• Journal of Positioning, Navigation, and Timing
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• v.9 no.4
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• pp.379-385
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• 2020

The satellite employs an adaptive beamformer to efficiently detect various signals and to suppress multiple interference signals, simultaneously. Although the adaptive beamforming satellite system needs Angle-of-Arrival (AOA) information of the desired signal, it is difficult to estimate the signal AOAs on the satellite environment. However, the AOA estimation on the ground control tower is more efficient and accurate comparing to the satellite environment. In this paper, we propose an adaptive beamforming satellite system based on the signal location information on the ground, consisting on an angle estimator, an adaptive beamformer, and signal processing & D/B unit. The ground control tower estimates the accurate location of the signal source, and it sends the estimated coordinates of the desired signal to the satellite. The angle estimator mounted on the satellite calculates the desired signal AOA, based on the signal location information transmitted from the ground control center. The satellite beamformer detects the desired signal and suppresses unwanted signals based on the signal AOA calculated by the angle estimator. We provide computer simulation results to present the performance of the proposed satellite adaptive beamforming system based on the signal location information.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.10a
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• pp.263-266
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• 2000

In this paper, we formed a transmitter and receiver by using two identical Chua's circuits and then formed wireless transmission line from the channel which was between those two circuits. We proposed a secure communication method in which the desired information signal was synthesized with the chaos signal created in a Chua's circuit and sent to the transmitter through channel. Then the signal was demodulated receiver of Chua's circuit. The method we used to accomplish the sun communication was synthesizing the desired information with the chaos circuit by parallel connection in a wireless transmission line. After transmitting the synthesized signal to the wireless transmission line, we confirmed the actuality of the secure communication by separating the information signal and the chaos signal in the receiver. In order to confirm the security, we compared the wiretapped signal and the recovery signal under the assumption that the wiretapping had taken place. In order to separate the two signals, we transformed the information signal to a current source in the transmitter and detected the current in the receiver.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.05a
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• pp.238-241
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• 2000

In this paper, we formed a transmitter and receiver by using two identical Chua's circuits and then formed wireless transmission line from the channel which was between those two circuits. We proposed a secure communication method in which the desired information signal was synthesized with the chaos signal created in a Chua's circuit and sent to the transmitter through channel. Then the signal was demodulated receiver of Chua's circuit. The Method we used to accomplish the secure communication was synthesizing the desired information with the chaos circuit by parallel connection in a wireless transmission line. After transmitting the synthesized signal to the wireless transmission line, we confirmed the actuality of the secure communication by separating the information signal and the chads signal in the receiver. In order to confirm the security, we compared the wiretapped signal and the recovery signal under the assumption that the wiretapping had taken place. In order to separate the two signals, we transformed the information signal to a current source in the transmitter and detected the current in the receiver.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1068-1081
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• 2019

Signal complexity is one point of view to analyze the biological signal. It arises as a result of the physiological signal produced by biological systems. Signal complexity can be used as a method in extracting the feature for a biological signal to differentiate a pathological signal from a normal signal. In this research, Hjorth descriptors, one of the signal complexity measurement techniques, were measured on signal sub-band as the features for lung sounds classification. Lung sound signal was decomposed using two wavelet analyses: discrete wavelet transform (DWT) and wavelet packet decomposition (WPD). Meanwhile, multi-layer perceptron and N-fold cross-validation were used in the classification stage. Using DWT, the highest accuracy was obtained at 97.98%, while using WPD, the highest one was found at 98.99%. This result was found better than the multi-scale Hjorth descriptor as in previous studies.

• Journal of Information Processing Systems
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• v.19 no.3
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• pp.377-384
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• 2023

The existence of abnormal breakpoint data leads to poor channel balance in wireless sensor networks (WSN). To enhance the communication quality of WSNs, a method for positioning abnormal breakpoint data in WSNs on the basis of signal reconstruction is studied. The WSN signal is collected using compressed sensing theory; the common part of the associated data set is mined by exchanging common information among the cluster head nodes, and the independent parts are updated within each cluster head node. To solve the non-convergence problem in the distributed computing, the approximate term is introduced into the optimization objective function to make the sub-optimization problem strictly convex. And the decompressed sensing signal reconstruction problem is addressed by the alternating direction multiplier method to realize the distributed signal reconstruction of WSNs. Based on the reconstructed WSN signal, the abnormal breakpoint data is located according to the characteristic information of the cross-power spectrum. The proposed method can accurately acquire and reconstruct the signal, reduce the bit error rate during signal transmission, and enhance the communication quality of the experimental object.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.8
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• pp.719-724
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• 2012

A traffic detection system is a device that collects traffic information around an intersection. Most existing traffic detection systems provide very limited traffic information for signal control due to the restriction of vehicle detection area. A signal control scheme determines the transition among signal phases and the time that a phase lasts for. However, the existing signal control scheme do not resolve the traffic congestion effectively since they use restricted traffic information. In this paper, a new traffic detection system with a zone division signal control scheme is proposed to provide correct and detail traffic information and decrease the vehicle's waiting time at the intersection. The traffic detection system obtains traffic information in a way of vehicle-to-roadside communication between vehicles and sensor network. A new signal control scheme is built to exploit the sufficient traffic information provided by the proposed traffic detection system efficiently. Simulation results show that the proposed signal control scheme has 121 % and 56 % lower waiting time and delay time of vehicles at an intersection than other fuzzy signal control scheme.

• International Journal of Internet, Broadcasting and Communication
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• v.12 no.3
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• pp.1-8
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• 2020

This paper studied a method of estimating target information using a radar in wireless communication. Position information on the target can be estimated angle, distance and velocity. The velocity information can be estimated since the Doppler frequency is changed in the moving target. The signal incident on the receiving array antenna is multiplied by the delay time and the reference signal to represent the output signal. This output signal is estimated by applying FFT (Fast Fourier Transform) after calculating signal correlation through correlation integrator. Since the output signal must be calculated within the correlator, it should be processed with the Dwell time. The correlation signal of the correlation integrator outside this Dwell time is indicated by the velocity measurement error. The FFT is applied to the signal that has passed through the correlated integrator in order to estimate the distance of the signal. The Doppler resolution must be improved because the FFT estimates target information using the Doppler information. The Doppler resolution decreases with increasing the integration time. The velocity information estimation should have no spread of the velocity. As a result of the simulation, there was no spread of the target velocity in this study.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.4
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• pp.434-439
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• 2019

This paper proposed a control algorithm to compensate the delay time to improve the signal to noise, and the proposed control algorithm estimate the target information to apply the continuous wave radar equation. The proposed control algorithm improves the output signal of each array element bv multiplying the weight of the receive signal to the signal to noise ratio. Radar radiate a signal in spatial and the target information is estimated by the echoed signal from the target. But the signal in the wireless communication environment occurs the delay time due to the multipath which appear human and natural structures. It is difficult to accurately estimate the desired information because of the degradation for the system performance due to the interference signal and the signal distortion. The target information can be improved by compensating the delay signal to apply the weight to the received signal by using the uniform linear array antenna. As a simulation result, we show that the performance of the proposed control algorithm and the non-compensated delay time are compared. The proposed control algorithm proved that the target distance estimation information is improved.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.6
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• pp.1146-1151
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• 2011

There are two methods for train control in information transmission by using track circuit system and installing wayside transmitter. Information signal is transmitted to the on-board antenna by using rails. Continuous information about train intervals, speed and route is received by on-board antenna in AF track circuit system. The information signal is included with carrier wave and received by magnetic coupling in the on-board antenna. Therefore, it is important to define standard current level in the AF track circuit system. When current flowed to rails is low, magnetic sensors are not operated by decreasing magnetic field intensity. Hence, SNR is decreased because electric field intensity is decreased. When the SNR is decreased, there is the serious influence of noise upon demodulation. So, the frequency of information signal is not extracted in frequency response. Thus, it is possible to happen to train accident and delay as the information signal is not analyzed in the on-board antenna. In this paper, standard energy density is calculated by using Parseval's theory in UM71c track circuit. Hence, detection time of information signal is presented.

• Journal of Mechanical Science and Technology
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• v.17 no.2
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• pp.197-206
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• 2003

It is intended to retrieve the time history of displacement from measured acceleration signal. In this study, the word retrieving means reconstructing the time history of original displacement signal from already measured acceleration signal not just extracting various information using relevant signal processing techniques. Unlike extracting required information from the signal, there are not many options to apply to retrieve the time history of displacement signal, once the acceleration signal is measured and recorded with given sampling rate. There are two methods, in general, to convert measured acceleration signal into displacement signal. One is directly integrating the acceleration signal in time domain. The other is dividing the Fourier transformed acceleration signal by the scale factor of - $\omega$$^2$and taking the inverse Fourier transform of it. It turned out both the methods produced a significant amount of errors depending on the sampling resolution in time and frequency domain when digitizing the acceleration signals. A simple and effective way to convert the time history of acceleration signal into the time history of displacement signal without significant errors is studied here with the analysis on the errors involved in the conversion process.