Vol. 43, No. 4,
Apr. 2016
Digital Library
Mipmap-Based Deferred Soft Shadow Mapping
Deferred Shading is a shading technique that postprocesses pixels in the screen space, following geometry-only rendering passes with depth buffering. Unlike typical shadow mapping techniques, this technique allows us to render shadows from multiple light sources without changing the structure of the rendering pipelines. This paper presents a deferred shadow mapping technique and its extension to soft shadows using mipmapping. Our technique first generates visibility maps from light sources, and blurs the visibility maps for deferred shading. This strategy leads to efficient soft-edged shadows, but does not incorporate depth variation, producing light bleeding to some extent. In order to suppress the light-bleeding artifacts, we also propose a depth-adaptive mipmap sampling technique in the screen space.
Detecting Road Intersections using Partially Similar Trajectories of Moving Objects
Bokuk Park, Jinkwan Park, Taeyong Kim, Hwan-Gue Cho
Automated road map generation poses significant research challenges since GPS-based navigation systems prevail in most general vehicles. This paper proposes an automated detecting method for intersection points using GPS vehicle trajectory data without any background digital map information. The proposed method exploits the fact that the trajectories are generally split into several branches at an intersection point. One problem in previous work on this intersection detecting is that those approaches require stopping points and direction changes for every testing vehicle. However our approach does not require such complex auxiliary information for intersection detecting. Our method is based on partial trajectory matching among trajectories since a set of incoming trajectories split other trajectory cluster branches at the intersection point. We tested our method on a real GPS data set with 1266 vehicles in Gangnam District, Seoul. Our experiment showed that the proposed method works well at some bigger intersection points in Gangnam. Our system scored 75% sensitivity and 78% specificity according to the test data. We believe that more GPS trajectory data would make our system more reliable and applicable in a practice.
Analyses of the Effect of System Environment on Filebench Benchmark
Yongju Song, Junghoon Kim, Dong Hyun Kang, Minho Lee, Young Ik Eom
In recent times, NAND flash memory has become widely used as secondary storage for computing devices. Accordingly, to take advantage of NAND flash memory, new file systems have been actively studied and proposed. The performance of these file systems is generally measured with benchmark tools. However, since benchmark tools are executed by software simulation methods, many researchers get non-uniform benchmark results depending on the system environments. In this paper, we use Filebench, one of the most popular and representative benchmark tools, to analyze benchmark results and study the reasons why the benchmark result variations occur. Our experimental results show the differences in benchmark results depending on the system environments. In addition, this study substantiates the fact that system performance is affected mainly by background I/O requests and fsync operations.
Generation of Triangular Mesh of Coronary Artery Using Mesh Merging
Yeonggul Jang, Dong Hwan Kim, Byunghwan Jeon, Dongjin Han, Hackjoon Shim, Hyuk-jae Chang
Generating a 3D surface model from coronary artery segmentation helps to not only improve the rendering efficiency but also the diagnostic accuracy by providing physiological informations such as fractional flow reserve using computational fluid dynamics (CFD). This paper proposes a method to generate a triangular surface mesh using vessel structure information acquired with coronary artery segmentation. The marching cube algorithm is a typical method for generating a triangular surface mesh from a segmentation result as bit mask. But it is difficult for methods based on marching cube algorithm to express the lumen of thin, small and winding vessels because the algorithm only works in a three-dimensional (3D) discrete space. The proposed method generates a more accurate triangular surface mesh for each singular vessel using vessel centerlines, normal vectors and lumen diameters estimated during the process of coronary artery segmentation as the input. Then, the meshes that are overlapped due to branching are processed by mesh merging and merged into a coronary mesh.
Probabilistic Segmentation and Tagging of Unknown Words
Processing of unknown words such as proper nouns and newly coined words is important for a morphological analyzer to process documents in various domains. In this study, a segmentation and tagging method for unknown Korean words is proposed for the 3-step probabilistic morphological analysis. For guessing unknown word, it uses rich suffixes that are attached to open class words, such as general nouns and proper nouns. We propose a method to learn the suffix patterns from a morpheme tagged corpus, and calculate their probabilities for unknown open word segmentation and tagging in the probabilistic morphological analysis model. Results of the experiment showed that the performance of unknown word processing is greatly improved in the documents containing many unregistered words.
Exemplar-Based Image Inpainting for Spherical Panoramic Image
Previous image processing techniques based on plane-to-plane transformations cannot be utilized for spherical panoramic images. In this paper, we propose a new method to inpaint a spherical panoramic image using exemplar, which is deformed by the location of the patch. Our proposed method makes the deformed exemplar patch by latitude and uses it as the reference patch to restore the damaged area. The exemplar-based inpainting method is based on the planar image coordinate system and thus the classical method cannot be applied to the spherical panoramic image. The merit of our proposed method is the fact that it is not dependent on the location of the damaged area. From the experimental results, we proved that our proposed method satisfies the original purpose of the exemplar-based inpainting technique for the spherical panoramic image.
SPARQL Query Processing System over Scalable Triple Data using SparkSQL Framework
MyungJoong Jeon, JinYoung Hong, YoungTack Park
Every year, RDFS data tends further toward scalability; hence, the manner of SPARQL processing needs to be changed for fast query. The query processing method of SPARQL has been studied using a scalable distributed processing framework. Current studies indicate that the query engine based on the scalable distributed processing framework i.e., Hadoop(MapReduce) is not suitable for real-time processing because of the repetitive tasks; in addition, it is difficult to construct a query engine based on an In-memory Distributed Query engine, because distributed structure on the low-level is required to be considered. In this paper, we proposed a method to construct a query engine for improving the speed of the query process with the mass triple data. The query engine processes the query of SPARQL using the SparkSQL, which is an In-memory based, distributed query processing framework. SparkSQL is a high-level distributed query engine that facilitates existing SQL statement. In order to process the SPARQL query, after generating the Algebra Tree using Jena, the Algebra Tree is required to be translated to Spark Algebra Tree for application in the Spark system, and construction of the system that generated the SparkSQL query. Furthermore, we proposed the design of triple property table based on DataFrame for more efficient query processing in the Spark system. Finally, we verified the validity through comparative evaluation with the query engine, which is the existing distributed processing framework.
Systematic Development of Mobile IoT Device Power Management : Feature-based Variability Modeling and Asset Development
Hyesun Lee, Kang Bok Lee, Hyo-Chan Bang
Internet of Things (IoT) is an environment where various devices are connected to each other via a wired/wireless network and where the devices gather, process, exchange, and share information. Some of the most important types of IoT devices are mobile IoT devices such as smartphones. These devices provide various high-performance services to users but cannot be supplied with power all the time; therefore, power management appropriate to a given IoT environment is necessary. Power management of mobile IoT devices involves complex relationships between various entities such as application processors (APs), HW modules inside/outside AP, Operating System (OS), platforms, and applications; a method is therefore needed to systematically analyze and manage these relationships. In addition, variabilities related to power management such as various policies, operational environments, and algorithms need to be analyzed and applied to power management development. In this paper, engineering principles and a method based on them are presented in order to address these challenges and support systematic development of IoT device power management. Power management of connected helmet systems was used to validate the feasibility of the proposed method.
Ontology and Sequential Rule Based Streaming Media Event Recognition
Chi-Seung Soh, Hyun-Kyu Park, Young-Tack Park
As the number of various types of media data such as UCC (User Created Contents) increases, research is actively being carried out in many different fields so as to provide meaningful media services. Amidst these studies, a semantic web-based media classification approach has been proposed; however, it encounters some limitations in video classification because of its underlying ontology derived from meta-information such as video tag and title. In this paper, we define recognized objects in a video and activity that is composed of video objects in a shot, and introduce a reasoning approach based on description logic. We define sequential rules for a sequence of shots in a video and describe how to classify it. For processing the large amount of increasing media data, we utilize Spark streaming, and a distributed in-memory big data processing framework, and describe how to classify media data in parallel. To evaluate the efficiency of the proposed approach, we conducted an experiment using a large amount of media ontology extracted from Youtube videos.
A Comparison of the Search Based Testing Algorithm with Metrics
Search-Based Software Testing (SBST) is an effective technique for test data generation on large domain size. Although the performance of SBST seems to be affected by the structural characteristics of Software Under Test (SUT), studies for the comparison of SBST techniques considering structural characteristics are rare. In addition to the comparison study for SBST, we analyzed the best algorithm with different structural characteristics of SUT. For the generalization of experimental results, we automatically generated 19,800 SUTs by combining four metrics, which are expected to affect the performance of SBST. According to the experiment results, Genetic algorithm showed the best performance for SUTs with high complexity and test data evaluation with count ≤ 20,000. On the other hand, the genetic simulated annealing and the simulated annealing showed relatively better performance for SUTs with high complexity and test data evaluation with count ≥ 50,000. Genetic simulated annealing, simulated annealing and hill climbing showed better performance for SUTs with low complexity.
A Study on Processing XML Documents
XML can effectively express structured or semi-structured data as well as relational databases. XQuery is a query language for retrieving information for such an XML document. In this paper, an XQuery composer is designed and implemented, with an API provided for XQuery processors, and a proper processor is registered. This composer shows query results immediately processed by the processor. As this composer contains a parser for XQuery, it can compose XQuery effectively using a diverse dialog box designed for XQuery grammar. A dialog box is affiliated with a clause region, which is a region that algebra operates from the parsing tree. It can compose path expressions for an XML document easily as it shows an element tree from DTD graphically. Path expressions are composed automatically by marking elements in the structural hierarchy and by specifying the predicate of an element partially.
Privacy-Preserving Self-Certified Public Auditing for Secure Cloud Storage
Mokryeon Baek, Dongmin Kim, Ik Rae Jeong
With a cloud storage service, data owners can easily access their outsourced data in cloud storage on different devices and at different locations, and can share their data with others. However, as the users no longer physically have possession of their outsourced data and the cloud still facing the existence of internal/external threats, the task of checking the data integrity is formidable. Over recent years, numerous schemes have been proposed to ensure data integrity in an untrusted cloud. However, the existing public auditing schemes use a third-party auditor(TPA) to execute high computation to check data integrity and may still face many security threats. In this paper, we first demonstrate that the scheme proposed by Zhang et al. is not secure against our two threat models, and then we propose a self-certified public auditing scheme to eliminate the security threats and guarantee a constant communication cost. Moreover, we prove the securities of our public auditing scheme under three security models.
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