scholarly journals Bayesian Super-Resolution of Spectroscopic Data

2021 ◽  
Author(s):  
Kota Tsujimori ◽  
Jun Hirotani ◽  
Shunta Harada
Keyword(s):  
Raman Spectra ◽  
Spectroscopic Data ◽  
Super Resolution ◽  
Si Substrate ◽  
Charge Coupled Device ◽  
Research Fields ◽  
Wide Range ◽  
Ccd Arrays ◽  
Ccd Detectors ◽  
The Ideal

Abstract The resolution of spectroscopy, which delivers valuable insights and knowledge in various research fields, has sometimes been limited by the number of multi-channel detectors employed. For example, in Raman spectroscopy using charge coupled device (CCD) detectors, the resolution is limited by the number of the CCD arrays and it is difficult to achieve spectroscopic data acquisition with high resolution over a wide range. Here we describe a methodology to increase the resolution as well as signal-to-noise (S/N) ratio by applying Bayesian super-resolution in the analysis of spectroscopic data. In our present method, first the hyperparameters for the Bayesian super-resolution are determined by a virtual experiment imitating actual experimental data, and the precision of the super-resolution reconstruction is confirmed by the calculation of errors from the ideal values. For validation of the super-resolution of spectroscopic data, we applied this method to the analysis of Raman spectra. From 200 Raman spectra of a reference Si substrate with a resolution of about 0.8 cm− 1, super-resolution reconstruction with resolution of 0.01 cm− 1 was successfully achieved with the promised precision. From the super-resolution spectrum, the Raman scattering peak of the reference Si substrate was estimated as 520.55 (+ 0.12, -0.09) cm− 1, which is comparable to the precisely determined value from previous works. The present methodology can be applied to various kinds of spectroscopic analysis, leading to increased precision in the analysis of spectroscopic data and the ability to detect slight differences in spectral peak positions and shapes.

scholarly journals Bayesian Super-Resolution of Spectroscopic Data

2021 ◽  
Author(s):  
Kota Tsujimori ◽  
Jun Hirotani ◽  
Shunta Harada
Keyword(s):  
Raman Spectra ◽  
Spectroscopic Data ◽  
Super Resolution ◽  
Si Substrate ◽  
Charge Coupled Device ◽  
Research Fields ◽  
Wide Range ◽  
Ccd Arrays ◽  
Ccd Detectors ◽  
The Ideal

Abstract The resolution of spectroscopy, which delivers valuable insights and knowledge in various research fields, has sometimes been limited by the number of multi-channel detectors employed. For example, in Raman spectroscopy using charge coupled device (CCD) detectors, the resolution is limited by the number of the CCD arrays and it is difficult to achieve spectroscopic data acquisition with high resolution over a wide range. Here we describe a methodology to increase the resolution as well as signal-to-noise (S/N) ratio by applying Bayesian super-resolution in the analysis of spectroscopic data. In our present method, first the hyperparameters for the Bayesian super-resolution are determined by a virtual experiment imitating actual experimental data, and the precision of the super-resolution reconstruction is confirmed by the calculation of errors from the ideal values. For validation of the super-resolution of spectroscopic data, we applied this method to the analysis of Raman spectra. From 200 Raman spectra of a reference Si substrate with a resolution of about 0.8 cm-1, super-resolution reconstruction with resolution of 0.01 cm-1 was successfully achieved with the promised precision. From the super-resolution spectrum, the Raman scattering peak of the reference Si substrate was estimated as 520.55 (+0.12, -0.09) cm-1, which is comparable to the precisely determined value from previous works. The present methodology can be applied to various kinds of spectroscopic analysis, leading to increased precision in the analysis of spectroscopic data and the ability to detect slight differences in spectral peak positions and shapes.

scholarly journals Bayesian Super-Resolution of Spectroscopic Data

2021 ◽  
Author(s):  
Kota Tsujimori ◽  
Jun Hirotani ◽  
Shunta Harada
Keyword(s):  
Raman Spectra ◽  
Spectroscopic Data ◽  
Super Resolution ◽  
Si Substrate ◽  
Charge Coupled Device ◽  
Research Fields ◽  
Wide Range ◽  
Ccd Arrays ◽  
Ccd Detectors ◽  
The Ideal

Abstract The resolution of spectroscopy, which delivers valuable insights and knowledge in various research fields, has sometimes been limited by the number of multi-channel detectors employed. For example, in Raman spectroscopy using charge coupled device (CCD) detectors, the resolution is limited by the number of the CCD arrays and it is difficult to achieve spectroscopic data acquisition with high resolution over a wide range. Here we describe a methodology to increase the resolution as well as signal-to-noise (S/N) ratio by applying Bayesian super-resolution in the analysis of spectroscopic data. In our present method, first the hyperparameters for the Bayesian super-resolution are determined by a virtual experiment imitating actual experimental data, and the precision of the super-resolution reconstruction is confirmed by the calculation of errors from the ideal values. For validation of the super-resolution of spectroscopic data, we applied this method to the analysis of Raman spectra. From 200 Raman spectra of a reference Si substrate with a resolution of about 0.8 cm-1, super-resolution reconstruction with resolution of 0.01 cm-1 was successfully achieved with the promised precision. From the super-resolution spectrum, the Raman scattering peak of the reference Si substrate was estimated as 520.55 (+ 0.12, -0.09) cm-1, which is comparable to the precisely determined value from previous works. The present methodology can be applied to various kinds of spectroscopic analysis, leading to increased precision in the analysis of spectroscopic data and the ability to detect slight differences in spectral peak positions and shapes.

scholarly journals Application of Bayesian Super-Resolution to Spectroscopic Data for Precise Characterization of Spectral Peak Shape

2022 ◽  
Author(s):  
Kota Tsujimori ◽  
Jun Hirotani ◽  
Shunta Harada
Keyword(s):  
Raman Spectra ◽  
Spectroscopic Data ◽  
Super Resolution ◽  
Spectral Peak ◽  
Peak Shape ◽  
Si Substrate ◽  
Precise Characterization ◽  
Data Points ◽  
The Ideal

AbstractThe number of data points of digitally recorded spectra have been limited by the number of multichannel detectors employed, which sometimes impedes the precise characterization of spectral peak shape. Here we describe a methodology to increase the number of data points as well as the signal-to-noise (S/N) ratio by applying Bayesian super-resolution in the analysis of spectroscopic data. In our present method, first, the hyperparameters for the Bayesian super-resolution are determined by a virtual experiment imitating actual experimental data, and the precision of the super-resolution reconstruction is confirmed by the calculation of errors from the ideal values. For validation of the super-resolution reconstruction of spectroscopic data, we applied this method to the analysis of Raman spectra. From 200 Raman spectra of a reference Si substrate with a data interval of about 0.8 cm−1, super-resolution reconstruction with a data interval of 0.01 cm−1 was successfully achieved with the promised precision. From the super-resolution spectrum, the Raman scattering peak of the reference Si substrate was estimated as 520.55 (+0.12, −0.09) cm−1, which is comparable to the precisely determined value reported in previous works. The present methodology can be applied to various kinds of spectroscopic analysis, leading to increased precision in the analysis of spectroscopic data and the ability to detect slight differences in spectral peak positions and shapes.

scholarly journals Application of Bayesian Super-Resolution to Spectroscopic Data for Precise Characterization of Spectral Peak Shape

2021 ◽  
Author(s):  
Kota Tsujimori ◽  
Jun Hirotani ◽  
Shunta Harada
Keyword(s):  
Raman Spectra ◽  
Spectroscopic Data ◽  
Super Resolution ◽  
Spectral Peak ◽  
Peak Shape ◽  
Si Substrate ◽  
Precise Characterization ◽  
Data Points ◽  
The Ideal

Abstract The number of data points of digitally recorded spectra have been limited by the number of multi-channel detectors employed, which sometimes inhibits the precise characterization of spectral peak shape. Here we describe a methodology to increase the number of data points as well as the signal-to-noise (S/N) ratio by applying Bayesian super-resolution in the analysis of spectroscopic data. In our present method, first the hyperparameters for the Bayesian super-resolution are determined by a virtual experiment imitating actual experimental data, and the precision of the super-resolution reconstruction is confirmed by the calculation of errors from the ideal values. For validation of the super-resolution reconstruction of spectroscopic data, we applied this method to the analysis of Raman spectra. From 200 Raman spectra of a reference Si substrate with a data interval of about 0.8 cm-1, super-resolution reconstruction with a data interval of 0.01 cm-1 was successfully achieved with the promised precision. From the super-resolution spectrum, the Raman scattering peak of the reference Si substrate was estimated as 520.55 (+0.12, -0.09) cm-1, which is comparable to the precisely determined value reported in previous works. The present methodology can be applied to various kinds of spectroscopic analysis, leading to increased precision in the analysis of spectroscopic data and the ability to detect slight differences in spectral peak positions and shapes.

A Simple Algorithm for Despiking Raman Spectra

2018 ◽  
Author(s):  
Darren Whitaker ◽  
Kevin Hayes
Keyword(s):  
Moving Average ◽  
Simple Algorithm ◽  
Mathematical Concepts ◽  
Charge Coupled Device ◽  
Analytical Technique ◽  
Minimal Sample ◽  
Minimal Distortion ◽  
Molecular Specificity ◽  
Ccd Detectors ◽  
Average Filter

Raman Spectroscopy is a widely used analytical technique, favoured when molecular specificity with minimal sample preparation is required.<br>The majority of Raman instruments use charge-coupled device (CCD) detectors, these are susceptible to cosmic rays and as such multiple spurious spikes can occur in the measurement. These spikes are problematic as they may hinder subsequent analysis, particularly if multivariate data analysis is required. In this work we present a new algorithm to remove these spikes from spectra after acquisition. Specifically we use calculation of modified <i>Z</i> scores to locate spikes followed by a simple moving average filter to remove them. The algorithm is very simple and its execution is essentially instantaneous, resulting in spike-free spectra with minimal distortion of actual Raman data. The presented algorithm represents an improvement on existing spike removal methods by utilising simple, easy to understand mathematical concepts, making it ideal for experts and non-experts alike. <br>

A Simple Algorithm for Despiking Raman Spectra

2018 ◽  
Author(s):  
Darren Whitaker ◽  
Kevin Hayes
Keyword(s):  
Moving Average ◽  
Simple Algorithm ◽  
Mathematical Concepts ◽  
Charge Coupled Device ◽  
Analytical Technique ◽  
Minimal Sample ◽  
Minimal Distortion ◽  
Molecular Specificity ◽  
Ccd Detectors ◽  
Average Filter

Raman Spectroscopy is a widely used analytical technique, favoured when molecular specificity with minimal sample preparation is required.<br>The majority of Raman instruments use charge-coupled device (CCD) detectors, these are susceptible to cosmic rays and as such multiple spurious spikes can occur in the measurement. These spikes are problematic as they may hinder subsequent analysis, particularly if multivariate data analysis is required. In this work we present a new algorithm to remove these spikes from spectra after acquisition. Specifically we use calculation of modified <i>Z</i> scores to locate spikes followed by a simple moving average filter to remove them. The algorithm is very simple and its execution is essentially instantaneous, resulting in spike-free spectra with minimal distortion of actual Raman data. The presented algorithm represents an improvement on existing spike removal methods by utilising simple, easy to understand mathematical concepts, making it ideal for experts and non-experts alike. <br>

A Simple Algorithm for Despiking Raman Spectra

2018 ◽  
Author(s):  
Darren Whitaker ◽  
Kevin Hayes
Keyword(s):  
Moving Average ◽  
Simple Algorithm ◽  
Mathematical Concepts ◽  
Charge Coupled Device ◽  
Analytical Technique ◽  
Minimal Sample ◽  
Minimal Distortion ◽  
Molecular Specificity ◽  
Ccd Detectors ◽  
Average Filter

Raman Spectroscopy is a widely used analytical technique, favoured when molecular specificity with minimal sample preparation is required.<br>The majority of Raman instruments use charge-coupled device (CCD) detectors, these are susceptible to cosmic rays and as such multiple spurious spikes can occur in the measurement. These spikes are problematic as they may hinder subsequent analysis, particularly if multivariate data analysis is required. In this work we present a new algorithm to remove these spikes from spectra after acquisition. Specifically we use calculation of modified <i>Z</i> scores to locate spikes followed by a simple moving average filter to remove them. The algorithm is very simple and its execution is essentially instantaneous, resulting in spike-free spectra with minimal distortion of actual Raman data. The presented algorithm represents an improvement on existing spike removal methods by utilising simple, easy to understand mathematical concepts, making it ideal for experts and non-experts alike. <br>

scholarly journals The IDEAL household energy dataset, electricity, gas, contextual sensor data and survey data for 255 UK homes

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Martin Pullinger ◽  
Jonathan Kilgour ◽  
Nigel Goddard ◽  
Niklas Berliner ◽  
Lynda Webb ◽  
...  
Keyword(s):  
Survey Data ◽  
Energy Demand ◽  
Secondary Data ◽  
Hot Water ◽  
Power Measurement ◽  
Sensor Data ◽  
Energy Awareness ◽  
Household Energy ◽  
Wide Range ◽  
The Ideal

AbstractThe IDEAL household energy dataset described here comprises electricity, gas and contextual data from 255 UK homes over a 23-month period ending in June 2018, with a mean participation duration of 286 days. Sensors gathered 1-second electricity data, pulse-level gas data, 12-second temperature, humidity and light data for each room, and 12-second temperature data from boiler pipes for central heating and hot water. 39 homes also included plug-level monitoring of selected electrical appliances, real-power measurement of mains electricity and key sub-circuits, and more detailed temperature monitoring of gas- and heat-using equipment, including radiators and taps. Survey data included occupant demographics, values, attitudes and self-reported energy awareness, household income, energy tariffs, and building, room and appliance characteristics. Linked secondary data comprises weather and level of urbanisation. The data is provided in comma-separated format with a custom-built API to facilitate usage, and has been cleaned and documented. The data has a wide range of applications, including investigating energy demand patterns and drivers, modelling building performance, and undertaking Non-Intrusive Load Monitoring research.

scholarly journals Light People: Professor Lin Li

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Hui Wang ◽  
Heng Gu
Keyword(s):  
Climate Change ◽  
Super Resolution ◽  
Development Trend ◽  
Optical Diffraction ◽  
Research Centre ◽  
Resistant Bacteria ◽  
Research Experience ◽  
Laser Material Processing ◽  
Healthy Human ◽  
Research Fields

EditorialHow to deal with climate change, how to mitigate or even reverse it, maybe the hottest scientific topic of the 21st century. Do you know that a Chinese scientist and his team contributed to climate change control by reducing PM2.5 in diesel car exhaust by 35–40%? That scientist is Prof. Lin Li, a Fellow of the Royal Academy of Engineering and founder of the Laser Processing Research Centre at The University of Manchester, United Kingdom. Of course, this is only one example of Professor Li’s scientific achievements. As a pioneer of microsphere super-resolution lens, his team, in collaboration with Singapore colleagues, broke the optical diffraction limit in optical microscopic imaging, making real-time observation of biological viruses without interference possible. He also used lasers to synthesize new nanomaterials which kill drug-resistant bacteria while remaining harmless to healthy human cells, which led to the development and breakthrough of related research fields. We are much honored to have Professor Lin Li for an exclusive interview in which he recalls his years of scientific research experience and talks about the future development trend of laser material processing.

scholarly journals A coma-free super-high resolution optical spectrometer using 44 high dispersion sub-gratings

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hua-Tian Tu ◽  
An-Qing Jiang ◽  
Jian-Ke Chen ◽  
Wei-Jie Lu ◽  
Kai-Yan Zang ◽  
...  
Keyword(s):  
High Resolution ◽  
Spectral Resolution ◽  
Wavelength Region ◽  
Ultra Violet ◽  
High Spectral Resolution ◽  
Visible Range ◽  
High Dispersion ◽  
Charge Coupled Device ◽  
Wide Range ◽  
High Resolution Spectrometer

AbstractUnlike the single grating Czerny–Turner configuration spectrometers, a super-high spectral resolution optical spectrometer with zero coma aberration is first experimentally demonstrated by using a compound integrated diffraction grating module consisting of 44 high dispersion sub-gratings and a two-dimensional backside-illuminated charge-coupled device array photodetector. The demonstrated super-high resolution spectrometer gives 0.005 nm (5 pm) spectral resolution in ultra-violet range and 0.01 nm spectral resolution in the visible range, as well as a uniform efficiency of diffraction in a broad 200 nm to 1000 nm wavelength region. Our new zero-off-axis spectrometer configuration has the unique merit that enables it to be used for a wide range of spectral sensing and measurement applications.

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