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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
> Journal Vol & Issue
Journal of Computing Science and Engineering
Journal Basic Information
Journal DOI :
Korean Institute of Information Scientists and Engineers
Editor in Chief :
In-Sup Lee / Il-Yeol Song / Jong C. Park / Tae-Whan Kim
Volume & Issues
Volume 7, Issue 4 - Dec 2013
Volume 7, Issue 3 - Sep 2013
Volume 7, Issue 2 - Jun 2013
Volume 7, Issue 1 - Mar 2013
Selecting the target year
The Life Cycle of the Rendezvous Problem of Cognitive Radio Ad Hoc Networks: A Survey
Htike, Zaw ; Hong, Choong Seon ; Lee, Sungwon ;
Journal of Computing Science and Engineering, volume 7, issue 2, 2013, Pages 81~88
DOI : 10.5626/JCSE.2013.7.2.81
In cognitive radio or dynamic spectrum access networks, a rendezvous represents meeting two or more users on a common channel, and negotiating to establish data communication. The rendezvous problem is one of the most challenging tasks in cognitive radio ad hoc networks. Generally, this problem is simplified by using two well-known mechanisms: the first uses a predefined common control channel, while the second employs a channel hopping procedure. Yet, these two mechanisms form a life cycle, when they simplify the rendezvous problem in cognitive radio networks. The main purpose of this paper is to point out how and why this cycle forms.
Overview of Real-Time Java Computing
Sun, Yu ; Zhang, Wei ;
Journal of Computing Science and Engineering, volume 7, issue 2, 2013, Pages 89~98
DOI : 10.5626/JCSE.2013.7.2.89
This paper presents a complete survey of recent techniques that are applied in the field of real-time Java computing. It focuses on the issues that are especially important for hard real-time applications, which include time predictable garbage collection, worst-case execution time analysis of Java programs, real-time Java threads scheduling and compiler techniques designed for real-time purpose. It also evaluates experimental frameworks that can be used for researching real-time Java. This overview is expected to help researchers understand the state-of-the-art and advance the research in real-time Java computing.
Direct Divergence Approximation between Probability Distributions and Its Applications in Machine Learning
Sugiyama, Masashi ; Liu, Song ; du Plessis, Marthinus Christoffel ; Yamanaka, Masao ; Yamada, Makoto ; Suzuki, Taiji ; Kanamori, Takafumi ;
Journal of Computing Science and Engineering, volume 7, issue 2, 2013, Pages 99~111
DOI : 10.5626/JCSE.2013.7.2.99
Approximating a divergence between two probability distributions from their samples is a fundamental challenge in statistics, information theory, and machine learning. A divergence approximator can be used for various purposes, such as two-sample homogeneity testing, change-point detection, and class-balance estimation. Furthermore, an approximator of a divergence between the joint distribution and the product of marginals can be used for independence testing, which has a wide range of applications, including feature selection and extraction, clustering, object matching, independent component analysis, and causal direction estimation. In this paper, we review recent advances in divergence approximation. Our emphasis is that directly approximating the divergence without estimating probability distributions is more sensible than a naive two-step approach of first estimating probability distributions and then approximating the divergence. Furthermore, despite the overwhelming popularity of the Kullback-Leibler divergence as a divergence measure, we argue that alternatives such as the Pearson divergence, the relative Pearson divergence, and the
-distance are more useful in practice because of their computationally efficient approximability, high numerical stability, and superior robustness against outliers.
Decoding Brain States during Auditory Perception by Supervising Unsupervised Learning
Porbadnigk, Anne K. ; Gornitz, Nico ; Kloft, Marius ; Muller, Klaus-Robert ;
Journal of Computing Science and Engineering, volume 7, issue 2, 2013, Pages 112~121
DOI : 10.5626/JCSE.2013.7.2.112
The last years have seen a rise of interest in using electroencephalography-based brain computer interfacing methodology for investigating non-medical questions, beyond the purpose of communication and control. One of these novel applications is to examine how signal quality is being processed neurally, which is of particular interest for industry, besides providing neuroscientific insights. As for most behavioral experiments in the neurosciences, the assessment of a given stimulus by a subject is required. Based on an EEG study on speech quality of phonemes, we will first discuss the information contained in the neural correlate of this judgement. Typically, this is done by analyzing the data along behavioral responses/labels. However, participants in such complex experiments often guess at the threshold of perception. This leads to labels that are only partly correct, and oftentimes random, which is a problematic scenario for using supervised learning. Therefore, we propose a novel supervised-unsupervised learning scheme, which aims to differentiate true labels from random ones in a data-driven way. We show that this approach provides a more crisp view of the brain states that experimenters are looking for, besides discovering additional brain states to which the classical analysis is blind.
The MPI CyberMotion Simulator: A Novel Research Platform to Investigate Human Control Behavior
Nieuwenhuizen, Frank M. ; Bulthoff, Heinrich H. ;
Journal of Computing Science and Engineering, volume 7, issue 2, 2013, Pages 122~131
DOI : 10.5626/JCSE.2013.7.2.122
The MPI CyberMotion Simulator provides a unique motion platform, as it features an anthropomorphic robot with a large workspace, combined with an actuated cabin and a linear track for lateral movement. This paper introduces the simulator as a tool for studying human perception, and compares its characteristics to conventional Stewart platforms. Furthermore, an experimental evaluation is presented in which multimodal human control behavior is studied by identifying the visual and vestibular responses of participants in a roll-lateral helicopter hover task. The results show that the simulator motion allows participants to increase tracking performance by changing their control strategy, shifting from reliance on visual error perception to reliance on simulator motion cues. The MPI CyberMotion Simulator has proven to be a state-of-the-art motion simulator for psychophysical research to study humans with various experimental paradigms, ranging from passive perception experiments to active control tasks, such as driving a car or flying a helicopter.
Brain Computer Interfacing: A Multi-Modal Perspective
Fazli, Siamac ; Lee, Seong-Whan ;
Journal of Computing Science and Engineering, volume 7, issue 2, 2013, Pages 132~138
DOI : 10.5626/JCSE.2013.7.2.132
Multi-modal techniques have received increasing interest in the neuroscientific and brain computer interface (BCI) communities in recent times. Two aspects of multi-modal imaging for BCI will be reviewed. First, the use of recordings of multiple subjects to help find subject-independent BCI classifiers is considered. Then, multi-modal neuroimaging methods involving combined electroencephalogram and near-infrared spectroscopy measurements are discussed, which can help achieve enhanced and robust BCI performance.
Brain-Computer Interface in Stroke Rehabilitation
Ang, Kai Keng ; Guan, Cuntai ;
Journal of Computing Science and Engineering, volume 7, issue 2, 2013, Pages 139~146
DOI : 10.5626/JCSE.2013.7.2.139
Recent advances in computer science enabled people with severe motor disabilities to use brain-computer interfaces (BCI) for communication, control, and even to restore their motor disabilities. This paper reviews the most recent works of BCI in stroke rehabilitation with a focus on methodology that reported on data collected from stroke patients and clinical studies that reported on the motor improvements of stroke patients. Both types of studies are important as the former advances the technology of BCI for stroke, and the latter demonstrates the clinical efficacy of BCI in stroke. Finally some challenges are discussed.