Good open Journal with Spectroscopy and Computer Journals.
ISSN:1932-6203
Impact Factor (2012 = 3.730)
Showing posts with label Spectroscopy. Show all posts
Showing posts with label Spectroscopy. Show all posts
Spectrum analysis A modern perspective
This Article covers the main topic of Spectrum analysis and will use as a base to my main research.
Abstract:
A summary of many of the new techniques developed in the last two decades for spectrum analysis of discrete time series is presented in this tutorial. An examination of the underlying time series model assumed by each technique serves as the common basis for understanding the differences among the various spectrum analysis approaches. Techniques discussed include the classical periodogram, classical Blackman-Tukey, autoregressive (maximum entropy), moving average, autotegressive-moving average, maximum likelihood, Prony, and Pisarenko methods. A summary table in the text provides a concise overview for all methods, including key references and appropriate equations for computation of each spectral estimate.
Link to Article:
Author(s) Kay, S.M. University of Rhode Island, Kingston, RI Marple, S.L., Jr.
Abstract:
A summary of many of the new techniques developed in the last two decades for spectrum analysis of discrete time series is presented in this tutorial. An examination of the underlying time series model assumed by each technique serves as the common basis for understanding the differences among the various spectrum analysis approaches. Techniques discussed include the classical periodogram, classical Blackman-Tukey, autoregressive (maximum entropy), moving average, autotegressive-moving average, maximum likelihood, Prony, and Pisarenko methods. A summary table in the text provides a concise overview for all methods, including key references and appropriate equations for computation of each spectral estimate.
Link to Article:
Author(s) Kay, S.M. University of Rhode Island, Kingston, RI Marple, S.L., Jr.
ISSN : 0018-9219Date of Current Version :28 June 2005Digital Object Identifier : 10.1109/PROC.1981.12184
NIR Fundamentals and a little More Presentation
Presentation Links good concise information on overview of NIR
NIRS - Advanced Alternative
Short-Wave Near-Infrared Spectroscopy of Milk Powder: Quantitative Analysis of Fat Content.
Image and Signal Processing, 2008. CISP'08. Congress on. Vol. 2. IEEE, 2008.
Wu, Di, et al.
Wu, Di, et al.
Measurement of sugar content of white vinegars using VIS/near-infrared.
This has all the attributes of my final keywords , and abstract contains my overall goals matching highly with my research. But not cited that much.
Abstract:
Visible and near infrared (VIS/NIR) spectroscopy combined with different calibration models was applied to predict the sugar content of white vinegars. The calibration set was composed of 240 samples, whereas 80 samples in the validation set. Partial least squares (PLS) models with or without pretreatments were developed and certain latent variables (LVs) were extracted by PLS analysis. The selected LVs were used as the inputs of BP neural network (BPNN) model. Finally, three models were developed. The prediction results indicated that PLS model with no pretreatment was better than that with pretreatments, and the best performance was obtained by BPNN model. The correlation coefficient, RMSEP and bias for validation set by BPNN model were 0.995, 0.135 and 0.035, respectively. The overall results indicated that VIS/NIR spectroscopy could be used as an alternative approach for the prediction of sugar content, and the BPNN models achieved the optimal prediction accuracy.
Link to Article:
Abstract:
Visible and near infrared (VIS/NIR) spectroscopy combined with different calibration models was applied to predict the sugar content of white vinegars. The calibration set was composed of 240 samples, whereas 80 samples in the validation set. Partial least squares (PLS) models with or without pretreatments were developed and certain latent variables (LVs) were extracted by PLS analysis. The selected LVs were used as the inputs of BP neural network (BPNN) model. Finally, three models were developed. The prediction results indicated that PLS model with no pretreatment was better than that with pretreatments, and the best performance was obtained by BPNN model. The correlation coefficient, RMSEP and bias for validation set by BPNN model were 0.995, 0.135 and 0.035, respectively. The overall results indicated that VIS/NIR spectroscopy could be used as an alternative approach for the prediction of sugar content, and the BPNN models achieved the optimal prediction accuracy.
Link to Article:
- Page(s):1311 - 1316
- Conference Location :Kunming
- Print ISBN: 978-1-4244-2095-7
Colorimeter and spectrcopy Digital Sensing using ANNS
Uses BP ANNS to analysis Tea , Using Near Infrared So a complete fit for my research fits my check-list very good except for citations.
Discrimination of varieties of tea using near infrared spectroscopy by principal component analysis and BP model Yong He, Xiaoli Li, Xunfei Deng College of Biosystems Engineering and Food Science, Zhejiang University, 310029, Hangzhou, China.
Digital Object Identifier: 10.1109/ICCIAS.2006.295409
A review of Bayesian neural networks with an application to near infrared spectroscopy
May be of use but very heavy maths will have to review better but matches my check-list well , has my exact keywords but will look at different Neural Nets and see what one will be the best for me.
Abstract:
MacKay's (1992) Bayesian framework for backpropagation is a practical and powerful means to improve the generalization ability of neural networks. It is based on a Gaussian approximation to the posterior weight distribution. The framework is extended, reviewed, and demonstrated in a pedagogical way. The notation is simplified using the ordinary weight decay parameter, and a detailed and explicit procedure for adjusting several weight decay parameters is given. Bayesian backprop is applied in the prediction of fat content in minced meat from near infrared spectra. It outperforms “early stopping” as well as quadratic regression. The evidence of a committee of differently trained networks is computed, and the corresponding improved generalization is verified. The error bars on the predictions of the fat content are computed. There are three contributors: The random noise, the uncertainty in the weights, and the deviation among the committee members. The Bayesian framework is compared to Moody's GPE (1992). Finally, MacKay and Neal's automatic relevance determination, in which the weight decay parameters depend on the input number, is applied to the data with improved results
Date of Publication: Jan 1996
- Page(s):
- 56 - 72
- ISSN :
- 1045-9227
- INSPEC Accession Number:
- 5181044
- Digital Object Identifier :
- 10.1109/72.478392
- Date of Current Version :
- 06 August 2002
- Issue Date :
- Jan 1996
- Sponsored by :
- IEEE Computational Intelligence Society
(ICNIRS) International Council for Near Infrared (NIR) Spectroscopy
ICNIRS, the International Council for Near Infrared (NIR) Spectroscopy.
http://www.icnirs.org/
The last Conference:
NIR 2013 - 16th International Conference on Near Infrared Spectroscopy
2 - 7 June 2013 - 34280 La Grande-Motte
A1 Agriculture, Environment
http://www.icnirs2013.org/
NIR 2013 - 16th International Conference on Near Infrared Spectroscopy
2 - 7 June 2013 - 34280 La Grande-Motte
A1 Agriculture, Environment
http://www.icnirs2013.org/
International Conference on Artificial Neural Networks (ICANN)
International Conference on Artificial Neural Networks (ICANN)
This is a list of the International their aim is to develop neural algorithms to apply to problems in many different areas , I can look at these to apply to my area Spectrometry.
The next conference 24rd International Conference on Artificial Neural Networks is to be held on September 15–19, 2014 in Hamburg, Germany.
23rd International Conference on Artificial Neural Networks
10-13 September 2013, Sofia, Bulgaria
http://www.icann2013.org/
The Technical Programme can be seen here: http://magazin.mageks-v.com/icann2013/index.php/technical-programme
This is a list of the International their aim is to develop neural algorithms to apply to problems in many different areas , I can look at these to apply to my area Spectrometry.
The next conference 24rd International Conference on Artificial Neural Networks is to be held on September 15–19, 2014 in Hamburg, Germany.
23rd International Conference on Artificial Neural Networks
10-13 September 2013, Sofia, Bulgaria
http://www.icann2013.org/
The Technical Programme can be seen here: http://magazin.mageks-v.com/icann2013/index.php/technical-programme
A machine vision system for grading lentils
A machine vision system for color grading of lentils was developed using a flatbed scanner as the image-gathering device. Grain samples belonging to different grades of large green lentils were scanned and analyzed over a two-crop season period. Image color, color distribution, and textural features were found to begood indicators of lentil grade. Linear discriminant analysis, k-nearest neighbors, and neural network based classifiers performed equally well in predicting sample grade. An online classification system was developed with a neural classifier that achieved an overall accuracy (agreement with the grain inspectors) of more than 90%.
Publication Number GRL# 809. Shahin, M.A. and Symons, S.J. 2001.
Keywords: lentils, grading, inspection, machine vision, color, image analysis, image classification, flatbed scanner
Publication Number GRL# 809. Shahin, M.A. and Symons, S.J. 2001.
Tricorder Project Releases Prototype Open Source 3D Printable Spectrometer
"As part of developing the next open source science tricorder model, Dr. Peter Jansen of the Tricorder project has released the source to an inexpensive 3D printable visible spectrometer prototype
intended for the next science tricorder, but also suitable for Arduino
or other embedded electronics projects for science education. With
access to a Makerbot-class 3D printer, the spectrometer can be build for
about $20 in materials. The source files including hardware
schematics, board layouts, Arduino/Processing sketches and example data
are available on Thingiverse, and potential contributors are encouraged to help improve the spectrometer design."
Full spectral imaging: a revisited approach to remote sensing
Current optical remote sensing instrument technology allows the acquisition and digitization of all of the reflected energy (light) across the full spectral range of interest. The current method for acquiring, transmitting, and processing this data is still based on the "multi-band" approach that has been used for the past thirty years. This approach was required due to limitations imposed by early instrument technology. This paper will present generalized concepts for acquiring, pre-processing, transmitting, and extracting information from full-spectral, remotely sensed data. The goal of the paper is to propose methods for changing from the current "bytes-per-band" approach to the "spectral curve" approach. The paper will describe how the Full Spectral Imaging (FSI) approach has the potential to greatly simplify instrument characterization and calibration and to significantly reduce data transmission and storage requirements. I will suggest how these improvements may be accomplished with no loss of remotely sensed information.
http://dx.doi.org/10.1117/12.510485
http://dx.doi.org/10.1117/12.510485
The spectral image processing system (SIPS)—interactive visualization and analysis of imaging spectrometer data
The Center for the Study of Earth from Space (CSES) at the University of Colorado, Boulder, has developed a prototype interactive software system called the Spectral Image Processing System (SIPS) using IDL (the Interactive Data Language) on UNIX-based workstations. SIPS is designed to take advantage of the combination of high spectral resolution and spatial data presentation unique to imaging spectrometers. It streamlines analysis of these data by allowing scientists to rapidly interact with entire datasets. SIPS provides visualization tools for rapid exploratory analysis and numerical tools for quantitative modeling. The user interface is X-Windows-based, user friendly, and provides “point and click” operation. SIPS is being used for multidisciplinary research concentrating on use of physically based analysis methods to enhance scientific results from imaging spectrometer data. The objective of this continuing effort is to develop operational techniques for quantitative analysis of imaging spectrometer data and to make them available to the scientific community prior to the launch of imaging spectrometer satellite systems such as the Earth Observing System (EOS) High Resolution Imaging Spectrometer (HIRIS).
Link to Journal:
Link to Journal:
Machine Learning Caltech
Outline
This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. It enables computational systems to adaptively improve their performance with experience accumulated from the observed data. ML has become one of the hottest fields of study today, taken up by undergraduate and graduate students from 15 different majors at Caltech. This course balances theory and practice, and covers the mathematical as well as the heuristic aspects. The lectures below follow each other in a story-like fashion:- What is learning?
- Can a machine learn?
- How to do it?
- How to do it well?
- Take-home lessons.
Overview of Diffraction Grating
Diffraction Grating
http://www.physics.smu.edu/~scalise/emmanual/diffraction/lab.html
Table of Characteristic IR Absorptions
| frequency, cm–1 | bond | functional group |
| 3640–3610 (s, sh) | O–H stretch, free hydroxyl | alcohols, phenols |
| 3500–3200 (s,b) | O–H stretch, H–bonded | alcohols, phenols |
| 3400–3250 (m) | N–H stretch | primary, secondary amines, amides |
| 3300–2500 (m) | O–H stretch | carboxylic acids |
| 3330–3270 (n, s) | –C(triple bond)C–H: C–H stretch | alkynes (terminal) |
| 3100–3000 (s) | C–H stretch | aromatics |
| 3100–3000 (m) | =C–H stretch | alkenes |
Nanometer-Scale Sizing Accuracy of Particle Suspensions on an Unmodified Cell Phone Using Elastic Light Scattering
Mobile technologies have been advancing at a rapid pace, with current mobile platforms' computing power approaching that of desktop machines. These advances in device computing have come alongside progress in mobile imaging technology, with current cell phone cameras using sophisticated back-thinned CMOS sensors coupled to high quality optics with relatively high numerical apertures. This progress has led several groups to explore the possibility of performing medical diagnostics, such as microscopic imaging [1]–[3], cell counting [4], and spectroscopy [2], using mobile devices. We present in this paper an attachment to a cellular phone that allows for accurate sizing of particles using elastic light scattering.
Link to Journal:
Cell-Phone-Based Platform for Biomedical Device Development and Education Applications
in this paper we report the development of two attachments to a
commercial cell phone that transform the phone's integrated lens and
image sensor into a 350× microscope and visible-light spectrometer. The
microscope is capable of transmission and polarized microscopy modes and
is shown to have 1.5 micron resolution and a usable field-of-view of 150×150 with no image processing, and approximately 350×350 when post-processing is applied. The spectrometer has a 300 nm bandwidth with a limiting
spectral resolution of close to 5 nm. We show applications of the
devices to medically relevant problems. In the case of the microscope,
we image both stained and unstained blood-smears showing the ability to
acquire images of similar quality to commercial microscope platforms,
thus allowing diagnosis of clinical pathologies. With the spectrometer
we demonstrate acquisition of a white-light transmission spectrum
through diffuse tissue as well as the acquisition of a fluorescence
spectrum. We also envision the devices to have immediate relevance in
the educational field.
Link to Journal:
Link to Journal:
On the Signal Features Analysis of a Pulse Induction Metal Detector Prototype
This paper describes two algorithms proposed for signal analysis in a pulse induction metal detector prototype. The first algorithm is based on combining Principal Component Analysis and Continuous Wavelet Transformation in order to determine metallic object position approaches to the detector coils, and the second algorithm is based on signal filtering in order to characterize object size. These algorithms have three functions; first, extracting time intervals in which the detector response gives information about an object approaching; second, the object position related to detector coils; third, information related to object size. The techniques used are based on time-frequency domains for raw signals acquired at the amplifier output in the prototype.
Link to Journal:
Keywords for Spectroscopy Disregarded:
machine vision,colorimeter,inferferogram,emmission and absorbion,fourier transform spectroscopy,FTIR, raman opticial activity,emmission spectrum,hyperspectral Imaging,blanco method,multi wavelength linear regression,lambda of caffeine,flurorescnce spectra,Quantitative method for spectral based materials, NIRS,
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