Image processing algorithms application for the modeling biogeochemical cycles of the Azov Sea

2021 ◽  
Vol 2131 (3) ◽  
pp. 032054
Author(s):  
Y V Belova ◽  
E O Rahimbaeva ◽  
E F Timofeeva
Keyword(s):  
Image Processing ◽  
Continuous Model ◽  
Common Species ◽  
Discrete Analogue ◽  
Biogenic Element ◽  
Computational Domain ◽  
Image Processing Algorithm ◽  
Software Module ◽  
Optimal Method ◽  
Azov Sea

Abstract In the article biogeochemical processes of the Azov Sea were researched. Mathematical non-stationary 3D model is proposed which describes the development dynamics of the two most common species of phytoplankton populations in the summer, the growth of which is limited by a single biogenic element, is proposed the linearization of continuous mathematical model on a uniform temporal grid is made. For a continuous model, a discrete analogue is constructed and an optimal method for grid equations solving is selected. To determine the boundary of the considered computational domain of a complex shape an image processing algorithm has been developed, implemented as a software module on Python, which makes it possible to obtain a dynamically changing contour of the Azov Sea from satellite images.

Estimating OLED Display Device Lifetime from pixel and screen brightness and its application

2019 ◽  
Vol 2019 (1) ◽  
pp. 331-338 ◽  
Author(s):  
Jérémie Gerhardt ◽  
Michael E. Miller ◽  
Hyunjin Yoo ◽  
Tara Akhavan
Keyword(s):  
Image Processing ◽  
Power Consumption ◽  
Model Parameters ◽  
Display Device ◽  
Image Processing Algorithm ◽  
Pixel Value ◽  
Device Lifetime ◽  
The Impact ◽  
Oled Display

In this paper we discuss a model to estimate the power consumption and lifetime (LT) of an OLED display based on its pixel value and the brightness setting of the screen (scbr). This model is used to illustrate the effect of OLED aging on display color characteristics. Model parameters are based on power consumption measurement of a given display for a number of pixel and scbr combinations. OLED LT is often given for the most stressful display operating situation, i.e. white image at maximum scbr, but having the ability to predict the LT for other configurations can be meaningful to estimate the impact and quality of new image processing algorithms. After explaining our model we present a use case to illustrate how we use it to evaluate the impact of an image processing algorithm for brightness adaptation.

Internal quality evaluation of chestnut using nuclear magnetic resonance

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Soo Hyun Park ◽  
Sang Ha Noh ◽  
Michael J. McCarthy ◽  
Seong Min Kim
Keyword(s):  
Image Processing ◽  
Nuclear Magnetic Resonance ◽  
Magnetic Resonance ◽  
Processing Algorithm ◽  
Internal Quality ◽  
Soluble Solid Content ◽  
Mr Images ◽  
Image Processing Algorithm ◽  
Internal Defects ◽  
Nuclear Magnetic

AbstractThis study was carried out to develop a prediction model for soluble solid content (SSC) of intact chestnut and to detect internal defects using nuclear magnetic resonance (NMR) relaxometry and magnetic resonance imaging (MRI). Inversion recovery and Carr–Purcell–Meiboom–Gill (CPMG) pulse sequences used to determine the longitudinal (T1) and transverse (T2) relaxation times, respectively. Partial least squares regression (PLSR) was adopted to predict SSCs of chestnuts with NMR data and histograms from MR images. The coefficient of determination (R2), root mean square error of prediction (RMSEP), ratio of prediction to deviation (RPD), and the ratio of error range (RER) of the optimized model to predict SSC were 0.77, 1.41 °Brix, 1.86, and 11.31 with a validation set. Furthermore, an image-processing algorithm has been developed to detect internal defects such as decay, mold, and cavity using MR images. The classification applied with the developed image processing algorithm was over 94% accurate to classify. Based on the results obtained, it was determined that the NMR signal could be applied for grading several levels by SSC, and MRI could be used to evaluate the internal qualities of chestnuts.

Simulation of classroom student behavior recognition based on PSO-kNN algorithm and emotional image processing

2020 ◽  
pp. 1-11
Author(s):  
Shilong Wu
Keyword(s):  
Image Processing ◽  
Emotion Recognition ◽  
Student Behavior ◽  
Classroom Behavior ◽  
Feature Recognition ◽  
Pso Algorithm ◽  
Cluster Center ◽  
Image Processing Algorithm ◽  
Behavior Recognition ◽  
Key Frame

Students’ classroom behavior recognition and emotion recognition effects directly determine the degree of teachers’ control of the classroom teaching process. At present, teachers and students belong to two groups in traditional teaching, and teachers cannot effectively mobilize students’ learning emotions. In order to improve the teaching effect, this paper combines the PSO algorithm and the KNN algorithm to obtain the PSO-KNN joint algorithm, and combines with the emotional image processing algorithm to construct an artificial intelligence-based classroom student behavior recognition model. Moreover, based on the image processing technology, this paper uses key frame detection for feature recognition, and this paper improves the recognition process based on the inter-frame similarity measurement algorithm and initial cluster center selection in the key frame extraction method of clustering. In addition, this paper analyzes the effect of the model constructed on the behavior recognition and emotion recognition of students. The research results show that the joint algorithm constructed in this paper has a high accuracy rate for students’ emotion recognition and behavior recognition, and can meet the actual teaching needs.

scholarly journals Simulation of the Photodetection in the PPS CMOS Sensors

2013 ◽  
Vol 8-9 ◽  
pp. 611-618
Author(s):  
Florin Toadere ◽  
Radu Arsinte
Keyword(s):  
Image Processing ◽  
Input Data ◽  
Signal To Noise Ratio ◽  
Image Processing Algorithm ◽  
Signal To Noise ◽  
Spectral Image ◽  
Image Capture ◽  
Visible Image ◽  
Optical Part ◽  
Electrical Part

The paper contains an analysis and simulation of passive pixel based sensors. The passive pixel CMOS image acquisition sensor (PPS) is the key part of a visible image capture systems. The PPS is a complex circuit composed by an optical part and an electrical part, both analog and digital. The goal of this paper is to simulate the functionality of the photodetection process that happens in the PPS sensor. The photodetector is responsible with the conversion from photons to electrical charges and then into current. In the optical part, the sensor is analyzed by a spectral image processing algorithm which uses as input data: the lenses array transmittance, the red, green and blue filters and the quantum efficiency of the PPS. In the electrical part of simulation, the program is computing the signal to noise ratio of the sensor taking into account the photon shot, white and fixed pattern noises. Our basic analysis is based on camera equation to which we add the noises.

An Image Processing Algorithm to Find New and Old Bruises

1995 ◽  
Vol 11 (5) ◽  
pp. 751-757 ◽  
Author(s):  
J. A. Throop ◽  
D. J. Aneshansley ◽  
B. L. Upchurch
Keyword(s):  
Image Processing ◽  
Processing Algorithm ◽  
Image Processing Algorithm

scholarly journals Image Processing Algorithm for Weight Estimation of Dairy Cattle

2011 ◽  
Vol 36 (1) ◽  
pp. 48-57 ◽  
Author(s):  
Kwang-Wook Seo ◽  
Hyeon-Tae Kim ◽  
Dae-Weon Lee ◽  
Yong-Cheol Yoon ◽  
Dong-Yoon Choi
Keyword(s):  
Image Processing ◽  
Dairy Cattle ◽  
Processing Algorithm ◽  
Weight Estimation ◽  
Image Processing Algorithm
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