Related papers: Demonstration of Sub-micron UCN Position Resolutio…
The TRIUMF Ultra-Cold Advanced Neutron (TUCAN) collaboration aims at a precision neutron electric dipole moment (nEDM) measurement with an uncertainty of $10^{-27}\,e\cdot\mathrm{cm}$, which is an order-of-magnitude better than the current…
The success of Convolutional Neural Networks (CNNs) in image classification has prompted efforts to study their use for classifying image data obtained in Particle Physics experiments. Here, we discuss our efforts to apply CNNs to 2D and 3D…
Traceability to the International System of Units (SI) is fundamental to measurement accuracy and reliability. In this study, we demonstrate subnanometer traceability of localization microscopy, establishing a metrological foundation for…
We consider the use of deep neural networks (DNNs) in the context of channel state information (CSI)-based localization for Massive MIMO cellular systems. We discuss the practical impairments that are likely to be present in practical CSI…
Single-shot imaging with femtosecond X-ray lasers is a powerful measurement technique that can achieve both high spatial and temporal resolution. However, its accuracy has been severely limited by the difficulty of applying conventional…
Deep learning (DL) methods have been recently proposed for user equipment (UE) localization in wireless communication networks, based on the channel state information (CSI) between a UE and each base station (BS) in the uplink. With the CSI…
Touchscreen-based interaction on display devices are ubiquitous nowadays. However, capacitive touch screens, the core technology that enables its widespread use, are prohibitively expensive to be used in large displays because the cost…
Energy-resolved neutron imaging at a pulsed source utilizes the energy-dependent neutron transmission measured via time-of-flight to extract quantitative information about the internal microstructure of an object. At the RADEN instrument at…
Neutron-antineutron oscillation (nnbar-osc) is a baryon-number-violating process and a sensitive probe for physics beyond the Standard Model. Ultra-cold neutrons (UCNs) are attractive for nnbar-osc searches because of their long storage…
Tracking the pose of instruments is a central problem in image-guided surgery. For microscopic scenarios, optical coherence tomography (OCT) is increasingly used as an imaging modality. OCT is suitable for accurate pose estimation due to…
The anatomical location of imaging features is of crucial importance for accurate diagnosis in many medical tasks. Convolutional neural networks (CNN) have had huge successes in computer vision, but they lack the natural ability to…
We previously proposed a method to detect neutrons by using a current-biased kinetic inductance detector (CB-KID), where neutrons are converted into charged particles using a 10B conversion layer. The charged particles are detected based on…
This work presents a novel Convolutional Neural Network (CNN) architecture and a training procedure to enable robust and accurate pose estimation of a noncooperative spacecraft. First, a new CNN architecture is introduced that has scored a…
We determined the ultracold neutron (UCN) production rate by superthermal conversion in the solid deuterium (sD$_2$) moderator of the UCN source at the Paul Scherrer Institute (PSI). In particular, we considered low amounts of less than…
Unsupervised change detection (UCD) in remote sensing aims to localise semantic changes between two images of the same region without relying on labelled data during training. Most recent approaches rely either on frozen foundation models…
Place recognition is one of the most challenging problems in computer vision, and has become a key part in mobile robotics and autonomous driving applications for performing loop closure in visual SLAM systems. Moreover, the difficulty of…
We explore means to advance source camera identification based on sensor noise in a data-driven framework. Our focus is on improving the sensor pattern noise (SPN) extraction from a single image at test time. Where existing works suppress…
Semantic labeling (or pixel-level land-cover classification) in ultra-high resolution imagery (< 10cm) requires statistical models able to learn high level concepts from spatial data, with large appearance variations. Convolutional Neural…
The accelerating expansion of the Universe, attributed to dark energy, has spurred interest in theories involving scalar fields such as chameleon field theories. These fields, which couple to matter with density-dependent effective mass,…
With an aim to increase the capture range and accelerate the performance of state-of-the-art inter-subject and subject-to-template 3D registration, we propose deep learning-based methods that are trained to find the 3D position of…