Seokhoon Kang

MAC Scheduling Scheme for VoIP Traffic Service in 3G LTE

3G Long Term Evolution, which aims for various mobile multimedia services provision by enhanced wireless performance, proposes the VoIP-based voice service through the PS domain. When delay and loss-sensitive VoIP traffic flows through the PS domain, more challenging technical difficulties are expected than in the existing 3G systems which provide the CS domain based voice service. Moreover, since 3G LTE, which adopts the OFDM as its physical layer, introduces Physical Resource Block (PRB) as the unit for the transmission resources, it becomes necessary to develop new types of resource management schemes. This paper proposes a MAC layer PRB scheduling algorithm for the efficient VoIP service in 3G LTE and shows the simulation results regarding its performance. The key idea of the algorithm consists of two parts; dynamic activation of a VoIP priority mode for the voice QoS satisfaction and adaptive adjustment of the VoIP priority mode duration in order to minimize the performance degradation induced by its priority mode application.

Enhanced self-configuration scheme for a robust ZigBee-based home automation

In recent, ZigBee is considered as a potential solution for a scalable and flexible home automation systems. However, several constraints of the ZigBee might create node failure or frequent link breakages and thus some nodes in the network might become orphans. This orphan problem of ZigBee might bring a big confusion to a home automation environment in which a number of consumer devices are interconnected in the multi-hop manner. Therefore, in this paper, an enhanced self-configuration (ESC) scheme is proposed, which can improve the robustness of the conventional ZigBee-based home automation systems by coping well with orphan propagation problem and dynamic error environments. Through experiments on real system implementation, it is demonstrated that the ESC is superior to the conventional ZigBee network in terms of delay and robustness.

Faces detection method based on skin color modeling

This study suggests a method to improve the speed of a sliding window type of face detector by way of skin color region detection. The face detection method by way of skin color region detection has been studied in various perspectives: Complicated background images because of the area whose color is similar to the skin color cause high false positive rates. In contrast, the face detection method based on appearance, which adopts a sliding window type, may involve high face detection rates but cause tremendous computational costs in the process of detection scanning as the image size increases, whereas the processing time is also extended accordingly. This study suggests a method to control the subwindow size and detection area of a sliding window by detecting and using the skin color region with the processing time reduced. By means of a face detector with haar wavelet and LBP features, 274 images were collected online in addition to Bao database images, and then an experiment was conducted with them. As a result, the face detection time in utilization of an existing sliding window decreased down to a maximum of 47%.

Improved Energy Aware Routing Protocol in Mobile Ad Hoc Network

In this paper, an improved routing protocol considering position and energy in mobile ad hoc networks is suggested. The suggested routing protocol adapts the continuous mobility by reflecting the position and energy of nodes, improves the safety of the route, and extends the node life by making the balanced energy consumption considering residual energy of nodes. In this paper, to compare performances among protocols, the network simulator (NS-2) is used. According to the results from the experiments using NS-2, the routing protocol (PE-AODV) suggested in this paper showed the improvement compared to the precious protocols by improving the holding time of the route by more than 2.5 times compared to the existing protocols, and reducing the numbers of nodes that spent all the energy by maximum of 54%.

Outgoing call recommendation using neural network

Personalized data collected from smartphones and similar devices reflect time dependency. Currently, research using such data as a basis for prediction of future data is ongoing. This suggests that not only is time dependency reflected in past data but also in newly produced data, and that time-dependent weights also are reflected therein. This paper analyzed the most prominent feature of personalized data, the call log. The random forest method was used to find highly correlated data between call data and new outgoing calls. This information was then learned through a neural network. Data were collected from 10 subjects, and 80% was learned, while 20% was used for prediction. The results showed that for six suggested numbers, the Hit Ratio was 77% while for nine suggested numbers, it was 90%. This indicates that when nine numbers are recommended, the probability that one of them will be called is high. “Recent call volume” and “changing data” showed a high correlation, and experiments were conducted so that time change could be adequately reflected. The problem of repetitions was addressed while maintaining the Hit Ratio.

Patch Classifier of Face Shape Outline Using Gray-Value Variance with Bilinear Interpolation

This paper proposes a method to classify whether a landmark, which consists of the outline in a face shape model in the shape model based approaches, is properly fitted to feature points. Through this method, the reliability of information can be determined in the process of managing and using the shape. The enlarged face image by image sensor is processed by bilinear interpolation. We use the gray-value variance that considers the texture feature of skin for classification of landmarks. The gray-value variance is calculated in skin area of the patch constructed around the landmark. In order to make a system strong to poses, we project the image of face to the frontal face shape model. And, to fill out each area, the area with insufficient pixel information is filled out with bilinear interpolation. When the fitting is properly done, it has the variance with a low value to be calculated for smooth skin texture. On the other hand, the variance for misaligned landmark shows a high variance by the background and facial contour gradient. We have proposed a classifier using this characteristic and, as a result, classified the true and false in the landmark with an accuracy of 83.32% through the patch classifier.

A robust fuzzy-bilateral filtering method and its application to video deinterlacing

Deinterlacing converts an interlaced field to progressive frames while sustaining and improving image details. This is one of the key operations of image processing. However, filter-based interpolation and detail enhancement are contradictory operations. Therefore, it is hard to implement two such operations simultaneously. In this paper, we propose a robust fuzzy-bilateral filtering method and its application to video deinterlacing. The proposed bilateral filtering concept considers the range and domain filters based on a fuzzy metric. This characteristic is adaptively applied to both existing pixel activity and the associated position between existing neighbor pixels and the missing pixels. Simulation results prove that the proposed method can efficiently interpolate the interlaced field while enhancing detail.

Noise Level Estimation for Image Processing

This paper compares the results between two well known noise level estimation algorithms. We address the issue of estimating the variance of additive white Gaussian noise in digital images, even with large textured areas. Noise can significantly influence the quality of digital images. This method is based on two approaches. Both algorithms were introduced and we tested both algorithms with 5 artificial images and 8 natural images. One approach searches intensity-homogeneous blocks and then we estimate the noise variance in these blocks. The other method is based on wavelet transform, we obtain variance of additive white Gaussian noise using coefficients of wavelet transform. Based on the results, we adaptively choose the method and obtain the most appropriate noise level.

Call admission control for next generation cellular networks using on demand round robin bandwidth sharing

In this paper, we propose a novel call admission control scheme which improves call blocking probabilities as well as the packet–level QoS in the multiservice environment of next generation cellular networks. In our scheme, we suggest that bandwidth–inefficient service sessions such as WWW browsing of which traffic pattern contains frequent think time are grouped dynamically into a set, called lock, and forced to share the bandwidth with others on the basis of on demand round robin instead of being allocated separate bandwidth especially when the resource availability is unfavorable. Compared with the corresponding results of a conventional reconfiguration strategy by the use of simulation study, our scheme shows improved packet delay and loss rate in the case of WWW traffic, and superior performance in terms of blocking probabilities of both new call and handoff cases.

Automatic Extension of Korean Predicate-Based Sub-categorization Dictionary from Sense Tagged Corpora

Analyzing sentences of Korean language, it is found that situation, meaning, and context perform an important role rather than syntactical characteristics. Thus it is difficult to disambiguate word sense by rule-based method, such as context-free grammar, only. In this study, sense-tagged corpora was semi-automatically constructed with the use of predicate-based sub-categorization dictionary. In this process, the information on the frequency of predicate-based sub-categorization patterns, the information on the collocation of predicates and nouns, and the information on the statistic cooccurence of declinable words could be obtained. Based on this information, the method of automatic extension of sub-categorization dictionary is suggested.