Analytical examples of reversal current, zero core current, and surface current, toroidal magnetostatic equilibria with nested flux surfaces

2012 ◽  
Vol 19 (7) ◽  
pp. 072517
Author(s):  
J.-J. Aly
2020 ◽  
Vol 91 (3) ◽  
pp. 30901
Author(s):  
Yibo Tang ◽  
Longhui He ◽  
Jianming Xu ◽  
Hailang He ◽  
Yuhan Li ◽  
...  

A dual-band microwave metamaterial absorber with single-peak regulation and wide-angle absorption has been proposed and illustrated. The designed metamaterial absorber is consisted of hollow-cross resonators, solid-cross resonators, dielectric substrate and metallic background plane. Strong absorption peak coefficients of 99.92% and 99.55% are achieved at 8.42 and 11.31 GHz, respectively, which is basically consistent with the experimental results. Surface current density and changing material properties are employed to illustrate the absorptive mechanism. More importantly, the proposed dual-band metamaterial absorber has the adjustable property of single absorption peak and could operate well at wide incidence angles for both transverse electric (TE) and transverse magnetic (TM) waves. Research results could provide and enrich instructive guidances for realizing a single-peak-regulation and wide-angle dual-band metamaterial absorber.


2016 ◽  
Vol 136 (2) ◽  
pp. 170-174 ◽  
Author(s):  
Tadashi Koshizuka ◽  
Yasuhiko Taniguchi ◽  
Eiichi Haginomori ◽  
Hisatoshi Ikeda ◽  
Keisuke Udagawa

1975 ◽  
Author(s):  
Bruce J. West ◽  
Bruce I. Cohen ◽  
Kenneth M. Watson

2020 ◽  
Vol 11 (1) ◽  
pp. 103
Author(s):  
Yadgar I. Abdulkarim ◽  
Fahmi F. Muhammadsharif ◽  
Mehmet Bakır ◽  
Halgurd N. Awl ◽  
Muharrem Karaaslan ◽  
...  

In this work, a new design for a real-time noninvasive metamaterial sensor, based on a corona-shaped resonator, is proposed. The sensor was designed numerically and fabricated experimentally in order to be utilized for efficient detection of glucose in aqueous solutions such as water and blood. The sensor was inspired by a corona in-plane-shaped design with the presumption that its circular structure might produce a broader interaction of the electromagnetic waves with the glucose samples. A clear shift in the resonance frequency was observed for various glucose samples, which implies that the proposed sensor has a good sensitivity and can be easily utilized to distinguish any glucose concentration, even though their dielectric coefficients are close. Results showed a superior performance in terms of resonance frequency shift (1.51 GHz) and quality factor (246) compared to those reported in the literature. The transmission variation level ∆|S21| was investigated for glucose concentration in both water and blood. The sensing mechanism was elaborated through the surface current, electric field and magnetic field distributions on the corona resonator. The proposed metamaterials sensor is considered to be a promising candidate for biosensor and medicine applications in human glycaemia monitoring.


2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Adam Gauci ◽  
Aldo Drago ◽  
John Abela

High frequency (HF) radar installations are becoming essential components of operational real-time marine monitoring systems. The underlying technology is being further enhanced to fully exploit the potential of mapping sea surface currents and wave fields over wide areas with high spatial and temporal resolution, even in adverse meteo-marine conditions. Data applications are opening to many different sectors, reaching out beyond research and monitoring, targeting downstream services in support to key national and regional stakeholders. In the CALYPSO project, the HF radar system composed of CODAR SeaSonde stations installed in the Malta Channel is specifically serving to assist in the response against marine oil spills and to support search and rescue at sea. One key drawback concerns the sporadic inconsistency in the spatial coverage of radar data which is dictated by the sea state as well as by interference from unknown sources that may be competing with transmissions in the same frequency band. This work investigates the use of Machine Learning techniques to fill in missing data in a high resolution grid. Past radar data and wind vectors obtained from satellites are used to predict missing information and provide a more consistent dataset.


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