Blind Pixel Analysis of InGaAs Detector and Optimization of P Electrode

2018 ◽  
Vol 47 (3) ◽  
pp. 304001 ◽  
Author(s):  
邓洪海 DENG Hong-hai ◽  
杨波 YANG Bo ◽  
夏辉 XIA Hui ◽  
邵海宝 SHAO Hai-bao ◽  
王强 WANG Qiang ◽  
...  
Author(s):  
P. Amudhavalli ◽  
N. Rajalakshmi ◽  
K.S. Sindhu

As Digital Marketing is becoming more popular, the number of customer’s interpretation on brands is increasing promptly which makes it firmer for companies to evaluate their brand image and to digital market their products on the web. The Forensic Analysis is used to determine and analyze patterns of fraudulent activities on images. Pixel Analysis and Least square support vector machine are used to compare and associate the scores acquired from the images into one result per tweet. We selected these techniques to compare and find the accuracy of the Digital Marketing images with the received product’s images to identify the fraudulent activities on images in Digital Marketing. As the result of this project the customer can identify whether the received product is exactly what is given in the online purchase website.


2021 ◽  
Vol 13 (19) ◽  
pp. 3870
Author(s):  
Hilma S. Nghiyalwa ◽  
Marcel Urban ◽  
Jussi Baade ◽  
Izak P. J. Smit ◽  
Abel Ramoelo ◽  
...  

Reliable estimates of savanna vegetation constituents (i.e., woody and herbaceous vegetation) are essential as they are both responders and drivers of global change. The savanna is a highly heterogenous biome with high variability in land cover types while also being very dynamic at both temporal and spatial scales. To understand the spatial-temporal dynamics of savannas, using Earth Observation (EO) data for mixed-pixel analysis is crucial. Mixed pixel analysis provides detailed land cover data at a sub-pixel level which are essential for conservation purposes, understanding food supply for herbivores, quantifying environmental change, such as bush encroachment, and fuel availability essential for understanding fire dynamics, and for accurate estimation of savanna biomass. This review paper consulted 197 studies employing mixed-pixel analysis in savanna ecosystems. The review indicates that studies have so far attempted to resolve the savanna mixed-pixel issues by using mainly coarse resolution data, such as Terra-Aqua MODIS and AVHRR and medium resolution Landsat, to provide fractional cover data. Hence, there is a lack of spatio-temporal mixed-pixel analysis for savannas at high spatial resolutions. Methods used for mixed-pixel analysis include parametric and non-parametric methods which range from pixel-unmixing models, such as linear spectral mixture analysis (SMA), time series decomposition, empirical methods to link the green vegetation parameters with Vegetation Indices (VIs), and machine learning methods, such as regression trees (RT) and random forests (RF). Most studies were undertaken at local and regional scale, highlighting a research gap for savanna mixed pixel studies at national, continental, and global level. Parametric methods for modeling spatio-temporal mixed pixel analysis were preferred for coarse to medium resolution remote sensing data, while non-parametric methods were preferred for very high to high spatial resolution data. The review indicates a gap for long time series spatio-temporal mixed-pixel analysis of savannas using high resolution data at various scales. There is potential to harmonize the available low resolution EO data with new high-resolution sensors to provide long time series of the savanna mixed pixel, which, according to this review, is missing.


1990 ◽  
Author(s):  
G. H. Olsen ◽  
A. M. Joshi ◽  
S. M. Mason ◽  
K. M. Woodruff ◽  
E. Mykietyn ◽  
...  

2012 ◽  
Vol 39 (5) ◽  
pp. 0507002
Author(s):  
王云姬 Wang Yunji ◽  
唐恒敬 Tang Hengjing ◽  
李雪 Li Xue ◽  
段微波 Duan Weibo ◽  
刘定权 Liu Dingquan ◽  
...  

2018 ◽  
Vol 10 (5) ◽  
pp. 669 ◽  
Author(s):  
Patrick Launeau ◽  
Manuel Giraud ◽  
Antoine Ba ◽  
Saïd Moussaoui ◽  
Marc Robin ◽  
...  

2020 ◽  
Vol 10 (7) ◽  
pp. 901-914
Author(s):  
D. Indumathy ◽  
S. Sudha

Cardiac arrest in human arises owing to blood vessel diseases or heart defects. Blood vessel diseases result due to the blockage of blood in the heart vessels, which leads to pain in the heart. Heart defects occur because of damage in the cardiac muscles indicated by abnormal heart rhythms. Cardiovascular diseases cause mortality which could be avoided through the earlier detection of cardiovascular diseases. The major cause for cardiovascular diseases is cholesterol deposition inside the artery walls which later forms plaques that block the blood flow. Until now, plaques have been detected through medical imaging only after the heart attack. The plaques are blasted through angioplasty or reduced with medicine. Classification of the plaques before treatment, leads to effective medication based on the type of plaque. The sub classification of the plaque types such as rupture-prone plaque, ruptured plaque with sub occlusive thrombus, erosion-prone plaque, calcified nodule and non-plaque has been segmented and identified. In this paper, we propose a novel Spatial Fuzzy Propensity Score Matching (SFPSM) method to classify the plaques. The SFPSM method consists of clustering, ranking the cluster and region-based pixel wise analysis. Pixel analysis inspects specific regions of sub pixel points and calibrates the plaque. From the experimental results, the classification of plaque based on the 50-image data set has exhibited accuracy of 85% after validation. The plaque accuracy of classification provides the standard digital number values for the sub classification of plaques.


Sign in / Sign up

Export Citation Format

Share Document