• Title/Summary/Keyword: Threshold Method

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A Study on Denoising Methods using Wavelet in AWGN environment (AWGN 환경에서 웨이브렛을 이용한 잡음 제거 방법에 관한 연구)

  • 배상범;김남호
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.5 no.5
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    • pp.853-860
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    • 2001
  • This paper presents the new two denoising methods using wavelet. One is new spatially selective noise filtration(NSSNF) using spatial correlation and the other is undecimated discrete wavelet transform (UDWT) threshold-based. NSSNF got the flexible gain special property of SNR adding new parameter at the existing SSNF and UDWT had superior denosing effect than orthogonal wavelet transform(OWT) applied soft-threshold by applied hard-threshold. We selected additive white gaussian noise(AWGN) in this test environment. Also we analyzed and compared ousting denoising method using SNR as standard of judgement of improvemental effect.

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Performance Analysis of Cooperative Spectrum Sensing Based on Sharing Threshold among cooperative users (협력 노드의 합리적 임계치 공유를 통한 센싱 검출 성능 분석)

  • Seo, SungIl;Lee, MiSun;Kim, Jinyoung
    • Journal of Satellite, Information and Communications
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    • v.8 no.1
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    • pp.66-70
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    • 2013
  • In this paper, Threshold setting method is proposed to improve detection probability for cooperative sensing. Even if cooperative users have all same false alarm rate, each user has different threshold due to pass ad-hoc channel. threshold level is related to detection probability. So, we select the highest threshold among cooperative users and then share threshold information for getting the high detection probability.

A Study on the Denoising Method by Multi-threshold for Underwater Transient Noise Measurement (수중 천이소음측정을 위한 다중 임계치 잡음제거기법 연구)

  • 최재용;도경철
    • The Journal of the Acoustical Society of Korea
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    • v.21 no.6
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    • pp.576-584
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    • 2002
  • This paper proposes a new denosing method using wavelet packet, to reject unknown external noise and white gaussian ambient noise for measuring the transient noise which is one of the important elements for ship classification. The previous denosing method applied the same wavelet threshold at each node of multi-single sensors for rejecting white noise is not adequate in the underwater environment existing lots of external noises. The proposed algorithm of this paper applies a modified soft-threshold to each node according to the discriminated threshold so as to reject unknown external noise and white gaussian ambient noise. It is verified by numerical simulation that the SNR is increased more than 25㏈. And the simulation results are confirmed through sea-trial using multi-single sensors.

Performance Analysis of Pulse Positioning Using Adaptive Threshold Detector (ATD)

  • Chang, Jae Won;Lee, Sang Jeong
    • Journal of Positioning, Navigation, and Timing
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    • v.7 no.1
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    • pp.25-35
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    • 2018
  • This paper describes the measurement of pulse positioning (input time) to calculate a time of arrival (TOA) that takes from transmitting a signal from the target of multilateration (MLAT) system to receiving the signal at the receiver. In this regard, this paper analyzes performances of simple threshold method and level adjust system (LAS) method, which is one of the adaptive threshold detector (ATD) methods, among many methods to calculate pulse positioning of signal received at the receiver. To this end, Cramer-rao lower bound (CRLB) with regard to pulse positioning, which was measured when signals transmitted from a transponder mounted at the target were received at the receiver, was induced and then deviation sizes with regard to pulse positioning, which was measured with simple threshold and LAS methods through MATLAB simulations, were compared. Next, problems occurring according to a difference in amplitude of signals inputted to each receiver are described when pulse positioning is measured at multiple receivers located at a different distance from the target as is the case in the MLAT system. Furthermore, LAS method to resolve the problems is explained. Lastly, this study analyzes whether a pulse positioning error occurring due to the signal noise satisfies the requirement (6 nsec. or lower) recommended for the MLAT system when using these two methods.

Comparison of Echogram Analysis Methods for Evaluating the Sound-scattering Layer (음향산란층의 식별을 위한 에코그램 분석 방법의 비교)

  • Choi, Seok-Gwan;Yoon, Eun-A;Han, Inwoo;Oh, Wooseok
    • Korean Journal of Fisheries and Aquatic Sciences
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    • v.49 no.6
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    • pp.856-861
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    • 2016
  • This study compared the density of fish determined using three different echogram methods: the frequency-difference, time variable, and threshold modification methods. An acoustic survey was conducted off the coast of Jeju Island after sunset. Data at 38 and 120 kHz frequencies were collected using a commercial fishing vessel. As a reference point, the value of ${\Delta}MVBS_{120-38kHz}$ that distinguished fish from zooplankton using the 38 and 120 kHz frequencies was set at < 2 dB. The estimated density of fish along the survey line was 0.1-30.4, 0.1-64.3, and $0.1-51.7m^2/nmi^2$ using the frequency difference, time variable threshold, and threshold modification methods, respectively. The results of this study constitute basic research for estimating fish densities.

An Effective Denoising Method for Images Contaminated with Mixed Noise Based on Adaptive Median Filtering and Wavelet Threshold Denoising

  • Lin, Lin
    • Journal of Information Processing Systems
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    • v.14 no.2
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    • pp.539-551
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    • 2018
  • Images are unavoidably contaminated with different types of noise during the processes of image acquisition and transmission. The main forms of noise are impulse noise (is also called salt and pepper noise) and Gaussian noise. In this paper, an effective method of removing mixed noise from images is proposed. In general, different types of denoising methods are designed for different types of noise; for example, the median filter displays good performance in removing impulse noise, and the wavelet denoising algorithm displays good performance in removing Gaussian noise. However, images are affected by more than one type of noise in many cases. To reduce both impulse noise and Gaussian noise, this paper proposes a denoising method that combines adaptive median filtering (AMF) based on impulse noise detection with the wavelet threshold denoising method based on a Gaussian mixture model (GMM). The simulation results show that the proposed method achieves much better denoising performance than the median filter or the wavelet denoising method for images contaminated with mixed noise.

The Change of Pressure Pain Threshold of Myofascial Trigger Points by Ultrasound Application Method (초음파 적용방식에 따른 근-근막 발통점의 압통각 역치 변화)

  • Lee, Jeong-Woo;Yoon, Se-Won
    • Journal of the Korean Academy of Clinical Electrophysiology
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    • v.1 no.2
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    • pp.61-68
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    • 2003
  • The purpose of this study was to compare the application method of ultrasound on the alteration of pressure pain threshold of myofascial trigger points. We used thirty patients with mayofascial pain syndrome in upper trapezius. Participants classified according to each group in non noxious dose, noxious dose by the ultrasound. The test was measured continuously pre test, post-test by algometer. The following results were obtained; 1. Pressure pain threshold were significantly increased in non-noxious dose and noxious dose group(p<.001). 2. In noxious dose group, pressure pain threshold were more significantly increased than non-noxious dose group(p<.001). These results lead us to the conclusion that non-noxious dose and noxious dose were significantly increased pressure pain threshold of upper trapezius trigger points. Therefore, a further direction of this study will be to provide more evidence that noxious dose have more effect on pressure pain threshold of myofascial trigger points.

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Local Binary Pattern Based Defocus Blur Detection Using Adaptive Threshold

  • Mahmood, Muhammad Tariq;Choi, Young Kyu
    • Journal of the Semiconductor & Display Technology
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    • v.19 no.3
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    • pp.7-11
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    • 2020
  • Enormous methods have been proposed for the detection and segmentation of blur and non-blur regions of the images. Due to the limited available information about the blur type, scenario and the level of blurriness, detection and segmentation is a challenging task. Hence, the performance of the blur measure operators is an essential factor and needs improvement to attain perfection. In this paper, we propose an effective blur measure based on the local binary pattern (LBP) with the adaptive threshold for blur detection. The sharpness metric developed based on LBP uses a fixed threshold irrespective of the blur type and level which may not be suitable for images with large variations in imaging conditions and blur type and level. Contradictory, the proposed measure uses an adaptive threshold for each image based on the image and the blur properties to generate an improved sharpness metric. The adaptive threshold is computed based on the model learned through the support vector machine (SVM). The performance of the proposed method is evaluated using a well-known dataset and compared with five state-of-the-art methods. The comparative analysis reveals that the proposed method performs significantly better qualitatively and quantitatively against all the methods.

Selection of Signal Strength and Detection Threshold for Optimal Tracking with Nearest Neighbor Filter (NN 필터 추적을 위한 최적 신호 강도 및 검출 문턱값 선택)

  • Jeong, Yeong-Heon;Gwon, Il-Hwan;Hong, Sun-Mok
    • Journal of the Institute of Electronics Engineers of Korea SC
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    • v.37 no.3
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    • pp.1-8
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    • 2000
  • In this paper, we formulate an optimal control problem to obtain the optimal signal strength and detection threshold for tracking with NN filter, First, we predict the tracking performance of NN filter by using the HYCA method. Based on this method, the predicted tracking performance is represented with respect to signal strength and detection threshold. Using this relation, we find the optimal parameters for following three examples: 1) the sequence of optimal detection threshold which minimizes sum of position estimation error; 2) the sequence of optimal detection threshold which minimizes sum of validation gate volume; and 3) the sequence of optimal signal strength and detection threshold which minimizes sum of signal strength.

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A Simple and Robustness Algorithm for ECG R- peak Detection

  • Rahman, Md Saifur;Choi, Chulhyung;Kim, Young-pil;Kim, Sikyung
    • Journal of Electrical Engineering and Technology
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    • v.13 no.5
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    • pp.2080-2085
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    • 2018
  • There have been numerous studies that extract the R-peak from electrocardiogram (ECG) signals. All of these studies can extract R-peak from ECG. However, these methods are complicated and difficult to implement in a real-time portable ECG device. After filtration choosing a threshold value for R-peak detection is a big challenge. Fixed threshold scheme is sometimes unable to detect low R-peak value and adaptive threshold sometime detect wrong R-peak for more adaptation. In this paper, a simple and robustness algorithm is proposed to detect R-peak with less complexity. This method also solves the problem of threshold value selection. Using the adaptive filter, the baseline drift can be removed from ECG signal. After filtration, an appropriate threshold value is automatically chosen by using the minimum and maximum value of an ECG signals. Then the neighborhood searching scheme is applied under threshold value to detect R-peak from ECG signals. Proposed method improves the detection and accuracy rate of R-peak detection. After R-peak detection, we calculate heart rate to know the heart condition.