scholarly journals Research on Frequency Domain Detection Method for 1553B Bus Based on Dual-Coaxial Mechanism

2020 ◽  
Vol 8 (1) ◽  
pp. 11-16
Author(s):  
Tingting Du ◽  
◽  
Qingzhong Jia
Keyword(s):  
Frequency Domain ◽  
Detection Method

A Frequency-Domain-Based Algorithm for Detecting Microseismicity Using Dense Surface Seismic Arrays

2021 ◽  
Author(s):  
Maria Mesimeri ◽  
Kristine L. Pankow ◽  
James Rutledge
Keyword(s):  
Frequency Domain ◽  
Detection Method ◽  
Maximum Amplitude ◽  
High Energy ◽  
Aftershock Sequence ◽  
Anthropogenic Activity ◽  
Small Scale ◽  
Seismic Events ◽  
Small Magnitude ◽  
Seismic Arrays

ABSTRACT We propose a new frequency-domain-based algorithm for detecting small-magnitude seismic events using dense surface seismic arrays. Our proposed method takes advantage of the high energy carried by S waves, and approximate known source locations, which are used to rotate the horizontal components to obtain the maximum amplitude. By surrounding the known source area with surface geophones, we achieve a favorable geometry for locating the detected seismic events with the backprojection method. To test our new detection method, we used a dense circular array, consisting of 151 5 Hz three-component geophones, over a 5 km aperture that was in operation at the Utah Frontier Observatory for Research in Geothermal Energy (FORGE) in southcentral Utah. We apply the new detection method during a small-scale test injection phase at FORGE, and during an aftershock sequence of an Mw 4.1 earthquake located ∼30  km north of the geophone array, within the Black Rock volcanic field. We are able to detect and locate microseismic events (Mw<0) during injections, despite the high level of anthropogenic activity, and several aftershocks that are missing from the regional catalog. By comparing our method with known algorithms that operate both in the time and frequency domain, we show that our proposed method performs better in the case of the FORGE injection monitoring, and equally well for the off-array aftershock sequence. Our new method has the potential to improve microseismic event detections even in extremely noisy environments, and the proposed location scheme serves as a direct discriminant between true and false detections.

Detection Method of Dissolved Oxygen Concentration in Water Based on Single Source Frequency Domain Fluorescence Lifetime

2020 ◽  
Vol 49 (3) ◽  
pp. 330002-330002
Author(s):  
陈朋 Peng CHEN ◽  
赵智 Zhi ZHAO ◽  
赵冬冬 Dong-dong ZHAO ◽  
韩洋洋 Yang-yang HAN ◽  
梁荣华 Rong-hua LIANG
Keyword(s):  
Dissolved Oxygen ◽  
Frequency Domain ◽  
Oxygen Concentration ◽  
Fluorescence Lifetime ◽  
Detection Method ◽  
Single Source ◽  
Dissolved Oxygen Concentration ◽  
Water Based

Multi-Resolution Salient Region Detection in Frequency Domain

2014 ◽  
Vol 6 (1) ◽  
pp. 60-70
Author(s):  
Yingchun Guo ◽  
Yanhong Feng ◽  
Gang Yan ◽  
Shuo Shi
Keyword(s):  
Frequency Domain ◽  
Euclidean Distance ◽  
Detection Method ◽  
Saliency Map ◽  
Normal Distribution Function ◽  
Salient Region Detection ◽  
Salient Region ◽  
Region Detection ◽  
Proposed Model ◽  
Salient Regions

Salient region detection is a challenge problem in computer vision, which is useful in image segmentation, region-based image retrieval, and so on. In this paper we present a multi-resolution salient region detection method in frequency domain which can highlight salient regions with well-defined boundaries of object. The original image is sub-sampled into three multi-resolution layers, and for each layer the luminance and color salient features are extracted in frequency domain. Then, the significant values are calculated by using invariant laws of Euclidean distance in Lab space and the normal distribution function is used to specify the salient map in each layer in order to remove noise and enhance the correlation among the vicinity pixels. The final saliency map is obtained by normalizing and merging the multi-resolution salient maps. Experimental evaluation depicts the promising results from the proposed model by outperforming the state-of-art frequency-tuned model.

A novel intercept detection method for low-SNR targets using frequency domain processing

2013 ◽  
Author(s):  
K. Manoj Kumar ◽  
P. J. Sijomon ◽  
K. Shamju Joseph ◽  
D. M. Premod ◽  
V. S. Shenoi ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Detection Method ◽  
Low Snr ◽  
Frequency Domain Processing

scholarly journals An effective frequency-domain feature of atrial fibrillation based on time-frequency analysis

2020 ◽  
Author(s):  
Yusong Hu ◽  
Yantao Zhao ◽  
Jihong Liu ◽  
Jin Pang ◽  
Chen Zhang ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Decision Tree ◽  
Sinus Rhythm ◽  
Frequency Domain ◽  
Frequency Spectrum ◽  
Detection Method ◽  
Maximum Amplitude ◽  
Time Frequency ◽  
Electrocardiogram Signals ◽  
Cart Algorithm

Abstract Background: Atrial fibrillation is a type of persistent arrhythmia that can lead to serious complications. Therefore, accurate and quick detection of atrial fibrillation by surface electrocardiogram has great importance on further treatment. The practical electrocardiogram signals contain various interferences in different frequencies, such as myoelectricity interference, power interference and so on. Detection speed and accuracy largely depend on the atrial fibrillation signal features extracted by the algorithm. But some of the discovered atrial fibrillation features are not well distinguishable, resulting in poor classification effect. Methods: This paper proposed a high distinguishable frequency feature - the frequency corresponding to the maximum amplitude in the frequency spectrum. We used the R-R interval detection method optimized with the mathematical morphology method and combined with the wavelet transform method for analysis. According to the two features - the maximum amplitude in the frequency spectrum and R-R interval irregular, we could recognize atrial fibrillation signals in electrocardiogram signals by decision tree classification algorithm. Results: The data used in the experiment come from the MIT-BIH database, which is publicly accessible via the web and with ethical approval and consent. Based on the input of time-domain and frequency-domain features, we classified sinus rhythm signals and AF signals using the decision tree generated by classification and regression tree (CART) algorithm. From the confusion matrix, we got the accuracy was 98.9%, sensitivity was 97.93% and specificity was 99.63%. Conclusions: The experimental results can prove the validity of the maximum amplitude in the frequency spectrum and the practicability and accuracy of the detection method, which applied this frequency-domain feature. Through the detection method, we obtained good accuracy of classifying sinus rhythm signals and atrial fibrillation signals. And the sensitivity and specificity of our method were pretty good by comparison with other studies.

scholarly journals An effective frequency-domain feature of atrial fibrillation based on time-frequency analysis

2020 ◽  
Author(s):  
Yusong Hu ◽  
Yantao Zhao ◽  
Jihong Liu ◽  
Jin Pang ◽  
Chen Zhang ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Decision Tree ◽  
Sinus Rhythm ◽  
Frequency Domain ◽  
Frequency Spectrum ◽  
Detection Method ◽  
Maximum Amplitude ◽  
Time Frequency ◽  
Electrocardiogram Signals ◽  
Cart Algorithm

Abstract Background: Atrial fibrillation is a type of persistent arrhythmia that can lead to serious complications. Therefore, accurate and quick detection of atrial fibrillation by surface electrocardiogram has great importance on further treatment. The practical electrocardiogram signals contain various interferences in different frequencies, such as myoelectricity interference, power interference and so on. Detection speed and accuracy largely depend on the atrial fibrillation signal features extracted by the algorithm. But some of the discovered atrial fibrillation features are not well distinguishable, resulting in poor classification effect. Methods: This paper proposed a high distinguishable frequency feature - the frequency corresponding to the maximum amplitude in the frequency spectrum. We used the R-R interval detection method optimized with the mathematical morphology method and combined with the wavelet transform method for analysis. According to the two features - the maximum amplitude in the frequency spectrum and R-R interval irregular, we could recognize atrial fibrillation signals in electrocardiogram signals by decision tree classification algorithm. Results: The data used in the experiment come from the MIT-BIH database, which is publicly accessible via the web and with ethical approval and consent. Based on the input of time-domain and frequency-domain features, we classified sinus rhythm signals and AF signals using the decision tree generated by classification and regression tree (CART) algorithm. From the confusion matrix, we got the accuracy was 98.9%, sensitivity was 97.93% and specificity was 99.63%. Conclusions: The experimental results can prove the validity of the maximum amplitude in the frequency spectrum and the practicability and accuracy of the detection method, which applied this frequency-domain feature. Through the detection method, we obtained good accuracy of classifying sinus rhythm signals and atrial fibrillation signals. And the sensitivity and specificity of our method were pretty good by comparison with other studies.

High-Frequency TSV Failure Detection Method with Z Parameter

2012 ◽  
Author(s):  
Joohee Kim ◽  
Daniel H. Jung ◽  
Jonghyun Cho ◽  
Jun So Pak ◽  
Joungho Kim ◽  
...  
Keyword(s):  
Frequency Domain ◽  
High Frequency ◽  
Detection Method ◽  
Failure Detection ◽  
Domain Analysis ◽  
Frequency Domain Analysis ◽  
Z Parameter ◽  
Die Stacking ◽  
Failure Types

Abstract As the TSV count increases, chip yield can be severely degraded due to failures during the TSV or die-stacking processes. This paper will present and discuss on the usage of failure masks designed to detect and differentiate failure types such as connection failure and insulator failure based on frequency-domain one point probing measurement. The failure masks are proposed on the basis of the frequency domain analysis of TSV failures with Z11 magnitudes.

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