MEMS-Scanner Testbench for High Field of View LiDAR Applications

LiDAR sensors are a key technology for enabling safe autonomous cars. For highway applications, such systems must have a long range, and the covered field of view (FoV) of >45° must be scanned with resolutions higher than 0.1°. These specifications can be met by modern MEMS scanners, which are chosen for their robustness and scalability. For the automotive market, these sensors, and especially the scanners within, must be tested to the highest standards. We propose a novel measurement setup for characterizing and validating these kinds of scanners based on a position-sensitive detector (PSD) by imaging a deflected laser beam from a diffuser screen onto the PSD. A so-called ray trace shifting technique (RTST) was used to minimize manual calibration effort, to reduce external mounting errors, and to enable dynamical one-shot measurements of the scanner’s steering angle over large FoVs. This paper describes the overall setup and the calibration method according to a standard camera calibration. We further show the setup’s capabilities by validating it with a statically set rotating stage and a dynamically oscillating MEMS scanner. The setup was found to be capable of measuring LiDAR MEMS scanners with a maximum FoV of 47° dynamically, with an uncertainty of less than 1%.

Seasonal Temperature Dependence of Availability of Under-Water Visible Optical Communications through Egypt Nile River Water

Underwater visible optical communications become very important for their high velocity and more data rate. But the optical is suffering from the high water attenuation. For optical communication. Pure water is the best of the ten water types with wavelengths λ = 455 and 486 mm. The Nile river water is a pure water without salinity (fresh water). The temperature of the water is daily changes and so the performance of optical communication underwater becomes temperature-dependent. A simplified expression very good accuracy of Egypt Nile water to determine the water refractive index, water dispersion, water attenuation, received optical power, and SNR as direct temperature dependence is done. The optical channel loss model is used to determine the received optical power and the ray trace model is used to define optical radiation pattern. The equation of optical received power by ray trace is the same as that by using the optical channel loss model except for the transmitter gain of them are different. For λ = 486nm with water temperature varying from 4oC to 30oC, the corresponding refractive index decreases from 1.3399 to 1.3379 (so, the optical velocity under fresh water increases from 2.239*108m/s to 2.2423*108 m/s), dispersion decreases from 0.6492 to 0.6459 (ps/m nm), attenuation factor decreases from 0.0378 m− 1 to 0.0345m− 1 and so the required transmitted optical power due to attenuation for 800 m long shrinks to 7.4 %. The Effect of temperature becomes more evident with more link distance. The required transmitted power to achieve the required SNR increases with the more data rate. To overcome the unavailability of the link due to water temperature, the transmitted power must be controlled by the daily water temperature. In this study, the temperature dependence of the performance of the optical link and a simulation proposed example design is done.

A ray-tracing algorithm for ab initio calculation of thermal load in undulator-based synchrotron beamlines

The OASYS suite and its powerful integration features are used to implement a ray-tracing algorithm to accurately calculate the thermal load in any component of an undulator-based synchrotron beamline. This is achieved by sampling and converting the SRW source of a given energy into a Shadow source and using the latter code to ray trace the full beamline. The accuracy of the algorithm is proved by reconstructing the full undulator radiation distribution through an aperture and comparing the result with direct calculaton of the total power using SRW. The algorithm is particularly suited to analyze cases with complex beamline layouts and optical elements, such as crystals, multilayers, and compound refractive lenses. Examples of its use to calculate the power load on elements of two of the feature beamlines at the Advanced Photon Source Upgrade Project and a comparison of the results with analytical calculations are presented.

Artificial Neural Networks in Radiation Heat Transfer Analysis

In the Monte Carlo ray-trace (MCRT) method, millions of rays are emitted and traced throughout an enclosure following the laws of geometrical optics. Each ray represents the path of a discrete quantum of energy emitted from surface element i and eventually absorbed by surface element j. The distribution of rays absorbed by the n surface elements making up the enclosure is interpreted in terms of a radiation distribution factor matrix whose elements represent the probability that energy emitted by element i will be absorbed by element j. Once obtained, the distribution factor matrix may be used to compute the net heat flux distribution on the walls of an enclosure corresponding to a specified surface temperature distribution. It is computationally very expensive to obtain high accuracy in the heat transfer calculation when high spatial resolution is required. This is especially true if a manifold of emissivities is to be considered in a parametric study in which each value of surface emissivity requires a new ray-trace to determine the corresponding distribution factor matrix. Artificial neural networks (ANNs) offer an alternative approach whose computational cost is greatly inferior to that of the traditional MCRT method. Significant computational efficiency is realized by eliminating the need to perform a new ray trace for each value of emissivity. The current contribution introduces and demonstrates through case studies estimation of radiation distribution factor matrices using ANNs and their subsequent use in radiation heat transfer calculations.