Vol. 42, No. 10,
Oct. 2015
Digital Library
Estimating Geometric Transformation of Planar Pattern in Spherical Panoramic Image
A spherical panoramic image does not conform to the pin-hole camera model, and, hence, it is not possible to utilize previous techniques consisting of plane-to-plane transformation. In this paper, we propose a new method to estimate the planar geometric transformation between the planar image and a spherical panoramic image. Our proposed method estimates the transformation parameters for latitude, longitude, rotation and scaling factors when the matching pairs between a spherical panoramic image and a planar image are given. A planar image is projected into a spherical panoramic image through two steps of nonlinear coordinate transformations, which makes it difficult to compute the geometric transformation. The advantage of using our method is that we can uncover each of the implicit factors as well as the overall transformation. The experiment results show that our proposed method can achieve estimation errors of around 1% and is not affected by deformation factors, such as the latitude and rotation.
An Approach of Scalable SHIF Ontology Reasoning using Spark Framework
For the management of a knowledge system, systems that automatically infer and manage scalable knowledge are required. Most of these systems use ontologies in order to exchange knowledge between machines and infer new knowledge. Therefore, approaches are needed that infer new knowledge for scalable ontology. In this paper, we propose an approach to perform rule based reasoning for scalable SHIF ontologies in a spark framework which works similarly to MapReduce in distributed memories on a cluster. For performing efficient reasoning in distributed memories, we focus on three areas. First, we define a data structure for splitting scalable ontology triples into small sets according to each reasoning rule and loading these triple sets in distributed memories. Second, a rule execution order and iteration conditions based on dependencies and correlations among the SHIF rules are defined. Finally, we explain the operations that are adapted to execute the rules, and these operations are based on reasoning algorithms. In order to evaluate the suggested methods in this paper, we perform an experiment with WebPie, which is a representative ontology reasoner based on a cluster using the LUBM set, which is formal data used to evaluate ontology inference and search speed. Consequently, the proposed approach shows that the throughput is improved by 28,400% (157k/sec) from WebPie(553/sec) with LUBM.
An Empirical Study on the Factors and Resolution Methods of the Smart Divide of Older Adults
Kihun Paek, Jinsook Bong, Yongtae Shin
This research was conducted both to analyze the determining factors of and to suggest resolution methods for the smart divide of adults over 65 years of age in the rapidly aging society of the 2000s and the smart society of the 2010s following the information society of the 1990s. The research model based on the Technology Acceptance Model (TAM) includes 6 determining factors derived from existing studies: Self Efficacy, Training, Accessibility, Playfulness, Cost Rationality, and Policy Support. Research data were collected through a survey given to a total of 243 older adults in 14 Senior Welfare Centers nationwide, and research hypotheses were verified by structural equation model (SEM) analysis. The results of this research that gives priority to the order of Political Support, Playfulness, Self Efficacy, Accessibility, Cost Rationality, and Training can be used to develop various resolution methods for the smart divide of adults over 65 years of age.
Anterior Cruciate Ligament Segmentation in Knee MRI with Locally-aligned Probabilistic Atlas and Iterative Graph Cuts
Segmentation of the anterior cruciate ligament (ACL) in knee MRI remains a challenging task due to its inhomogeneous signal intensity and low contrast with surrounding soft tissues. In this paper, we propose a multi-atlas-based segmentation of the ACL in knee MRI with locally-aligned probabilistic atlas (PA) in an iterative graph cuts framework. First, a novel PA generation method is proposed with global and local multi-atlas alignment by means of rigid registration. Second, with the generated PA, segmentation of the ACL is performed by maximum-aposteriori (MAP) estimation and then by graph cuts. Third, refinement of ACL segmentation is performed by improving shape prior through mask-based PA generation and iterative graph cuts. Experiments were performed with a Dice similarity coefficients of 75.0%, an average surface distance of 1.7 pixels, and a root mean squared distance of 2.7 pixels, which increased accuracy by 12.8%, 22.7%, and 22.9%, respectively, from the graph cuts with patient-specific shape constraints.
Artificial Potential Function for Driving a Road with Traffic Light
Traffic light rules are one among the most common and important safety rules as the directly correlate with the safety of pedestrians. Consequently, an algorithm is required to cause an automated (or semi-automated) vehicle to observe traffic light signals. We present a novel, artificial potential function to guide an automated vehicle through traffic lights. Our function consists of three potential function components representing the three traffic light colors: green, yellow, and red. The traffic light potential function smoothly changes an artificial potential field using the elapsed time for the current light and light conversion. Our traffic light potential function is combined with other potential functions to guide vehicles’ movement and constructs the final artificial potential field. Using various simulations, we found or method successfully guided the vehicle to observe traffic lights while behaving like human-controlled cars.
Automated Cell Counting Method for HeLa Cells Image based on Cell Membrane Extraction and Back-tracking Algorithm
Minyoung Kyoung, Jeong-Hoh Park, Myoung gu Kim, Sang-Mo Shin, Hyunbean Yi
Cell counting is extensively used to analyze cell growth in biomedical research, and as a result automated cell counting methods have been developed to provide a more convenient and means to analyze cell growth. However, there are still many challenges to improving the accuracy of the cell counting for cells that proliferate abnormally, divide rapidly, and cluster easily, such as cancer cells. In this paper, we present an automated cell counting method for HeLa cells, which are used as reference for cancer research. We recognize and classify the morphological conditions of the cells by using a cell segmentation algorithm based on cell membrane extraction, and we then apply a cell back-tracking algorithm to improve the cell counting accuracy in cell clusters that have indistinct cell boundary lines. The experimental results indicate that our proposed segmentation method can identify each of the cells more accurately when compared to existing methods and, consequently, can improve the cell counting accuracy.
A Space-Efficient Inverted Index Technique using Data Rearrangement for String Similarity Searches
An inverted index structure is widely used for efficient string similarity search. One of the main requirements of similarity search is a fast response time; to this end, most techniques use an in-memory index structure. Since the size of an inverted index structure usually very large, however, it is not practical to assume that an index structure will fit into the main memory. To alleviate this problem, we propose a novel technique that reduces the size of an inverted index. In order to reduce the size of an index, the proposed technique rearranges data strings so that the data strings containing the same q-grams can be placed close to one other. Then, the technique encodes those multiple strings into a range. Through an experimental study using real data sets, we show that our technique significantly reduces the size of an inverted index without sacrificing query processing time.
An Interference Reduction Scheme Using AP Aggregation and Transmit Power Control on OpenFlow-based WLAN
Mi-Rim Do, Sang-Hwa Chung, Chang-Woo Ahn
Recently, excessive installations of APs have caused WLAN interference, and many techniques have been suggested to solve this problem. The AP aggregation technique serves to reduce active APs by moving station connections to a certain AP. Since this technique forcibly moves station connections, the transmission performance of some stations may deteriorate. The AP transmit power control technique may cause station disconnection or deterioration of transmission performance when power is reduced under a certain level. The combination of these two techniques can reduce interference through AP aggregation and narrow the range of interferences further through detailed power adjustment. However, simply combining these techniques may decrease the probability of power adjustment after aggregation and increase station disconnections upon power control. As a result, improvement in performance may be insignificant. Hence, this study suggests a scheme to combine the AP aggregation and the AP transmit power control techniques in OpenFlow-based WLAN to ameliorate the disadvantages of each technique and to reduce interferences efficiently by performing aggregation for the purpose of increasing the probability of adjusting transmission power. Simulations reveal that the average transmission delay of the suggested scheme is reduced by as much as 12.8% compared to the aggregation scheme and by as much as 18.1% compared to the power control scheme. The packet loss rate due to interference is reduced by as much as 24.9% compared to the aggregation scheme and by as much as 46.7% compared to the power control scheme. In addition, the aggregation scheme and the power control scheme decrease the throughput of several stations as a side effect, but our scheme increases the total data throughput without decreasing the throughput of each station.
A Routing Scheme Considering Bottleneck and Route Link Quality in RPL-based IoT Wireless Networks
Ik-Joo Jung, Sang-Hwa Chung, Sung-Jun Lee
In order to manage a large number of devices connected to the Internet of Things (IoT), the Internet Engineering Task Force (IETF) proposed the IPv6 Routing Protocol for Low Power and Lossy Networks (RPL). The route of the RPL network is generated through the use of an Objective Function (OF) that is suitable for the service that is required for the IoT network. Since the route of the RPL network is conventionally simply chosen only by considering the link quality between the nodes, it is sensible to seek an OF that can also provide better Quality of Service (QoS). In previous studies, the end-to-end delay might possibly be sub-optimal because they only deal with problems related to the reduction of energy consumption and not to the link quality on the path to the sink node. In this study, we propose a scheme that reduces the end-to-end delay but also gives full consideration to both the quality on the entire route to the destination and to the expected lifetime of nodes with bottlenecks from heaped traffic. Weighting factors for the proposed scheme are chosen by experiments and the proposed scheme can reduce the end-to-end delay and the energy consumption of previous studies by 20.8% and 10.5%, respectively.
Regular Expression Matching Processor Architecture Supporting Character Class Matching
Many hardware-based regular expression matching architectures are proposed for high performance matching. In particular, regular expression processors such as ReCPU and SMPU perform pattern matching in a similar approach to that used in general purpose processors, which provide the flexibility when updating patterns. However, these processors are inefficient in performing class matching since they do not provide character class matching capabilities. This paper proposes an instruction set and architecture of a regular expression matching processor, which can support character class matching. The proposed processor can efficiently perform character class matching since it includes character class, character range, and negated character class matching capabilities.
A Method of Seller Reputation Computation Based on Rating Separation in e-Marketplace
Hyun-Kyo Oh, Yoohan Noh, Sang-Wook Kim, Sunju Park
Most e-marketplaces build a reputation system that provides potential buyers with reputation scores of sellers in order for buyers to identify the sellers that are more reliable and trustworthy. The reputation scores are computed based on the aggregation of buyers’ ratings. However, when these ratings are used to compute the reputation scores, the existing reputation systems do not make a distinction according to the following two criteria: the capability of the seller and the quality of an item. We claim that a reputation system needs to separate the two criteria in order to provide more precise information about the seller. In this paper, we propose a method to compute seller’s reputation by separating the rating into the seller’s score and the item’s score. The proposed method computes the reputation of the seller’s capability by using only the ‘seller’s score’ and helps potential buyers to find reliable sellers who provide fast delivery and better service. In experiments, we propose a simulation strategy that reflects the real life of an E-marketplace and verify the effectiveness of our method by using the generated simulation data.
Perceptual Color Difference based Image Quality Assessment Method and Evaluation System according to the Types of Distortion
A lot of image quality assessment metrics that can precisely reflect the human visual system (HVS) have previously been researched. The Structural SIMilarity (SSIM) index is a remarkable HVS-aware metric that utilizes structural information, since the HVS is sensitive to the overall structure of an image. However, SSIM fails to deal with color difference in terms of the HVS. In order to solve this problem, the Structural and Hue SIMilarity (SHSIM) index has been selected with the Hue, Saturation, Intensity (HSI) model as a color space, but it cannot reflect the HVS-aware color difference between two color images. In this paper, we propose a new image quality assessment method for a color image by using a CIE Lab color space. In addition, by using a support vector machine (SVM) classifier, we also propose an optimization system for applying optimal metric according to the types of distortion. To evaluate the proposed index, a LIVE database, which is the most well-known in the area of image quality assessment, is employed and four criteria are used. Experimental results show that the proposed index is more consistent with the other methods.
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