Search results for "Reconstruction algorithm"
showing 10 items of 34 documents
A charge reconstruction algorithm for DAMPE silicon microstrip detectors
2019
Abstract The DArk Matter Particle Explorer (DAMPE) can detect electrons and photons from 5 GeV to 10 TeV and charged nuclei from a few tens of GeV to 100 TeV. The silicon–tungstentracker (STK), which is composed of 768 singled-sided silicon microstrip detectors, is one of four subdetectors in DAMPE providing photon conversion , track reconstruction, and charge identification for relativistic charged particles. This paper focuses on the charge identification performance of the STK detector. The charge response depends mainly on the incident angle and the impact position of the incoming particle. To improve the charge resolution, a reconstruction algorithm to correct for these parameters was …
Conductivity imaging with interior potential measurements
2011
In this article, we present two reconstruction methods intended to be used for conductivity imaging with data obtained from a planar electrical impedance tomography device for breast cancer detection. The inverse problem to solve is different from the classical inverse conductivity problem. We reconstruct the electrical conductivity of a two-dimensional domain from boundary measurements of currents and interior measurements of the potential. One reconstruction algorithm is based on a discrete resistor model; the other one is an integral equation approach for smooth conductivity distributions.
QR-Factorization Algorithm for Computed Tomography (CT): Comparison With FDK and Conjugate Gradient (CG) Algorithms
2018
[EN] Even though QR-factorization of the system matrix for tomographic devices has been already used for medical imaging, to date, no satisfactory solution has been found for solving large linear systems, such as those used in computed tomography (CT) (in the order of 106 equations). In CT, the Feldkamp, Davis, and Kress back projection algorithm (FDK) and iterative methods like conjugate gradient (CG) are the standard methods used for image reconstruction. As the image reconstruction problem can be modeled by a large linear system of equations, QR-factorization of the system matrix could be used to solve this system. Current advances in computer science enable the use of direct methods for…
Forbidden Factors and Fragment Assembly
2002
In this paper we approach the fragment assembly problem by using the notion of minimal forbidden factors introduced in previous paper. Denoting by M(w) the set of minimal forbidden factors of a word w, we first focus on the evaluation of the size of elements in M(w) and on designing of an algorithm to recover the word w from M(w). Actually we prove that for a word w randomly generated by a memoryless source with identical symbol probabilities, the maximal length m(w) of words in M(w) is logarithmic and that the reconstruction algorithm runs in linear time. These results have an interesting application to the fragment assembly problem, i.e. reconstruct a word w from a given set I of substrin…
A memetic approach to discrete tomography from noisy projections
2010
Discrete tomography deals with the reconstruction of images from very few projections, which is, in the general case, an NP-hard problem. This paper describes a new memetic reconstruction algorithm. It generates a set of initial images by network flows, related to two of the input projections, and lets them evolve towards a possible solution, by using crossover and mutation. Switch and compactness operators improve the quality of the reconstructed images during each generation, while the selection of the best images addresses the evolution to an optimal result. One of the most important issues in discrete tomography is known as the stability problem and it is tackled here, in the case of no…
A Spatial-Temporal Correlation Approach for Data Reduction in Cluster-Based Sensor Networks
2019
International audience; In a resource-constrained Wireless Sensor Networks (WSNs), the optimization of the sampling and the transmission rates of each individual node is a crucial issue. A high volume of redundant data transmitted through the network will result in collisions, data loss, and energy dissipation. This paper proposes a novel data reduction scheme, that exploits the spatial-temporal correlation among sensor data in order to determine the optimal sampling strategy for the deployed sensor nodes. This strategy reduces the overall sampling/transmission rates while preserving the quality of the data. Moreover, a back-end reconstruction algorithm is deployed on the workstation (Sink)…
Optical encryption with compressive ghost imaging
2011
Ghost imaging (GI) is a novel technique where the optical information of an object is encoded in the correlation of the intensity fluctuations of a light source. Computational GI (CGI) is a variant of the standard procedure that uses a single bucket detector. Recently, we proposed to use CGI to encrypt and transmit the object information to a remote party [1]. The optical encryption scheme shows compressibility and robustness to eavesdropping attacks. The reconstruction algorithm provides a relative low quality images and requires high acquisitions times. A procedure to overcome such limitations is to combine CGI with compressive sampling (CS), an advanced signal processing theory that expl…
Effective elastic properties of biocomposites using 3D computational homogenization and X-ray microcomputed tomography
2021
A 3D computational homogenization method based on X-ray microcomputed tomography (μCT) was proposed and implemented to investigate how the fiber weight fraction, orthotropy and orientation distribution affect the effective elastic properties of regenerated cellulose fiber-polylactic acid (PLA) biocomposites. Three-dimensional microstructures reconstructed by means of the X-ray μCT were used as the representative volume elements (RVEs) and incorporated into the finite element solver within the computational homogenization framework. The present method used Euclidean bipartite matching technique so as to eliminate the generation of artificial periodic boundaries and use the in-situ solution d…
How much geometry it takes to reconstruct a 2-manifold in R 3
2009
Known algorithms for reconstructing a 2-manifold from a point sample in R 3 are naturally based on decisions/predicates that take the geometry of the point sample into account. Facing the always present problem of round-off errors that easily compromise the exactness of those predicate decisions, an exact and robust implementation of these algorithms is far from being trivial and typically requires employment of advanced datatypes for exact arithmetic, as provided by libraries like CORE, LEDA, or GMP. In this article, we present a new reconstruction algorithm, one whose main novelties is to throw away geometry information early on in the reconstruction process and to mainly operate combina…
Implementation and performance of the ATLAS second level jet trigger
2008
ATLAS is one of the four major LHC experiments, designed to cover a wide range of physics topics. In order to cope with a rate of 40 MHz and 25 interactions per bunch crossing, the ATLAS trigger system is divided in three different levels. The first one (LVL1, hardware based) identifies signatures in 2 microseconds that are confirmed by the the following trigger levels (software based). The Second Level Trigger (LVL2) only looks at a region of the space around the LVL1 signature (called Region of Interest or ROI), confirming/rejecting the event in about 10 ms, while the Event Filter (Third Level Trigger, EF) has potential full event access and larger processing times, of the order of 1 s. T…