• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.10
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• pp.3-12
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• 2013

Wireless Sensor Network (WSN) is used in the railway field in terms of efficient management and maintenance. Sensor communication technology based on IEEE 802.15.4/ZigBee is used in low speed train. However, it is difficult to apply in the high speed train that exposed to severe wireless channel environments. In this paper, we propose the sensor communication algorithm for high speed train environment. we improve error rate and throughput using Equalizer, Multiple Input Multiple Output (MIMO), Flexible Spreading Factor (SF) and Modulation. Also, we have analyzed the performance of the IEEE 802.15.4/ZigBee based on the standard of physical layer of 2.4GHz band in each algorithms. Simulation results show that proposed algorithms can improve error rate and throughput of conventional system.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.8
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• pp.28-38
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• 2013

In this paper, we propose a improved turbo equalizer which generates a feedback signal through a simple calculation to improve performance in single carrier system with the LMS(least mean square) algorithm based equalizer and LDPC(low density parity check) codes. LDPC codes can approach the Shannon limit performance closely. However, computational complexity of LDPC codes is greatly increased by increasing the repetition of the LDPC codes and using a long parity check matrix in harsh environments. Turbo equalization based on LDPC code is used for improvement of system performance. In this system, there is a disadvantage of very large amount of computation due to the increase of the repetition number. To less down the amount of this complicated calculation, The proposed improved turbo equalizer adjusts the adoptive equalizer after the soft decision and the LDPC code. Through the simulation results, it's confirmed that performance of improved turbo equalizer is close to the SISO-MMSE(soft input soft output minimum mean square error) turbo equalizer based on LDPC code with the smaller amount of calculation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.1B
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• pp.183-192
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• 2000

In this paper, the dual looped CMOS PLL with 3-250MHz input locking range at a single 13.3V is designed. This paper proposed a new PLL architecture with a current pumping algorithm to improve voltage-to-frequencylinearity of VCO(Voltage Controlled Oscillator). The designed VCO operates at a wide frequency range of75.8MHz-lGHz with a high linearity. Also, PFD(Phase frequency Detector) circuit preventing voltage fluctuation of the charge pump with loop filter circuit under the locked condition is designed. The simulation results of the PLL using 0.6 um N-well single poly triple metal CMOS technology illustrate a locking time of 3.5 us, a power dissipation of 92mW at 1GHz operating frequency with 125MHz of input frequency. Measured results show that the phase noise of VCO with V-I converter is -100.3dBc/Hz at a 100kHz offset frequency.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.1B
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• pp.89-98
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• 2000

This paper presents an active noise cancelling model using wavelet transform and subband filter banks based on adaptive filter. The analysis filter banks decompose input and error signals into QMF filter banks of lowpass and highpass bands. Each filter bank uses wavelet filter with dyadic tree structure. The decomposed input and error signals are iterated by adaptive filter coefficients of each subband using filtered-X LMS algorithm. The synthesis filter banks make output signal of wideband with perfect reconstruction to prepare adaptive filter output signals of each subband. The analysis and synthesis niter hants use conjugate quadrature filters for Pefect reconstruction. Also, The delayed LMS algorithm model for on-line identification of error path transfer characteristics is used gain and acoustic time delay factors. The proposed adaptive active noise cancelling modelis suggested by system retaining the computational and convergence speed advantage using wavelet subband filter banks.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.12A
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• pp.1209-1216
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• 2008

In order to maximize spectrum efficiency and data rate MIMO(Multiple Input Multiple Output) is adopted to wireless system. OFDM-based WiMAX and LTE accommodate MIMO as mandatory technology. STC(Space Time Coding) and SM(Spatial Multiplexing) are used in downlink while in uplink C-MIMO(Collaborative MIMO) is used to improve data throughput. In this paper conventional pairing schemes, RPS(Random Pairing Scheduling) and DPS(Determinant Pairing Scheduling) are analyzed. From the analysis the performance of DPS algorithm is better than that of RPS because DPS measures orthogonal factor between paired users. However, there are potential problems such as hardware complexity and performance. To overcome the issues Power-Based Scheduling(PBS) algorithm is proposed for C-MIMO. PBS can provide higher performance compared to RPS and dramatically reduce hardware complexity compared to DPS

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.7
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• pp.1686-1694
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• 2014

In this paper, a new scheme to recognize a finger shape in the depth image captured by Kinect is proposed. Rigid transformation of an input finger shape is pre-processed for its robustness against the shape angle of input fingers. After extracting contour map from hand region, observing the change of contour pixel location is performed to calculate rotational compensation angle. For the finger shape recognition, we first acquire three pixel points, the most left, right, and top located pixel points. In the proposed algorithm, we first acquire three pixel points, the most left, right, and top located pixel points for the finger shape recognition, also we use geometrical features of human fingers such as Euclidean distance, the angle of the finger and the pixel area of hand region between each pixel points to recognize the finger shape. Through experimental results, we show that the proposed algorithm performs better than old schemes.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.10C
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• pp.1013-1022
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• 2003

This paper implemented the real-time object tracking algorithm that extracts and tracks the moving object adaptively to input frame sequence by using non-parametric image processing method and Kalman filter-based dynamic AR(2) process method. By applying non-parametric image processing to input frames, the moving object was extracted from the background adaptively to diverse environmental conditions. And the movement of object was able to be adaptively estimated and tracked by modeling the various movement of object as dynamic AR(2) process and estimating based on the Kalman filter the parameters of AR(2) process dynamically changing along time. The experiments of the implemented object tracking system showed that the proposed method tracked the moving object as more approximately as the estimation error became about l/2.5∼1/50 of one of the traditional tracking method based on linear Kalman filter.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.4
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• pp.602-610
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• 2018

The model predictive control is an effective method to optimize the current control input that predicts the current control state and the future error using the predictive model of the control system when the reference trajectory is known. Since the control input can not have a physically infinitely large value, a predictive controller design with constraints should be considered. In addition, the reference model $A_r$ and the weight matrices Q, R that determine the control performance of the predictive controller are not optimized as arbitrarily designated should be considered in the controller design. In this study, we construct a predictive controller of a mobile robot by transforming it into a quadratic programming problem with constraints, The control performance of the mobile robot can be improved by optimizing the control parameters of the predictive controller that determines the control performance of the mobile robot using genetic algorithm. Through the computer simulation, the superiority of the proposed method is confirmed by comparing with the existing method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.1C
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• pp.43-50
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• 2007

In the paper, we address QRM-MLD (Maximum Likelihood Detection with QR Decomposition and M-algorithm) signal detection method for spatially multiplexed MIMO (Multiple Input Multiple Output) systems. Recently, the QRM-MLD signal detection method which can achieve 1Gbps transmission speed for next generation mobile communication was implemented in a MIMO testbed for the mobile moving at a pedestrian speed. In the paper, we propose a novel signal detection method 'reduced complexity QRM-MLD' that achieves identical error performance as the QRM-MLD while reducing the computational complexity significantly. We rigorously compare the two detection methods in terms of computational complexity to show the complexity reduction of the proposed method. We also perform a set of computer simulations to demonstrate that two detection methods achieve identical error performance.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.2 no.3
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• pp.79-86
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• 2009

This study proposed a method of detecting the facial area by calculating Euclidian distances among skin color elements and extracting the characteristics of the face. The proposed algorithm is composed of light calibration and face detection. The light calibration process performs calibration for the change of light. The face detection process extracts the area of skin color by calculating Euclidian distances to the input images using as characteristic vectors color and chroma in 20 skin color sample images. From the extracted facial area candidate, the eyes were detected in space C of color model CMY, and the mouth was detected in space Q of color model YIQ. From the extracted facial area candidate, the facial area was detected based on the knowledge of an ordinary face. When an experiment was conducted with 40 color images of face as input images, the method showed a face detection rate of 100%.