On the 3D point clouds-palm and coconut trees data set extraction and their usages.

OBJECTIVE: Drone image data set can be utilized for field surveying and image data collection which can be useful for analytics. With the current drone mapping software, useful 3D object reconstruction is possible. This research aims to learn the 3D data set construction process for trees with open-source software along with their usage. Thus, we research the tools used for 3D data set construction, especially in the agriculture field. Due to the growing open-source community, we demonstrate the case study of our palm and coconut data sets against the open-source ones. RESULTS: The methodology for achieving the point cloud data set was based on the tools: OpenDroneMap, CloudCompare, and Open3D. As a result, 40 palm trees and 40 coconut tree point clouds were extracted. Examples of the usages are provided in the area of volume estimation and graph analytics.

Micro-architecture design exploration template for AutoML case study on SqueezeSEMAuto.

Convolutional Neural Network (CNN) models have been commonly used primarily in image recognition tasks in the deep learning area. Finding the right architecture needs a lot of hand-tune experiments which are time-consuming. In this paper, we exploit an AutoML framework that adds to the exploration of the micro-architecture block and the multi-input option. The proposed adaption has been applied to SqueezeNet with SE blocks combined with the residual block combinations. The experiments assume three search strategies: Random, Hyperband, and Bayesian algorithms. Such combinations can lead to solutions with superior accuracy while the model size can be monitored. We demonstrate the application of the approach against benchmarks: CIFAR-10 and Tsinghua Facial Expression datasets. The searches allow the designer to find the architectures with better accuracy than the traditional architectures without hand-tune efforts. For example, CIFAR-10, leads to the SqueezeNet architecture using only 4 fire modules with 59% accuracy. When exploring SE block insertion, the model with good insertion points can lead to an accuracy of 78% while the traditional SqueezeNet can achieve an accuracy of around 50%. For other tasks, such as facial expression recognition, the proposed approach can lead up to an accuracy of 71% with the proper insertion of SE blocks, the appropriate number of fire modules, and adequate input merging, while the traditional model can achieve the accuracy under 20%.

OCTAve: 2D En Face Optical Coherence Tomography Angiography Vessel Segmentation in Weakly-Supervised Learning With Locality Augmentation.

OBJECTIVE: While the microvasculature annotation within Optical Coherence Tomography Angiography (OCTA) can be leveraged using deep-learning techniques, expensive annotation processes are required to create sufficient training data. One way to avoid the expensive annotation is to use a type of weak annotation in which only the center of the vessel is annotated. However, retaining the final segmentation quality with roughly annotated data remains a challenge. METHODS: Our proposed methods, called OCTAve, provide a new way of using weak-annotation for microvasculature segmentation. Since the centerline labels are similar to scribble annotations, we attempted to solve this problem by using the scribble-based weakly-supervised learning method. Even though the initial results look promising, we found that the method could be significantly improved by adding our novel self-supervised deep supervision method based on Kullback-Liebler divergence. RESULTS: The study on large public datasets with different annotation styles (i.e., ROSE, OCTA-500) demonstrates that our proposed method gives better quantitative and qualitative results than the baseline methods and a naive approach, with a p-value less than 0.001 on dice's coefficient and a lot fewer artifacts. CONCLUSION: The segmentation results are both qualitatively and quantitatively superior to baseline weakly-supervised methods when using scribble-based weakly-supervised learning augmented with self-supervised deep supervision, with an average drop in segmentation performance of less than 10%. SIGNIFICANCE: This work gives a new perspective on how weakly-supervised learning can be used to reduce the cost of annotating microvasculature, which can make the annotating process easier and reduce the amount of work for domain experts.

Ontology construction and application in practice case study of health tourism in Thailand.

Ontology is one of the key components in semantic webs. It contains the core knowledge for an effective search. However, building ontology requires the carefully-collected knowledge which is very domain-sensitive. In this work, we present the practice of ontology construction for a case study of health tourism in Thailand. The whole process follows the METHONTOLOGY approach, which consists of phases: information gathering, corpus study, ontology engineering, evaluation, publishing, and the application construction. Different sources of data such as structure web documents like HTML and other documents are acquired in the information gathering process. The tourism corpora from various tourism texts and standards are explored. The ontology is evaluated in two aspects: automatic reasoning using Pellet, and RacerPro, and the questionnaires, used to evaluate by experts of the domains: tourism domain experts and ontology experts. The ontology usability is demonstrated via the semantic web application and via example axioms. The developed ontology is actually the first health tourism ontology in Thailand with the published application.

Parallel simulation of HGMS of weakly magnetic nanoparticles in irrotational flow of inviscid fluid.

The process of high gradient magnetic separation (HGMS) using a microferromagnetic wire for capturing weakly magnetic nanoparticles in the irrotational flow of inviscid fluid is simulated by using parallel algorithm developed based on openMP. The two-dimensional problem of particle transport under the influences of magnetic force and fluid flow is considered in an annular domain surrounding the wire with inner radius equal to that of the wire and outer radius equal to various multiples of wire radius. The differential equations governing particle transport are solved numerically as an initial and boundary values problem by using the finite-difference method. Concentration distribution of the particles around the wire is investigated and compared with some previously reported results and shows the good agreement between them. The results show the feasibility of accumulating weakly magnetic nanoparticles in specific regions on the wire surface which is useful for applications in biomedical and environmental works. The speedup of parallel simulation ranges from 1.8 to 21 depending on the number of threads and the domain problem size as well as the number of iterations. With the nature of computing in the application and current multicore technology, it is observed that 4-8 threads are sufficient to obtain the optimized speedup.