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

2006 ◽  
Author(s):  
Peck H. Koh ◽  
Clare E. Elwell ◽  
David T. Delpy
Keyword(s):  
Data Processing ◽  
Signal Analysis ◽  
Software Tool ◽  
Optical Signal ◽  
Nirs Data

MITK Diffusion Imaging

2012 ◽  
Vol 51 (05) ◽  
pp. 441-448 ◽  
Author(s):  
P. F. Neher ◽  
I. Reicht ◽  
T. van Bruggen ◽  
C. Goch ◽  
M. Reisert ◽  
...  
Keyword(s):  
Data Processing ◽  
Open Source ◽  
High Performance ◽  
Diffusion Imaging ◽  
Software Tool ◽  
Brain Anatomy ◽  
Sustainable Evaluation ◽  
Imaging Research ◽  
Interactive Data

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.

scholarly journals The methods of EMG data processing

2017 ◽  
Vol 3 ◽  
pp. 38-45
Author(s):  
Michał Serej ◽  
Maria Skublewska - Paszkowska
Keyword(s):  
Data Processing ◽  
Motion Capture ◽  
Signal Analysis ◽  
Emg Signal ◽  
Capture Technique

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.

Optical SNR estimation using machine learning

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Palash Rai ◽  
Rahul Kaushik
Keyword(s):  
Machine Learning ◽  
Signal Analysis ◽  
Nearest Neighbor ◽  
Signal To Noise Ratio ◽  
Optical Signal ◽  
Machine Learning Algorithms ◽  
K Nearest Neighbor ◽  
Eye Diagram ◽  
Test Instrument ◽  
Data Signal

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.

Implementation of Polarization-Encoded Quantum Fredkin Gate Using Kerr Effect

2019 ◽  
Vol 0 (0) ◽  
Author(s):  
Debajyoti Samanta
Keyword(s):  
Quantum Computing ◽  
Data Processing ◽  
Kerr Effect ◽  
Optical Signal ◽  
Nonlinear Material ◽  
Optical Nonlinear Material ◽  
Fredkin Gate ◽  
Switching Property

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.

A software tool for investigating optical signal processing

2005 ◽  
Author(s):  
J.G. Keating ◽  
J. Halligan ◽  
K. Adamson ◽  
D.J. Gleeson ◽  
S.M.P. McKema-Lawlor ◽  
...  
Keyword(s):  
Signal Processing ◽  
Optical Signal Processing ◽  
Software Tool ◽  
Optical Signal

Microdamage and optical signal analysis of impact-induced fracture in smart structures

1990 ◽  
Author(s):  
Andrew K. Tay ◽  
Dale A. Wilson ◽  
A. C. Demirdogen ◽  
J. R. Houghton ◽  
Robert L. Wood
Keyword(s):  
Signal Analysis ◽  
Smart Structures ◽  
Optical Signal

OPTICAL SIGNAL ANALYSIS AND PROCESSING OF OPTICAL IMAGING WITH STRAY LIGHT BEAMS

2010 ◽  
Vol 21 (09) ◽  
pp. 1183-1195
Author(s):  
YING LIU ◽  
YUANPING ZHOU ◽  
WEIGUO WANG ◽  
WENMING TANG
Keyword(s):  
Optical Imaging ◽  
Signal Analysis ◽  
Optical Signal ◽  
Optical Image ◽  
Stray Light ◽  
Analysis Method ◽  
Image Edge ◽  
The Mean ◽  
The Difference ◽  
Light Beams

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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