• 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.

• Journal of Advanced Navigation Technology
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• v.18 no.1
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• pp.7-13
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• 2014

In this paper, an effect on a GPS receiver by spoofing signal is analyzed and a GPS spoofing signal detection algorithm for GPS L1 C/A spoofing signal is proposed. A proposed detection algorithm monitors the correlation function distortion by the spoofing signal. If detected distortion is over a detection threshold, we can determine that the spoofing signal is received. The detection threshold is calculated from the statistical characteristics of a thermal noise. For verifying the suggested algorithm, a MATLAB-based simulation platform is implemented. This platform has functionalities to track GPS signal and measure the correlation values. By using this platform, the correlation function distortion by spoofing signal is observed. Also a performance of the algorithm proposed in this paper is applied and confirm the detection of a spoofing signal.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.2
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• pp.757-770
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• 2020

We propose a hand gesture recognition method that is compatible with a head-up display (HUD) including small processing resource. For fast link adaptation with HUD, it is necessary to rapidly process gesture recognition and send the minimum amount of driver hand gesture data from the wearable device. Therefore, we use a method that recognizes each hand gesture with an inertial measurement unit (IMU) sensor based on revised correlation matching. The method of gesture recognition is executed by calculating the correlation between every axis of the acquired data set. By classifying pre-defined gesture values and actions, the proposed method enables rapid recognition. Furthermore, we evaluate the performance of the algorithm, which can be implanted within wearable bands, requiring a minimal process load. The experimental results evaluated the feasibility and effectiveness of our decomposed correlation matching method. Furthermore, we tested the proposed algorithm to confirm the effectiveness of the system using pre-defined gestures of specific motions with a wearable platform device. The experimental results validated the feasibility and effectiveness of the proposed hand gesture recognition system. Despite being based on a very simple concept, the proposed algorithm showed good performance in recognition accuracy.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.5
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• pp.913-919
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• 2011

In this paper, we present a new efficient event detection algorithm for sensor networks with faults. We focus on multi-attributed events, which are sets of data points that correspond to interesting or unusual patterns in the underlying phenomenon that the network monitors. Conventional algorithms cannot detect some events because they treat only their own sensor readings which can be affected easily by environmental or physical problem. Our approach exploits spatio-temporal correlation of sensor readings. Sensor nodes exchange a fault-tolerant code encoded their own readings with neighbors, organize virtual sensor readings which have spatio-temporal correlation, and determine a result for multi-attributed events from them. In the result, our proposed algorithm provides improvement of detecting multi-attributed events and reduces the number of false-negatives due to negative environmental effects.

• Journal of the Korean Society of Visualization
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• v.3 no.2
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• pp.71-78
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• 2005

Uncertainty introduced by a cross-correlation algorithm based on FFT has been investigated using PIV standard images. The standard images were generated by the Monte Carlo simulation method. Both bias and random errors from the velocity vector have been analyzed with regard to the particle diameter, displacement, and the number of particles. The uncertainty of velocity is evaluated based upon the IS0/IEC standard. As a result, a total error of $0.26\%$ is included in the PIV cross-correlation algorithm. In addition, the uncertainty budget is presented, where the effect of the above three variables is examined. According to the budget, the variation of the number of particles within the interrogation window mainly contributes to the combined standard uncertainty of the real measured velocity field when excluding the effect of errors by the experiments itself. Finally, the expanded uncertainty is found to be about $12\%$ at the $95\%$ confidence level.

• Journal of Multimedia Information System
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• v.3 no.4
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• pp.119-128
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• 2016

This study aims to suggest a new algorithm for detecting two-wheelers on road that have various shapes according to the viewing angle for vision based intelligent vehicles. This article describes a new approach to two-wheelers detection algorithm riding on people based on modified Histogram of Oriented Gradients (HOG) using correlation coefficient (CC). The CC between two local area variables, in which one is the person riding a bike and other is its background, can represent correlation relation. First, we extract edge vectors using HOG which includes gradient information and differential magnitude as cell based. And then, the value, which is calculated by the CC between the area of each cell and one of two-wheelers, can be extracted as the weighting factor in process for normalizing the modified HOG cell. This paper applied the Adaboost algorithm to make a strong classification from weak classification. In this experiment, we can get the result that the detection rate of the proposed method is higher than that of the traditional method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.9A
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• pp.858-866
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• 2005

This paper proposes the method of frequency synchronization using the spectrum correlation in ATSC terrestrial DTV system. If the spectrum around pilot is severely distorted by multipath or mobile reception environment, the conventional algorithm using the pilot signal makes the estimation error of the frequency offset. Because the proposed algorithm acquires frequency synchronization using the correlation between the received specalm and the standard spectrum without the use of pilot, the proposed method can acquire frequency synchronization faster and more accurate than conventional algorithm. And this paper proposes new method for ATSC frequency synchronization without the use of pilot.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.4
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• pp.248-255
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• 2003

This paper presents a fingerprint recognition algorithm using cross-correlation function. This algorithm consists of minutiae extraction, minutiae alignment and minutiae matching, where we propose a new minutiae alignment method. In our alignment method, the rotation angle between two fingerprints is obtained by using cross-correlation function of the minutia directions, thereafter the displacement is obtained from the rotated fingerprint. This alignment method is capable of finding rotation angle and displacement of two fingerprints without resorting to exhaustive search. Our fingerprint recognition algorithm has been tested on fingerprint images captured with inkless scanner. The experiment results show that 17.299% false rejection ratio(FRR) at 2.086% false acceptance ratio(FAR).

• Smart Structures and Systems
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• v.26 no.2
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• pp.241-251
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• 2020

Due to the undeniable importance of approximating the concrete compressive strength (CSC) in civil engineering, this paper focuses on presenting four novel optimizations of multi-layer perceptron (MLP) neural network, namely artificial bee colony (ABC-MLP), grasshopper optimization algorithm (GOA-MLP), shuffled frog leaping algorithm (SFLA-MLP), and salp swarm algorithm (SSA-MLP) for predicting this crucial parameter. The used dataset consists of 103 rows of information concerning seven influential parameters (cement, slag, water, fly ash, superplasticizer, fine aggregate, and coarse aggregate). In this work, the best-fitted complexity of each ensemble is determined by a population-based sensitivity analysis. The GOA distinguished its self by the least complexity (population size = 50) and emerged as the second time-effective optimizer. Referring to the prediction results, all tested algorithms are able to construct reliable networks. However, the SSA (Correlation = 0.9652 and Error = 1.3939) and GOA (Correlation = 0.9629 and Error = 1.3922) performed more accurately than ABC (Correlation = 0.7060 and Error = 4.0161) and SFLA (Correlation = 0.8890 and Error = 2.5480). Therefore, the SSA-MLP and GOA-MLP can be promising alternatives to laboratorial and traditional CSC evaluative methods.

• Journal of the Society of Naval Architects of Korea
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• v.44 no.6
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• pp.579-589
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• 2007

Contrary to the conventional single-point measuring devices such as LDV, pitot-tube, hot-wire, etc., it would be possible to measure instantaneously 3-D flow fields with a stereoscopic PIV system. In this paper, we present an analysis algorithm for a stereoscopic PIV system using the normalized cross-correlation (NCC) and a 3-D calibration based reconstruction method. The evaluation method based on NCC is one of the most accurate correlation-based methods. We validated the developed algorithm through a benchmarking comparison with 3-D artificial SPIV images and calibration target images.