Scattering | EPFL Graph Search

Scattering is a term used in physics to describe a wide range of physical processes where moving particles or radiation of some form, such as light or sound, are forced to deviate from a straight trajectory by localized non-uniformities (including particles and radiation) in the medium through which they pass. In conventional use, this also includes deviation of reflected radiation from the angle predicted by the law of reflection. Reflections of radiation that undergo scattering are often called diffuse reflections and unscattered reflections are called specular (mirror-like) reflections. Originally, the term was confined to light scattering (going back at least as far as Isaac Newton in the 17th century). As more "ray"-like phenomena were discovered, the idea of scattering was extended to them, so that William Herschel could refer to the scattering of "heat rays" (not then recognized as electromagnetic in nature) in 1800. John Tyndall, a pioneer in light scattering research, noted th

Diffusion de neutrons de 14 MeV par le carbone 12

Claude Joseph

The angular distributions of the differential cross-sections for scattering of 14,1 MeV neutrons from 12C, Q = 0(0+); – 4,43(2+); – 7,65(0+) and – 9,63(8–) MeV, have been measured using a time-of-flight spectrometer. An indication can be found of the excitation of a wide level at about 10 MeV. A Monte-Carlo programme has been worked up and used to correct the results for multiple scattering; it calculates time-of-flight spectra which can be compared with the measured ones in order to deduce the best values of the cross-sections. The results are finally confronted with other experimental determinations and existing theoretical predictions.

s.n.1967

The Neutron Scattering Structure Factor

Jérôme Egger

In the experience of neutron scattering, the structure factor is a value that can be almost directly measured (through the differential inelastic neutron scattering cross section). Thus it can be interesting to have a calculation which could give a theoretical point of view, even if the system used in the calculation cannot be as complex as in the real case. Although this calculation (of a simplified system) could be a bad representation of the real system, it is already a point of comparison which yields finally to a better understanding. The present work has the aim to provide a useful matlab code for the computation of the structure factor of small magnetic cluster in “spin-only” scattering. A concrete exemple and the result for a special configuration will be also given.

2010

3D Reconstruction of Optical Diffraction Tomography Based on a Neural Network Model

Morteza Hasani Shoreh

Optical tomography has been widely investigated for biomedical imaging applications. In recent years, it has been combined with digital holography and has been employed to produce high quality images of phase objects such as cells. In this Thesis, we look into some of the newest optical Diffraction Tomography (DT) based techniques to solve Three-Dimensional (3D) reconstruction problems and discuss and compare some of the leading ideas and papers. Then we propose a neural-network-based algorithm to solve this problem and apply it on both synthetic and biological samples. Conventional phase tomography with coherent light and off axis recording is performed. The Beam Propagation Method (BPM) is used to model scattering and each x-y plane is modeled by a layer of neurons in the BPM. The network's output (simulated data) is compared to the experimental measurements and the error is used for correcting the weights of the neurons (the refractive indices of the nodes) using standard error back-propagation techniques. The proposed algorithm is detailed and investigated. Then, we look into resolution-conserving regularization and discuss a method for selecting regularizing parameters. In addition, the local minima and phase unwrapping problems are discussed and ways of avoiding them are investigated. It is shown that the proposed learning tomography (LT) achieves better performance than other techniques such as, DT especially when insufficient number or incomplete set of measurements is available. We also explore the role of regularization in obtaining higher fidelity images without losing resolution. It is experimentally shown that due to overcoming multiple scattering, the LT reconstruction greatly outperforms the DT when the sample contains two or more layers of cells or beads. Then, reconstruction using intensity measurements is investigated. 3D reconstruction of a live cell during apoptosis is presented in a time-lapse format. At the end, we present a final comparison with leading papers and commercially available systems. It is shown that -compared to other existing algorithms- the results of the proposed method have better quality. In particular, parasitic granular structures and the missing cone artifact are improved. Overall, the perspectives of our approach are pretty rich for high-resolution tomographic imaging in a range of practical applications.

EPFL2018

3D Reconstruction of Optical Diffraction Tomography Based on a Neural Network Model

Morteza Hasani Shoreh

EPFL2018