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

Data-Driven Segmentation of Post-mortem Iris Images

This paper presents a method for segmenting iris images obtained from the deceased subjects, by training a deep convolutional neural network (DCNN) designed for the purpose of semantic segmentation. Post-mortem iris recognition has recently emerged as an alternative, or additional, method useful in forensic analysis. At the same time it poses many new challenges from the technological standpoint, one of them being the image segmentation stage, which has proven difficult to be reliably executed by conventional iris recognition methods. Our approach is based on the SegNet architecture, fine-tuned with 1,300 manually segmented post-mortem iris images taken from the Warsaw-BioBase-Post-Mortem-Iris v1.0 database. The experiments presented in this paper show that this data-driven solution is able to learn specific deformations present in post-mortem samples, which are missing from alive irises, and offers a considerable improvement over the state-of-the-art, conventional segmentation algorithm (OSIRIS): the Intersection over Union (IoU) metric was improved from 73.6% (for OSIRIS) to 83% (for DCNN-based presented in this paper) averaged over subject-disjoint, multiple splits of the data into train and test subsets. This paper offers the first known to us method of automatic processing of post-mortem iris images. We offer source codes with the trained DCNN that perform end-to-end segmentation of post-mortem iris images, as described in this paper. Also, we offer binary masks corresponding to manual segmentation of samples from Warsaw-BioBase-Post-Mortem-Iris v1.0 database to facilitate development of alternative methods for post-mortem iris segmentation

Development of CUiris: A Dark-Skinned African Iris Dataset for Enhancement of Image Analysis and Robust Personal Recognition

Iris recognition algorithms, especially with the emergence of large-scale iris-based identification systems, must be tested for speed and accuracy and evaluated with a wide range of templates – large size, long-range, visible and different origins. This paper presents the acquisition of eye-iris images of dark-skinned subjects in Africa, a predominant case of verydark- brown iris images, under near-infrared illumination. The peculiarity of these iris images is highlighted from the histogram and normal probability distribution of their grayscale image entropy (GiE) values, in comparison to Asian and Caucasian iris images. The acquisition of eye-images for the African iris dataset is ongoing and will be made publiclyavailable as soon as it is sufficiently populated

Deep Neural Network and Data Augmentation Methodology for off-axis iris segmentation in wearable headsets

A data augmentation methodology is presented and applied to generate a large dataset of off-axis iris regions and train a low-complexity deep neural network. Although of low complexity the resulting network achieves a high level of accuracy in iris region segmentation for challenging off-axis eye-patches. Interestingly, this network is also shown to achieve high levels of performance for regular, frontal, segmentation of iris regions, comparing favorably with state-of-the-art techniques of significantly higher complexity. Due to its lower complexity, this network is well suited for deployment in embedded applications such as augmented and mixed reality headsets