A new detection method for noisy channels with time-varying offset

2022 ◽  
pp. 1-1
Author(s):  
Kees A. Schouhamer Immink ◽  
Jos H. Weber
Keyword(s):  
Detection Method ◽  
Time Varying ◽  
Noisy Channels

scholarly journals Precise detection of the size of monodisperse droplets considering the fluctuations induced by the droplet generation process

2021 ◽  
Author(s):  
Wen Zeng ◽  
Hai Fu
Keyword(s):  
Droplet Size ◽  
Detection Method ◽  
Droplet Microfluidics ◽  
Generation Process ◽  
Time Varying ◽  
Electrical Detection ◽  
Flow Focusing ◽  
Wide Range ◽  
Droplet Production ◽  
Monodisperse Droplets

Abstract For droplet microfluidics, the electrical-detection method which can precisely detect the size of monodisperse droplets is demonstrated in this paper. In a Flow-focusing microdroplet generator, three pairs of the microelectrodes are allocated along the microchannel, and during the passing-by process of each droplet, both the length, the velocity and the production speed of the droplets can be obtained from the experimental measurements of the time-varying capacitance between each pair of the microelectrodes. Particularly, for different geometries of the Flow-focusing microchannel, the method of the electrical-detection is validated experimentally over a wide range of the typical conditions of monodisperse droplet production. In addition, the droplet size measured by the electrical-detection method is compared with that by the method of image processing, and the detection precision of the electrical-detection method is verified experimentally. Most importantly, by calculating the root-mean-square value of the droplet lengths for three pairs of the microelectrodes, the detection precision of the droplet size can be increased drastically.

scholarly journals Dynamic Traffic Detection and Modeling for Beidou Satellite Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yanyu Qu ◽  
Fangling Pu ◽  
Jianguo Yin ◽  
Lingzi Liu ◽  
Xin Xu
Keyword(s):  
Network Traffic ◽  
Spatial Information ◽  
Detection Method ◽  
Data Communication ◽  
Traffic Monitoring ◽  
Integrated System ◽  
Time Varying ◽  
Dynamic Traffic ◽  
Ground Station ◽  
Traffic Detection

Beidou navigation system (BDS) has been developed as an integrated system. The third BDS, BSD-3, will be capable of providing not only global positioning and navigation but also data communication. When the volume of data transmitted through BDS-3 continues to increase, BDS-3 will encounter network traffic congestion, unbalanced resource usage, or security attacks as terrestrial networks. The network traffic monitoring is essential for automatic management and safety assurance of BDS-3. A dynamic traffic detection method including traffic prediction by Long Short-Term Memory (LSTM) and a dynamically adjusting polling strategy is proposed to unevenly sample the traffic of each link. A distributed traffic detection architecture is designed for collection of the detected traffic and its related temporal and spatial information with low delay. A time-varying graph (TVG) model is introduced to represent the dynamic topology, the time-varying link, and its traffic. The BDS-3 network is simulated by STK. The WIDE dataset is used to simulate the traffic between the satellite and ground station. Simulation results show that the dynamic traffic detection method can follow the variation of the traffic of each link with uneven sampling. The detected traffic can be transmitted to the ground station in near real time through the distributed traffic detection architecture. The traffic and its related information are stored by using Neo4j in terms of the TVG model. The nodes, edges, and traffic of BDS-3 can be quickly queried through Neo4j. The presented dynamic traffic detection and representation schemes will support BDS-3 to establish automatic management and security system and develop business.

An agile multi-frame detection method for targets with time-varying existence

2019 ◽  
Vol 165 ◽  
pp. 133-143 ◽  
Author(s):  
Jinghe Wang ◽  
Wei Yi ◽  
Reza Hoseinnezhad ◽  
Lingjiang Kong
Keyword(s):  
Detection Method ◽  
Time Varying

A Bi-Parametric Clutter-Map CFAR Detection Method in Non-Gaussian Environment for Foreign Objects Debris on Runways

2013 ◽  
Vol 401-403 ◽  
pp. 1173-1176 ◽  
Author(s):  
Jing Wu ◽  
Hong Wang ◽  
Xue Lian Yu
Keyword(s):  
Detection Method ◽  
Parameters Estimation ◽  
Time Varying ◽  
Recursive Filter ◽  
Cfar Detection ◽  
Foreign Objects ◽  
Primary Interference ◽  
Non Gaussian

Time-varying land clutter is primary interference for Foreign Objects Debris (FOD) detection on airport runways. Traditional clutter-map CFAR (CM-CFAR) algorithms were ineffect-ive to detect targets in non-Gaussian clutter. In this paper, a Bi-parametric CM-CFAR (Bi-CM-CFAR) algorithm based on bi-parameters estimation is proposed. Clutter-level estimation is obtained with video integrator, parameters estimator and recursive filter; and updated in each scanning period. Moreover, simulations verify effectiveness of this method for FOD detection.

scholarly journals Extremum Seeking using Synchronous Detection Method with Time-Varying Parameters

2018 ◽  
Vol 51 (13) ◽  
pp. 297-302
Author(s):  
César U. Solis ◽  
Julio B. Clempner ◽  
Alexander S. Poznyak
Keyword(s):  
Detection Method ◽  
Extremum Seeking ◽  
Time Varying ◽  
Synchronous Detection ◽  
Varying Parameters ◽  
Time Varying Parameters

scholarly journals Adaptive Fault Detection with Two Time-Varying Control Limits for Nonlinear and Multimodal Processes

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Jinna Li ◽  
Yuan Li ◽  
Yanhong Xie ◽  
Xuejun Zong
Keyword(s):  
Fault Detection ◽  
Detection Method ◽  
Nearest Neighbors ◽  
Training Data ◽  
Time Varying ◽  
Training Samples ◽  
Mean And Variance ◽  
The Mean ◽  
Control Limits

A novel fault detection method is proposed for detection process with nonlinearity and multimodal batches. Calculating the Mahalanobis distance of samples, the data with the similar characteristics are replaced by the mean of them; thus, the number of training data is reduced easily. Moreover, the super ball regions of mean and variance of training data are presented, which not only retains the statistical properties of original training data but also avoids the reduction of data unlimitedly. To accurately identify faults, two control limits are determined during investigating the distributions of distances and angles between training samples to their nearest neighboring samples in the reduced database; thus, the traditionalk-nearest neighbors (only considering distances) fault detection (FD-kNN) method is developed. Another feature of the proposed detection method is that the control limits vary with updating database such that an adaptive fault detection technique is obtained. Finally, numerical examples and case study are given to illustrate the effectiveness and advantages of the proposed method.

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