AIMC Topic: Photons

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Noise Suppression With Similarity-Based Self-Supervised Deep Learning.

IEEE transactions on medical imaging
Image denoising is a prerequisite for downstream tasks in many fields. Low-dose and photon-counting computed tomography (CT) denoising can optimize diagnostic performance at minimized radiation dose. Supervised deep denoising methods are popular but ...

Learnable latent embeddings for joint behavioural and neural analysis.

Nature
Mapping behavioural actions to neural activity is a fundamental goal of neuroscience. As our ability to record large neural and behavioural data increases, there is growing interest in modelling neural dynamics during adaptive behaviours to probe neu...

Multi-Scale Histogram-Based Probabilistic Deep Neural Network for Super-Resolution 3D LiDAR Imaging.

Sensors (Basel, Switzerland)
LiDAR (Light Detection and Ranging) imaging based on SPAD (Single-Photon Avalanche Diode) technology suffers from severe area penalty for large on-chip histogram peak detection circuits required by the high precision of measured depth values. In this...

Quantization-aware training for low precision photonic neural networks.

Neural networks : the official journal of the International Neural Network Society
Recent advances in Deep Learning (DL) fueled the interest in developing neuromorphic hardware accelerators that can improve the computational speed and energy efficiency of existing accelerators. Among the most promising research directions towards t...

Photon-counting Detector CT with Deep Learning Noise Reduction to Detect Multiple Myeloma.

Radiology
Background Photon-counting detector (PCD) CT and deep learning noise reduction may improve spatial resolution at lower radiation doses compared with energy-integrating detector (EID) CT. Purpose To demonstrate the diagnostic impact of improved spatia...

Advances in Emerging Photonic Memristive and Memristive-Like Devices.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Possessing the merits of high efficiency, low consumption, and versatility, emerging photonic memristive and memristive-like devices exhibit an attractive future in constructing novel neuromorphic computing and miniaturized bionic electronic system. ...

Simulation study on 3D convolutional neural networks for time-of-flight prediction in monolithic PET detectors using digitized waveforms.

Physics in medicine and biology
We investigate the use of 3D convolutional neural networks for gamma arrival time estimation in monolithic scintillation detectors.The required data is obtained by Monte Carlo simulation in GATE v8.2, based on a 50 × 50 × 16 mmmonolithic LYSO crystal...

Use of gamma radiation and artificial neural network techniques to monitor characteristics of polyduct transport of petroleum by-products.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine
This study presents a methodology based on the dual-mode gamma densitometry technique in combination with artificial neural networks to simultaneously determine type and quantity of four different fluids (Gasoline, Glycerol, Kerosene and Fuel Oil) to...

Antimony as a Programmable Element in Integrated Nanophotonics.

Nano letters
The use of nonlinear elements with memory as photonic computing components has seen a huge surge in interest in recent years with the rise of artificial intelligence and machine learning. A key component is the nonlinear element itself. A class of ma...

Tri-view two-photon microscopic image registration and deblurring with convolutional neural networks.

Neural networks : the official journal of the International Neural Network Society
Two-photon fluorescence microscopy has enabled the three-dimensional (3D) neural imaging of deep cortical regions. While it can capture the detailed neural structures in the x-y image space, the image quality along the depth direction is lower becaus...