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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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Journal of Information Processing Systems
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Korea Information Processing Society
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Volume 12, Issue 3 - Sep 2016
Volume 12, Issue 2 - Jun 2016
Volume 12, Issue 1 - Mar 2016
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A Comprehensive Review of Emerging Computational Methods for Gene Identification
Yu, Ning ; Yu, Zeng ; Li, Bing ; Gu, Feng ; Pan, Yi ;
Journal of Information Processing Systems, volume 12, issue 1, 2016, Pages 1~34
DOI : 10.3745/JIPS.04.0023
Gene identification is at the center of genomic studies. Although the first phase of the Encyclopedia of DNA Elements (ENCODE) project has been claimed to be complete, the annotation of the functional elements is far from being so. Computational methods in gene identification continue to play important roles in this area and other relevant issues. So far, a lot of work has been performed on this area, and a plethora of computational methods and avenues have been developed. Many review papers have summarized these methods and other related work. However, most of them focus on the methodologies from a particular aspect or perspective. Different from these existing bodies of research, this paper aims to comprehensively summarize the mainstream computational methods in gene identification and tries to provide a short but concise technical reference for future studies. Moreover, this review sheds light on the emerging trends and cutting-edge techniques that are believed to be capable of leading the research on this field in the future.
X-Ray Image Enhancement Using a Boundary Division Wiener Filter and Wavelet-Based Image Fusion Approach
Khan, Sajid Ullah ; Chai, Wang Yin ; See, Chai Soo ; Khan, Amjad ;
Journal of Information Processing Systems, volume 12, issue 1, 2016, Pages 35~45
DOI : 10.3745/JIPS.02.0029
To resolve the problems of Poisson/impulse noise, blurriness, and sharpness in degraded X-ray images, a novel and efficient enhancement algorithm based on X-ray image fusion using a discrete wavelet transform is proposed in this paper. The proposed algorithm consists of two basics. First, it applies the techniques of boundary division to detect Poisson and impulse noise corrupted pixels and then uses the Wiener filter approach to restore those corrupted pixels. Second, it applies the sharpening technique to the same degraded X-ray image. Thus, it has two source X-ray images, which individually preserve the enhancement effects. The details and approximations of these sources X-ray images are fused via different fusion rules in the wavelet domain. The results of the experiment show that the proposed algorithm successfully combines the merits of the Wiener filter and sharpening and achieves a significant proficiency in the enhancement of degraded X-ray images exhibiting Poisson noise, blurriness, and edge details.
Content Based Dynamic Texture Analysis and Synthesis Based on SPIHT with GPU
Ghadekar, Premanand P. ; Chopade, Nilkanth B. ;
Journal of Information Processing Systems, volume 12, issue 1, 2016, Pages 46~56
DOI : 10.3745/JIPS.02.0009
Dynamic textures are videos that exhibit a stationary property with respect to time (i.e., they have patterns that repeat themselves over a large number of frames). These patterns can easily be tracked by a linear dynamic system. In this paper, a model that identifies the underlying linear dynamic system using wavelet coefficients, rather than a raw sequence, is proposed. Content based threshold filtering based on Set Partitioning in a Hierarchical Tree (SPIHT) helps to get another representation of the same frames that only have low frequency components. The main idea of this paper is to apply SPIHT based threshold filtering on different bands of wavelet transform so as to have more significant information in fewer parameters for singular value decomposition (SVD). In this case, more flexibility is given for the component selection, as SVD is independently applied to the different bands of frames of a dynamic texture. To minimize the time complexity, the proposed model is implemented on a graphics processing unit (GPU). Test results show that the proposed dynamic system, along with a discrete wavelet and SPIHT, achieve a highly compact model with better visual quality, than the available LDS, Fourier descriptor model, and higher-order SVD (HOSVD).
Resilient Packet Transmission (RPT) for the Buffer Based Routing (BBR) Protocol
Rathee, Geetanjali ; Rakesh, Nitin ;
Journal of Information Processing Systems, volume 12, issue 1, 2016, Pages 57~72
DOI : 10.3745/JIPS.03.0014
To provide effective communication in the wireless mesh network (WMN), several algorithms have been proposed. Since the possibilities of numerous failures always exist during communication, resiliency has been proven to be an important aspect for WMN to recover from these failures. In general, resiliency is the diligence of the reliability and availability in network. Several types of resiliency based routing algorithms have been proposed (i.e., Resilient Multicast, ROMER, etc.). Resilient Multicast establishes a two-node disjoint path and ROMER uses a credit-based approach to provide resiliency in the network. However, these proposed approaches have some disadvantages in terms of network throughput and network congestion. Previously, the buffer based routing (BBR) approach has been proposed to overcome these disadvantages. We proved earlier that BBR is more efficient in regards to w.r.t throughput, network performance, and reliability. In this paper, we consider the node/link failure issues and analogous performance of BBR. For these items we have proposed a resilient packet transmission (RPT) algorithm as a remedy for BBR during these types of failures. We also share the comparative performance analysis of previous approaches as compared to our proposed approach. Network throughput, network congestion, and resiliency against node/link failure are particular performance metrics that are examined over different sized WMNs.
An Experimental Implementation of a Cross-Layer Approach for Improving TCP Performance over Cognitive Radio Networks
Byun, Sang-Seon ;
Journal of Information Processing Systems, volume 12, issue 1, 2016, Pages 73~82
DOI : 10.3745/JIPS.03.0041
In cognitive radio networks (CRNs), the performance of the transmission control protocol (TCP) at the secondary user (SU) severely drops due to the mistrigger of congestion control. A long disruption is caused by the transmission of primary user, leading to the mistrigger. In this paper, we propose a cross-layer approach, called a CR-aware scheme that enhances TCP performance at the SU. The scheme is a sender side addition to the standard TCP (i.e., TCP-NewReno), and utilizes an explicit cross-layer signal delivered from a physical (or link) layer and the signal gives an indication of detecting the primary transmission (i.e., transmission of the primary user). We evaluated our scheme by implementing it onto a software radio platform, the Universal Software Radio Peripheral (USRP), where many parts of lower layer operations (i.e., operations in a link or physical layer) run as user processes. In our implementation, we ran our CR-aware scheme over IEEE 802.15.4. Furthermore, for the purpose of comparison, we implemented a selective ACK-based local recovery scheme that helps TCP isolate congestive loss from a random loss in a wireless section.
A Robust Fingerprint Matching System Using Orientation Features
Kumar, Ravinder ; Chandra, Pravin ; Hanmandlu, Madasu ;
Journal of Information Processing Systems, volume 12, issue 1, 2016, Pages 83~99
DOI : 10.3745/JIPS.02.0020
The latest research on the image-based fingerprint matching approaches indicates that they are less complex than the minutiae-based approaches when it comes to dealing with low quality images. Most of the approaches in the literature are not robust to fingerprint rotation and translation. In this paper, we develop a robust fingerprint matching system by extracting the circular region of interest (ROI) of a radius of 50 pixels centered at the core point. Maximizing their orientation correlation aligns two fingerprints that are to be matched. The modified Euclidean distance computed between the extracted orientation features of the sample and query images is used for matching. Extensive experiments were conducted over four benchmark fingerprint datasets of FVC2002 and two other proprietary databases of RFVC 2002 and the AITDB. The experimental results show the superiority of our proposed method over the well-known image-based approaches in the literature.
Number Plate Detection with a Multi-Convolutional Neural Network Approach with Optical Character Recognition for Mobile Devices
Gerber, Christian ; Chung, Mokdong ;
Journal of Information Processing Systems, volume 12, issue 1, 2016, Pages 100~108
DOI : 10.3745/JIPS.04.0022
In this paper, we propose a method to achieve improved number plate detection for mobile devices by applying a multiple convolutional neural network (CNN) approach. First, we processed supervised CNN-verified car detection and then we applied the detected car regions to the next supervised CNN-verifier for number plate detection. In the final step, the detected number plate regions were verified through optical character recognition by another CNN-verifier. Since mobile devices are limited in computation power, we are proposing a fast method to recognize number plates. We expect for it to be used in the field of intelligent transportation systems.
Soft Set Theory Oriented Forecast Combination Method for Business Failure Prediction
Xu, Wei ; Xiao, Zhi ;
Journal of Information Processing Systems, volume 12, issue 1, 2016, Pages 109~128
DOI : 10.3745/JIPS.04.0016
This paper presents a new combined forecasting method that is guided by the soft set theory (CFBSS) to predict business failures with different sample sizes. The proposed method combines both qualitative analysis and quantitative analysis to improve forecasting performance. We considered an expert system (ES), logistic regression (LR), and support vector machine (SVM) as forecasting components whose weights are determined by the receiver operating characteristic (ROC) curve. The proposed procedure was applied to real data sets from Chinese listed firms. For performance comparison, single ES, LR, and SVM methods, the combined forecasting method based on equal weights (CFBEWs), the combined forecasting method based on neural networks (CFBNNs), and the combined forecasting method based on rough sets and the D-S theory (CFBRSDS) were also included in the empirical experiment. CFBSS obtains the highest forecasting accuracy and the second-best forecasting stability. The empirical results demonstrate the superior forecasting performance of our method in terms of accuracy and stability.
Analysis of Semantic Relations Between Multimodal Medical Images Based on Coronary Anatomy for Acute Myocardial Infarction
Park, Yeseul ; Lee, Meeyeon ; Kim, Myung-Hee ; Lee, Jung-Won ;
Journal of Information Processing Systems, volume 12, issue 1, 2016, Pages 129~148
DOI : 10.3745/JIPS.04.0021
Acute myocardial infarction (AMI) is one of the three emergency diseases that require urgent diagnosis and treatment in the golden hour. It is important to identify the status of the coronary artery in AMI due to the nature of disease. Therefore, multi-modal medical images, which can effectively show the status of the coronary artery, have been widely used to diagnose AMI. However, the legacy system has provided multi-modal medical images with flat and unstructured data. It has a lack of semantic information between multi-modal images, which are distributed and stored individually. If we can see the status of the coronary artery all at once by integrating the core information extracted from multi-modal medical images, the time for diagnosis and treatment will be reduced. In this paper, we analyze semantic relations between multi-modal medical images based on coronary anatomy for AMI. First, we selected a coronary arteriogram, coronary angiography, and echocardiography as the representative medical images for AMI and extracted semantic features from them, respectively. We then analyzed the semantic relations between them and defined the convergence data model for AMI. As a result, we show that the data model can present core information from multi-modal medical images and enable to diagnose through the united view of AMI intuitively.
Feasibility Study of a Distributed and Parallel Environment for Implementing the Standard Version of AAM Model
Naoui, Moulkheir ; Mahmoudi, Said ; Belalem, Ghalem ;
Journal of Information Processing Systems, volume 12, issue 1, 2016, Pages 149~168
DOI : 10.3745/JIPS.02.0039
The Active Appearance Model (AAM) is a class of deformable models, which, in the segmentation process, integrates the priori knowledge on the shape and the texture and deformation of the structures studied. This model in its sequential form is computationally intensive and operates on large data sets. This paper presents another framework to implement the standard version of the AAM model. We suggest a distributed and parallel approach justified by the characteristics of the model and their potentialities. We introduce a schema for the representation of the overall model and we study of operations that can be parallelized. This approach is intended to exploit the benefits build in the area of advanced image processing.
The Effects of Industry Classification on a Successful ERP Implementation Model
Lee, Sangmin ; Kim, Dongho ;
Journal of Information Processing Systems, volume 12, issue 1, 2016, Pages 169~181
DOI : 10.3745/JIPS.03.0047
Organizations in some industries are still hesitant to adopt the Enterprise Resource Planning (ERP) system due to its high risk of failures. This study examined how industry classification affects the successful implementation of the ERP system. To achieve this goal, we reinvestigated the existing ERP Success Model that was developed by Chung with the data from various industry sectors, since Chung validated the model only in the engineering and construction industries. In order to test to see if the Chung model can be applicable outside the engineering and construction industries, the relationships between the ERP success indicators and the critical success factors in the Chung model and those in the sample data collected from ten different industry sectors were compared and investigated. The ten industry sectors were selected based on the Global Industry Classification Standard (GICS). We found that the impact of success factors on the success of implementing an ERP system varied across industry sectors. This means that the success of ERP system implementation can be industry-specific. Thus, industry classification should be considered as another factor to help IT decision makers or top-management avoid ERP system failures when they plan to implement a new ERP system.