Application of the grating, slit and edge image analysis in the inspiration of UV high-performance lenses

1990 ◽  
Author(s):  
Rolph Hey ◽  
Christian Hofmann ◽  
Jens-Ullrich Mueller
Keyword(s):  
Image Analysis ◽  
High Performance ◽  
Edge Image

Optical measurement of voids in situ during infusion of carbon reinforcements

2020 ◽  
pp. 002199832095982
Author(s):  
C Lystrup ◽  
A George ◽  
B Zobell ◽  
K Boster ◽  
C Childs ◽  
...  
Keyword(s):  
Image Analysis ◽  
Carbon Fiber ◽  
High Performance ◽  
Optical Measurement ◽  
Void Formation ◽  
Digital Camera ◽  
Void Content ◽  
Liquid Composite Molding ◽  
Automated Method

Liquid composite molding (LCM) is growing in importance as an alternative to traditional prepreg-autoclave methods for manufacture high-performance composites. The most significant roadblock to industry’s implementation of LCM is the usually higher void content compared with prepreg processing. One tool for reducing void levels in LCM involves optimization of flow velocity, which requires models to be developed to describe void formation at a given velocity. To help solve this problem, the following research illustrates the first known method for optical void measurement in situ during infusion in a carbon fiber reinforcement. Similar to previous studies on glass fiber, this work utilizes fluorescent dye and a digital camera to produce sufficient contrast and resolution for image analysis. Visible bubbles are photographed against the opaque carbon fiber background. An automated method of image analysis is outlined, which was used to analyze 230 images for three different flow orientations of a single fabric, producing the highest amount of experimental data seen so far on in situ void measurement. The resulting data identifies a minimum velocity threshold for minimal macro-void formation. The resultant void characterization framework will better enable optimization of LCM processing for high-performance composites based on carbon reinforcements.

HIPERCIR: a low-cost high-performance 3D radiology image analysis system

1999 ◽  
Author(s):  
Ignacio Blanquer ◽  
Vincente Hernandez ◽  
Javier Ramirez ◽  
Antonio M. Vidal ◽  
Mariano L. Alcaniz-Raya ◽  
...  
Keyword(s):  
Image Analysis ◽  
High Performance ◽  
Low Cost ◽  
Image Analysis System ◽  
Analysis System ◽  
Radiology Image

FACILITATING HIGH-PERFORMANCE IMAGE ANALYSIS ON REDUCED HYPERCUBE (RH) PARALLEL COMPUTERS

1995 ◽  
Vol 09 (04) ◽  
pp. 679-698 ◽  
Author(s):  
SOTIRIOS G. ZIAVRAS ◽  
MICHALIS A. SIDERAS
Keyword(s):  
Image Analysis ◽  
Interconnection Networks ◽  
High Performance ◽  
Interconnection Network ◽  
Parallel Computers ◽  
Number Of Publications ◽  
Low Dimensional ◽  
High Level ◽  
The Cost ◽  
Cube Connected Cycles

The direct binary hypercube interconnection network has been very popular for the design of parallel computers, because it provides a low diameter and can emulate efficiently the majority of the topologies frequently employed in the development of algorithms. The last fifteen years have seen major efforts to develop image analysis algorithms for hypercube-based parallel computers. The results of these efforts have culminated in a large number of publications included in prestigious scholarly journals and conference proceedings. Nevertheless, the aforementioned powerful properties of the hypercube come at the cost of high VLSI complexity due to the increase in the number of communication ports and channels per PE (processing element) with an increase in the total number of PE’s. The high VLSI complexity of hypercube systems is undoubtedly their dominant drawback; it results in the construction of systems that contain either a large number of primitive PE’s or a small number of powerful PE’s. Therefore, low-dimensional k-ary n-cubes with lower VSLI complexity have recently drawn the attention of many designers of parallel computers. Alternative solutions reduce the hypercube’s VLSI complexity without jeopardizing its performance. Such an effort by Ziavras has resulted in the introduction of reduced hypercubes (RH’s). Taking advantage of existing high-performance routing techniques, such as wormhole routing, an RH is obtained by a uniform reduction in the number of edges for each hypercube node. An RH can also be viewed as several connected copies of the well-known cube-connected-cycles network. The objective here is to prove that parallel computers comprising RH interconnection networks are definitely good choices for all levels of image analysis. Since the exact requirements of high-level image analysis are difficult to identify, but it is believed that versatile interconnection networks, such as the hypercube, are suitable for relevant tasks, we investigate the problem of emulating hypercubes on RH’s. The ring (or linear array), the torus (or mesh), and the binary tree are the most frequently used topologies for the development of algorithms in low-level and intermediate-level image analysis. Thus, to prove the viability of the RH for the two lower levels of image analysis, we introduce techniques for embedding the aforementioned three topologies into RH’s. The results prove the suitability of RH’s for all levels of image analysis.

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