Study of the filter method for neutron pulse-height distributions measured with organic scintillators

Nowadays, during the process of Image acquisition and transmission, image information data can be corrupted by impulse noise. That noise is classified as salt and pepper noise and random impulse noise depending on the noise values. A median filter is widely used digital nonlinear filter in edge preservation, removing of impulse noise and smoothing of signals. Median filter is the widely used to remove salt and pepper noise than rank order filter, morphological filter, and unsharp masking filter. The median filter replaces a sample with the middle value among all the samples present inside the sample window. A median filter will be of two types depending on the number of samples processed at the same cycle i.e, bit level architecture and word level architecture.. In this paper, Carry Look-ahead Adder median filter method will be introduced to improve the hardware resources used in median filter architecture for 5 window and 9 window for 8 bit and 16 bit median filter architecture.

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Air quality. Determination of the number concentration of airborne inorganic fibres by phase contrast optical microscopy. Membrane filter method

10.3403/30266221u ◽

2015 ◽

Keyword(s):

Air Quality ◽

Optical Microscopy ◽

Phase Contrast ◽

Membrane Filter ◽

Number Concentration ◽

Filter Method ◽

Membrane Filter Method

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Improvement of the Zeta-Plus Filter Method for Concentration of Viruses from Water

Water Science & Technology ◽

10.2166/wst.1991.0061 ◽

1991 ◽

Vol 24 (2) ◽

pp. 217-220 ◽

Cited By ~ 4

Author(s):

Kazuyoshi Yano ◽

Yasuko Yoshida ◽

Mitsumi Kaneko

Keyword(s):

Filter Method

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The Effectiveness of the Fused Weighted Filter Feature Selection Method to Improve Software Fault Prediction

Journal of Communications Technology Electronics and Computer Science ◽

10.22385/jctecs.v8i0.96 ◽

2016 ◽

Vol 8 ◽

pp. 5 ◽

Cited By ~ 1

Author(s):

Fatemeh Alighardashi ◽

Mohammad Ali Zare Chahooki

Keyword(s):

Feature Selection ◽

Feature Selection Method ◽

Selection Method ◽

Machine Learning Algorithms ◽

Fault Prediction ◽

Filter Method ◽

Selection Methods ◽

Software Projects ◽

Software Fault Prediction ◽

Software Fault

Improving the software product quality before releasing by periodic tests is one of the most expensive activities in software projects. Due to limited resources to modules test in software projects, it is important to identify fault-prone modules and use the test sources for fault prediction in these modules. Software fault predictors based on machine learning algorithms, are effective tools for identifying fault-prone modules. Extensive studies are being done in this field to find the connection between features of software modules, and their fault-prone. Some of features in predictive algorithms are ineffective and reduce the accuracy of prediction process. So, feature selection methods to increase performance of prediction models in fault-prone modules are widely used. In this study, we proposed a feature selection method for effective selection of features, by using combination of filter feature selection methods. In the proposed filter method, the combination of several filter feature selection methods presented as fused weighed filter method. Then, the proposed method caused convergence rate of feature selection as well as the accuracy improvement. The obtained results on NASA and PROMISE with ten datasets, indicates the effectiveness of proposed method in improvement of accuracy and convergence of software fault prediction.

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Performance evaluation of GPS / BDS combined single-point positioning based on RTKLIB

E3S Web of Conferences ◽

10.1051/e3sconf/202016503009 ◽

2020 ◽

Vol 165 ◽

pp. 03009

Author(s):

Li Yan-yi ◽

Huang Jin ◽

Tang Ming-xiu

Keyword(s):

Kalman Filter ◽

Extended Kalman Filter ◽

Single Point ◽

Least Square Method ◽

Least Square ◽

Filter Method ◽

Single System ◽

Positioning Error ◽

Dual Mode ◽

Kalman Filter Method

In order to evaluate the performance of GPS / BDS, RTKLIB, an open-source software of GNSS, is used in this paper. In this paper, the least square method, the weighted least square method and the extended Kalman filter method are respectively applied to BDS / GPS single system for data solution. Then, the BDS system and GPS system are used for fusion positioning and the positioning results of the two systems are compared with that of the single system. Through the comparison of experiments, on the premise of using the extended Kalman filter method for positioning, when the GPS signal is not good, BDS data is introduced for dual-mode positioning, the positioning error in e direction is reduced by 36.97%, the positioning error in U direction is reduced by 22.95%, and the spatial positioning error is reduced by 16.01%, which further reflects the advantages of dual-mode positioning in improving a system robustness and reducing the error.

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