Source Multiplicity Examined with Minimum Entropy Deconvolution

1992 ◽  
Author(s):  
I. H. Henson ◽  
R. K. Cessaro
Keyword(s):  
Minimum Entropy

Weighted Minimum Entropy Autofocus Algorithm for ISAR Imaging

2011 ◽  
Vol 33 (8) ◽  
pp. 1809-1815
Author(s):  
Gang Xu ◽  
Lei Yang ◽  
Lei Zhang ◽  
Ya-chao Li ◽  
Meng-dao Xing
Keyword(s):  
Minimum Entropy ◽  
Isar Imaging ◽  
Autofocus Algorithm

Source Multiplicity Examined with Minimum Entropy Deconvolution

1992 ◽  
Author(s):  
I. H. Henson ◽  
R. K. Cessaro
Keyword(s):  
Minimum Entropy

scholarly journals Neuro-Fuzzy Transformation with Minimize Entropy Principle to Create New Features for Particulate Matter Prediction

2021 ◽  
Vol 11 (14) ◽  
pp. 6590
Author(s):  
Krittakom Srijiranon ◽  
Narissara Eiamkanitchat
Keyword(s):  
Particulate Matter ◽  
Fuzzy Model ◽  
Original Data ◽  
Northern Region ◽  
National Standard ◽  
Minimum Entropy ◽  
Fuzzy Transformation ◽  
Neuro Fuzzy ◽  
Proposed Model ◽  
Entropy Principle

Air pollution is a major global issue. In Thailand, this issue continues to increase every year, similar to other countries, especially during the dry season in the northern region. In this period, particulate matter with aerodynamic diameters smaller than 10 and 2.5 micrometers, known as PM10 and PM2.5, are important pollutants, most of which exceed the national standard levels, the so-called Thailand air quality index (T-AQI). Therefore, this study created a prediction model to classify T-AQI calculated from both types of PM. The neuro-fuzzy model with a minimum entropy principle model is proposed to transform the original data into new informative features. The processes in this model are able to discover appropriate separation points of the trapezoidal membership function by applying the minimum entropy principle. The membership value of the fuzzy section is then passed to the neural section to create a new data feature, the PM level, for each hour of the day. Finally, as an analytical process to obtain new knowledge, predictive models are created using new data features for better classification results. Various experiments were utilized to find an appropriate structure with high prediction accuracy. The results of the proposed model were favorable for predicting both types of PM up to three hours in advance. The proposed model can help people who are planning short-term outdoor activities.

scholarly journals An enhanced binarization framework for degraded historical document images

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Wei Xiong ◽  
Lei Zhou ◽  
Ling Yue ◽  
Lirong Li ◽  
Song Wang
Keyword(s):  
Document Image ◽  
Morphological Operations ◽  
Document Images ◽  
Minimum Entropy ◽  
Stroke Width ◽  
Background Estimation ◽  
Structuring Element ◽  
Document Image Binarization ◽  
Benchmark Datasets ◽  
Stroke Width Transform

AbstractBinarization plays an important role in document analysis and recognition (DAR) systems. In this paper, we present our winning algorithm in ICFHR 2018 competition on handwritten document image binarization (H-DIBCO 2018), which is based on background estimation and energy minimization. First, we adopt mathematical morphological operations to estimate and compensate the document background. It uses a disk-shaped structuring element, whose radius is computed by the minimum entropy-based stroke width transform (SWT). Second, we perform Laplacian energy-based segmentation on the compensated document images. Finally, we implement post-processing to preserve text stroke connectivity and eliminate isolated noise. Experimental results indicate that the proposed method outperforms other state-of-the-art techniques on several public available benchmark datasets.

Maximization of the Signal Impulsiveness Combining Envelope Technique with Minimum Entropy Deconvolution

2013 ◽  
Vol 392 ◽  
pp. 725-729 ◽  
Author(s):  
Rafael José Gomes de Oliveira ◽  
Mauro Hugo Mathias
Keyword(s):  
Test Bench ◽  
Minimum Entropy ◽  
Combined Application ◽  
Recent Developments ◽  
Gear Trains ◽  
The Subject ◽  
Using Data ◽  
High Frequency Resonance ◽  
Combined Technique ◽  
Experimental Test Bench

The application of the HFRT (High-Frequency Resonance Technique), a demodulation based technique, is a technique for evaluation the condition of bearings and other components in rotating machinery. Another technique MED (Minimum Entropy Deconvolution) has been the subject of recent developments for application in condition monitoring of gear trains and roller bearings. This article demonstrates the effectiveness of the combined application of the MED technique with HFRT in order to enhance the capacity of HFRT to identify the characteristic fault frequencies of damaged bearings by increasing the signal impulsivity. All tests were done using data collected from an experimental test bench in laboratory. The Kurtosis value is used as an indicator of effectiveness of the combined technique and the results shown an increase of five times the original kurtosis value with the application of MED filter together with the HFRT.

scholarly journals The principle of minimum entropy production and snow structure

2004 ◽  
Vol 50 (170) ◽  
pp. 342-352 ◽  
Author(s):  
Perry Bartelt ◽  
Othmar Buser
Keyword(s):  
Mass Transfer ◽  
Temperature Gradient ◽  
Entropy Production ◽  
Surface Curvature ◽  
The State ◽  
Temperature Gradients ◽  
Curvature Radius ◽  
Minimum Entropy ◽  
Snow Layer ◽  
Minimum Entropy Production

AbstractAn essential problem in snow science is to predict the changing form of ice grains within a snow layer. Present theories are based on the idea that form changes are driven by mass diffusion induced by temperature gradients within the snow cover. This leads to the well-established theory of isothermal- and temperature-gradient metamorphism. Although diffusion theory treats mass transfer, it does not treat the influence of this mass transfer on the form — the curvature radius of the grains and bonds — directly. Empirical relations, based on observations, are additionally required to predict flat or rounded surfaces. In the following, we postulate that metamorphism, the change of ice surface curvature and size, is a process of thermodynamic optimization in which entropy production is minimized. That is, there exists an optimal surface curvature of the ice grains for a given thermodynamic state at which entropy production is stationary. This state is defined by differences in ice and air temperature and vapor pressure across the interfacial boundary layer. The optimal form corresponds to the state of least wasted work, the state of minimum entropy production. We show that temperature gradients produce a thermal non-equilibrium between the ice and air such that, depending on the temperature, flat surfaces are required to mimimize entropy production. When the temperatures of the ice and air are equal, larger curvature radii are found at low temperatures than at high temperatures. Thus, what is known as isothermal metamorphism corresponds to minimum entropy production at equilibrium temperatures, and so-called temperature-gradient metamorphism corresponds to minimum entropy production at none-quilibrium temperatures. The theory is in good agreement with general observations of crystal form development in dry seasonal alpine snow.

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