Art2 Network with Neoteny Learning Law and and its application to color pixel analysis

2006 ◽  
Author(s):  
Zhong Chen ◽  
Xiaojing Xu ◽  
Zixing Cai
Keyword(s):  
Pixel Analysis ◽  
Color Pixel ◽  
Learning Law

Learning Law

2019 ◽  
Author(s):  
Anthony Marinac ◽  
Brian Simpson ◽  
Caroline Hart ◽  
Rhianna Chisholm ◽  
Jennifer Nielsen ◽  
...  
Keyword(s):  
Learning Law

P-53: A MisAddressing-Free Triangular Color-pixel Arrangement Structure for High Efficacy ACPDPs

2003 ◽  
Vol 34 (1) ◽  
pp. 418 ◽  
Author(s):  
Jung Hun Kim ◽  
Byoung-Kuk Min ◽  
Hyun-il Park ◽  
Kwang-Yeol Choi
Keyword(s):  
High Efficacy ◽  
Color Pixel ◽  
P 53

Filter-free color pixel sensor using gated PIN photodiodes and machine learning techniques

2021 ◽  
pp. 105337
Author(s):  
João Batista Junior ◽  
Arianne Pereira ◽  
Rudolf Buhler ◽  
André Perin ◽  
Carla Novo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Color Pixel ◽  
Pin Photodiodes

Iterative learning algorithm with a quadratic criterion for linear time-varying systems

2002 ◽  
Vol 216 (3) ◽  
pp. 309-316 ◽  
Author(s):  
S N Huang ◽  
K K Tan ◽  
T H Lee
Keyword(s):  
Learning Algorithm ◽  
Linear Time ◽  
Tracking Error ◽  
Iterative Learning ◽  
Tracking Performance ◽  
Time Varying ◽  
Control Scheme ◽  
Time Varying Systems ◽  
Simulation Results ◽  
Learning Law

A novel iterative learning controller for linear time-varying systems is developed. The learning law is derived on the basis of a quadratic criterion. This control scheme does not include package information. The advantage of the proposed learning law is that the convergence is guaranteed without the need for empirical choice of parameters. Furthermore, the tracking error on the final iteration will be a class K function of the bounds on the uncertainties. Finally, simulation results reveal that the proposed control has a good setpoint tracking performance.

scholarly journals Deduplication Analysis of Products In Digital Marketing

2018 ◽  
Vol 10 (1) ◽  
pp. 392
Author(s):  
P. Amudhavalli ◽  
N. Rajalakshmi ◽  
K.S. Sindhu
Keyword(s):  
Support Vector Machine ◽  
Brand Image ◽  
Forensic Analysis ◽  
Least Square ◽  
Digital Marketing ◽  
Support Vector ◽  
Online Purchase ◽  
Pixel Analysis ◽  
The Web

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.

scholarly journals Spatio-Temporal Mixed Pixel Analysis of Savanna Ecosystems: A Review

2021 ◽  
Vol 13 (19) ◽  
pp. 3870
Author(s):  
Hilma S. Nghiyalwa ◽  
Marcel Urban ◽  
Jussi Baade ◽  
Izak P. J. Smit ◽  
Abel Ramoelo ◽  
...  
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Land Cover ◽  
Parametric Methods ◽  
Mixed Pixel ◽  
Medium Resolution ◽  
Long Time ◽  
Pixel Analysis ◽  
Spatio Temporal ◽  
Resolution Data

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.

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