Functional Optical Signal Analysis (fOSA): A Software Tool for NIRS Data Processing

2006 ◽  
Author(s):  
Peck H. Koh ◽  
Clare E. Elwell ◽  
David T. Delpy
2012 ◽  
Vol 51 (05) ◽  
pp. 441-448 ◽  
Author(s):  
P. F. Neher ◽  
I. Reicht ◽  
T. van Bruggen ◽  
C. Goch ◽  
M. Reisert ◽  
...  

SummaryBackground: Diffusion-MRI provides a unique window on brain anatomy and insights into aspects of tissue structure in living humans that could not be studied previously. There is a major effort in this rapidly evolving field of research to develop the algorithmic tools necessary to cope with the complexity of the datasets.Objectives: This work illustrates our strategy that encompasses the development of a modularized and open software tool for data processing, visualization and interactive exploration in diffusion imaging research and aims at reinforcing sustainable evaluation and progress in the field.Methods: In this paper, the usability and capabilities of a new application and toolkit component of the Medical Imaging and Interaction Toolkit (MITK, www.mitk.org), MITKDI, are demonstrated using in-vivo datasets.Results: MITK-DI provides a comprehensive software framework for high-performance data processing, analysis and interactive data exploration, which is designed in a modular, extensible fashion (using CTK) and in adherence to widely accepted coding standards (e.g. ITK, VTK). MITK-DI is available both as an open source software development toolkit and as a ready-to-use in stallable application.Conclusions: The open source release of the modular MITK-DI tools will increase verifiability and comparability within the research community and will also be an important step towards bringing many of the current techniques towards clinical application.


2017 ◽  
Vol 3 ◽  
pp. 38-45
Author(s):  
Michał Serej ◽  
Maria Skublewska - Paszkowska

The article presents both the methods of data processing of electromyography (EMG), and EMG signal analysis using the implemented piece of software. This application is used to load the EMG signal stored in a file with the .C3D extension. The analysis was conducted in terms of the highest muscles activaton during exercise recorded with Motion Capture technique.


2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Palash Rai ◽  
Rahul Kaushik

Abstract A technique for the estimation of an optical signal-to-noise ratio (OSNR) using machine learning algorithms has been proposed. The algorithms are trained with parameters derived from eye-diagram via simulation in 10 Gb/s On-Off Keying (OOK) nonreturn-to-zero (NRZ) data signal. The performance of different machine learning (ML) techniques namely, multiple linear regression, random forest, and K-nearest neighbor (K-NN) for OSNR estimation in terms of mean square error and R-squared value has been compared. The proposed methods may be useful for intelligent signal analysis in a test instrument and to monitor optical performance.


2019 ◽  
Vol 0 (0) ◽  
Author(s):  
Debajyoti Samanta

AbstractOptics has strong potentiality in data processing. Here, quantum Fredkin gate is implemented with the help of switching property of optical nonlinear material and polarization-based encoding and decoding technique of optical signal. The gate is very much useful for quantum computing and communications.


Author(s):  
J.G. Keating ◽  
J. Halligan ◽  
K. Adamson ◽  
D.J. Gleeson ◽  
S.M.P. McKema-Lawlor ◽  
...  

1990 ◽  
Author(s):  
Andrew K. Tay ◽  
Dale A. Wilson ◽  
A. C. Demirdogen ◽  
J. R. Houghton ◽  
Robert L. Wood

2010 ◽  
Vol 21 (09) ◽  
pp. 1183-1195
Author(s):  
YING LIU ◽  
YUANPING ZHOU ◽  
WEIGUO WANG ◽  
WENMING TANG

For the optical signal analysis and processing of optical imaging with stray light beams, a new analysis method for smoothing the optical image edge and maintaining the spectrum signal well is presented in this paper. By calculating the mean gray value of the light pixel in the filter window of adaptive algorithm, we can analyze and compare the difference between the mean gray value and the current gray value with the light pixel of stray light beams, and can determine the smoothness of the optical image edge. This method can increase greatly the ability of optical image analysis and processing, and effectively reduce the false edge and the edge loss for the optical imaging with stray light beams, so that the optical image with high quality can be obtained.


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