scholarly journals Separation of a single photon and products of the  0,  andK0smeson neutral decay channels using neural network

2004 ◽  
Vol 2004 (04) ◽  
pp. 007-007 ◽  
Author(s):  
D.V Bandurin ◽  
N.B Skachkov
Keyword(s):  
Neural Network ◽  
Single Photon ◽  
Decay Channels ◽  
Neutral Decay

scholarly journals Introduction and Analysis of a Method for the Investigation of QCD-Like Tree Data

Entropy ◽  
2022 ◽  
Vol 24 (1) ◽  
pp. 104
Author(s):  
Marko Jercic ◽  
Ivan Jercic ◽  
Nikola Poljak
Keyword(s):  
Neural Network ◽  
Iterative Method ◽  
Probability Distributions ◽  
Test System ◽  
Jet Formation ◽  
Decay Probability ◽  
Decay Channels ◽  
System Of Particles ◽  
Tree Data ◽  
True System

The properties of decays that take place during jet formation cannot be easily deduced from the final distribution of particles in a detector. In this work, we first simulate a system of particles with well-defined masses, decay channels, and decay probabilities. This presents the “true system” for which we want to reproduce the decay probability distributions. Assuming we only have the data that this system produces in the detector, we decided to employ an iterative method which uses a neural network as a classifier between events produced in the detector by the “true system” and some arbitrary “test system”. In the end, we compare the distributions obtained with the iterative method to the “true” distributions.

scholarly journals Quality Index of Supervised Data for Convolutional Neural Network-Based Localization

2019 ◽  
Vol 9 (10) ◽  
pp. 1983 ◽  
Author(s):  
Seigo Ito ◽  
Mineki Soga ◽  
Shigeyoshi Hiratsuka ◽  
Hiroyuki Matsubara ◽  
Masaru Ogawa
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Quality Index ◽  
Single Photon ◽  
Automated Guided Vehicles ◽  
Training Data ◽  
Supervised Training ◽  
Localization Performance ◽  
Imaging Sensor

Automated guided vehicles (AGVs) are important in modern factories. The main functions of an AGV are its own localization and object detection, for which both sensor and localization methods are crucial. For localization, we used a small imaging sensor named a single-photon avalanche diode (SPAD) light detection and ranging (LiDAR), which uses the time-of-flight principle and arrays of SPADs. The SPAD LiDAR works both indoors and outdoors and is suitable for AGV applications. We utilized a deep convolutional neural network (CNN) as a localization method. For accurate CNN-based localization, the quality of the supervised data is important. The localization results can be poor or good if the supervised training data are noisy or clean, respectively. To address this issue, we propose a quality index for supervised data based on correlations between consecutive frames visualizing the important pixels for CNN-based localization. First, the important pixels for CNN-based localization are determined, and the quality index of supervised data is defined based on differences in these pixels. We evaluated the quality index in indoor-environment localization using the SPAD LiDAR and compared the localization performance. Our results demonstrate that the index correlates well to the quality of supervised training data for CNN-based localization.

scholarly journals Neural network identification of people hidden from view with a single-pixel, single-photon detector

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Piergiorgio Caramazza ◽  
Alessandro Boccolini ◽  
Daniel Buschek ◽  
Matthias Hullin ◽  
Catherine F. Higham ◽  
...  
Keyword(s):  
Neural Network ◽  
Single Photon ◽  
Photon Detector ◽  
Single Photon Detector ◽  
Network Identification ◽  
Single Pixel

scholarly journals Comparing traditional and deep-learning techniques of kinematic reconstruction for polarization discrimination in vector boson scattering

2020 ◽  
Vol 80 (12) ◽  
Author(s):  
M. Grossi ◽  
J. Novak ◽  
B. Kerševan ◽  
D. Rebuzzi
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Vector Boson ◽  
Higgs Mechanism ◽  
Beyond The Standard Model ◽  
Decay Channels ◽  
Leptonic Decays ◽  
Learning Techniques ◽  
The Standard Model ◽  
Vector Boson Scattering

AbstractMeasuring longitudinally polarized vector boson scattering in $$\mathrm {WW}$$ WW channel is a promising way to investigate unitarity restoration with the Higgs mechanism and to search for possible physics beyond the Standard Model. In order to perform such a measurement, it is crucial to develop an efficient reconstruction of the full $$\mathrm {W}$$ W boson kinematics in leptonic decays with the focus on polarization measurements. We investigated several approaches, from traditional ones up to advanced deep neural network structures, and we compared their abilities in reconstructing the $$\mathrm {W}$$ W boson reference frame and in consequently measuring the longitudinal fraction $$\mathrm {W}_{\text {L}}$$ W L in both semi-leptonic and fully-leptonic $$\mathrm {WW}$$ WW decay channels.

Feature extraction and neural network-based multi-peak analysis on time-correlated LiDAR histograms

2022 ◽  
Author(s):  
Gongbo Chen ◽  
Felix Landmeyer ◽  
Christian Wiede ◽  
Rainer Kokozinski
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Single Photon ◽  
Photon Counting ◽  
Recovery Process ◽  
Digital Processing ◽  
Target Distance ◽  
Learning Ability ◽  
High Background ◽  
Background Light

Abstract Time correlated single photon counting (TCSPC) is a statistical method to generate time-correlated histograms (TC-Hists), which are based on the time-of-flight (TOF) information measured by photon detectors such as single-photon avalanche diodes. With restricted measurements per histogram and the presence of high background light, it is challenging to obtain the target distance in a TC-Hist. In order to improve the data processing robustness under these conditions, the concept of machine learning is applied to the TC-Hist. Using the neural network-based multi-peak analysis (NNMPA), introduced by us, including a physics-guided feature extraction, a neural network multi-classifier, and a distance recovery process, the analysis is focused on a small amount of critical features in the TC-Hist. Based on these features, possible target distances with correlated certainty values are inferred. Furthermore, two optimization approaches regarding the learning ability and real-time performance are discussed. In particular, variants of the NNMPA are evaluated on both synthetic and real datasets. The proposed method not only has higher robustness in allocating the coarse position (±5 %) of the target distance in harsh conditions, but also is faster than the classical digital processing with an average-filter. Thus, it can be applied to improve the system robustness, especially in the case of high background light and middle-range detections.

Rapid prediction of drug inhibition under heat stress: single-photon imaging combined with a convolutional neural network

Nanoscale ◽  
2020 ◽  
Vol 12 (45) ◽  
pp. 23134-23139
Author(s):  
Hongxin Lin ◽  
Jianlei Xie ◽  
Taojian Fan ◽  
Youwu He ◽  
Jianxin Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Heat Stress ◽  
Convolutional Neural Network ◽  
Single Photon ◽  
Prediction Method ◽  
Rapid Prediction ◽  
Drug Inhibition ◽  
Photon Imaging

A novel prediction method for cellular drug inhibition under heat stress.

scholarly journals Convolutional neural network-based automatic heart segmentation and quantitation in 123I-metaiodobenzylguanidine SPECT imaging

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Shintaro Saito ◽  
Kenichi Nakajima ◽  
Lars Edenbrandt ◽  
Olof Enqvist ◽  
Johannes Ulén ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Single Photon ◽  
Photon Emission ◽  
Spect Imaging ◽  
Background Correction ◽  
Emission Computed Tomography ◽  
Planar Imaging ◽  
Cardiac Region ◽  
Spect Images

Abstract Background Since three-dimensional segmentation of cardiac region in 123I-metaiodobenzylguanidine (MIBG) study has not been established, this study aimed to achieve organ segmentation using a convolutional neural network (CNN) with 123I-MIBG single photon emission computed tomography (SPECT) imaging, to calculate heart counts and washout rates (WR) automatically and to compare with conventional quantitation based on planar imaging. Methods We assessed 48 patients (aged 68.4 ± 11.7 years) with heart and neurological diseases, including chronic heart failure, dementia with Lewy bodies, and Parkinson's disease. All patients were assessed by early and late 123I-MIBG planar and SPECT imaging. The CNN was initially trained to individually segment the lungs and liver on early and late SPECT images. The segmentation masks were aligned, and then, the CNN was trained to directly segment the heart, and all models were evaluated using fourfold cross-validation. The CNN-based average heart counts and WR were calculated and compared with those determined using planar parameters. The CNN-based SPECT and conventional planar heart counts were corrected by physical time decay, injected dose of 123I-MIBG, and body weight. We also divided WR into normal and abnormal groups from linear regression lines determined by the relationship between planar WR and CNN-based WR and then analyzed agreement between them. Results The CNN segmented the cardiac region in patients with normal and reduced uptake. The CNN-based SPECT heart counts significantly correlated with conventional planar heart counts with and without background correction and a planar heart-to-mediastinum ratio (R2 = 0.862, 0.827, and 0.729, p < 0.0001, respectively). The CNN-based and planar WRs also correlated with and without background correction and WR based on heart-to-mediastinum ratios of R2 = 0.584, 0.568 and 0.507, respectively (p < 0.0001). Contingency table findings of high and low WR (cutoffs: 34% and 30% for planar and SPECT studies, respectively) showed 87.2% agreement between CNN-based and planar methods. Conclusions The CNN could create segmentation from SPECT images, and average heart counts and WR were reliably calculated three-dimensionally, which might be a novel approach to quantifying SPECT images of innervation.

scholarly journals Low-Parameter Small Convolutional Neural Network Applied to Functional Medical Imaging of Tc-99m Trodat-1 Brain Single-Photon Emission Computed Tomography for Parkinson’s Disease

2021 ◽  
Vol 12 (1) ◽  
pp. 1
Author(s):  
Yu-Chieh Chang ◽  
Te-Chun Hsieh ◽  
Jui-Cheng Chen ◽  
Kuan-Pin Wang ◽  
Zong-Kai Hsu ◽  
...  
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Functional Imaging ◽  
Dopaminergic Neuron ◽  
Single Photon ◽  
Photon Emission ◽  
Emission Computed Tomography ◽  
Single Photon Emission ◽  
Single Photon Emission Computed ◽  
Photon Emission Computed Tomography

Parkinson’s disease (PD), a progressive disease that affects movement, is related to dopaminergic neuron degeneration. Tc-99m Trodat-1 brain (TRODAT) single-photon emission computed tomography (SPECT) aids the functional imaging of dopamine transporters and is used for dopaminergic neuron enumeration. Herein, we employed a convolutional neural network to facilitate PD diagnosis through TRODAT SPECT, which is simpler than models such as VGG16 and ResNet50. We retrospectively collected the data of 3188 patients (age range 20–107 years) who underwent TRODAT SPECT between June 2011 and December 2019. We developed a set of functional imaging multiclassification deep learning algorithms suitable for TRODAT SPECT on the basis of the annotations of medical experts. We then applied our self-proposed model and compared its results with those of four other models, including deep and machine learning models. TRODAT SPECT included three images collected from each patient: one presenting the maximum absorption of the metabolic function of the striatum and two adjacent images. An expert physician determined that our model’s accuracy, precision, recall, and F1-score were 0.98, 0.98, 0.98, and 0.98, respectively. Our TRODAT SPECT model provides an objective, more standardized classification correlating to the severity of PD-related diseases, thereby facilitating clinical diagnosis and preventing observer bias.

Sign in / Sign up

Export Citation Format

Share Document