• 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.

• Journal of the Korean Institute of Telematics and Electronics
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• v.13 no.5
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• pp.24-29
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• 1976

In the phase locked AM signal detection, phase locked loop is used to extract a synchronous carrier from an input AM signal. Under the assumption that input noise is white Gaussian and free-running frequency of voltage controlled oscillator is the same that of an input carrier, operational behaviours of phase locked loop is analyzed and signal to noise ratio of the detection is derived quentitatively. The results show that the phase locked AM signal detection method offers a higher degree of noise mmunity than conventional AM signal detections.

• Journal of Satellite, Information and Communications
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• v.9 no.1
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• pp.28-32
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• 2014

In this paper, we analyzed performance of signal detection for terrestrial digital multimedia broadcasting (T-DMB) system in various channel environments that applied gaussian, Rayleigh and COST 207 environments that estimate response of real channel state. In order to detect the signal, a signal detection scheme based on energy detection is employed. We generated T-DMB signals so that estimate properly simulation result. For evaluating the signal detection performance, detection probability is derived. We consider two kinds of detection segment, which are three segments and five segments. And we compare the system performances in accordance with the detection segment. From simulation results, it is confirmed that the performance of signal detection is raised as increase the number of segments. The result of this paper can be applied to implement signal detection of broadcasting system.

• Journal of Positioning, Navigation, and Timing
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• v.4 no.4
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• pp.173-180
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• 2015

In this study, an algorithm that detects a spoofing signal for a GPS L1 signal was proposed, and the performance was verified through RF spoofing signal simulation. The proposed algorithm determines the reception of a spoofing signal by detecting a correlation distortion of GPS L1 C/A code caused by the spoofing signal. To detect the correlation distortion, a detection criterion of a spoofing signal was derived from the relationship among the Early, Prompt, and Late tap correlation values of a receiver correlator; and a detection threshold was calculated from the false alarm probability of spoofing signal detection. In this study, an RF spoofing environment was built using the GSS 8000 simulator (Spirent). For the RF spoofing signal generated from the simulator, the RF spoofing environment was verified using the commercial receiver DL-V3 (Novatel Inc.). To verify the performance of the proposed algorithm, the RF signal was stored as IF band data using a USRP signal collector (NI) so that the data could be processed by a CNU software receiver (software defined radio). For the performance of the proposed algorithm, results were obtained using the correlation value of the software receiver, and the performance was verified through the detection of a spoofing signal and the detection time of a spoofing signal.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.6
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• pp.289-296
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• 2018

Drone detection in FMCW radar system needs complex techniques because a drone beat frequency is highly dynamic and unpredictable. Therefore, the current static signal processing algorithms cannot show appropriate detection accuracy. With dynamic signal fluctuation and environmental clutters, it can fail to detect a drone or make false detection. It affects to the radar system integrity and safety. Constant false alarm rate (CFAR), one of famous static signal process algorithm is effective for static environment. But for drone detection, it shows low detection accuracy. In this paper, we suggest neural network based FMCW radar system for detecting a drone. We use recurrent neural network (RNN) because it is the effective neural network for signal processing. In our FMCW radar system, one transmitter emits FMCW signal and four-way fixed receivers detect reflected drone beat frequency. The coordinate of the drone can be calculated with four receivers information by triangulation. Therefore, RNN only learns and inferences reflected drone beat frequency. It helps higher learning and detection accuracy. With several drone flight experiments, RNN shows false detection rate and detection accuracy as 21.1% and 96.4%, respectively.

• Phonetics and Speech Sciences
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• v.4 no.3
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• pp.103-110
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• 2012

In this paper, a target signal detection method using multiple signal classification (MUSIC) algorithm is proposed. The MUSIC algorithm is a subspace-based direction of arrival (DOA) estimation method. Using the inverse of the eigenvalue-weighted eigen spectra, the algorithm detects the DOAs of multiple sources. To apply the algorithm in target signal detection for GSC-based beamforming, we utilize its spectral response for the DOA of the target source in noisy conditions. The performance of the proposed target signal detection method is compared with those of the normalized cross-correlation (NCC), the fixed beamforming, and the power ratio method. Experimental results show that the proposed algorithm significantly outperforms the conventional ones in receiver operating characteristics (ROC) curves.

• Journal of Ocean Engineering and Technology
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• v.8 no.1
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• pp.62-70
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• 1994

In order to estimate the detection range of a active SONAR system, the SONAR equation is commonly used. In this paper, an algorithm to calculate detection range in active SONAR system as function of SONAR depth and target depth is presented. For given SONAR parameters and environment, the transmission loss and background level are found, signal excess is computed. Using log-normal distribution, signal excess is converted to detection probability at each range. Then, the detection range is obtained by integrating the detection probability as function of range for each depth. The proposed algorithm have been applied to the case of omni-directional source with center frequency 30Hz for summer and winter sound profiles. It is found that the optimal search depth is the source depth since the detection range increase at source depth where the signal excess is maximized.

• Journal of Korea Multimedia Society
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• v.9 no.12
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• pp.1572-1579
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• 2006

An usual acoustic emission(AE) event has two widely characterized parameters in time domain, peak amplitude and event duration. But noise in AE measuring may disturb the signals with its parameters and aggrandize the signal incertitude. Experiment activity of detection of the nick inside of porcelain with AE was made and study on AE signal processing with statistic be presented in this paper in order to pick-up information expected from the signal with noise. Effort is concentrated on developing a novel arithmetic to improve extraction of the characteristic from stochastic signal and to enhance the voracity of detection. The main purpose discussed in this paper is to treat with signals on amplitudes with statistic mutuality and power density spectrum in frequency domain, and farther more to select samples for neural networks training by means of least-squares algorithm between real measuring signal and deterministic signals under laboratory condition. By seeking optimization with the algorithm, the parameters representing characteristic of the porcelain object are selected, while the stochastic interfere be weakened, then study for detection on neural networks is developed based on processing above.

• Proceedings of the IEEK Conference
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• 2005.11a
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• pp.1015-1018
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• 2005

In this paper, we introduce about a new class to analysis of partial discharge signal based on Fuzzy model. We can early diagnose life of power cable through detection of partial discharge signal. However, partial discharge signal detector is difficult because of partial discharge signal is very non-linear. Also, it is very difficult work that separate partial discharge signal from noise. We constructed partial discharge accumulation detection system that use Labview for detection of non-linear partial discharge signal. And analyzed partial discharge signal that is detected by Labview system utilizing Fuzzy model.

• ETRI Journal
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• v.35 no.4
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• pp.644-654
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• 2013

In this paper, a signal detection scheme for cognitive radio (CR) based on the Bussgang theorem is proposed. The proposed scheme calculates the statistical difference between Gaussian noise and the primary user signal by applying the Bussgang theorem to the received signal. Therefore, the proposed scheme overcomes the noise uncertainty and gives scalable complexity according to the zero-memory nonlinear function for a mobile device. We also present the theoretical analysis on the detection threshold and the detection performance in the additive white Gaussian noise channel. The proposed detection scheme is evaluated by computer simulations based on the IEEE 802.22 standard for the wireless regional area network. Our results show that the proposed scheme is robust to the noise uncertainty and works well in a very low signal-to-noise ratio.