scholarly journals A statistical selection strategy for normalization procedures in LC-MS proteomics experiments through dataset-dependent ranking of normalization scaling factors

PROTEOMICS ◽  
2011 ◽  
Vol 11 (24) ◽  
pp. 4736-4741 ◽  
Author(s):  
Bobbie-Jo M. Webb-Robertson ◽  
Melissa M. Matzke ◽  
Jon M. Jacobs ◽  
Joel G. Pounds ◽  
Katrina M. Waters
Keyword(s):  
Selection Strategy ◽  
Scaling Factors ◽  
Statistical Selection

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.

Optimal Route Selection Strategy in Wireless Mesh Networks

2018 ◽  
Vol 6 (1) ◽  
pp. 238-243
Author(s):  
Pushpender Sarao ◽  
◽  
T. Raghavendra Gupta ◽  
S. Suresh ◽  
◽  
...  
Keyword(s):  
Wireless Mesh Networks ◽  
Mesh Networks ◽  
Selection Strategy ◽  
Route Selection ◽  
Optimal Route ◽  
Wireless Mesh

Identifying the Predictors of Self-Management Behaviors in Patients with Diabetes Based on Ecological Approach: A Systematic Review

2020 ◽  
Vol 16 ◽  
Author(s):  
Mahnaz Davari ◽  
Hamed Rezakhani Moghaddam ◽  
Aghil Habibi Soola
Keyword(s):  
Scientific Information ◽  
The Self ◽  
Self Management ◽  
Ecological Approach ◽  
Selection Strategy ◽  
Diabetic Patients ◽  
Personal Factors ◽  
Management Interventions ◽  
Number Of Patients ◽  
Management Behaviors

Background: Recognizing and promoting the factors that affect the self-management behaviors of diabetes leads to a reduction in the number of patients and an improvement in the quality of care. The ecological approach focuses on the nature of people's interactions with their physical and socio-cultural environments. Objective: The purpose of this study was to identify the predictors of self-management behaviors with a comprehensive approach in these patients. Methods: The Keywords were investigated in the relevant national and international databases, including PubMed, Google Scholar, Science Direct, Scopus, and Scientific Information Database, Magiran, and Iran Medex to obtain the articles published from 2009 to 2019. The search and article selection strategy was developed based on the Prisma checklist and was carried out in three steps. Results: Most studies have shown that personal factors had the highest prediction power for the self-management of diabetes. Then, the interpersonal factors, society and policy-making factors, and group and organization factors were most frequently reported predictors of self-management behaviors in diabetic patients. Conclusion: Self-management of diabetes is necessary for controlling it because 95% of care is done by the patient. When designing self-management interventions, factors based on the individual level that increasing self-management behaviors should be taken into account.

User-Driven Network Selection during Vertical Handover in 4G Networks

2017 ◽  
Vol 10 ◽  
pp. 1
Author(s):  
Abubakar Muhammad Miyim ◽  
Mahamod Ismail ◽  
Rosdiadee Nordin
Keyword(s):  
Blocking Probability ◽  
Service Selection ◽  
Vertical Handover ◽  
Network Selection ◽  
Selection Strategy ◽  
Intelligent Network ◽  
Decision Algorithm ◽  
Radio Access ◽  
Service Application ◽  
Multi Access

The importance of network selection for wireless networks, is to facilitate users with various personal wireless devices to access their desired services via a range of available radio access networks. The inability of these networks to provide broadband data service applications to users poses a serious challenge in the wireless environment. Network Optimization has therefore become necessary, so as to accommodate the increasing number of users’ service application demands while maintaining the required quality of services. To achieve that, the need to incorporate intelligent and fast mechanism as a solution to select the best value network for the user arises. This paper provides an intelligent network selection strategy based on the user- and network-valued metrics to suit their preferences when communicating in multi-access environment. A user-driven network selection strategy that employs Multi-Access Service Selection Vertical Handover Decision Algorithm (MASS-VHDA) via three interfaces; Wi-Fi, WiMAX and LTE-A is proposed, numerically evaluated and simulated. The results from the performance analysis demonstrate some improvement in the QoS and network blocking probability to satisfy user application requests for multiple simultaneous services.

scholarly journals Semi-Supervised PolSAR Image Classification Based on Self-Training and Superpixels

2019 ◽  
Vol 11 (16) ◽  
pp. 1933 ◽  
Author(s):  
Yangyang Li ◽  
Ruoting Xing ◽  
Licheng Jiao ◽  
Yanqiao Chen ◽  
Yingte Chai ◽  
...  
Keyword(s):  
Image Classification ◽  
State Of The Art ◽  
Sample Selection ◽  
Spatial Relations ◽  
Speckle Noise ◽  
Classification Performance ◽  
Selection Strategy ◽  
Training Set ◽  
Polarimetric Synthetic Aperture Radar ◽  
Unlabeled Sample

Polarimetric synthetic aperture radar (PolSAR) image classification is a recent technology with great practical value in the field of remote sensing. However, due to the time-consuming and labor-intensive data collection, there are few labeled datasets available. Furthermore, most available state-of-the-art classification methods heavily suffer from the speckle noise. To solve these problems, in this paper, a novel semi-supervised algorithm based on self-training and superpixels is proposed. First, the Pauli-RGB image is over-segmented into superpixels to obtain a large number of homogeneous areas. Then, features that can mitigate the effects of the speckle noise are obtained using spatial weighting in the same superpixel. Next, the training set is expanded iteratively utilizing a semi-supervised unlabeled sample selection strategy that elaborately makes use of spatial relations provided by superpixels. In addition, a stacked sparse auto-encoder is self-trained using the expanded training set to obtain classification results. Experiments on two typical PolSAR datasets verified its capability of suppressing the speckle noise and showed excellent classification performance with limited labeled data.

scholarly journals Autonomous Mobile Robot Navigation in Sparse LiDAR Feature Environments

2021 ◽  
Vol 11 (13) ◽  
pp. 5963
Author(s):  
Phuc Thanh-Thien Nguyen ◽  
Shao-Wei Yan ◽  
Jia-Fu Liao ◽  
Chung-Hsien Kuo
Keyword(s):  
Robot Navigation ◽  
Tracking Error ◽  
Unmanned Vehicles ◽  
Mobile Robot Navigation ◽  
Selection Strategy ◽  
Localization And Mapping ◽  
Pure Pursuit ◽  
Stable Performance ◽  
Large Curve ◽  
Moving Speed

In the industrial environment, Autonomous Guided Vehicles (AGVs) generally run on a planned route. Among trajectory-tracking algorithms for unmanned vehicles, the Pure Pursuit (PP) algorithm is prevalent in many real-world applications because of its simple and easy implementation. However, it is challenging to decelerate the AGV’s moving speed when turning on a large curve path. Moreover, this paper addresses the kidnapped-robot problem occurring in spare LiDAR environments. This paper proposes an improved Pure Pursuit algorithm so that the AGV can predict the trajectory and decelerate for turning, thus increasing the accuracy of the path tracking. To solve the kidnapped-robot problem, we use a learning-based classifier to detect the repetitive pattern scenario (e.g., long corridor) regarding 2D LiDAR features for switching the localization system between Simultaneous Localization And Mapping (SLAM) method and Odometer method. As experimental results in practice, the improved Pure Pursuit algorithm can reduce the tracking error while performing more efficiently. Moreover, the learning-based localization selection strategy helps the robot navigation task achieve stable performance, with 36.25% in completion rate more than only using SLAM. The results demonstrate that the proposed method is feasible and reliable in actual conditions.

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