Singularity of the conjugate function for a point ?-detector

1973 ◽  
Vol 14 (1) ◽  
pp. 81-85
Author(s):  
A. M. Kol'chuzhkin ◽  
V. V. Uchaikin
Keyword(s):  
Conjugate Function ◽  
Point Detector

A confocal laser scanning microscope for fluorescence and reflection at video rates

1989 ◽  
Vol 47 ◽  
pp. 134-135
Author(s):  
P.M. Houpt ◽  
A. Draaijer
Keyword(s):  
Light Source ◽  
Laser Scanning ◽  
Focal Point ◽  
Confocal Laser Scanning Microscope ◽  
Confocal Laser ◽  
Point Light Source ◽  
Volume Of Interest ◽  
Ion Laser ◽  
Point Light ◽  
Point Detector

In confocal microscopy, the object is scanned by the coinciding focal points (confocal) of a point light source and a point detector both focused on a certain plane in the object. Only light coming from the focal point is detected and, even more important, out-of-focus light is rejected.This makes it possible to slice up optically the ‘volume of interest’ in the object by moving it axially while scanning the focused point light source (X-Y) laterally. The successive confocal sections can be stored in a computer and used to reconstruct the object in a 3D image display.The instrument described is able to scan the object laterally with an Ar ion laser (488 nm) at video rates. The image of one confocal section of an object can be displayed within 40 milliseconds (1000 х 1000 pixels). The time to record the total information within the ‘volume of interest’ normally depends on the number of slices needed to cover it, but rarely exceeds a few seconds.

scholarly journals Conjugate function theory in weak∗ Dirichlet algebras

1974 ◽  
Vol 16 (4) ◽  
pp. 359-371 ◽  
Author(s):  
I.I. Hirschman ◽  
Richard Rochberg
Keyword(s):  
Function Theory ◽  
Conjugate Function

scholarly journals Practical implications for the quality assurance of modulated radiation therapy techniques using point detector arrays

2017 ◽  
Vol 18 (6) ◽  
pp. 20-31 ◽  
Author(s):  
Steffi Kantz ◽  
Almut Troeller McDermott ◽  
Matthias Söhn ◽  
Sabine Reinhardt ◽  
Claus Belka ◽  
...  
Keyword(s):  
Quality Assurance ◽  
Radiation Therapy ◽  
Detector Arrays ◽  
Point Detector ◽  
Practical Implications

scholarly journals Selection of Satellite Image Series for the Determination of Forest Pathology Dynamics Taking Into Account Cloud Coverage and Image Distortions Based on the Data Obtained from the Key Point Detector

2019 ◽  
Author(s):  
Евгений Трубаков ◽  
Evgeniy Trubakov ◽  
Андрей Трубаков ◽  
Andrey Trubakov ◽  
Дмитрий Коростелёв ◽  
...  
Keyword(s):  
Satellite Image ◽  
Forest Pathology ◽  
Acceptable Result ◽  
New Approach ◽  
Cloud Coverage ◽  
Point Detector ◽  
Automated Methods ◽  
Selection Of

Remote sensing of the earth and monitoring of various phenomena have been and still remain an important task for solving various problems. One of them is the forest pathology dynamics determining. Assuming its dependence on various factors forest pathology can be either short-term or long-term. Sometimes it is necessary to analyze satellite images within a period of several years in order to determine the dynamics of forest pathology. So it is connected with some special aspects and makes such analysis in manual mode impossible. At the same time automated methods face the problem of identifying a series of suitable images even though they are not covered by clouds, shadows, turbulence and other distortions. Classical methods of nebulosity determination based either on neural network or decision functions do not always give an acceptable result, because the cloud coverage by itself can be either of cirrus intortus type or insignificant within the image, but in case of cloudiness it can be the reason for wrong analysis of the area under examination. The article proposes a new approach for the analysis and selection of images based on key point detectors connected neither with cloudiness determination nor distorted area identification, but with the extraction of suitable images eliminating those that by their characteristics are unfit for forest pathology determination. Experiments have shown that the accuracy of this approach is higher than of currently used method in GIS, which is based on cloud detector.

scholarly journals On the Exponential Integrability of Conjugate Functions

2021 ◽  
Vol 27 (6) ◽  
Author(s):  
H. Gissy ◽  
S. Miihkinen ◽  
J. A. Virtanen
Keyword(s):  
Conjugate Function ◽  
Conjugate Functions ◽  
Exponential Integrability ◽  
Related Theorem

AbstractWe relate the exponential integrability of the conjugate function $${\tilde{f}}$$ f ~ to the size of the gap in the essential range of f. Our main result complements a related theorem of Zygmund.

scholarly journals Growth estimates for a Dirichlet series and its derivative

2020 ◽  
Vol 53 (1) ◽  
pp. 3-12
Author(s):  
S.I. Fedynyak ◽  
P.V. Filevych
Keyword(s):  
Continuous Function ◽  
Dirichlet Series ◽  
Half Plane ◽  
Conjugate Function ◽  
Nonnegative Sequence

Let $A\in(-\infty,+\infty]$, $\Phi$ be a continuous function on $[a,A)$ such that for every $x\in\mathbb{R}$ we have$x\sigma-\Phi(\sigma)\to-\infty$ as $\sigma\uparrow A$, $\widetilde{\Phi}(x)=\max\{x\sigma -\Phi(\sigma)\colon \sigma\in [a,A)\}$ be the Young-conjugate function of $\Phi$, $\overline{\Phi}(x)=\widetilde{\Phi}(x)/x$ for all sufficiently large $x$, $(\lambda_n)$ be a nonnegative sequence increasing to $+\infty$, $F(s)=\sum a_ne^{s\lambda_n}$ be a Dirichlet series absolutely convergent in the half-plane $\operatorname{Re}s<A$, $M(\sigma,F)=\sup\{|F(s)|\colon \operatorname{Re}s=\sigma\}$ and $G(\sigma,F)=\sum |a_n|e^{\sigma\lambda_n}$ for each $\sigma<A$. It is proved that if $\ln G(\sigma,F)\le(1+o(1))\Phi(\sigma)$, $\sigma\uparrow A$, then the inequality$$\varlimsup_{\sigma\uparrow A}\frac{M(\sigma,F')}{M(\sigma,F)\overline{\Phi}\,^{-1}(\sigma)}\le1$$holds, and this inequality is sharp. % Abstract (in English)

New fast point detector of FIR laser radiation

1993 ◽  
Author(s):  
Sergey D. Ganichev ◽  
K. Y. Glooch ◽  
I. N. Kotel'nikov ◽  
N. A. Mordovets ◽  
A. Y. Shul'man ◽  
...  
Keyword(s):  
Laser Radiation ◽  
Fir Laser ◽  
Point Detector
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