• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.2
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• pp.850-857
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• 2013

The objective of this paper is to provide service continuity based on an efficient subchannel allocation in OFDMA systems. The resource management for handover are necessary to maintain the QoS requirements of different multimedia applications because the service continuity may be defected by some delay and information loss. Therefore we propose two subchannel management schemes applied to OFDMA systems. Firstly, a superposition allocation of interference subchannels is achieved by modifying a frequency reuse scheme, using co-subchannel interference principle. Secondly for handover applications, we suggest a novel subchannel reservation scheme to adjust the number of allocated channels, depending on the different characteristics and diverse quality of mobile multimedia applications. Simulation results show that the total throughput for the proposed method is increased up to 20% at average and peak arrivals and the handover failure rate is decreased to about 25%, as compared to the conventional method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.05a
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• pp.165-170
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• 2003

This paper presents an integrated approach to unconstrained face recognition in arbitrary scenes. The front end of the system comprises of a scale and pose tolerant face detector. Scale normalization is achieved through novel combination of a skin color segmentation and log-polar mapping procedure. Principal component analysis is used with the multi-view approach proposed in[10] to handle the pose variations. For a given color input image, the detector encloses a face in a complex scene within a circular boundary and indicates the position of the nose. Next, for recognition, a radial grid mapping centered on the nose yields a feature vector within the circular boundary. As the width of the color segmented region provides an estimated size for the face, the extracted feature vector is scale normalized by the estimated size. The feature vector is input to a trained neural network classifier for face identification. The system was evaluated using a database of 20 person's faces with varying scale and pose obtained on different complex backgrounds. The performance of the face recognizer was also quite good except for sensitivity to small scale face images. The integrated system achieved average recognition rates of 87% to 92%.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.12
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• pp.1079-1085
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• 2013

Due to the limited power supply resources, it is essential that the minimization of algorithmic operation and the reduction of the hardware logical-resources in the design of the satellite transponder. It is also required that the transponder process the signals of various bandwidth efficiently, that is suitble for the SDR-based implementation. This paper proposes a variable rate down sampler which can provide variable bandwidth and data rate for carrier, ranging and sub-band command signals respectively. The proposed down sampler can provide multiple $2^M$ decimated outputs from a single half band filter with recursive arithmetic architecture, which can minimize the hardware resources as well as the arithmetic operations. The algorithm for hardware implementation as well as the analysis for the passband flatness and aliasing is presented and varified by the FPGA implementation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.12
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• pp.3675-3681
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• 2009

One of the most important factors in the lifetime of WSN(Wireless Sensor Network) is the limited resources and static control problem of the sensor nodes. In order to achieve energy efficiency and network utilities, sensor nodes can be well organized into one cluster and selected head node and normal node by dynamic conditions. Various clustering algorithms have been proposed as an efficient way to organize method based on LEACH algorithm. In this paper, we propose an efficient clustering algorithm using information entropy theory based on LEACH algorithm, which is able to recognize environmental differences according to changes from data of sensor nodes. To measure and analyze the changes of clusters, we simply compute the entropy of sensor data and applied it to probability based clustering algorithm. In experiments, we simulate the proposed method and LEACH algorithm. We have shown that our data balanced and energy efficient scheme, has high energy efficiency and network lifetime in two conditions.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.5
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• pp.447-455
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• 2014

In this paper, we consider a representative integral imaging method in glasses-free 3D image processing and propose a prioritized transmission scheme for guaranteeing a received video quality in error-prone environments. According to the correlation of pixels consisting of each voxel of integral image, we set the priority differently and apply the modulation level according to this priority value. That is to say, the corresponding pixels with small variance are set to a high priority and transmitted by using a low level modulation that is robust under transmission errors, but the corresponding pixels with greater variance are set to a lower priority and transmitted by using a high level modulation that has a high bit error rate but fast transmission rate. Result shows that the proposed scheme that applies the error-robust modulation level to the important image bit stream with the high priority improves the peak to sidelobe ratio (PSR) of the received 3D image, compared with a typical method that use the same modulation level without distinction of priorities.

• Journal of Korean Society of Transportation
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• v.24 no.7 s.93
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• pp.81-90
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• 2006

It is important to clarify the relationship between traffic accidents and various influencing factors in order to reduce the number of traffic accidents. This study developed a traffic accident frequency prediction model using by multi-linear regression and qualification theories which are commonly applied in the field of traffic safety to verify the influences of various factors into the traffic accident frequency The data were collected on the Korean National Highway 17 which shows the highest accident frequencies and fatality rates in Chonbuk province. In order to minimize the uncertainty of the data, the fuzzy theory and neural network theory were applied. The neural network theory can provide fair learning performance by modeling the human neural system mathematically. Tn conclusion, this study focused on the practicability of the fuzzy reasoning theory and the neural network theory for traffic safety analysis.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.6
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• pp.1312-1317
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• 2003

This paper presents an integrated approach to unconstrained face recognition in arbitrary scenes. The front end of the system comprises of a scale and pose tolerant face detector. Scale normalization is achieved through novel combination of a skin color segmentation and log-polar mapping procedure. Principal component analysis is used with the multi-view approach proposed in[10] to handle the pose variations. For a given color input image, the detector encloses a face in a complex scene within a circular boundary and indicates the position of the nose. Next, for recognition, a radial grid mapping centered on the nose yields a feature vector within the circular boundary. As the width of the color segmented region provides an estimated size for the face, the extracted feature vector is scale normalized by the estimated size. The feature vector is input to a trained neural network classifier for face identification. The system was evaluated using a database of 20 person's faces with varying scale and pose obtained on different complex backgrounds. The performance of the face recognizer was also quite good except for sensitivity to small scale face images. The integrated system achieved average recognition rates of 87% to 92%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.11
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• pp.2553-2562
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• 2013

It is difficult for TCP congestion control algorithm to ensure the bandwidth and delay bound required for media streaming services in broadband wireless network environments. In this paper, we propose the COIN TCP (COncave INcrease TCP) scheme for providing a high-quality media streaming services. The COIN TCP concavely increases the congestion window size by adjusting the increment rate of congestion window, that is inversely proportional to the amount of data accumulated in the router queue. As a result, our scheme can quickly occupy the available bandwidth and prevent the heavy congestion. It also improves the link utilization by adjusting the decrement rate of congestion window according to the packet loss rate with the random loss. Through the simulation results, we prove that our scheme improves the total throughput in broadband wireless network.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.123-125
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• 2015

Wireless ad hoc network which is comprised of wireless nodes that have the limited communication range is utilized to monitoring disaster area, tracing object, and tactical system. But in the case of wireless node on the ground, a network performance decrease because wireless channel is affected from obstacle or the node deployment is restricted. In this paper, we consider wireless network based on UAV(Unmanned Aerial Vehicle) which has little spatial constraint and quickly deploy a position. We implement test-bed included ground nodes and UAV, and measure throughput and PDR(Packet Delivery Ratio) according to the usage of UAV. We show that network performance is improved by relaying data on UAV.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.38 no.3
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• pp.61-67
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• 2001

In this paper, a method that apply LP algorithm to image for speech recognition is suggested, using both speech and image information for recogniton of korean numeral speech. The input speech signal is pre-emphasized with parameter value 0.95, analyzed for B th LP coefficients using Hamming window, autocorrelation and Levinson-Durbin algorithm. Also, a gray image signal is analyzed for 2-dimensional LP coefficients using autocorrelation and Levinson-Durbin algorithm like speech. These parameters are used for input parameters of neural network using back-propagation algorithm. The recognition experiment was carried out at each noise level, three numeral speechs, '3','5', and '9' were enhanced. Thus, in case of recognizing speech with 2-dimensional LP parameters, it results in a high recognition rate, a low parameter size, and a simple algorithm with no additional feature extraction algorithm.