• Journal of Institute of Control, Robotics and Systems
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• v.21 no.3
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• pp.283-289
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• 2015

In this paper, a GNSS interference detection algorithm based on an adaptive fading Kalman filter is proposed to detect a spoofing signal which is one of the threatening GNSS intentional interferences. To detect and mitigate the spoofing signal, the fading factor of the filter is used as a detection parameter. For simulation, the effect of the spoofing signal is modeled by the ramp type bias error of the pseudorange to emulate a smart spoofer and the change of the fading factor value according to ramp type bias error is quantitatively analyzed. In addition, the detection threshold is established to detect the spoofing signal by analyzing the change of the error covariance and the effect of spoofing is mitigated by controlling the Kalman gain of the filter. To verify the performance analysis of the proposed algorithm, various simulations are implemented. Through the results of simulations, we confirmed that the proposed algorithm works well.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5800-5818
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• 2018

Interference may occur when several co-located wireless body area networks (WBANs) share the same channel simultaneously, which is compressed by resource scheduling generally. In this paper, a QoS-aware Adaptive Coloring (QAC) scheduling algorithm is proposed, which contains two components: interference sets determination and time slots assignment. The highlight of QAC is to determine the interference graph based on the relay scheme and adapted to the network QoS by multi-coloring approach. However, the frequent resource assignment brings in extra energy consumption and packet loss. Thus we come up with a launch condition for the QAC scheduling algorithm, that is if the interference duration is longer than a threshold predetermined, time slots rescheduling is activated. Furthermore, based on the relative distance and moving speed between WBANs, a prediction model for interference duration is proposed. The simulation results show that compared with the state-of-the-art approaches, the QAC scheduling algorithm has better performance in terms of network capacity, average delay and resource utility.

• Journal of the Korean Institute of Telematics and Electronics
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• v.24 no.5
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• pp.841-848
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• 1987

This paper presents a new two-stage edge detection algorithm which can give thin edges and can find edges between low contrast regions. In the first stage, the potential edge points are obtained using the proposed edge operator which is motivated by the concept of the E filter. And in the second stage, the real edge points adre detected by comparing the potential edge values with the lically adaptive threshold values of the corresponding potential edge points and by testing whether the potential edge points are zero-crossing points or not. It is shown quantitatively that the proposed algorithm can find edges effectively in an image with impulse noise, and also the results of applying the proposed algorithm to real images are presented.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.7
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• pp.1721-1736
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• 2013

Black and gray hole attack is one kind of routing disturbing attacks and can bring great damage to the network. As a result, an efficient algorithm to detect black and gray attack is important. This paper demonstrate an adaptive approach to detecting black and gray hole attacks in ad hoc network based on a cross layer design. In network layer, we proposed a path-based method to overhear the next hop's action. This scheme does not send out extra control packets and saves the system resources of the detecting node. In MAC layer, a collision rate reporting system is established to estimate dynamic detecting threshold so as to lower the false positive rate under high network overload. We choose DSR protocol to test our algorithm and ns-2 as our simulation tool. Our experiment result verifies our theory: the average detection rate is above 90% and the false positive rate is below 10%. Moreover, the adaptive threshold strategy contributes to decrease the false positive rate.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.1
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• pp.107-117
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• 2016

STD method using a mean and standard deviation is widely used in various inspection systems. The result of detection using the STD method is very dependent on the threshold value. This paper proposes an adaptive defect detection algorithm to with a precise detection of an ultimate defect. The proposed method is determined threshold value adaptively using a skewness that indicates a similarity of intensity and normal distribution of image. In the experiment, we used a various TFT-LCD images for a quantitative evaluation of defect detection performance evaluation result to prove the performance of the proposed algorithm.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.41 no.3
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• pp.163-174
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• 2004

The 3D tooth model in which each tooth can be manipulated individualy is essential component for the orthodontic simulation and implant simulation in dental field. For the reconstruction of such a tooth model, we need an image segmentation algorithm capable of separating individual tooth from neighboring teeth and alveolar bone. In this paper we propose a CT image normalization method and adaptive optimal thresholding algorithm for the segmenation of tooth region in CT image slices. The proposed segmentation algorithm is based on the fact that the shape and intensity of tooth change gradually among CT image slices. It generates temporary boundary of a tooth by using the threshold value estimated in the previous imge slice, and compute histograms for the inner region and the outer region seperated by the temporary boundary. The optimal threshold value generating the finnal tooth region is computed based on these two histogram.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.1
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• pp.658-665
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• 2015

In the production of medical images, noise reduction and contrast enhancement are important methods to increase qualities of processing results. By using the edge-based denoising and adaptive nonlinear histogram stretching, a novel medical image enhancement algorithm is proposed. First, a medical image is decomposed by wavelet transform, and then all high frequency sub-images are decomposed by Haar transform. At the same time, edge detection with Sobel operator is performed. Second, noises in all high frequency sub-images are reduced by edge-based soft-threshold method. Third, high frequency coefficients are further enhanced by adaptive weight values in different sub-images. Finally, an adaptive nonlinear histogram stretching method is applied to increase the contrast of resultant image. Experimental results show that the proposed algorithm can enhance a low contrast medical image while preserving edges effectively without blurring the details.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.9
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• pp.2021-2030
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• 2012

Premature ventricular contraction(PVC) is the most common disease among arrhythmia and it may cause serious situations such as ventricular fibrillation and ventricular tachycardia. Particularly, in the healthcare system that must continuously monitor patient's situation, it is necessary to process ECG (Electrocardiography) signal in realtime. In other words, the design of algorithm that exactly detects R wave using minimal computation and classifies PVC by analyzing the persons's physical condition and/or environment is needed. Thus, the patient adaptive pattern matching algorithm for the classification of PVC is presented in this paper. For this purpose, we detected R wave through the preprocessing method, adaptive threshold and window. Also, we applied pattern matching method to classify each patient's normal cardiac behavior through the Hash function. The performance of R wave detection and abnormal beat classification is evaluated by using MIT-BIH arrhythmia database. The achieved scores indicate the average of 99.33% in R wave detection and the rate of 0.32% in abnormal beat classification error.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.42 no.6
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• pp.195-202
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• 2005

An adaptive segmentation algorithm is used to efficiently target decisions in local non-stationary images. Until now, several adaptive approaches have been proposed as a method of segmentation. However, they can't be directly used for radar target detection because a radar signal has different characteristics from general images. Generally, a histogram of radar signal shows that targets have a relatively small number of frequency functions compared to the background and distribution of background, which have several shapes as the environment changes. In this paper, we propose an adaptive segmentation algorithm using a histogram chord which is a right-down line from maximum pick of frequency function. The proposed method provides thresholds which are optimum for several radar environments because the used chord for threshold search is not significantly effected by interference conditions. Simulation results show that the proposed method is superior to the traditional algorithms, global threshold method and distribution median method, with respect to detection performance.

• The Journal of the Korea institute of electronic communication sciences
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• v.4 no.3
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• pp.190-196
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• 2009

A new time adapted threshold using the standard deviations of Wavelet coefficients after Wavelet transform by frame scale is proposed. The time adapted threshold is set up using the sum of standard deviations of Wavelet coefficient in level 3 approximation and weighted level 1 detail. Level 3 approximation coefficients represent the voiced sound with low frequency and level 1 detail coefficients represent the unvoiced sound with high frequency. After reducing noise by soft thresholding with the proposed time adapted threshold, there are still residual noises in silent interval. To reduce residual noises in silent interval, a detection algorithm of silent interval is proposed. From simulation results, it can be noticed that SNR and MSE of the proposed algorithm are improved than those of Wavelet transform and than those of Wavelet packet transform.