Vision-based Crack Identification on the Concrete Slab Surface using Fuzzy Reasoning Rules and Self-Organizing

Identifying cracks on the surface of concrete slab structure is important for structure stability maintenance. In order to avoid subjective visual inspection, it is necessary to develop an automated identification and measuring system by vision based method. Although there have been some intelligent computerized inspection methods, they are sensitive to noise due to the brightness contrast and objects such as forms and joints of certain size often falsely classified as cracks. In this paper, we propose a new fuzzy logic based image processing method that extracts cracks from concrete slab structure including small cracks that were often neglected as noise. We extract candidate crack areas by applying fuzzy method with three color channel values of concrete slab structure. Then further refinement processes are performed with Self Organizing Map algorithm and density based noise removal process to obtain basic crack characteristic attributes for further analysis. Experimental result verifies that the proposed method is sufficiently identified cracks with various sizes with high accuracy (97.3%) among 1319 ground truth cracks from 30 images

Accurate Corner Detection using 4-directional Edge Labeling and Corner Positioning Templates

Abstract -Corner positioning templates are proposed in order to detect the accurate positions of corners that are extracted using 4-directional edge labeling. Top-down and bottom-up directional labeling are used to label the edge segments with four kinds of labels according to their directions. The points whose labels have changed are then determined as corners. The exact positions of the missing corners due to the disconnected edges are detected through the corner positioning templates that are determined according to the labels of start-points and end-points after the two-pass edge labeling. Experiment results show that the proposed method can detect the exact positions of the real corners

Automatic Segmentation of Brachial Artery based on Fuzzy C-Means Pixel Clustering from Ultrasound Images

Automatic extraction of brachial artery and measuring associated indices such as flow-mediated dilatation and Intima-media thickness are important for early detection of cardiovascular disease and other vascular endothelial malfunctions. In this paper, we propose the basic but important component of such decision-assisting medical software development – noise tolerant fully automatic segmentation of brachial artery from ultrasound images. Pixel clustering with Fuzzy C-Means algorithm in the quantization process is the key component of that segmentation with various image processing algorithms involved. This algorithm could be an alternative choice of segmentation process that can replace speckle noise-suffering edge detection procedures in this application domain

NKT cells promote antibody-induced joint inflammation by suppressing transforming growth factor β1 production

Although NKT cells has been known to exert protective roles in the development of autoimmune diseases, the functional roles of NKT cells in the downstream events of antibody-induced joint inflammation remain unknown. Thus, we explored the functional roles of NKT cells in antibody-induced arthritis using the K/BxN serum transfer model. NKT cell–deficient mice were resistant to the development of arthritis, and wild-type mice administrated with α-galactosyl ceramide, a potent NKT cell activator, aggravated arthritis. In CD1d−/− mice, transforming growth factor (TGF)-β1 was found to be elevated in joint tissues, and the blockade of TGF-β1 using neutralizing monoclonal antibodies restored arthritis. The administration of recombinant TGF-β1 into C57BL/6 mice reduced joint inflammation. Moreover, the adoptive transfer of NKT cells into CD1d−/− mice restored arthritis and reduced TGF-β1 production. In vitro assay demonstrated that interleukin (IL)-4 and interferon (IFN)-γ were involved in suppressing TGF-β1 production in joint cells. The adoptive transfer of NKT cells from IL-4−/− or IFN-γ−/− mice did not reverse arthritis and TGF-β1 production in CD1d−/− mice. In conclusion, NKT cells producing IL-4 and IFN-γ play a role in immune complex–induced joint inflammation by regulating TGF-β1

Recovery of the mitochondrial COI barcode region in diverse Hexapoda through tRNA-based primers

<p>Abstract</p> <p>Background</p> <p>DNA barcoding uses a 650 bp segment of the mitochondrial cytochrome <it>c </it>oxidase I (COI) gene as the basis for an identification system for members of the animal kingdom and some other groups of eukaryotes. PCR amplification of the barcode region is a key step in the analytical chain, but it sometimes fails because of a lack of homology between the standard primer sets and target DNA.</p> <p>Results</p> <p>Two forward PCR primers were developed following analysis of all known arthropod mitochondrial genome arrangements and sequence alignment of the tRNA-W gene which was usually located within 200 bp upstream of the COI gene. These two primers were combined with a standard reverse primer (LepR1) to produce a cocktail which generated a barcode amplicon from 125 of 141 species that included representatives of 121 different families of Hexapoda. High quality sequences were recovered from 79% of the species including groups, such as scale insects, that invariably fail to amplify with standard primers.</p> <p>Conclusions</p> <p>A cocktail of two tRNA-W forward primers coupled with a standard reverse primer amplifies COI for most hexapods, allowing characterization of the standard barcode primer binding region in COI 5' as well as the barcode segment. The current results show that primers designed to bind to highly conserved gene regions upstream of COI will aid the amplification of this gene region in species where standard primers fail and provide valuable information to design a primer for problem groups.</p

A Distributed ADMM Approach to Non-Myopic Path Planning for Multi-Target Tracking

This paper investigates non-myopic path planning of mobile sensors for multi-target tracking. Such problem has posed a high computational complexity issue and/or the necessity of high-level decision making. Existing works tackle these issues by heuristically assigning targets to each sensing agent and solving the split problem for each agent. However, such heuristic methods reduce the target estimation performance in the absence of considering the changes of target state estimation along time. In this work, we detour the task-assignment problem by reformulating the general non-myopic planning problem to a distributed optimization problem with respect to targets. By combining alternating direction method of multipliers (ADMM) and local trajectory optimization method, we solve the problem and induce consensus (i.e., high-level decisions) automatically among the targets. In addition, we propose a modified receding-horizon control (RHC) scheme and edge-cutting method for efficient real-time operation. The proposed algorithm is validated through simulations in various scenarios.Comment: Copyright 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

Computer Vision Based Automatic Extraction and Thickness Measurement of Deep Cervical Flexor from Ultrasonic Images

Deep Cervical Flexor (DCF) muscles are important in monitoring and controlling neck pain. While ultrasonographic analysis is useful in this area, it has intrinsic subjectivity problem. In this paper, we propose automatic DCF extractor/analyzer software based on computer vision. One of the major difficulties in developing such an automatic analyzer is to detect important organs and their boundaries under very low brightness contrast environment. Our fuzzy sigma binarization process is one of the answers for that problem. Another difficulty is to compensate information loss that happened during such image processing procedures. Many morphologically motivated image processing algorithms are applied for that purpose. The proposed method is verified as successful in extracting DCFs and measuring thicknesses in experiment using two hundred 800 × 600 DICOM ultrasonography images with 98.5% extraction rate. Also, the thickness of DCFs automatically measured by this software has small difference (less than 0.3 cm) for 89.8% of extracted DCFs