• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.842-844
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• 2012

This paper introduces a 5GHz FIR filter low noise amplifier (LNA) based on a distributed amplifier and analyzes the its characteristics. The proposed FIR filter-LNA has the MA(moving average) filter characteristic which improves the frequency selectivity of the amplifier. Proto-type circuits with FR4 and ${\varepsilon}_r=10.2$ PCB have been realized and simulated using ADS (Advanced Design System). The simulation results verified that the designed LNA had a gain of about 10dB and the frequency characteristic of the MA FIR filter. It is expected that the proposed FIR filter LNA can be applicable to the various applications using an amplifier and a filter in RF systems.

• Proceedings of the IEEK Conference
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• 2003.07a
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• pp.234-237
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• 2003

The accuracy of channel estimation significantly affects the performance of coherent OFDM receiver. It is desirable to employ a good channel estimator while requiring low implementation complexity. In this paper, we propose a channel estimator that employs a simple two-dimensional (2-D) moving average (MA) filter as the channel estimation filter. The optimum tap size of the 2-D MA FIR filter is analytically designed in the time and frequency domain in association with the channel condition and pilot signal to interference power ratio. The analytic results can be applied to the design of adaptive channel estimator. Finally, the performance of the proposed channel estimator is verified by computer simulation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.8
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• pp.1753-1758
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• 2012

This paper introduces analog FIR filters based on a distributed amplifier and analyzes the proposed filter's characteristics. A simple design method of an analog FIR filter based on the digital filter design technique is also introduced. The proposed analog FIR filters are a moving average(MA) and a comb type filters with no multiplier. This simple structures of the proposed filters may enable to operate at multi-GHz frequency range and applicable to combine a filter and an amplifier of RF system. The proposed analog FIR filters were implemented with standard $0.18{\mu}m$ CMOS technology. The designed GHz analog FIR filters are simulated by Cadence Spectre and compared to the results of digital FIR filters obtained from MATLAB simulations. From the simulation results, the characteristics of the proposed analog FIR filters are fairly well matched with those of digital FIR filters.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.6A
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• pp.341-350
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• 2003

In this paper, we evaluate the performance of a moving average (MA) channel estimation filter when space-time block coding transmit diversity (STBC-TD) is applied to the wideband direct sequence code division multiple access (WCDMA) forward link. And we present the infinite impulse response (IIR) filter scheme that can reduce the required memory buffer and the channel estimation delay time. This paper also compares the performance between MA filter scheme and IIR filter scheme in various Rayleigh fading channel environments through the bit error rate (BER) and the frame error rate (FER). Extensive computer simulation results show that transmission with STBC-TD provides a significant gain in performance over no transmit diversity technique, particularly at pedestrian speeds. If STBC-TD technique is employed in the channel estimator based on MA filter, it provides considerable performance gains against Rayleigh fading and reduces the optimum filter tap number. Consequently, the channel estimation delay time and the complexity of the receiver are reduced. In addition, the channel estimator based on IIR filter has the advantages such as little memory requirement and no delay time compared to the MA scheme. However, IIR filter coefficients is very sensitive to the mobile speed change and it exerts a serious influence upon the performance. For that reason, it is important to set uP the optimum IIR filter coefficients.

• Journal of Korea Multimedia Society
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• v.10 no.11
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• pp.1530-1537
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• 2007

In this paper, we implemented a speech emotion recognition system that can distinguish human emotional states from recorded speech captured by a single microphone and classify them into four categories: neutrality, happiness, sadness and anger. In general, a speech recorded with a microphone contains background noises due to the speaker environment and the microphone characteristic, which can result in serious system performance degradation. In order to minimize the effect of these noises and to improve the system performance, a MA(Moving Average) filter with a relatively simple structure and low computational complexity was adopted. Then a SFS(Sequential Forward Selection) feature optimization method was implemented to further improve and stabilize the system performance. For speech emotion classification, a SVM pattern classifier is used. The experimental results indicate the emotional classification performance around 65% in the computer simulation and 62% on the ARM platform.

• The Journal of the Acoustical Society of Korea
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• v.25 no.6
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• pp.269-276
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• 2006

In this paper, we propose an emotion recognition system that can discriminate human emotional state into neutral or anger from the speech captured by a cellular-phone in real time. In general. the speech through the mobile network contains environment noise and network noise, thus it can causes serious System performance degradation due to the distortion in emotional features of the query speech. In order to minimize the effect of these noise and so improve the system performance, we adopt a simple MA (Moving Average) filter which has relatively simple structure and low computational complexity, to alleviate the distortion in the emotional feature vector. Then a SFS (Sequential Forward Selection) feature optimization method is implemented to further improve and stabilize the system performance. Two pattern recognition method such as k-NN and SVM is compared for emotional state classification. The experimental results indicate that the proposed method provides very stable and successful emotional classification performance such as 86.5%. so that it will be very useful in application areas such as customer call-center.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.86-89
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• 1992

One of the most difficult problems in depth control for underwater vehicle is the effect of seaway disturbance. One component of the seaway forces is of large magnitude with a relatively narrow-band, first order component. The other component is generaly of somewhat smaller magnitude, second order component. Since the magnitude of the first order component is generally much greater than the compensating force that can be generating by the planes, it is undesirable for the controller to generate a control command. In this paper, we disigned adaptive notch filtering system using filter bank structure. Energies of each band-passed signal are obtained by MA(Moving Average) method and compared to produce center frequency. By adapting this parameter to notch filter, 1st order seaway disturbance can be removed, which lead to the improvement of automatic depth control system.

• The Transactions of the Korean Institute of Power Electronics
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• v.9 no.1
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• pp.42-49
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• 2004

This paper presents a new method of external load disturbance compensation using deadbeat load torque observer and gain compensation by parameter estimator. The response of the permanent magnet synchronous motor(PMSM) follows the nominal plant. The load torque compensation method is composed of a deadbeat observer. To reduce the noise effect, the post-filter implemented by moving average(MA) process is adopted. The parameter compensator with recursive least square method(RLSM) parameter estimator is suggested to make the new system work as same as the name plate system which in used to take gains. The proposed estimator is combined with a high performance load torque observer to resolve the problems. As a result, the proposed control system has a robust and precise system against the load torque and the parameter variation. A stability and usefulness are verified by computer simulation and experiment.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.53 no.3
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• pp.188-195
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• 2004

This paper presents neural load torque observer that is used to deadbeat load torque observer and gain compensation by parameter estimator As a result, the response of the PMSM(permanent magnet synchronous motor) follows that nominal plant. The load torque compensation method is composed of a neural deadbeat observer To reduce the noise effect, the post-filter implemented by MA(moving average) process, is adopted. The parameter compensator with RLSM (recursive least square method) parameter estimator is adopted to increase the performance of the load torque observer and main controller The parameter estimator is combined with a high performance neural load torque observer to resolve the problems. The neural network is trained in on-line phases and it is composed by a feed forward recall and error back-propagation training. During the normal operation, the input-output response is sampled and the weighting value is trained multi-times by error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. As a result, the proposed control system has a robust and precise system against the load torque and the Parameter variation. A stability and usefulness are verified by computer simulation and experiment.

• Journal of Power Electronics
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• v.8 no.4
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• pp.354-362
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• 2008

This paper presents neural load torque compensation method which is composed of a deadbeat load torque observer and gains compensation by a parameter estimator. As a result, the response of the PMSM (permanent magnet synchronous motor) obtains better precision position control. To reduce the noise effect, the post-filter is implemented by a MA (moving average) process. The parameter compensator with an RLSM (recursive least square method) parameter estimator is adopted to increase the performance of the load torque observer and main controller. The parameter estimator is combined with a high performance neural load torque observer to resolve problems. The neural network is trained in online phases and it is composed by a feed forward recall and error back-propagation training. During normal operation, the input-output response is sampled and the weighting value is trained multi-times by the error back-propagation method at each sample period to accommodate the possible variations in the parameters or load torque. As a result, the proposed control system has a robust and precise system against load torque and parameter variation. Stability and usefulness are verified by computer simulation and experiment.