Statistical features of Electromagnetic Gaussian-SchellModel Beam Propagating through Smoke AerosolEnvironment

2022 ◽  
Author(s):  
Hemant Singh ◽  
Deepa Joshi ◽  
Bhaskar Kanseri
Keyword(s):  
Statistical Features

scholarly journals RSSI-Based for Device-Free Localization Using Deep Learning Technique

Smart Cities ◽  
2020 ◽  
Vol 3 (2) ◽  
pp. 444-455
Author(s):  
Abdul Syafiq Abdull Sukor ◽  
Latifah Munirah Kamarudin ◽  
Ammar Zakaria ◽  
Norasmadi Abdul Rahim ◽  
Sukhairi Sudin ◽  
...  
Keyword(s):  
Deep Learning ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Statistical Features ◽  
Received Signal Strength Indicator ◽  
Privacy Concerns ◽  
Physical Discomfort ◽  
Learning Technique ◽  
Device Free ◽  
The Internet Of Things

Device-free localization (DFL) has become a hot topic in the paradigm of the Internet of Things. Traditional localization methods are focused on locating users with attached wearable devices. This involves privacy concerns and physical discomfort especially to users that need to wear and activate those devices daily. DFL makes use of the received signal strength indicator (RSSI) to characterize the user’s location based on their influence on wireless signals. Existing work utilizes statistical features extracted from wireless signals. However, some features may not perform well in different environments. They need to be manually designed for a specific application. Thus, data processing is an important step towards producing robust input data for the classification process. This paper presents experimental procedures using the deep learning approach to automatically learn discriminative features and classify the user’s location. Extensive experiments performed in an indoor laboratory environment demonstrate that the approach can achieve 84.2% accuracy compared to the other basic machine learning algorithms.

RPPAware: A software suite to preprocess, analyze and visualize reverse phase protein array data

2018 ◽  
Vol 16 (03) ◽  
pp. 1850001 ◽  
Author(s):  
A. Ranjitha Dhanasekaran ◽  
Katheleen J. Gardiner
Keyword(s):  
Brain Regions ◽  
Large Datasets ◽  
Ease Of Use ◽  
Statistical Features ◽  
Software Suite ◽  
Reverse Phase ◽  
High Throughput Technology ◽  
Phase Protein ◽  
Biological Discovery

Reverse Phase Protein Arrays (RPPA) is a high-throughput technology used to profile levels of protein expression. Handling the large datasets generated by RPPA can be facilitated by appropriate software tools. Here, we describe RPPAware, a free and intuitive software suite that was developed specifically for analysis and visualization of RPPA data. RPPAware is a portable tool that requires no installation and was built using Java. Many modules of the tool invoke R to utilize the statistical features. To demonstrate the utility of RPPAware, data generated from screening brain regions of a mouse model of Down syndrome with 62 antibodies were used as a case study. The ease of use and efficiency of RPPAware can accelerate data analysis to facilitate biological discovery. RPPAware 1.0 is freely available under GNU General Public License from the project website at http://downsyndrome.ucdenver.edu/iddrc/rppaware/home.htm along with a full documentation of the tool.

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