Hovering control of quadrotor system using a selection algorithm for control gains and gain-scaling factors

2020 ◽  
Vol 69 (12) ◽  
pp. 1916-1928
Author(s):  
Ji-Sun Park ◽  
Sang-Young Oh ◽  
Ho-Lim Choi
Keyword(s):  
Selection Algorithm ◽  
Scaling Factors ◽  
Hovering Control ◽  
Quadrotor System

Exact Optimum RAN Selection Algorithm for Heterogeneous-Type Cognitive Radio Networks

2014 ◽  
Vol 1 ◽  
pp. 652-655
Author(s):  
Takumi.Matsui Takumi.Matsui ◽  
Mikio.Hasegawa Mikio.Hasegawa ◽  
Hiroshi.Hirai Hiroshi.Hirai ◽  
Kiyohito.Nagano Kiyohito.Nagano ◽  
Kazuyuki.Aihara Kazuyuki.Aihara
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Radio Networks ◽  
Selection Algorithm

A SVD and Modified Firefly Optimization Based Robust Digital Image Watermarking Technique

2017 ◽  
Vol 7 (7) ◽  
pp. 268
Author(s):  
Chauhan Usha ◽  
Singh Rajeev Kumar
Keyword(s):  
Digital Media ◽  
Firefly Algorithm ◽  
Image Watermarking ◽  
Singular Values ◽  
Robust Watermarking ◽  
Scaling Factors ◽  
Geometric Attacks ◽  
Host Image ◽  
Firefly Optimization ◽  
Value Decomposition

Digital Watermarking is a technology, to facilitate the authentication, copyright protection and Security of digital media. The objective of developing a robust watermarking technique is to incorporate the maximum possible robustness without compromising with the transparency. Singular Value Decomposition (SVD) using Firefly Algorithm provides this objective of an optimal robust watermarking technique. Multiple scaling factors are used to embed the watermark image into the host by multiplying these scaling factors with the Singular Values (SV) of the host image. Firefly Algorithm is used to optimize the modified host image to achieve the highest possible robustness and transparency. This approach can significantly increase the quality of watermarked image and provide more robustness to the embedded watermark against various attacks such as noise, geometric attacks, filtering attacks etc.

An Enhancement of Feature Selection Algorithm for EDM: A Review

2018 ◽  
Vol 8 (5) ◽  
pp. 29
Author(s):  
Manpreet Kaur ◽  
Chamkaur Singh
Keyword(s):  
Feature Selection ◽  
Educational Data Mining ◽  
Problem Formulation ◽  
Research Area ◽  
Education Quality ◽  
Educational Institutions ◽  
Selection Algorithm ◽  
Positive Role ◽  
Data Set ◽  
Selection Algorithms

Educational Data Mining (EDM) is an emerging research area help the educational institutions to improve the performance of their students. Feature Selection (FS) algorithms remove irrelevant data from the educational dataset and hence increases the performance of classifiers used in EDM techniques. This paper present an analysis of the performance of feature selection algorithms on student data set. .In this papers the different problems that are defined in problem formulation. All these problems are resolved in future. Furthermore the paper is an attempt of playing a positive role in the improvement of education quality, as well as guides new researchers in making academic intervention.

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