A repository & source of cutting edge news about emerging terahertz technology, it's commercialization & innovations in THz devices, quality & process control, medical diagnostics, security, astronomy, communications, applications in graphene, metamaterials, CMOS, compressive sensing, 3d printing, and the Internet of Nanothings. NOTHING POSTED IS INVESTMENT ADVICE! REPOSTED COPYRIGHT IS FOR EDUCATIONAL USE.
Showing posts with label Klaus Szielasko. Show all posts
Showing posts with label Klaus Szielasko. Show all posts
Saturday, June 16, 2018
Abstract-Raspberry-like supraparticles from nanoparticle building-blocks as code-objects for hidden signatures readable by terahertz rays
Christopher Stumm, Klaus Szielasko, Tim Granath, Claudia Stauch, Karl Mandel,
https://www.sciencedirect.com/science/article/pii/S2352492818301764
Supraparticles, i.e., raspberry-like microparticles which are composed of nanoparticles (iron oxide), reveal specific interaction properties with terahertz (THz) rays. Depending on the density of the clustering of the nanoparticles within the raspberry-like supraparticle, characteristic THz components are altered upon transmission. The clustering can be adjusted upon supraparticle assembly via modification of the nanoparticles’ surfaces. By employing very densely and very loosely clustered supraparticles, a graphical coding system can be developed which allows creating signatures that are hidden in the bulk of a material (an object) and are easily and unambiguously decodable with THz rays.
Saturday, May 12, 2018
Abstract-An Infrared-Induced Terahertz Imaging Modality for Foreign Insert Detection in A Glass Fiber-Skinned Lightweight Honeycomb Composite Panel
Hai Zhang, Stefano Sfarra, Ahmad Osman, Klaus Szielasko, Christopher Stumm, Marc Genest, Xavier Maldague
https://ieeexplore.ieee.org/document/8353417/
In this paper,
terahertz time-domain spectroscopy (THz-TDS) is used for the first time to
detect fabricated defects in a glass fiber-skinned lightweight honeycomb
composite panel. A novel amplitude polynomial regression (APR) algorithm is
proposed as a pre-processing method. This method segments the
amplitude-frequency curves to simulate the heating and the cooling monotonic
behavior as in infrared thermography. Then, the method of empirical orthogonal
function (EOF) imaging is applied on the APR pre-processed data as a
post-processing algorithm. Signal-to-noise ratio analysis is performed to
verify the image improvement of the proposed APR-EOF modality from a
quantitative point of view. Finally, the experimental results and the physical
analysis show that THz is more suitable with respect to the detection of
defects in glass fiber lightweight honeycomb composites.
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