Micro scalar patterning for printing ultra fine solid lines in flexographic printing process

This research focuses on the study of ultra-fine solid lines printing by using Micro-flexographic machine which is combination of flexography and micro-contact printing technique. Flexography is one of the famous and high speed roll to roll printing techniques that are possible to create graphic and electronic device on variable substrates. Micro-contact printing is a low cost technique that usually uses for micro to nano scale image especially in fine solid lines image structure. Graphene is nano material that can be used as printing ink which usually uses in producing micro to nano scale electronic devices. Lanthanum is a rare earth metal that has potential in printing industry. The combination of both printing techniques is known as Micro-flexographic printing has been successfully produced the lowest fine solid lines width and gap. The new printing technique could print fine solid lines image below 10 μm on biaxially oriented polypropylene (BOPP) substrate by using graphene as printing ink. The Micro-flexographic printing technique has been successfully printed fine solid lines with 2.6 μm width. This study also elaborates the imprint lithography process in achieving micro down to nano fine solid lines structure below 10 μm. In an additional, the lanthanum target has been successful printed on variable substrates with good surface adhesion property. This research illustrates the ultra-fine solid lines printing capability for the application of printing electronic, graphic and bio-medical

Empirical Study of Car License Plates Recognition

The number of vehicles on the road has increased drastically in recent years. The license plate is an identity card for a vehicle. It can map to the owner and further information about vehicle. License plate information is useful to help traffic management systems. For example, traffic management systems can check for vehicles moving at speeds not permitted by law and can also be installed in parking areas to se-cure the entrance or exit way for vehicles. License plate recognition algorithms have been proposed by many researchers. License plate recognition requires license plate detection, segmentation, and charac-ters recognition. The algorithm detects the position of a license plate and extracts the characters. Various license plate recognition algorithms have been implemented, and each algorithm has its strengths and weaknesses. In this research, I implement three algorithms for detecting license plates, three algorithms for segmenting license plates, and two algorithms for recognizing license plate characters. I evaluate each of these algorithms on the same two datasets, one from Greece and one from Thailand. For detecting li-cense plates, the best result is obtained by a Haar cascade algorithm. After the best result of license plate detection is obtained, for the segmentation part a Laplacian based method has the highest accuracy. Last, the license plate recognition experiment shows that a neural network has better accuracy than other algo-rithm. I summarize and analyze the overall performance of each method for comparison

The image ray transform for structural feature detection

The use of analogies to physical phenomena is an exciting paradigm in computer vision that allows unorthodox approaches to feature extraction, creating new techniques with unique properties. A technique known as the "image ray transform" has been developed based upon an analogy to the propagation of light as rays. The transform analogises an image to a set of glass blocks with refractive index linked to pixel properties and then casts a large number of rays through the image. The course of these rays is accumulated into an output image. The technique can successfully extract tubular and circular features and we show successful circle detection, ear biometrics and retinal vessel extraction. The transform has also been extended through the use of multiple rays arranged as a beam to increase robustness to noise, and we show quantitative results for fully automatic ear recognition, achieving 95.2% rank one recognition across 63 subjects