Packaging Electronics on Textiles: Identifying Fiber Junctions for Automated Placement

Electronic textile (E-textile) research requires an understanding of the mechanical properties of fabric substrates used to build and support electronics. Because fibers are often non-uniform and fabrics are easily deformed, locating fiber junctions on the irregular surface is challenging, yet is essential for packaging electronics on textiles at the resolution of single fibers that deliver power and signals. In this paper, we demonstrate the need to identify fiber junctions in a task where microelectromechanical structures (MEMS) are integrated on fabrics. We discuss the benefits of fiber-aligned placement compared with random placement. Thereafter we compare three image processing algorithms to extract fiber junction locations from sample fabric images. The Hough line transform algorithm implemented in MATLAB derives line segments from the image to model the fibers, identifying crossings by the intersections of the line segments. The binary image analysis algorithm implemented in MATLAB searches the image for unique patterns of 1s and 0s that represent the fiber intersection. The pattern matching algorithm implemented in Vision Assistant - LabVIEW, uses a pyramid value correlation function to match a reference template to the remainder of the fabric image to identify the crossings. Of the three algorithms, the binary image analysis method had the highest accuracy, while the pattern matching algorithm was fastest.

A novel pattern matching algorithm for genomic patterns related to protein motifs

Background: Patterns on proteins and genomic sequences are vastly analyzed, extracted and collected in databases. Although protein patterns originate from genomic coding regions, very few works have directly or indirectly dealt with coding region patterns induced from protein patterns. Results: In this paper, we have defined a new genomic pattern structure suitable for representing induced patterns from proteins. The provided pattern structure, which is called “Consecutive Positions Scoring Matrix (CPSSM)”, is a replacement for protein patterns and profiles in the genomic context. CPSSMs can be identified, discovered, and searched in genomes. Then, we have presented a novel pattern matching algorithm between the defined genomic pattern and genomic sequences based on dynamic programming. In addition, we have modified the provided algorithm to support intronic gaps and huge sequences. We have implemented and tested the provided algorithm on real data. The results on Saccharomyces cerevisiae’s genome show 132% more true positives and no false negatives and the results on human genome show no false negatives and 10 times as many true positives as those in previous works. Conclusion: CPSSM and provided methods could be used for open reading frame detection and gene finding. The application is available with source codes to run and download at http://app.foroughmand.ir/cpssm/ .