Direct Linear Mapping Between Cartesian Hill Frame Coordinates and Orbit Element Differences

2009 ◽  
pp. 779-780
Keyword(s):  
Linear Mapping ◽  
Orbit Element

Synchrotron X-Ray Topography Study on the Relationship between Local Basal Plane Bending and Basal Plane Dislocations in PVT-Grown 4H-SiC Substrate Wafers

2020 ◽  
Vol 1004 ◽  
pp. 393-400
Author(s):  
Tuerxun Ailihumaer ◽  
Hongyu Peng ◽  
Balaji Raghothamachar ◽  
Michael Dudley ◽  
Gilyong Chung ◽  
...  
Keyword(s):  
Basal Plane ◽  
Local Strain ◽  
Grazing Incidence ◽  
Peak Shift ◽  
Linear Mapping ◽  
Incidence Geometry ◽  
Convex Shape ◽  
X Ray ◽  
Black And White ◽  
Plane Bending

Synchrotron monochromatic beam X-ray topography (SMBXT) in grazing incidence geometry shows black and white contrast for basal plane dislocations (BPDs) with Burgers vectors of opposite signs as demonstrated using ray tracing simulations. The inhomogeneous distribution of these dislocations is associated with the concave/convex shape of the basal plane. Therefore, the distribution of these two BPD types were examined for several 6-inch diameter 4H-SiC substrates and the net BPD density distribution was used for evaluating the nature and magnitude of basal plane bending in these wafers. Results show different bending behaviors along the two radial directions - [110] and [100] directions, indicating the existence of non-isotropic bending. Linear mapping of the peak shift of the 0008 reflection along the two directions was carried out using HRXRD to correlate with the results from the SMBXT measurements. Basal-plane-tilt angle calculated using the net BPD density derived from SMBXT shows a good correlation with those obtained from HRXRD measurements, which further confirmed that bending in basal plane is caused by the non-uniform distribution of BPDs. Regions of severe bending were found to be associated with both large tilt angles (95% black contrast BPDs to 5% white contrast BPDs) and abrupt changes in a and c lattice parameters i.e. local strain.

The LLE and a linear mapping

2006 ◽  
Vol 39 (9) ◽  
pp. 1799-1804 ◽  
Author(s):  
F.C. Wu ◽  
Z.Y. Hu
Keyword(s):  
Linear Mapping

Deep learning approach for UHF RFID-based indoor localization

2021 ◽  
pp. 1-13
Author(s):  
Haishu Ma ◽  
Zongzheng Ma ◽  
Lixia Li ◽  
Ya Gao
Keyword(s):  
Radio Frequency Identification ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Estimation Error ◽  
Research Direction ◽  
Linear Interpolation ◽  
Linear Mapping ◽  
Uhf Rfid ◽  
Indoor Location ◽  
Main Research

Due to the proliferation of the IoT devices, indoor location-based service is bringing huge business values and potentials. The positioning accuracy is restricted by the variability and complexity of the indoor environment. Radio Frequency Identification (RFID), as a key technology of the Internet of Things, has became the main research direction in the field of indoor positioning because of its non-contact, non-line-of-sight and strong anti-interference abilities. This paper proposes the deep leaning approach for RFID based indoor localization. Since the measured Received Signal Strength Indicator (RSSI) can be influenced by many indoor environment factors, Kalman filter is applied to erase the fluctuation. Furthermore, linear interpolation is adopted to increase the density of the reference tags. In order to improve the processing ability of the fingerprint database, deep neural network is adopted together with the fingerprinting method to optimize the non-linear mapping between fingerprints and indoor coordinates. The experimental results show that the proposed method achieves high accuracy with a mean estimation error of 0.347 m.

scholarly journals Artificial neural networks for inverse design of resonant nanophotonic components with oscillatory loss landscapes

Nanophotonics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 385-392
Author(s):  
Joeri Lenaerts ◽  
Hannah Pinson ◽  
Vincent Ginis
Keyword(s):  
Machine Learning ◽  
Optimization Procedure ◽  
Linear Mapping ◽  
Inverse Design ◽  
Design Parameters ◽  
Second Step ◽  
Input Parameters ◽  
Novel Variant ◽  
Resonant Systems ◽  
The Fourier Transform

AbstractMachine learning offers the potential to revolutionize the inverse design of complex nanophotonic components. Here, we propose a novel variant of this formalism specifically suited for the design of resonant nanophotonic components. Typically, the first step of an inverse design process based on machine learning is training a neural network to approximate the non-linear mapping from a set of input parameters to a given optical system’s features. The second step starts from the desired features, e.g. a transmission spectrum, and propagates back through the trained network to find the optimal input parameters. For resonant systems, this second step corresponds to a gradient descent in a highly oscillatory loss landscape. As a result, the algorithm often converges into a local minimum. We significantly improve this method’s efficiency by adding the Fourier transform of the desired spectrum to the optimization procedure. We demonstrate our method by retrieving the optimal design parameters for desired transmission and reflection spectra of Fabry–Pérot resonators and Bragg reflectors, two canonical optical components whose functionality is based on wave interference. Our results can be extended to the optimization of more complex nanophotonic components interacting with structured incident fields.

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