• The Journal of Korean Institute of Communications and Information Sciences
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• v.38A no.1
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• pp.44-57
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• 2013

In this paper, we propose a femtocell searching technique which can prevent a macrocell UE(user equipment) from losing synchronism to its serving macrocell near closed access femtocells in co-channel deployment due to the leakage of femtocell signals by using a CS(Common Signal). The CS, commonly transmitted by femtocells in a macrocell at the same time, enables the macrocell UEs to be kept synchronized with their serving macrocells since the CINR(Carrier to Interference and Noise Ratio) of base stations in macrocell can be kept high even near closed access femtocells. Also, the CS is designed in such a way that a macrocell UE can recognize the existence of femtocell by using the metric CSCINR(Common Signal Carrier to Interference and Ratio) measured with CS. In addition, the proposed femtocell searching technique can reduce the frequency of femtocell searching trial by using the metric on mobility of a macrocell UE defined in this paper, and the reduction of the frequency of handover trial can be also expected as a byproduct.

• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.295-296
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• 1998

With xeroradiography appearance, DR (Digital Radiography) system have been studying for X-ray detection using photoreceptor. Also detection method for receptor charge change have been developing variably. We use photoreceptor material of a-Se(Amorphous Selenium) with high DQE, high SNR(Signal to Noise Ratio) and high transformation efficiency of X-ray signals into electrical signals. After a-Se receptor is uniformly charged by using Arc discharge, X-ray is exposed. Then a-Se receptor produce subtle charge variation and MOSFET detect charge variations. The detected signal pass A/D converter and signal processing by PC. As results, the initial voltage is 8V. It has wide dynamic range needed digital radiography system. In this study, we obtained data with changing kVp(tube potential voltage) and fixed 8mAs(tube current by exposure time) in X-ray system. However MOSFET detector for X-ray signal is not tested X-ray mAs variations. But if MOSFET detector is tested X-ray mAs variation and exactly calibrated multichannel is made and noise-reduction is done, suitable DR system readout method will be done.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.1
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• pp.45-50
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• 2022

This paper compares the equalization performance of the DSE-MMA (Dithered Signed Error-MMA) and DQE-MMA (Dithered Quantized Error-MMA) adaptive equalization algorithm based on the dither signal in order to reduce the intersymbol interference occurs at communication channel. These algorithm was emerged in ordr to reduction of arithmetic operation than current MMA, it makes the independent and identical distribute the quantized error component by performing the 1 or N bit quautizer after adding the dither singal in obtaining the error signal for adapting process. It is possible to improve the robustness performance of adaptive algorithm, but degrade the MSE performance in steady state by dither signal. The paper directly compare the DSE-MMA and DQE-MMA adaptive equalization performance of the same concept of dithering in the same communication channel and signal to noise ratio by computer simulation. As a result of simulation, the DQE-MMA has more better in the every performance index, equalizer output constellation, residual isi, MSE and SER performance, but not in convergence speed.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.6
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• pp.1103-1108
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• 2023

In this paper, we propose a new algorithm for attenuating the background noises in acoustic signal. This algorithm improves the noise attenuation performance by using the FNN(: Full-connected Neural Network) deep learning algorithm instead of the existing adaptive filter after wavelet transform. After wavelet transforming the input signal for each short-time period, noise is removed from a single input audio signal containing noise by using a 1024-1024-512-neuron FNN deep learning model. This transforms the time-domain voice signal into the time-frequency domain so that the noise characteristics are well expressed, and effectively predicts voice in a noisy environment through supervised learning using the conversion parameter of the pure voice signal for the conversion parameter. In order to verify the performance of the noise reduction system proposed in this study, a simulation program using Tensorflow and Keras libraries was written and a simulation was performed. As a result of the experiment, the proposed deep learning algorithm improved Mean Square Error (MSE) by 30% compared to the case of using the existing adaptive filter and by 20% compared to the case of using the STFT(: Short-Time Fourier Transform) transform effect was obtained.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.6
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• pp.182-195
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• 2023

Recently, research on traffic signal control using artificial intelligence algorithms has been receiving attention, and many traffic signal control models are being studied. However, most studies either focused on independent intersections or are theoretical studies that calculate signal cycle length according to changes in traffic volume. Therefore, this study was conducted on a signalized intersection - roundabout in Gajwa-ro. The Particle Swarm Optimization - Bacterial Foraging Optimization (PSO-BFO) algorithm was proposed, which is developed from the GA and PSO algorithms for minimizing congestion at two intersections. As a result, optimum cycle length was determined to be 158 seconds. The Verkehr In Stadten - SIMulationsmodell (VISSIM) results showed that there was 3.4% increased capacity, 8.2% reduced delay and 8.3% reduced number of stops at the Gajwa-ro signalized intersection. Additionally, at the roundabout, a 9.2% increase in capacity, a 7.1% reduction in delay, and a 27.2% decrease in the number of stops was observed.

• Investigative Magnetic Resonance Imaging
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• v.8 no.1
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• pp.1-8
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• 2004

Purpose : Information about electrical activity inside the brain during fMRl scans is very useful in monitoring physiological function of the patient or locating the spatial position of the activated region in the brain. However, many additional noises appear in the EEG signal acquired during the MRI scan. Gradient induced noise is the biggest one among the noises. In this work, we propose a gradient noise reduction method using the independent component analysis (ICA) method. Materials and Methods : We used a 29-channel MR-compatible EEG measurement system and a 3.0 Tesla MRI system. We measured EEG signals on a subject lying inside the magnet during EPI scans. We selectively removed the gradient noise from the measured EEG signal using the ICA method. We compared the results with the ones obtained with conventional averaging method and PCA method. Results : All the noise reduction methods including the averaging and PCA methods were effective in removing the noise in some extent. However, the proposed ICA method was found to be superior to the other methods. Conclusion : Gradient noise in EEG signals acquired during fMRI scans can be effectively reduced by the ICA method. The noise-reduced EEG signal can be used in fMRI studies of epileptic patients or combinatory studies of fMRI and EEG.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.26 no.9
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• pp.1234-1240
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• 2002

A new feedback control law is proposed and tested for suppressing the vortex shedding from a circular cylinder in a uniform flow. The lift coefficient ( $C_{L}$) is employed as a feedback control signal and the control forcing is given by a rotational oscillation of the cylinder. The influence of the feedback transfer function on the $C_{L}$ reduction is examined. The main rationale of the feedback control is that a feedback control forcing is imposed at a phase which is located outside the range of lock-on. By applying the feedback control law, $C_{L}$ is reduced significantly. Furthermore, the reduction mechanism of $C_{L}$ is analyzed by showing the vortex formation modes with respect to the forcing phase.e.ase.e.

• ETRI Journal
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• v.37 no.2
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• pp.338-347
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• 2015

The multi-carrier transmission signal in Multi-Carrier Code Division Multiple Access (MC-CDMA) has a high peak-to-average power ratio (PAPR), which results in nonlinear distortion and deteriorative system performance. An n-tuple selective mapping method is proposed to reduce the PAPR, in this paper. This method generates $2^n$ sequences of an original data sequence by adding n-tuple of n PAPR control bits to it followed by an interleaver and error-control code (ECC) to reduce its PAPR. The convolutional, Golay, and Hamming codes are used as ECCs in the proposed scheme. The proposed method uses different numbers of the n PAPR control bits to accomplish a noteworthy PAPR reduction and also avoids the need for a side-information transmission. The simulation results authenticate the effectiveness of the proposed method.

• ETRI Journal
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• v.29 no.2
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• pp.231-233
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• 2007

One of the drawbacks in an OFDM system is the high peak-to-average power ratio (PAPR). Among a number of techniques to reduce the high PAPR, simple amplitude predistortion (SAP), a form of active constellation extension, has been proposed to effectively achieve the desired PAPR. In this letter, a novel scheme, simple amplitude and phase predistortion (SAPP), is proposed. In SAP the carriers' amplitude is utilized to combat the peak signal. Each amplitude is amplified according to its degree of contribution as a metric. In addition to amplitude, SAPP also utilizes the phase. Simulation results indicate that the proposed scheme provides better PAPR reduction than SAP.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.106-109
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• 2000

There have been proposed two types of low bit rate vocoder upto now : One is MBE type using the spectrum modeling and another is CELP type using the hybrid coding method. CELP type vocoder has mainly studied between them. Specially, much of intensity is concentrated in CELP vocoder due to the emergence of Internet Phone and PCS in a domestic. In order to improve the speech quality in CELP vocoder, in this paper, we proposed a new spectrum analysis algorithm with variable window, In CELP vocoder, the spectrum of the synthesised speech signal is distorted because the fixed size windows is used for spectrum analysis. So we have measured the spectral leakage and in order to minimize the spectral leakage have adjusted the window size. Applying this method G.723.1 ACELP, we can get SD(Spectral Distortion) reduction 0.084(dB), residual energy reduction 6.3% and MOS(Mean Opinion Score) improvement 0.1.