custom implementation of a 2D Convolutional Neural Network (CNN) as a viable application for real-time data selection in high-resolution and high-rate particle imaging detectors, making use of hardware acceleration in high-end…

Introduction: Real-time dosimetry of surface doses in electron beams has not been widely established yet. Plastic scintillation detectors (PSD) promise high spatial resolution and real-time dosimetry with minimum perturbation of the…

Pulse-shape discrimination (PSD) in high-purity germanium (HPGe) detectors is central to rare-event searches such as neutrinoless double-beta decay (0vBB), yet conventional approaches compress each waveform into a small set of summary…

Tissue characterization has long been an important component of Computer Aided Diagnosis (CAD) systems for automatic lesion detection and further clinical planning. Motivated by the superior performance of deep learning methods on various…

This work reports on the performance of a novel neutron-sensitive scintillating detector fabricated using Fused-Deposition Modelling (FDM) additive manufacturing. FDM is a cost-effective 3D-printing method employing flexible plastic…

A 3-D position sensitive neutron detector is being developed based on a plastic scintillator array. A double side SiPM readout is used to determine the depth of interaction (DOI) in each scintillator unit. In the preliminary test, the DOI…

The next generation of very-short-baseline reactor experiments will require compact detectors operating at surface level and close to a nuclear reactor. This paper presents a new detector concept based on a composite solid scintillator…