Singular-Value Optimization in Plane-Polar Near-Field Antenna Characterization

2010 ◽  
Vol 52 (2) ◽  
pp. 103-112 ◽  
Author(s):  
A. Capozzoli ◽  
C. Curcio ◽  
G. D'Elia ◽  
A. Liseno
Keyword(s):  
Near Field ◽  
Singular Value

Introduction [Singular-value optimization in plane-polar near-field antenna characterization

2010 ◽  
Vol 52 (2) ◽  
pp. 103-103 ◽  
Author(s):  
A. Capozzoli ◽  
C. Curcio ◽  
G. D'Elia ◽  
A. Liseno
Keyword(s):  
Near Field ◽  
Singular Value

scholarly journals On the Optimal Field Sensing in Near-Field Characterization

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4460
Author(s):  
Amedeo Capozzoli ◽  
Claudio Curcio ◽  
Angelo Liseno
Keyword(s):  
Near Field ◽  
Gaussian Quadrature ◽  
Field Measurements ◽  
Singular Value ◽  
Low Frequencies ◽  
Virtual Sensors ◽  
Phase Measurements ◽  
Singular Functions ◽  
Virtual Field ◽  
Value Decomposition

We deal with the problem of characterizing a source or scatterer from electromagnetic radiated or scattered field measurements. The problem refers to the amplitude and phase measurements which has applications also to interferometric approaches at optical frequencies. From low frequencies (microwaves) to high frequencies or optics, application examples are near-field/far-field transformations, object restoration from measurements within a pupil, near-field THz imaging, optical coherence tomography and ptychography. When analyzing the transmitting-sensing system, we can define “optimal virtual" sensors by using the Singular Value Decomposition (SVD) approach which has been, since long time, recognized as the “optimal” tool to manage linear algebraic problems. The problem however emerges of discretizing the relevant singular functions, thus defining the field sampling. To this end, we have recently developed an approach based on the Singular Value Optimization (SVO) technique. To make the “virtual” sensors physically realizable, in this paper, two approaches are considered: casting the “virtual” field sensors into arrays reaching the same performance of the “virtual” ones; operating a segmentation of the receiver. Concerning the array case, two ways are followed: synthesize the array by a generalized Gaussian quadrature discretizing the linear reception functionals and use elementary sensors according to SVO. We show that SVO is “optimal” in the sense that it leads to the use of elementary, non-uniformly located field sensors having the same performance of the “virtual” sensors and that generalized Gaussian quadrature has essentially the same performance.

scholarly journals Different Metrics for Singular Value Optimization in Near-Field Antenna Characterization

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2122
Author(s):  
Amedeo Capozzoli ◽  
Claudio Curcio ◽  
Angelo Liseno
Keyword(s):  
Electromagnetic Field ◽  
Near Field ◽  
A Priori ◽  
Singular Value ◽  
Maximum Amount ◽  
A Priori Information ◽  
Field Sample ◽  
Information Measures ◽  
Field Samples ◽  
Priori Information

We deal with the use of different metrics in the framework of the Singular Value Optimization (SVO) technique for near-field antenna characterization. SVO extracts the maximum amount of information on an electromagnetic field over a certain domain from field samples on an acquisition domain, with a priori information on the source, e.g., support information. It determines the field sample positions by optimizing a functional featuring the singular value dynamics of the radiation operator and representing a measure of the information collected by the field samples. Here, we discuss in detail and compare the use, in the framework of SVO, of different objective functionals and so of different information measures: Shannon number, mutual information, and Fisher information. The numerical results show that they yield a similar performance.

Near-field scanning optical microscopy

1986 ◽  
Vol 44 ◽  
pp. 642-643
Author(s):  
E. Betzig ◽  
A. Harootunian ◽  
M. Isaacson ◽  
A. Lewis
Keyword(s):  
Near Field ◽  
Optical Microscope ◽  
Visible Radiation ◽  
Field Methods ◽  
Optical Elements ◽  
Optical Region ◽  
Order Of Magnitude ◽  
Nondestructive Imaging ◽  
Scanning Optical Microscopy ◽  
Near Field Scanning

In general, conventional methods of optical imaging are limited in spatial resolution by either the wavelength of the radiation used or by the aberrations of the optical elements. This is true whether one uses a scanning probe or a fixed beam method. The reason for the wavelength limit of resolution is due to the far field methods of producing or detecting the radiation. If one resorts to restricting our probes to the near field optical region, then the possibility exists of obtaining spatial resolutions more than an order of magnitude smaller than the optical wavelength of the radiation used. In this paper, we will describe the principles underlying such "near field" imaging and present some preliminary results from a near field scanning optical microscope (NS0M) that uses visible radiation and is capable of resolutions comparable to an SEM. The advantage of such a technique is the possibility of completely nondestructive imaging in air at spatial resolutions of about 50nm.

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