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A Pragmatic Introduction to Signal Processing: with applications in scientific measurement

19 May 2016-
TL;DR: A practical introduction to signal processing in scientific measurement, for scientists, engineers, researchers, instructors, and students working in academia, industry, environmental, medical, engineering, earth science, space, military, financial, and agriculture.
Abstract: A practical introduction to signal processing in scientific measurement, for scientists, engineers, researchers, instructors, and students working in academia, industry, environmental, medical, engineering, earth science, space, military, financial, and agriculture. Includes such topics as smoothing, differentiation, peak detection, integration and peak area measurements, harmonic analysis, convolution and deconvolution, Fourier filtering, least-squares curve fitting, multi-component spectroscopy, nonlinear iterative least-squares peak fitting. Provides many demonstrations and real data examples, plus download links to many free Matlab/Octave scripts and functions plus spreadsheet templates which has been widely used and have been cited in over 160 published papers, theses, and patents. Includes an appendix containing case studies, examples, and simulations, plus an alphabetical index.
Citations
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Journal ArticleDOI
03 May 2018-Sensors
TL;DR: The objective is to develop a method for direct reflectance measurements for drone-based remote sensing based on an imaging spectrometer and irradiance spectrumeter based on a tuneable Fabry-Pérot Interferometer -based hyperspectral camera.
Abstract: Drone-based remote sensing has evolved rapidly in recent years. Miniaturized hyperspectral imaging sensors are becoming more common as they provide more abundant information of the object compared to traditional cameras. Reflectance is a physically defined object property and therefore often preferred output of the remote sensing data capture to be used in the further processes. Absolute calibration of the sensor provides a possibility for physical modelling of the imaging process and enables efficient procedures for reflectance correction. Our objective is to develop a method for direct reflectance measurements for drone-based remote sensing. It is based on an imaging spectrometer and irradiance spectrometer. This approach is highly attractive for many practical applications as it does not require in situ reflectance panels for converting the sensor radiance to ground reflectance factors. We performed SI-traceable spectral and radiance calibration of a tuneable Fabry-Perot Interferometer -based (FPI) hyperspectral camera at the National Physical Laboratory NPL (Teddington, UK). The camera represents novel technology by collecting 2D format hyperspectral image cubes using time sequential spectral scanning principle. The radiance accuracy of different channels varied between ±4% when evaluated using independent test data, and linearity of the camera response was on average 0.9994. The spectral response calibration showed side peaks on several channels that were due to the multiple orders of interference of the FPI. The drone-based direct reflectance measurement system showed promising results with imagery collected over Wytham Forest (Oxford, UK).

63 citations


Cites methods from "A Pragmatic Introduction to Signal ..."

  • ...The Gaussian fitting was done with PeakFit -function from O’Haver [30]....

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01 Jan 2016
TL;DR: Data analysis and signal processing in chromatography will help people to read a good book with a cup of tea in the afternoon, instead they are facing with some infectious virus inside their computer.
Abstract: Thank you for downloading data analysis and signal processing in chromatography. Maybe you have knowledge that, people have look hundreds times for their chosen readings like this data analysis and signal processing in chromatography, but end up in harmful downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they are facing with some infectious virus inside their computer.

58 citations

Journal ArticleDOI
TL;DR: In this paper, the authors determined bulk and site-specific hydrogen diffusivities in two diopsides and an augite by heating initially homogeneous water-bearing samples in a 1-atm CO/CO2 gas-mixing furnace at 800-1000°C and observing H-loss profiles.
Abstract: The rate of hydrogen diffusion in clinopyroxene is relevant to interpreting hydrogen (“water”) concentrations in xenoliths, phenocrysts, and clinopyroxene-hosted melt inclusions to provide insight into the deep-earth water cycle and volcanic explosivity. Here, we determine bulk and site-specific hydrogen diffusivities in two diopsides and an augite by heating initially homogeneous water-bearing samples in a 1-atm CO/CO2 gas-mixing furnace at 800–1000 °C and oxygen fugacity at the quartz–fayalite–magnetite buffer and observing H-loss profiles. The O–H stretching range between wavenumbers 3000 and 4000 cm−1 in FTIR spectra is resolved into 4–6 peaks, each of which is assumed to represent a distinct defect site for the hydrogen, to determine peak-specific diffusivities using our previously published whole-block method. For the diopside from the Kunlun Mts. in China, Arrhenius relations are reported for peaks at 3645, 3617, 3540, 3443, and 3355 cm−1 based on measurements at 816, 904, and 1000 °C. Bulk and site-specific diffusivities are determined for the same set of peaks at 904 °C for the second diopside (Jaipur). The augite (PMR-53) was a triangular thin slab, and hydrogen diffusivities were determined for bulk hydrogen and peaks at 3620, 3550, 3460, and 3355 cm−1 in the thickness direction at 800 °C. Bulk hydrogen diffusivity in the Jaipur diopside is consistent with previous work, and hydrogen diffusivity in augite PMR-53 is slightly lower than the fast direction diffusivities measured || [100] and [001]* in Jaipur diopside. Both diopsides show 1–2 orders of magnitude differences in the peaks-specific diffusivities, with the fastest diffusivities at 3450 cm−1 and the slowest at 3645 cm−1. However, the hydrogen diffusivities in Jaipur diopside are 2–4 orders of magnitude higher than those in Kunlun diopside for bulk hydrogen and all peaks. Thus, peak-specific differences cannot by themselves adequately explain the 5 orders of magnitude range in hydrogen diffusivities observed experimentally in different diopsides. The results are broadly consistent with a previously proposed increase in hydrogen diffusivity in diopside with Fe up to 0.6–0.8 a.p.f.u., although there may be an opposing relationship with Al(IV). The results of this study and others predict high water diffusivities in Fe-bearing mantle xenolith clinopyroxene, on the order of ~10−9 to 10−11 m2/s at 1000 °C. The common observation of hydrogen zonation in mantle xenolith olivine, but not in clinopyroxene implies that hydrogen diffusion is much faster in olivine than in pyroxene, which then requires the operation of the fastest diffusion mechanism quantified in olivine and diffusivities in clinopyroxene at the lower end of this 2 orders of magnitude range. Such high diffusivities strongly suggest that water in mantle xenoliths has at least partially equilibrated with the host magma, and that the diffusion profiles observed in mantle xenolith olivine reflect only the final stage of ascent after water begins to exsolve.

52 citations


Cites methods from "A Pragmatic Introduction to Signal ..."

  • ...…the diopside spectra were resolved into a series of individual Gaussian curves either by hand or using the peak-fitting program peakfit.m in MATLAB (O’Haver 2015) using fixed positions that were chosen based on previous knowledge of where to expect prominent peaks (Skogby et al. 1990) and to…...

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  • ...After baseline subtraction, the diopside spectra were resolved into a series of individual Gaussian curves either by hand or using the peak-fitting program peakfit.m in MATLAB (O’Haver 2015) using fixed positions that were chosen based on previous knowledge of where to expect prominent peaks (Skogby et al. 1990) and to provide some consistency between these two samples....

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Journal ArticleDOI
TL;DR: In this paper, the authors present the results of deformation experiments on olivine single crystals and aggregates conducted in a deformation-DIA at confining pressures of 5 to 9 GPa and temperatures of 298 to 1473 K.
Abstract: Plastic deformation of olivine at relatively low temperatures (i.e., low‐temperature plasticity) likely controls the strength of the lithospheric mantle in a variety of geodynamic contexts. Unfortunately, laboratory estimates of the strength of olivine deforming by low‐temperature plasticity vary considerably from study to study, limiting confidence in extrapolation to geological conditions. Here we present the results of deformation experiments on olivine single crystals and aggregates conducted in a deformation‐DIA at confining pressures of 5 to 9 GPa and temperatures of 298 to 1473 K. These results demonstrate that, under conditions in which low‐temperature plasticity is the dominant deformation mechanism, fine‐grained samples are stronger at yield than coarse‐grained samples, and the yield stress decreases with increasing temperature. All samples exhibited significant strain hardening until an approximately constant flow stress was reached. The magnitude of the increase in stress from the yield stress to the flow stress was independent of grain size and temperature. Cyclical loading experiments revealed a Bauschinger effect, wherein the initial yield strength is higher than the yield strength during subsequent cycles. Both strain hardening and the Bauschinger effect are interpreted to result from the development of back stresses associated with long‐range dislocation interactions. We calibrated a constitutive model based on these observations, and extrapolation of the model to geological conditions predicts that the strength of the lithosphere at yield is low compared to previous experimental predictions but increases significantly with increasing strain. Our results resolve apparent discrepancies in recent observational estimates of the strength of the oceanic lithosphere.

48 citations


Cites methods from "A Pragmatic Introduction to Signal ..."

  • ...Peaks were chosen using the peakfit function for MATLAB® (O'Haver, 2018, pp. 340–358), fitting the (130) peak with a single Gaussian and the doublet containing the (112) and (131) peaks with two equal‐width Gaussians....

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  • ...Best fit values for all parameters aside from γ were obtained using a nonlinear optimization routine in the MATLAB® optimization toolbox to minimize the misfit between measured and predicted stresses....

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Journal ArticleDOI
TL;DR: An inertial measurement unit (IMU)-based method for automated assessment of squat quality is proposed to provide clinicians with a quantitative measure of SLS performance and showed that both flexion at the hip and knee, as well as hip and ankle internal rotation are discriminative features.
Abstract: Many clinical assessment protocols of the lower limb rely on the evaluation of functional movement tests such as the single leg squat (SLS), which are often assessed visually. Visual assessment is subjective and depends on the experience of the clinician. In this paper, an inertial measurement unit (IMU)-based method for automated assessment of squat quality is proposed to provide clinicians with a quantitative measure of SLS performance. A set of three IMUs was used to estimate the joint angles, velocities, and accelerations of the squatting leg. Statistical time domain features were generated from these measurements. The most informative features were used for classifier training. A data set of SLS performed by healthy participants was collected and labeled by three expert clinical raters using two different labeling criteria: “observed amount of knee valgus” and “overall risk of injury”. The results showed that both flexion at the hip and knee, as well as hip and ankle internal rotation are discriminative features, and that participants with “poor” squats bend the hip and knee less than those with better squat performance. Furthermore, improved classification performance is achieved for females by training separate classifiers stratified by gender. Classification results showed excellent accuracy, 95.7 % for classifying squat quality as “poor” or “good” and 94.6% for differentiating between high and no risk of injury.

42 citations


Cites methods from "A Pragmatic Introduction to Signal ..."

  • ...For segmentation, a peak detection method developed by [17] was applied to the knee flexion angle....

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References
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Book
01 Jan 1997
TL;DR: Getting Started with DSPs 30: Complex Numbers 31: The Complex Fourier Transform 32: The Laplace Transform 33: The z-Transform Chapter 27 Data Compression / JPEG (Transform Compression)
Abstract: In early 1980s, DSP was taught as a graduate level course in electrical engineering. A decade later, DSP had become a standart part of the ungraduate curriculum.

3,046 citations

Journal ArticleDOI
01 Jun 1995
TL;DR: The mathematics have been worked out in excruciating detail, and wavelet theory is now in the refinement stage, which involves generalizing and extending wavelets, such as in extending wavelet packet techniques.
Abstract: Wavelets were developed independently by mathematicians, quantum physicists, electrical engineers and geologists, but collaborations among these fields during the last decade have led to new and varied applications. What are wavelets, and why might they be useful to you? The fundamental idea behind wavelets is to analyze according to scale. Indeed, some researchers feel that using wavelets means adopting a whole new mind-set or perspective in processing data. Wavelets are functions that satisfy certain mathematical requirements and are used in representing data or other functions. Most of the basic wavelet theory has now been done. The mathematics have been worked out in excruciating detail, and wavelet theory is now in the refinement stage. This involves generalizing and extending wavelets, such as in extending wavelet packet techniques. The future of wavelets lies in the as-yet uncharted territory of applications. Wavelet techniques have not been thoroughly worked out in such applications as practical data analysis, where, for example, discretely sampled time-series data might need to be analyzed. Such applications offer exciting avenues for exploration. >

3,022 citations

Journal ArticleDOI
TL;DR: In this article, the Fourier self-deconvolution (FDS) method was proposed to resolve overlapped lines that can not be instrumentally resolved due to their intrinsic linewidth.
Abstract: The general theory of Fourier self-deconvolution, i.e., spectral deconvolution using Fourier transforms and the intrinsic line-shape, is developed. The method provides a way of computationally resolving overlapped lines that can not be instrumentally resolved due to their intrinsic linewidth. Examples of the application of the technique to synthetic and experimental infrared spectra are presented, and potential applications are discussed. It is shown that lines in spectra having moderate signal/noise ratios (∼1000) can readily be reduced in width by a factor of 3. The method is applicable to a variety of spectroscopic techniques.

1,213 citations

Journal ArticleDOI
TL;DR: Comparing the performance of single spectrum based peak detection methods shows that the continuous wavelet-based algorithm provides the best average performance.
Abstract: In mass spectrometry (MS) based proteomic data analysis, peak detection is an essential step for subsequent analysis Recently, there has been significant progress in the development of various peak detection algorithms However, neither a comprehensive survey nor an experimental comparison of these algorithms is yet available The main objective of this paper is to provide such a survey and to compare the performance of single spectrum based peak detection methods In general, we can decompose a peak detection procedure into three consequent parts: smoothing, baseline correction and peak finding We first categorize existing peak detection algorithms according to the techniques used in different phases Such a categorization reveals the differences and similarities among existing peak detection algorithms Then, we choose five typical peak detection algorithms to conduct a comprehensive experimental study using both simulation data and real MALDI MS data The results of comparison show that the continuous wavelet-based algorithm provides the best average performance

284 citations