scholarly journals Three‐dimensional transfer function of optical microscopes in reflection mode

2021 ◽  
Author(s):  
Peter Lehmann ◽  
Tobias Pahl
Keyword(s):  
Transfer Function ◽  
Three Dimensional ◽  
Reflection Mode

Three-dimensional coherent transfer function in reflection-mode confocal microscopy with two-zone phase filters

Optik ◽  
2018 ◽  
Vol 170 ◽  
pp. 70-81 ◽  
Author(s):  
Lingxiao Li ◽  
Yi He ◽  
Yuanyuan Wang ◽  
Xiqi Li ◽  
Ling Wei ◽  
...  
Keyword(s):  
Transfer Function ◽  
Confocal Microscopy ◽  
Three Dimensional ◽  
Reflection Mode

Density-based discrimination of protein and RNA in the ribosome

1992 ◽  
Vol 50 (1) ◽  
pp. 464-465
Author(s):  
Joachim Frank
Keyword(s):  
Transfer Function ◽  
Spatial Frequency ◽  
Three Dimensional ◽  
Wiener Filter ◽  
Dark Field Image ◽  
Dark Field ◽  
Frequency Range ◽  
Bright Field ◽  
Contrast Transfer Function ◽  
Pass Filter

Cryo-electron microscopy combined with single-particle reconstruction techniques has allowed us to form a three-dimensional image of the Escherichia coli ribosome.In the interior, we observe strong density variations which may be attributed to the difference in scattering density between ribosomal RNA (rRNA) and protein. This identification can only be tentative, and lacks quantitation at this stage, because of the nature of image formation by bright field phase contrast. Apart from limiting the resolution, the contrast transfer function acts as a high-pass filter which produces edge enhancement effects that can explain at least part of the observed variations. As a step toward a more quantitative analysis, it is necessary to correct the transfer function in the low-spatial-frequency range. Unfortunately, it is in that range where Fourier components unrelated to elastic bright-field imaging are found, and a Wiener-filter type restoration would lead to incorrect results. Depending upon the thickness of the ice layer, a varying contribution to the Fourier components in the low-spatial-frequency range originates from an “inelastic dark field” image. The only prospect to obtain quantitatively interpretable images (i.e., which would allow discrimination between rRNA and protein by application of a density threshold set to the average RNA scattering density may therefore lie in the use of energy-filtering microscopes.

Optical transfer function of three-dimensional photonic crystals by volume holographic recording

2013 ◽  
Vol 103 (4) ◽  
pp. 041106 ◽  
Author(s):  
Susanna Orlic ◽  
Fabian Bernstein ◽  
Christoph Kratz ◽  
Alexander Schlösser
Keyword(s):  
Transfer Function ◽  
Photonic Crystals ◽  
Three Dimensional ◽  
Holographic Recording ◽  
Optical Transfer Function ◽  
Optical Transfer

scholarly journals Gravity wave propagation in the realistic atmosphere based on a three-dimensional transfer function model

2007 ◽  
Vol 25 (9) ◽  
pp. 1979-1986 ◽  
Author(s):  
L. Sun ◽  
W. Wan ◽  
F. Ding ◽  
T. Mao
Keyword(s):  
Transfer Function ◽  
Gravity Wave ◽  
Gravity Waves ◽  
Minimum Energy ◽  
Three Dimensional ◽  
Wave Number ◽  
Transfer Function Model ◽  
Wave Band ◽  
Function Model ◽  
Ionospheric Height

Abstract. In order to study the filter effect of the background winds on the propagation of gravity waves, a three-dimensional transfer function model is developed on the basis of the complex dispersion relation of internal gravity waves in a stratified dissipative atmosphere with background winds. Our model has successfully represented the main results of the ray tracing method, e.g. the trend of the gravity waves to travel in the anti-windward direction. Furthermore, some interesting characteristics are manifest as follows: (1) The method provides the distribution characteristic of whole wave fields which propagate in the way of the distorted concentric circles at the same altitude under the control of the winds. (2) Through analyzing the frequency and wave number response curve of the transfer function, we find that the gravity waves in a wave band of about 15–30 min periods and of about 200–400 km horizontal wave lengths are most likely to propagate to the 300-km ionospheric height. Furthermore, there is an obvious frequency deviation for gravity waves propagating with winds in the frequency domain. The maximum power of the transfer function with background winds is smaller than that without background winds. (3) The atmospheric winds may act as a directional filter that will permit gravity wave packets propagating against the winds to reach the ionospheric height with minimum energy loss.

Dynamic Observer Method Based on Modified Green’s Functions for Robust and More Stable Inverse Algorithms

2008 ◽  
Author(s):  
Priscila F. B. Sousa ◽  
Ana P. Fernandes ◽  
Vale´rio Luiz Borges ◽  
George S. Dulikravich ◽  
Gilmar Guimara˜es
Keyword(s):  
Heat Transfer ◽  
Transfer Function ◽  
Green's Functions ◽  
Transfer Functions ◽  
Green’S Functions ◽  
Three Dimensional ◽  
Original Method ◽  
Identification Algorithm ◽  
Transfer Function Model ◽  
Function Model

This work presents a modified procedure to use the concept of dynamic observers based on Green’s functions to solve inverse problems. The original method can be divided in two distinct steps: i) obtaining a transfer function model GH and; ii) obtaining heat transfer functions GQ and GN and building an identification algorithm. The transfer function model, GH, is obtained from the equivalent dynamic systems theory using Green’s functions. The modification presented here proposes two different improvements in the original technique: i) A different method of obtaining the transfer function model, GH, using analytical functions instead of numerical procedures, and ii) Definition of a new concept of GH to allow the use of more than one response temperature. Obtaining the heat transfer functions represents an important role in the observer method and is crucial to allow the technique to be directly applied to two or three-dimensional heat conduction problems. The idea of defining the new GH function is to improve the robustness and stability of the algorithm. A new dynamic equivalent system for the thermal model is then defined in order to allow the use of two or more temperature measurements. Heat transfer function, GH can be obtained numerically or analytically using Green’s function method. The great advantage of deriving GH analytically is to simplify the procedure and minimize the estimative errors.

Spatial Frequency Domain Analysis of Heat Transfer in Microelectronic Chips With Applications to Temperature Aware Computing

2007 ◽  
Author(s):  
Keivan Etessam-Yazdani ◽  
Hendrik F. Hamann ◽  
Mehdi Asheghi
Keyword(s):  
Transfer Function ◽  
Spatial Frequency ◽  
Frequency Domain ◽  
Thermal Power ◽  
Three Dimensional ◽  
Heat Removal ◽  
Frequency Domain Analysis ◽  
Heat Diffusion ◽  
The Impact ◽  
Spatial Frequency Domain

In this paper we present a novel analytical approach for obtaining the thermal transfer function of multi-layer chips in the spatial frequency domain. The behavior of the transfer function is used to address a number of key issues such as 1) the appropriate power granularity required for microarchitecture thermal-power analysis, and 2) the impact of packaging and cooling solutions on heat removal from chip hotspots. The merit of the presented method is in 1) simplicity, such that even for rather complicated multi-layer structures the analysis takes only a fraction of a second, and 2) accuracy, because the approach is based on the exact solution of three-dimensional heat diffusion equations.

Sign in / Sign up

Export Citation Format

Share Document