Search results for "Pixel"

Characterisation and mitigation of beam-induced backgrounds observed in the ATLAS detector during the 2011 proton-proton run

2013

This paper presents a summary of beam-induced backgrounds observed in the ATLAS detector and discusses methods to tag and remove background contaminated events in data. Triggerrate based monitoring of beam-related backgrounds is presented. The correlations of backgrounds with machine conditions, such as residual pressure in the beam-pipe, are discussed. Results from dedicated beam-background simulations are shown, and their qualitative agreement with data is evaluated. Data taken during the passage of unpaired, i.e. non-colliding, proton bunches is used to obtain background-enriched data samples. These are used to identify characteristic features of beam-induced backgrounds, which then are …

Energy Recovery of Multiple Charge Sharing Events in Room Temperature Semiconductor Pixel Detectors

2021

Multiple coincidence events from charge-sharing and fluorescent cross-talk are typical drawbacks in room-temperature semiconductor pixel detectors. The mitigation of these distortions in the measured energy spectra, using charge-sharing discrimination (CSD) and charge-sharing addition (CSA) techniques, is always a trade-off between counting efficiency and energy resolution. The energy recovery of multiple coincidence events is still challenging due to the presence of charge losses after CSA. In this work, we will present original techniques able to correct charge losses after CSA even when multiple pixels are involved. Sub-millimeter cadmium–zinc–telluride (CdZnTe or CZT) pixel detectors we…

A neural network clustering algorithm for the ATLAS silicon pixel detector

2014

A novel technique to identify and split clusters created by multiple charged particles in the ATLAS pixel detector using a set of artificial neural networks is presented. Such merged clusters are a common feature of tracks originating from highly energetic objects, such as jets. Neural networks are trained using Monte Carlo samples produced with a detailed detector simulation. This technique replaces the former clustering approach based on a connected component analysis and charge interpolation. The performance of the neural network splitting technique is quantified using data from proton-proton collisions at the LHC collected by the ATLAS detector in 2011 and from Monte Carlo simulations. …

Data quality monitors of vertex detectors at the start of the Belle II experiment

2019

The Belle II experiment features a substantial upgrade of the Belle detector and will operate at the SuperKEKB energy-asymmetric e+e− collider at KEK in Tsukuba, Japan. The accelerator completed its first phase of commissioning in 2016, and the Belle II detector saw its first electron-positron collisions in April 2018. Belle II features a newly designed silicon vertex detector based on double-sided strip layers and DEPFET pixel layers. A subset of the vertex detector was operated in 2018 to determine background conditions (Phase 2 operation). The collaboration completed full detector installation in January 2019, and the experiment started full data taking. This paper will report on the fin…

A Study for Cloud Parameter Retrieval from the IR Cloud Cameras of the AUGER Observatory

2009

The Pierre Auger Observatory operative in Argentina, studies the ultra-high energy cosmic rays with energies above 1018eV. The atmosphere is also monitored by a collection of different instruments. In this paper we present a study on the retrieval of the cloud coverage from the atmospheric monitoring data collected by the four IR cloud cameras placed in the sites of the Observatory. We discuss two different algorithms that supply pixel by pixel cloudiness information in the form of binary masks. The final objective of the study is collecting different algorithms to obtain a reliable set that allow to overcome most of the more frequent ambiguities due to particular cloud configurations and a…

OPTIMIZING STOCHASTIC SUSCEPTIBILITY MODELLING FOR DEBRIS FLOW LANDSLIDES: PIXEL SIZE EFFECTS, PROBLEMS IN CHRONO-VALIDATION, 2D SPATIALLY DISTRIBUTE…

Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks

2016

This paper proposes Markovian Generative Adversarial Networks (MGANs), a method for training generative networks for efficient texture synthesis. While deep neural network approaches have recently demonstrated remarkable results in terms of synthesis quality, they still come at considerable computational costs (minutes of run-time for low-res images). Our paper addresses this efficiency issue. Instead of a numerical deconvolution in previous work, we precompute a feed-forward, strided convolutional network that captures the feature statistics of Markovian patches and is able to directly generate outputs of arbitrary dimensions. Such network can directly decode brown noise to realistic textu…

Leveraging Uncertainty Estimates to Improve Segmentation Performance in Cardiac MR

2021

In medical image segmentation, several studies have used Bayesian neural networks to segment and quantify the uncertainty of the images. These studies show that there might be an increased epistemic uncertainty in areas where there are semantically and visually challenging pixels. The uncertain areas of the image can be of a great interest as they can possibly indicate the regions of incorrect segmentation. To leverage the uncertainty information, we propose a segmentation model that incorporates the uncertainty into its learning process. Firstly, we generate the uncertainty estimate (sample variance) using Monte-Carlo dropout during training. Then we incorporate it into the loss function t…

The impact of grain size on the efficiency of embedded SIMD image processing architectures

2004

Pixel-per-processing element (PPE) ratio-the amount of image data directly mapped to each processing element-has a significant impact on the area and energy efficiency of embedded SIMD architectures for image processing applications. This paper quantitatively evaluates the impact of PPE ratio on system performance and efficiency for focal-plane SIMD image processing architectures by comparing throughput, area efficiency, and energy efficiency for a range of common application kernels using architectural and workload simulation. While the impact of grain size is affected by the mix of executed instructions within an application program, the most efficient PPE ratio often does not occur at PE…

Post-processing of Pixel and Object-Based Land Cover Classifications of Very High Spatial Resolution Images

2020

The state of the art is plenty of classification methods. Pixel-based methods include the most traditional ones. Although these achieved high accuracy when classifying remote sensing images, some limits emerged with the advent of very high-resolution images that enhanced the spectral heterogeneity within a class. Therefore, in the last decade, new classification methods capable of overcoming these limits have undergone considerable development. Within this research, we compared the performances of an Object-based and a Pixel-Based classification method, the Random Forests (RF) and the Object-Based Image Analysis (OBIA), respectively. Their ability to quantify the extension and the perimeter…