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Object detection in Unmanned Aerial Vehicle (UAV) images has emerged as a focal area of research, which presents two significant challenges: i) objects are typically small and dense within vast images; ii) computational resource constraints…
CT organ segmentation on computed tomography (CT) images becomes a significant brick for modern medical image analysis, supporting clinic workflows in multiple domains. Previous segmentation methods include 2D convolution neural networks…
Advances in atomic resolution in situ environmental transmission electron microscopy for direct probing of gas-solid reactions, including at very high temperatures are described. In addition, recent developments of dynamic real time in situ…
The neutron electric dipole moment (EDM) is a sensitive probe for currently undiscovered sources of charge-parity symmetry violation. As part of the TRIUMF Ultracold Advanced Neutron (TUCAN) collaboration, we are developing spin analyzers…
Existing RGB-D SOD methods mainly rely on a symmetric two-stream CNN-based network to extract RGB and depth channel features separately. However, there are two problems with the symmetric conventional network structure: first, the ability…
The authors report on the use of carbon nanofiber nanoemitters to ionize deuterium atoms for the generation of neutrons in a deuterium-deuterium reaction in a preloaded target. Acceleration voltages in the range of 50-80 kV are used. Field…
Pulmonary nodule detection, false positive reduction and segmentation represent three of the most common tasks in the computeraided analysis of chest CT images. Methods have been proposed for eachtask with deep learning based methods…
Object segmentation and structure localization are important steps in automated image analysis pipelines for microscopy images. We present a convolution neural network (CNN) based deep learning architecture for segmentation of objects in…
This article describes the design features and the first test measurements obtained during the installation of a novel high resolution 2D neutron detection technique. The technique proposed in this work consists of a boron layer (enriched…
Neutron detection provides an effective method to detect, locate, and characterize sources of interest to nuclear security applications. Current neutron imaging systems based on double-scatter kinematic reconstruction provide good signal…
The development of compact neutron sources for applications is extensive and features many approaches. Let alone ion-based approaches, several projects with different parameters exist. This article focuses on ion-based neutron production…
In this chapter, we present a brief overview of the recent development in object detection using convolutional neural networks (CNN). Several classical CNN-based detectors are presented. Some developments are based on the detector…
This paper proposes a Diffusion Model-Optimized Neural Radiance Field (DT-NeRF) method, aimed at enhancing detail recovery and multi-view consistency in 3D scene reconstruction. By combining diffusion models with Transformers, DT-NeRF…
Model efficiency has become increasingly important in computer vision. In this paper, we systematically study neural network architecture design choices for object detection and propose several key optimizations to improve efficiency.…
Recently, many attempts have been made to construct a transformer base U-shaped architecture, and new methods have been proposed that outperformed CNN-based rivals. However, serious problems such as blockiness and cropped edges in predicted…
We describe an apparatus used to measure the triple-correlation term (\D \hat{\sigma}_n\cdot p_e\times p_\nu) in the beta-decay of polarized neutrons. The \D-coefficient is sensitive to possible violations of time reversal invariance. The…
The micropattern gaseous detector Micromegas has been developed for several years in Saclay and presents good performance for neutron detection. A prototype for neutron imaging has been designed and new results obtained in thermal neutron…
We compare existing segmented plastic antineutrino detectors with our new geometrically improved design for antineutrino detection and light collection efficiency. The purpose of this study is to determine the most suitable design style for…
In this work, a new neutron and {\gamma}(n/{\gamma}) discrimination method based on an Elman Neural Network (ENN) is proposed to improve the discrimination performance of liquid scintillator (LS) detectors. Neutron and {\gamma} data were…
Deep learning-based coastline detection algorithms have begun to outshine traditional statistical methods in recent years. However, they are usually trained only as single-purpose models to either segment land and water or delineate the…