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Optical Sensing and Imaging, from UV to THz Range

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (31 January 2019) | Viewed by 72194

Special Issue Editors


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Dipartimento Interateneo di Fisica (Department of Physics) Politecnico di Bari, Via Edoardo Orabona n. 4, 70125 Bari, Italy
Interests: optoacoustic gas sensing; quantum cascade lasers; spectroscopic techniques for real-time device monitoring; thermal modeling of optoelectronic devices
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Optoelectronics and Nanoscale Electronics, Pollard Institute, School of Electronic and Electrical Engineering, University of Leeds, Woodhouse Lane, Leeds LS2 9JT, UK
Interests: optical absorption; semiconductor lasers; mid-infrared and terahertz lasers and detectors; quantum-cascade lasers; infrared and terahertz sensing and imaging, medical sensing and imaging
Special Issues, Collections and Topics in MDPI journals

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Laboratoire de Physico-Chimie de l'Atmosphère, Université du Littoral Côte d'Opale, 189A, Avenue Maurice Schumann 59140 Dunkerque, France
Interests: developments of photonic instrumentation involving high-sensitivity spectroscopic techniques for applied spectroscopy; optical metrology of key atmospheric species (trace gases and aerosols) for applications in atmospheric and environmental sciences; optical parametric laser source generation by frequency conversion and its applications to applied spectroscopy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The recent advance in optical sources and detectors has opened up new opportunities for sensing and imaging techniques and applications. This 2018 Special Issue of the journal, Sensors, entitled “Optical Sensing and Imaging, from UV to THz Range” will focus on all aspects of the research and development related to these areas. Original research papers that focus on the optical sources developments for sensing, the design and experimental verification of new sensors and imaging systems, as well as papers that focus on their field-testing and campaign measurement, are welcome. Both reviews and original research articles will be published. Reviews should provide an up-to-date well-balanced overview of the current state of the art in a particular field and include main results from other groups. We look forward to and welcome your participation in this Special Issue.

Prof. Dr. Vincenzo Spagnolo
Prof. Dr. Dragan Indjin
Prof. Dr. Weidong Chen
Guest Editors

Manuscript Submission Information

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Keywords

  • Optical sources for sensing
  • Sensing mechanisms
  • Gas and liquid sensors
  • Fiber optic sensors
  • Chemical sensors
  • Bio-medical sensors
  • Physical sensors
  • Imaging
  • Spectral imaging
  • Multispectral imaging
  • Chemical imaging
  • Imaging spectroscopy

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Published Papers (12 papers)

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10 pages, 2747 KiB  
Article
Quartz Enhanced Photoacoustic Spectroscopy Based on a Custom Quartz Tuning Fork
by Maxime Duquesnoy, Guillaume Aoust, Jean-Michel Melkonian, Raphaël Lévy, Myriam Raybaut and Antoine Godard
Sensors 2019, 19(6), 1362; https://doi.org/10.3390/s19061362 - 19 Mar 2019
Cited by 43 | Viewed by 4051
Abstract
We have designed and fabricated a custom quartz tuning fork (QTF) with a reduced fundamental frequency; a larger gap between the prongs; and the best quality factor in air at atmospheric conditions ever reported, to our knowledge. Acoustic microresonators have been added to [...] Read more.
We have designed and fabricated a custom quartz tuning fork (QTF) with a reduced fundamental frequency; a larger gap between the prongs; and the best quality factor in air at atmospheric conditions ever reported, to our knowledge. Acoustic microresonators have been added to the QTF in order to enhance the sensor sensitivity. We demonstrate a normalized noise equivalent absorption (NNEA) of 3.7 × 10−9 W.cm−1.Hz−1/2 for CO2 detection at atmospheric pressure. The influence of the inner diameter and length of the microresonators has been studied, as well as the penetration depth between the QTF’s prongs. We investigated the acoustic isolation of our system and measured the Allan deviation of the sensor. Full article
(This article belongs to the Special Issue Optical Sensing and Imaging, from UV to THz Range)
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Figure 1

Figure 1
<p>(<b>a</b>) Picture of the custom quartz tuning fork used for our experiment. The quartz tuning fork (QTF) presents a 2 mm gap between the prongs. (<b>b</b>) Picture of a commercial fork.</p>
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<p>(<b>a</b>) Acoustic radiation of our QTF simulated by finite element modeling (FEM). The QTF was excited by applying a pressure force on the QTF prongs. (<b>b</b>) Principle scheme of acoustic recovery—firstly, emission of acoustic waves by the QTF; and secondly, reflection thanks to the acoustic recovery device.</p>
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<p>(<b>a</b>) Picture of the QTF within its acoustic cavity: the microresonators for acoustic amplification and the surrounding cylinder for acoustic recovery. (<b>b</b>) Schematic of our QTF used with its acoustic recovery cavity and acoustic microresonators, with a laser beam passing through (in red).</p>
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<p>Normalized sensitivity to the external sound, for the bare C2 QTF (red) and for C2 with its acoustic recovery cavity without microresonators (blue).</p>
Full article ">Figure 5
<p>(<b>a</b>) Quality factor of the system (QTF + microresonators) as a function of the length of the tubes, for two different diameters of the tubes and a penetration depth of the tubes of −0.8 mm with respect to the face of the prongs. (<b>b</b>) Same as (<b>a</b>) for tubes of same length and diameter, depending on the depth of penetration of the tubes within the gap between the QTF’s prongs. The quality factor is measured with an impedance-meter. (<b>c</b>) Schematic of the penetration depth.</p>
Full article ">Figure 6
<p>Calculated absorption spectra of CO<sub>2</sub> (red) and H<sub>2</sub>O (blue) at atmospheric conditions (<span class="html-italic">P</span> = 101,325 Pa, <span class="html-italic">T</span> = 300K) using the HITRAN database.</p>
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<p>Detection scheme for quartz enhanced photoacoustic spectroscopy (QEPAS) CO<sub>2</sub> detection.</p>
Full article ">Figure 8
<p>Allan deviation of the measured absorption of 2.7% of CO<sub>2</sub> at 6490.05 cm<sup>−1</sup>.</p>
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13 pages, 4108 KiB  
Article
Dual-Gas Sensor of CH4/C2H6 Based on Wavelength Modulation Spectroscopy Coupled to a Home-Made Compact Dense-Pattern Multipass Cell
by Xing Tian, Yuan Cao, Jiajin Chen, Kun Liu, Guishi Wang, Tu Tan, Jiaoxu Mei, Weidong Chen and Xiaoming Gao
Sensors 2019, 19(4), 820; https://doi.org/10.3390/s19040820 - 17 Feb 2019
Cited by 30 | Viewed by 5749
Abstract
A sensitive dual-gas sensor for the detection of CH4 and C2H6 is demonstrated. Two tunable semiconductor lasers operating at 1.653 μm (for CH4 monitoring) and 1.684 μm (for C2H6) were used as the light [...] Read more.
A sensitive dual-gas sensor for the detection of CH4 and C2H6 is demonstrated. Two tunable semiconductor lasers operating at 1.653 μm (for CH4 monitoring) and 1.684 μm (for C2H6) were used as the light source for spectroscopic measurements of CH4 and C2H6. Long-path absorption in a home-made compact dense-pattern multipass cell (Leff = 29.37 m) was employed, combined with wavelength modulation and second harmonic detection. Simultaneous detection of CH4 and C2H6 was achieved by separated wavelength modulations of the two lasers. Modulation frequencies and amplitudes were optimized for sensitivity detection of CH4 and C2H6 simultaneously. The dual-gas sensor exhibits 1σ detection limits of 1.5 ppbv for CH4 in 140 s averaging time and 100 ppbv for C2H6 in 200 s. Full article
(This article belongs to the Special Issue Optical Sensing and Imaging, from UV to THz Range)
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Figure 1
<p>Schematic diagram of the dual-gas sensor. Laser #1 and #2: lasers for CH<sub>4</sub>/C<sub>2</sub>H<sub>6</sub>; DWBC: single mode standard coupler; CL: collimator; FL: focusing lens; IGA: InGaAs detector.</p>
Full article ">Figure 2
<p>Photograph of the dual-gas sensor associated with a beam pattern (shown with a red laser) on one mirror of a home-made dense-pattern multipass cell.</p>
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<p>Absorption spectrum of ethane near 5937.3 cm<sup>−1</sup>. Left axis: laser current (vs. laser frequency); Right axis: absorbance of ethane in the tuning wavelength range.</p>
Full article ">Figure 4
<p>Absorption spectrum of methane near 6046.95 cm<sup>−1</sup>. Left axis: laser current (vs. laser frequency); Right axis: absorbance of the methane absorption line.</p>
Full article ">Figure 5
<p>The 2f signals of CH<sub>4</sub> and C<sub>2</sub>H<sub>6</sub> vs. the modulation voltage amplitude.</p>
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<p>The 2f signal amplitudes of CH<sub>4</sub> and C<sub>2</sub>H<sub>6</sub> vs. modulation phase.</p>
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<p>The 2f signal amplitudes vs. modulation frequencies for CH<sub>4</sub> (<b>a</b>) and C<sub>2</sub>H<sub>6</sub> (<b>b</b>).</p>
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<p>Second harmonic signals of CH<sub>4</sub> modulated at 6 kHz (<b>a</b>) and C<sub>2</sub>H<sub>6</sub> modulated at 5.8 kHz (<b>b</b>) showing cross-talk induced interference fringes superimposed on the second harmonic signal.</p>
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<p>Comparison of the second harmonic signal amplitudes with two lasers on and only one laser on. (<b>a</b>) Methane detection and (<b>b</b>) ethane detection.</p>
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<p>Baselines of nitrogen measured in the methane (<b>a</b>) and ethane (<b>b</b>) channels.</p>
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<p>(<b>a</b>) 2f signal of 0.6 ppmv CH<sub>4</sub> associated with baseline and (<b>b</b>) 2f signal of 0.6 ppmv CH<sub>4</sub> after subtracting the baseline.</p>
Full article ">Figure 12
<p>Second harmonic signals of CH<sub>4</sub> (<b>a</b>) and C<sub>2</sub>H<sub>6</sub> (<b>b</b>) at different concentrations ranging from 10 ppmv to 100 ppmv.</p>
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<p>2f signal amplitudes vs. gas concentrations of CH<sub>4</sub> (<b>a</b>) and C<sub>2</sub>H<sub>6</sub> (<b>b</b>).</p>
Full article ">Figure 14
<p>Time series measurements of CH<sub>4</sub> (<b>a</b>) and C<sub>2</sub>H<sub>6</sub> (<b>b</b>). Allan deviation plots for the CH<sub>4</sub> (<b>c</b>) and the C<sub>2</sub>H<sub>6</sub> (<b>d</b>) measurements.</p>
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<p>Simultaneous detection of methane and ethane “leakage”.</p>
Full article ">
14 pages, 5538 KiB  
Article
SWIR AOTF Imaging Spectrometer Based on Single-pixel Imaging
by Huijie Zhao, Zefu Xu, Hongzhi Jiang and Guorui Jia
Sensors 2019, 19(2), 390; https://doi.org/10.3390/s19020390 - 18 Jan 2019
Cited by 4 | Viewed by 4123
Abstract
An acousto-optic tunable filter (AOTF) is a new type of mono-wavelength generator, and an AOTF imaging spectrometer can obtain spectral images of interest. However, due to the limitation of AOTF aperture and acceptance angle, the light passing through the AOTF imaging spectrometer is [...] Read more.
An acousto-optic tunable filter (AOTF) is a new type of mono-wavelength generator, and an AOTF imaging spectrometer can obtain spectral images of interest. However, due to the limitation of AOTF aperture and acceptance angle, the light passing through the AOTF imaging spectrometer is weak, especially in the short-wave infrared (SWIR) region. In weak light conditions, the noise of a non-deep cooling mercury cadmium telluride (MCT) detector is high compared to the camera response. Thus, effective spectral images cannot be obtained. In this study, the single-pixel imaging (SPI) technique was applied to the AOTF imaging spectrometer, which can obtain spectral images due to the short-focus lens that collects light into a small area. In our experiment, we proved that the irradiance of a short-focus system is much higher than that of a long-focus system in relation to the AOTF imaging spectrometer. Then, an SPI experimental setup was built to obtain spectral images in which traditional systems cannot obtain. This work provides an efficient way to detect spectral images from 1000 to 2200 nm. Full article
(This article belongs to the Special Issue Optical Sensing and Imaging, from UV to THz Range)
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Figure 1

Figure 1
<p>Principle of an acousto-optic tunable filter (AOTF) device. The frequency of the acoustic wave is changed by the AOTF drive and the wavelength of diffracted light changes together.</p>
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<p>Simplified structure of the traditional short-wave infrared (SWIR) AOTF imaging spectrometer. The focal length of the imaging lens is 73 mm. The AOTF aperture is an aperture stop in the system. The AOTF and the detector are controlled by software. The target object is a checkerboard.</p>
Full article ">Figure 3
<p>Spectral images acquired by the traditional SWIR AOTF imaging spectrometer (the detector is a non-deep cooling mercury cadmium telluride (MCT) focal plane array (FPA)). The curve on the right is the row of data contained in the red line on the left. The data here are the results of 8-bit quantization and 255 is the max number of pixels. The integration times of images (<b>a</b>–<b>c</b>) are 5, 10, and 20 ms, respectively. The target object is a checkerboard, but the signals will be submerged by a dark current no matter which integration time is adopted.</p>
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<p>The setup of an AOTF imaging spectrometer based on single-pixel imaging (SPI). The focal length of the imaging lens was 8 mm. Lens 1 was focused on the digital micromirror device (DMD) and Lens 2 was the collimating lens. The DMD displayed the mask.</p>
Full article ">Figure 5
<p>Process of image transformation. The short-focus lens collected the light into a small area on the focal plane. The focal length of the short-focus lens was 8 mm in this case.</p>
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<p>Original Hadamard mask and its inverse: (<b>a</b>) the original Hadamard mask and (<b>b</b>) the inverse of (<b>a</b>).</p>
Full article ">Figure 7
<p>The experiment setup of the camera response contrast. The cut-off wavelength of the AOTF was 1319 nm and the AOTF aperture was the aperture stop in the experiment. The focal lengths of the lenses were 8 mm and 73 mm.</p>
Full article ">Figure 8
<p>Images of strong light conditions. (<b>a</b>) Image acquired by the 8-mm lens, (<b>b</b>) image acquired by the 73-mm lens, (<b>c</b>) the row of data from the red line shown in (<b>a</b>), (<b>d</b>) the row of data from the red line shown in (<b>b</b>). The data here are the results of 16-bit quantization. For a better display, (<b>a</b>,<b>b</b>) are not shown in the same grayscale. The response of the 8-mm focus system was larger than that of the 73-mm focus system. However, the images from the 8-mm lens had a low resolution.</p>
Full article ">Figure 9
<p>The relationship between the camera response and irradiance.</p>
Full article ">Figure 10
<p>Images of weak light conditions. (<b>a</b>) Image acquired by an 8-mm lens, (<b>b</b>) image acquired by a 73 mm lens, (<b>c</b>) the row of data from the red line shown in (<b>a</b>), (<b>d</b>) the row of data from the red line shown in (<b>b</b>). In weak light conditions, the traditional system was no longer able to obtain a signal.</p>
Full article ">Figure 11
<p>Real conditions of the experimental setup. A polarizer was used to suppress stray light. The lens was focused on the DMD.</p>
Full article ">Figure 12
<p>Different target objects and their image results. The objects in (<b>a</b>–<b>d</b>) correspond to the white circle, oblique stripe, checkerboard, and cut leaf, respectively.</p>
Full article ">Figure 13
<p>Oblique square images acquired by the SWIR AOTF imaging spectrometer on the basis of SPI. (<b>a</b>) High-resolution image, (<b>b</b>) low-resolution image.</p>
Full article ">Figure 14
<p>Images with different bands acquired by the SWIR AOTF imaging spectrometer based on SPI. In the 1465 nm and 1935 nm bands, the leaf exhibited less reflectance because H<sub>2</sub>O occupies an absorption peak in these bands.</p>
Full article ">Figure 15
<p>(<b>a</b>) Real leaf and (<b>c</b>) fake leaf. (<b>b</b>,<b>d</b>) Images acquired by the imaging spectrometer at 1935 nm. The real leaf is dark and the fake leaf is bright.</p>
Full article ">
13 pages, 3807 KiB  
Article
An Optical Fiber Sensor Based on La2O2S:Eu Scintillator for Detecting Ultraviolet Radiation in Real-Time
by Yongji Yan, Xu Zhang, Haopeng Li, Yu Ma, Tianci Xie, Zhuang Qin, Shuangqiang Liu, Weimin Sun and Elfed Lewis
Sensors 2018, 18(11), 3754; https://doi.org/10.3390/s18113754 - 2 Nov 2018
Cited by 15 | Viewed by 4124
Abstract
A novel ultraviolet (UV) optical fiber sensor (UVOFS) based on the scintillating material La2O2S:Eu has been designed, tested, and its performance compared with other scintillating materials and other conventional UV detectors. The UVOFS is based on PMMA (polymethyl methacrylate) [...] Read more.
A novel ultraviolet (UV) optical fiber sensor (UVOFS) based on the scintillating material La2O2S:Eu has been designed, tested, and its performance compared with other scintillating materials and other conventional UV detectors. The UVOFS is based on PMMA (polymethyl methacrylate) optical fiber which includes a scintillating material. Scintillating materials provide a unique opportunity to measure UV light intensity even in the presence of strong electromagnetic interference. Five scintillating materials were compared in order to select the most appropriate one for the UVOFS. The characteristics of the sensor are reported, including a highly linear response to radiation intensity, reproducibility, temperature response, and response time (to pulsed light) based on emission from a UV source (UV fluorescence tube) centered on a wavelength of 308 nm. A direct comparison with the commercially available semiconductor-based UV sensor proves the UVOFS of this investigation shows superior performance in terms of accuracy, long-term reliability, response time and linearity. Full article
(This article belongs to the Special Issue Optical Sensing and Imaging, from UV to THz Range)
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Figure 1
<p>The photosensitive tube schematic diagram.</p>
Full article ">Figure 2
<p>The structure of the UVOFS. (<b>a</b>) the geometry of the UVOFS, (<b>b</b>) the schematic of the optical signal transmission in the sensor’s probe.</p>
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<p>The experimentally measured optical spectrum of the La<sub>2</sub>O<sub>2</sub>S:Tb scintillator based sensor.</p>
Full article ">Figure 4
<p>The spectrum of the CsI:Tl Scintillator with a Gaussian fitting calculation (<b>a</b>) the ultraviolet-exciting (310 nm) photoluminescence spectrum (PL) (<b>b</b>) the X-ray exciting radiation luminescence spectrum (RL) [<a href="#B20-sensors-18-03754" class="html-bibr">20</a>].</p>
Full article ">Figure 5
<p>The emission spectra of the Gd<sub>2</sub>O<sub>2</sub>S (<b>a</b>) doped with Tb (<b>b</b>) doped with Pr.</p>
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<p>The emission spectra of the La<sub>2</sub>O<sub>2</sub>S (<b>a</b>) doped with Tb (<b>b</b>) doped with Eu.</p>
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<p>The experimental devices and method.</p>
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<p>The comparison of the transmitted spectra from UVOFS without the shielding box and with the shielding box under visible light excitation.</p>
Full article ">Figure 9
<p>(<b>a</b>) The structure of the shielding box (<b>b</b>) The shielding cover (<b>c</b>) The structure of the UV radiometer (XINBAO U340B) with the OSRAM’s BPW66C photodiode, UVA + UVB detecting range, 2.5 times/second sampling rate and ±(4%FS + 2DGT) accuracy.</p>
Full article ">Figure 10
<p>The measured relationship between the UV stimulation and response of the UVOFS based on the materials (<b>a</b>) CsI:Tl, (<b>b</b>) Gd<sub>2</sub>O<sub>2</sub>S:Tb, (<b>c</b>) Gd<sub>2</sub>O<sub>2</sub>S:Pr, (<b>d</b>) La<sub>2</sub>O<sub>2</sub>S:Tb, (<b>e</b>) La<sub>2</sub>O<sub>2</sub>S:Eu.</p>
Full article ">Figure 10 Cont.
<p>The measured relationship between the UV stimulation and response of the UVOFS based on the materials (<b>a</b>) CsI:Tl, (<b>b</b>) Gd<sub>2</sub>O<sub>2</sub>S:Tb, (<b>c</b>) Gd<sub>2</sub>O<sub>2</sub>S:Pr, (<b>d</b>) La<sub>2</sub>O<sub>2</sub>S:Tb, (<b>e</b>) La<sub>2</sub>O<sub>2</sub>S:Eu.</p>
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<p>The response of the output intensity of UVOFS versus UV stimulation repeated in 6 cycles to establish the repeatability of the sensor.</p>
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<p>The response of the UVOFS versus temperature.</p>
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<p>The time-resolved output signals from the UVOFS captured using the MPPC detector with the gate time set to 0.1 ms (<b>a</b>) within 0.1 s; (<b>b</b>) within 0.015 s.</p>
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<p>The comparison of the (<b>a</b>) ultraviolet radiometer and (<b>b</b>) UVOFS.</p>
Full article ">
14 pages, 3177 KiB  
Article
Sub-Diffraction Visible Imaging Using Macroscopic Fourier Ptychography and Regularization by Denoising
by Zhixin Li, Desheng Wen, Zongxi Song, Gang Liu, Weikang Zhang and Xin Wei
Sensors 2018, 18(9), 3154; https://doi.org/10.3390/s18093154 - 18 Sep 2018
Cited by 7 | Viewed by 4515
Abstract
Imaging past the diffraction limit is of significance to an optical system. Fourier ptychography (FP) is a novel coherent imaging technique that can achieve this goal and it is widely used in microscopic imaging. Most phase retrieval algorithms for FP reconstruction are based [...] Read more.
Imaging past the diffraction limit is of significance to an optical system. Fourier ptychography (FP) is a novel coherent imaging technique that can achieve this goal and it is widely used in microscopic imaging. Most phase retrieval algorithms for FP reconstruction are based on Gaussian measurements which cannot extend straightforwardly to long range, sub-diffraction imaging setup because of laser speckle noise corruption. In this work, a new FP reconstruction framework is proposed for macroscopic visible imaging. When compared with existing research, the reweighted amplitude flow algorithm is adopted for better signal modeling, and the Regularization by Denoising (RED) scheme is introduced to reduce the effects of speckle. Experiments demonstrate that the proposed method can obtain state-of-the-art recovered results on both visual and quantitative metrics without increasing computation cost, and it is flexible for real imaging applications. Full article
(This article belongs to the Special Issue Optical Sensing and Imaging, from UV to THz Range)
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Figure 1
<p>Transfer functions of the imaging system with incoherent illumination, coherent illumination and macroscopic Fourier ptychography, respectively.</p>
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<p>The imaging process of Fourier ptychography.</p>
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<p>Peak signal to noise ratio (PSNR) for Reweighted Amplitude Flow for Fourier Ptychography (RAFP) algorithm with different weights.</p>
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<p>Block scheme of the simulation experiments.</p>
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<p>Quantitative comparison of the reconstruction results by different methods under Gaussian noise and speckle noise.</p>
Full article ">Figure 5 Cont.
<p>Quantitative comparison of the reconstruction results by different methods under Gaussian noise and speckle noise.</p>
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<p>Visual comparison of the reconstruction results by different methods under Gaussian noise and speckle noise.</p>
Full article ">Figure 6 Cont.
<p>Visual comparison of the reconstruction results by different methods under Gaussian noise and speckle noise.</p>
Full article ">
22 pages, 10933 KiB  
Article
New Digital Plug and Imaging Sensor for a Proton Therapy Monitoring System Based on Positron Emission Tomography
by Nicola D’Ascenzo, Min Gao, Emanuele Antonecchia, Paolo Gnudi, Hsien-Hsin Chen, Fang-Hsin Chen, Ji-Hong Hong, Ing-Tsung Hsiao, Tzu-Chen Yen, Weidong Wang, Daoming Xi, Bo Zhang and Qingguo Xie
Sensors 2018, 18(9), 3006; https://doi.org/10.3390/s18093006 - 8 Sep 2018
Cited by 22 | Viewed by 5286
Abstract
One of the most challenging areas of sensor development for nuclear medicine is the design of proton therapy monitoring systems. Sensors are operated in a high detection rate regime in beam-on conditions. We realized a prototype of a monitoring system for proton therapy [...] Read more.
One of the most challenging areas of sensor development for nuclear medicine is the design of proton therapy monitoring systems. Sensors are operated in a high detection rate regime in beam-on conditions. We realized a prototype of a monitoring system for proton therapy based on the technique of positron emission tomography. We used the Plug and Imaging (P&I) technology in this application. This sensing system includes LYSO/silicon photomultiplier (SiPM) detection elements, fast digital multi voltage threshold (MVT) readout electronics and dedicated image reconstruction algorithms. In this paper, we show that the P&I sensor system has a uniform response and is controllable in the experimental conditions of the proton therapy room. The prototype of PET monitoring device based on the P&I sensor system has an intrinsic experimental spatial resolution of approximately 3 mm (FWHM), obtained operating the prototype both during the beam irradiation and right after it. The count-rate performance of the P&I sensor approaches 5 Mcps and allows the collection of relevant statistics for the nuclide analysis. The measurement of both the half life and the relative abundance of the positron emitters generated in the target volume through irradiation of 10 10 protons in approximately 15 s is performed with 0.5% and 5 % accuracy, respectively. Full article
(This article belongs to the Special Issue Optical Sensing and Imaging, from UV to THz Range)
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Figure 1
<p>The P&amp;I sensor system. A <math display="inline"><semantics> <mrow> <mn>6</mn> <mo>×</mo> <mn>6</mn> </mrow> </semantics></math> array of LYSO crystals with size <math display="inline"><semantics> <mrow> <mn>3.9</mn> <mo>×</mo> <mn>3.9</mn> <mo>×</mo> <mn>20</mn> </mrow> </semantics></math> mm<sup>3</sup> is read out by an array of <math display="inline"><semantics> <mrow> <mn>6</mn> <mo>×</mo> <mn>6</mn> </mrow> </semantics></math> SiPM. The assembled block has a dedicated plug-in system for the readout electronics on the back.</p>
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<p>The P&amp;I sensor system. Two P&amp;I modules are connected to a MVT readout board.</p>
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<p>The P&amp;I sensor system. Schematics of the digital readout electronics based on the MVT concept.</p>
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<p>Different views of the design of the prototype of P&amp;I PET monitoring system for proton beam therapy. The detector modules (flat black panels), the electronic board housing (side structures) and the central positioning support for the alignment of phantoms and detectors are placed on a movable base adaptable to the proton therapy room and fixable to the proton therapy patient’s bed.</p>
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<p>Installation of the prototype of Plug and Imaging sensor PET inside the proton therapy treatment room at the proton therapy center of the Chang Gung Memorial Hospital. The prototype is shown: during the table rotation (<b>a</b>); and in the final position, aligned with the proton beam (<b>b</b>).</p>
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<p>Response of a LYSO/SIPM channel after the detection of a 511 keV photon from a <sup>22</sup>Na source. In (<b>a</b>) the MVT digitized signal (black points) and the fitted function reconstructing the original signal (red line) are shown. The fitted function is integrated within a time window of 200 ns and the distribution of its integral for a small sub-sample of 30,000 events is shown in (<b>b</b>): the photoelectric peak and the Compton continuum are visible in the obtained spectrum.</p>
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<p>System calibration: position (<math display="inline"><semantics> <msub> <mi>ph</mi> <mi>pos</mi> </msub> </semantics></math>) (<b>a</b>) and energy resolution (FWHM/<math display="inline"><semantics> <msub> <mi>ph</mi> <mi>pos</mi> </msub> </semantics></math>) (<b>b</b>) of the 511 keV photoelectric peak measured during a <sup>22</sup>Na calibration run in the 2880 channels.</p>
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<p>2D histogram of the energy resolution (FWHM/<math display="inline"><semantics> <msub> <mi>ph</mi> <mi>pos</mi> </msub> </semantics></math>) obtained in the prototype PET monitoring system for the left (<b>a</b>) and right (<b>b</b>) module with respect to the beam direction.</p>
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<p>Position of the 511 keV photoelectric peak taken at a time distance of ten hours.</p>
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<p>Reconstructed lateral profile at <span class="html-italic">y</span> = 10 cm (<b>a</b>); and one-dimensional longitudinal profile (<b>b</b>) of the positron emitters produced by a 150 MeV proton pencil beam with approximately 2 mm spot-size and 0.3 MeV energy spread within a PMMA target volume.</p>
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<p>Time dependence of the measured coincidence rate. The exponential decay fitting function and the separate components corresponding to the activated elements are shown.</p>
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<p>Histogram of the energy deposited in one of the crystals of the PET monitoring system during beam-on operation before (blue line) and after (filled red) 5 ns time coincidence.</p>
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<p>Reconstructed lateral profile at at <span class="html-italic">y</span> = 10 cm (<b>a</b>); and one-dimensional longitudinal profile (<b>b</b>) of the positron emitters produced by a 150 MeV proton pencil beam with approximately 2 mm spot-size and 0.3 MeV energy spread within a water target volume during beam-on operation.</p>
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9 pages, 3293 KiB  
Article
Quartz-Enhanced Photoacoustic Spectroscopy Sensor with a Small-Gap Quartz Tuning Fork
by Yu-Fei Ma, Yao Tong, Ying He, Jin-Hu Long and Xin Yu
Sensors 2018, 18(7), 2047; https://doi.org/10.3390/s18072047 - 27 Jun 2018
Cited by 16 | Viewed by 4162
Abstract
A highly sensitive quartz-enhanced photoacoustic spectroscopy (QEPAS) sensor based on a custom quartz tuning fork (QTF) with a small-gap of 200 μm was demonstrated. With the help of the finite element modeling (FEM) simulation software COMSOL, the change tendency of the QEPAS signal [...] Read more.
A highly sensitive quartz-enhanced photoacoustic spectroscopy (QEPAS) sensor based on a custom quartz tuning fork (QTF) with a small-gap of 200 μm was demonstrated. With the help of the finite element modeling (FEM) simulation software COMSOL, the change tendency of the QEPAS signal under the influence of the laser beam vertical position and the length of the micro-resonator (mR) were calculated theoretically. Water vapor (H2O) was selected as the target analyte. The experimental results agreed well with those of the simulation, which verified the correctness of the theoretical model. An 11-fold signal enhancement was achieved with the addition of an mR with an optimal length of 5 mm in comparison to the bare QTF. Finally, the H2O-QEPAS sensor, which was based on a small-gap QTF, achieved a minimum detection limit (MDL) of 1.3 ppm, indicating an improvement of the sensor performance when compared to the standard QTF that has a gap of 300 μm. Full article
(This article belongs to the Special Issue Optical Sensing and Imaging, from UV to THz Range)
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<p>The schematic diagram of a quartz tuning fork (QTF), the laser excitation beam, and the generated sound wave.</p>
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<p>Schematic of the quartz-enhanced photoacoustic spectroscopy (QEPAS) sensor system.</p>
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<p>Calculated deformation with various acoustic wave excitation positions (L) for two different QTFs: (<b>a</b>–<b>c</b>) for the QTF with a 200 μm gap; (<b>d</b>–<b>f</b>) for the standard QTF with a 300 μm gap.</p>
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<p>Normalized calculated displacement as a function of vertical height (L) for two different QTFs.</p>
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<p>Calculated displacement with various micro-resonators (mRs) for two different QTFs: (<b>a</b>–<b>c</b>) for the QTF with a 200 μm gap; (<b>d</b>–<b>f</b>) for the standard QTF with a 300 μm gap.</p>
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<p>Calculated normalized displacement as a function of mR length.</p>
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<p>Experimental measured H<sub>2</sub>O-QEPAS signal amplitude as a function of the vertical height of the laser beam.</p>
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<p>Experimental measured H<sub>2</sub>O-QEPAS signal amplitude as a function of modulation depth.</p>
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<p>Experimental measured 2<span class="html-italic">f</span> QEPAS signals using mRs with different lengths</p>
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12 pages, 4402 KiB  
Article
Flash Smelting Copper Concentrates Spectral Emission Measurements
by Luis Arias, Sergio Torres, Carlos Toro, Eduardo Balladares, Roberto Parra, Claudia Loeza, Camilo Villagrán and Pablo Coelho
Sensors 2018, 18(7), 2009; https://doi.org/10.3390/s18072009 - 22 Jun 2018
Cited by 26 | Viewed by 4266
Abstract
In this paper, we report on spectral features emitted by a reaction shaft occurring in flash smelting of copper concentrates containing sulfide copper minerals such as chalcopyrite (CuFeS2), bornite (Cu5FeS4) and pyrite (FeS2). Different combustion [...] Read more.
In this paper, we report on spectral features emitted by a reaction shaft occurring in flash smelting of copper concentrates containing sulfide copper minerals such as chalcopyrite (CuFeS2), bornite (Cu5FeS4) and pyrite (FeS2). Different combustion conditions are addressed, such as sulfur-copper ratio and oxygen excess. Temperature and spectral emissivity features are estimated for each case by using the two wavelength method and radiometric models. The most relevant results have shown an increasing intensity behavior for higher sulfur-copper ratios and oxygen contents, where emissivity is almost constant along the visible spectrum range for all cases, which validates the gray body assumption. CuO and FeO emission line features along the visible spectrum appear to be a sensing alternative for describing the combustion reactions. Full article
(This article belongs to the Special Issue Optical Sensing and Imaging, from UV to THz Range)
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<p>Experimental setup: (<b>a</b>) Experimental setup picture depicting main components; (<b>b</b>) Optical setup of a constructed pinhole to shaft spectra access.</p>
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<p>Emitted spectra during the flash smelting process, for three different copper concentrates with a: (<b>a</b>) S/Cu ratio of 1.07; (<b>b</b>) S/Cu ratio of 1.27 and (<b>c</b>) S/Cu of 1.85.</p>
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<p>Industrial measurement setup: (<b>a</b>) smelter top peephole; (<b>b</b>) steel cooled protecting probe and cooled optical fiber; (<b>c</b>) mounted probe.</p>
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<p>Emitted spectra during the flash smelting process in a flash smelter (real plant): (<b>a</b>) Uncalibrated spectra; (<b>b</b>) Calibrated spectra.</p>
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<p>Mean estimated temperature by using two wavelength method and standard deviation as a function of wavelength interval Δλ.</p>
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<p>Spectral emissivity during the flash smelting process, for three different copper concentrates of a: (<b>a</b>) S/Cu ratio of 1.07; (<b>b</b>) S/Cu ratio of 1.27 and (<b>c</b>) S/Cu of 1.85.</p>
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<p>Spectral emissivity during the industrial flash smelting process, for two measurements.</p>
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17 pages, 4734 KiB  
Article
Filter Selection for Optimizing the Spectral Sensitivity of Broadband Multispectral Cameras Based on Maximum Linear Independence
by Sui-Xian Li
Sensors 2018, 18(5), 1455; https://doi.org/10.3390/s18051455 - 7 May 2018
Cited by 14 | Viewed by 4342
Abstract
Previous research has shown that the effectiveness of selecting filter sets from among a large set of commercial broadband filters by a vector analysis method based on maximum linear independence (MLI). However, the traditional MLI approach is suboptimal due to the need to [...] Read more.
Previous research has shown that the effectiveness of selecting filter sets from among a large set of commercial broadband filters by a vector analysis method based on maximum linear independence (MLI). However, the traditional MLI approach is suboptimal due to the need to predefine the first filter of the selected filter set to be the maximum ℓ2 norm among all available filters. An exhaustive imaging simulation with every single filter serving as the first filter is conducted to investigate the features of the most competent filter set. From the simulation, the characteristics of the most competent filter set are discovered. Besides minimization of the condition number, the geometric features of the best-performed filter set comprise a distinct transmittance peak along the wavelength axis of the first filter, a generally uniform distribution for the peaks of the filters and substantial overlaps of the transmittance curves of the adjacent filters. Therefore, the best-performed filter sets can be recognized intuitively by simple vector analysis and just a few experimental verifications. A practical two-step framework for selecting optimal filter set is recommended, which guarantees a significant enhancement of the performance of the systems. This work should be useful for optimizing the spectral sensitivity of broadband multispectral imaging sensors. Full article
(This article belongs to the Special Issue Optical Sensing and Imaging, from UV to THz Range)
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<p>The data sets used for simulation are visualized above: (<b>a</b>) transmittances of 45 single filters; (<b>b</b>) all 1035 transmittances of single filters and two single filter combinations; (<b>c</b>) camera sensitivity of a Basler 302f camera sensor; (<b>d</b>) CIE standard illuminant D65; (<b>e</b>) The first eigenvectors of spectral data of the Macbeth ColorChecker; (<b>f</b>) CIE standard illuminant A and (<b>g</b>) color display of spectral data cubic of the Macbeth Color Checker.</p>
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<p>The data sets used for simulation are visualized above: (<b>a</b>) transmittances of 45 single filters; (<b>b</b>) all 1035 transmittances of single filters and two single filter combinations; (<b>c</b>) camera sensitivity of a Basler 302f camera sensor; (<b>d</b>) CIE standard illuminant D65; (<b>e</b>) The first eigenvectors of spectral data of the Macbeth ColorChecker; (<b>f</b>) CIE standard illuminant A and (<b>g</b>) color display of spectral data cubic of the Macbeth Color Checker.</p>
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<p>Overall performances of the 45 series of filter sets in terms of GFC, where the upper round marker denotes the maximum value; the middle square denotes the mean; and the lower round denotes the minimum (some of the minimums with negative value less than −2 are not be displayed).</p>
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<p>Overall performances of the 45 series of filter sets for 6 channels in terms of GFC, where the upper round marker denotes the maximum value; the middle square denotes the mean; and the lower round denotes the minimum (some of the minimums with negative value less than −2 are not be displayed).</p>
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<p>Statistics of the top 20% best-performed filter set series in terms of the four indices, PSNR, GFC, MSE and CIEDE2000: (<b>a</b>) frequency and (<b>b</b>) cumulative scores.</p>
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<p>Radar charts of the condition numbers versus the corresponding filter sets in terms of several channel numbers. The radical coordinates denote condition numbers and the angular coordinates denote the No. of the filter sets, therefore the markers denote the position of condition number of the corresponding filter sets. In the radar charts at seven and eight channels, some of the condition numbers cannot be seen because they are too large to be displayed, however only the filter sets with the smaller ones deserve for consideration.</p>
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<p>Transmittance curves of the verified best-performed filter sets under CIE standard illuminant D65 at 4, 5, 6, 7and 8 channels (No. 45, 44 and 38) and the filter set series with maximum <math display="inline"> <semantics> <mrow> <mo> </mo> <msub> <mo>ℓ</mo> <mn>2</mn> </msub> </mrow> </semantics> </math> norm first filter (No. 2), where the first filter of the filter sets is graphed in bold line with solid round markers. The filter sets are selected the same way as the traditional MLI algorithm charted in <a href="#sec2-sensors-18-01455" class="html-sec">Section 2</a> by minimizing condition number at each channel with the designated filter as the first filter of the corresponding filter set. The notation, for example, No. 38 (5 channels) denotes the No. 38 filter set is superior to others when the number of channels is 5, and the No. of the first filter of the filter set is 38.</p>
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<p>Transmittances of the best-performed filter sets, No. 16 (5), under CIE standard illuminant A according to the cumulative scores listed in <a href="#sensors-18-01455-t006" class="html-table">Table 6</a>.</p>
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14 pages, 960 KiB  
Article
Classification and Discrimination of Different Fungal Diseases of Three Infection Levels on Peaches Using Hyperspectral Reflectance Imaging Analysis
by Ye Sun, Kangli Wei, Qiang Liu, Leiqing Pan and Kang Tu
Sensors 2018, 18(4), 1295; https://doi.org/10.3390/s18041295 - 23 Apr 2018
Cited by 40 | Viewed by 7349
Abstract
Peaches are susceptible to infection from several postharvest diseases. In order to control disease and avoid potential health risks, it is important to identify suitable treatments for each disease type. In this study, the spectral and imaging information from hyperspectral reflectance (400~1000 nm) [...] Read more.
Peaches are susceptible to infection from several postharvest diseases. In order to control disease and avoid potential health risks, it is important to identify suitable treatments for each disease type. In this study, the spectral and imaging information from hyperspectral reflectance (400~1000 nm) was used to evaluate and classify three kinds of common peach disease. To reduce the large dimensionality of the hyperspectral imaging, principal component analysis (PCA) was applied to analyse each wavelength image as a whole, and the first principal component was selected to extract the imaging features. A total of 54 parameters were extracted as imaging features for one sample. Three decayed stages (slight, moderate and severe decayed peaches) were considered for classification by deep belief network (DBN) and partial least squares discriminant analysis (PLSDA) in this study. The results showed that the DBN model has better classification results than the classification accuracy of the PLSDA model. The DBN model based on integrated information (494 features) showed the highest classification results for the three diseases, with accuracies of 82.5%, 92.5%, and 100% for slightly-decayed, moderately-decayed and severely-decayed samples, respectively. The successive projections algorithm (SPA) was used to select the optimal features from the integrated information; then, six optimal features were selected from a total of 494 features to establish the simple model. The SPA-PLSDA model showed better results which were more feasible for industrial application. The results showed that the hyperspectral reflectance imaging technique is feasible for detecting different kinds of diseased peaches, especially at the moderately- and severely-decayed levels. Full article
(This article belongs to the Special Issue Optical Sensing and Imaging, from UV to THz Range)
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<p>Flowchart of the data analysis procedures used to classify different fungal diseases. (PCA: principal components analysis; PC1: first principal component image; SPA: successive projections algorithm; RGB: red, blue, and green; HIS: hue, saturation, and lightness; GLCM: gray level co-occurrence matrix; PLSDA: partial least squares discrimination analysis; DBN: deep belief network).</p>
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<p>Average reflectance spectra of three kinds of disease and control group using the entire spectral region from 400 to 1000 nm for (<b>A</b>) different decay stages of all kinds of diseases, (<b>B</b>) different kinds of diseases of all decay stages, (<b>C</b>) slightly-decayed samples of different diseases, (<b>D</b>) moderately-decayed samples, (<b>E</b>) severely-decayed samples.</p>
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<p>RGB images of the three kinds of diseases at each level of decay.</p>
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<p>The common procedure for image processing.</p>
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Review

Jump to: Research

15 pages, 2033 KiB  
Review
Acoustic Detection Module Design of a Quartz-Enhanced Photoacoustic Sensor
by Tingting Wei, Hongpeng Wu, Lei Dong and Frank K. Tittel
Sensors 2019, 19(5), 1093; https://doi.org/10.3390/s19051093 - 4 Mar 2019
Cited by 11 | Viewed by 4463
Abstract
This review aims to discuss the latest advancements of an acoustic detection module (ADM) based on quartz-enhanced photoacoustic spectroscopy (QEPAS). Starting from guidelines for the design of an ADM, the ADM design philosophy is described. This is followed by a review of the [...] Read more.
This review aims to discuss the latest advancements of an acoustic detection module (ADM) based on quartz-enhanced photoacoustic spectroscopy (QEPAS). Starting from guidelines for the design of an ADM, the ADM design philosophy is described. This is followed by a review of the earliest standard quartz tuning fork (QTF)-based ADM for laboratory applications. Subsequently, the design of industrial fiber-coupled and free-space ADMs based on a standard QTF for near-infrared and mid-infrared laser sources respectively are described. Furthermore, an overview of the latest development of a QEPAS ADM employing a custom QTF is reported. Numerous application examples of four QEPAS ADMs are described in order to demonstrate their reliability and robustness. Full article
(This article belongs to the Special Issue Optical Sensing and Imaging, from UV to THz Range)
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<p>Different gas detection techniques are compared in terms of the sensitivity, complexity, and robustness of the sensor. The font sizes stand for their current importance for trace gases detection. DAS: direct absorption spectroscopy, WMAS: wavelength modulation absorption spectroscopy, MPAS: multi-pass absorption spectroscopy, FMS: frequency modulation spectroscopy, ICOS: integrated cavity output spectroscopy, OA-ICOS: off-integrated cavity output spectroscopy axis, CRDS: cavity ring-down spectroscopy, OFCEAS: optical-feedback cavity-enhanced absorption spectroscopy, DCEAS: direct cavity-enhanced absorption spectroscopy and NICE-OHMS: noise-immune cavity-enhanced optical heterodyne molecular spectroscopy.</p>
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<p>Comparison of transmission ranges for different window materials.</p>
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<p>(<b>a</b>) A supporter to hold the AmR; (<b>b</b>) The length L, the thickness T, the width W and the prong spacing g of a standard QTF.</p>
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<p>(<b>a</b>) CAD image of the KF25 vacuum cap; (<b>b</b>) CAD image of the KF25 vacuum base; (<b>c</b>) Photo of the KF25 cap; (<b>d</b>) Photo of the KF25 base.</p>
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<p>(<b>a</b>) CAD image of single-mode fiber-coupled ADM; (<b>b</b>) Photo of single-mode fiber-coupled ADM.</p>
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<p>(<b>a</b>) CAD image of the free space MIR ADM; (<b>b</b>) photo of the top cap with spectrophone; (<b>c</b>) photo of the free space MIR ADM.</p>
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<p>(<b>a</b>) CAD image of the KF40 vacuum cap; (<b>b</b>) CAD image of the KF40 vacuum base; (<b>c</b>) Photo of the KF40 vacuum cap; (<b>d</b>) Photo of the KF40 vacuum base.</p>
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32 pages, 3516 KiB  
Review
Recent Applications of Multispectral Imaging in Seed Phenotyping and Quality Monitoring—An Overview
by Gamal ElMasry, Nasser Mandour, Salim Al-Rejaie, Etienne Belin and David Rousseau
Sensors 2019, 19(5), 1090; https://doi.org/10.3390/s19051090 - 4 Mar 2019
Cited by 132 | Viewed by 18631
Abstract
As a synergistic integration between spectroscopy and imaging technologies, spectral imaging modalities have been emerged to tackle quality evaluation dilemmas by proposing different designs with effective and practical applications in food and agriculture. With the advantage of acquiring spatio-spectral data across a wide [...] Read more.
As a synergistic integration between spectroscopy and imaging technologies, spectral imaging modalities have been emerged to tackle quality evaluation dilemmas by proposing different designs with effective and practical applications in food and agriculture. With the advantage of acquiring spatio-spectral data across a wide range of the electromagnetic spectrum, the state-of-the-art multispectral imaging in tandem with different multivariate chemometric analysis scenarios has been successfully implemented not only for food quality and safety control purposes, but also in dealing with critical research challenges in seed science and technology. This paper will shed some light on the fundamental configuration of the systems and give a birds-eye view of all recent approaches in the acquisition, processing and reproduction of multispectral images for various applications in seed quality assessment and seed phenotyping issues. This review article continues from where earlier review papers stopped but it only focused on fully-operated multispectral imaging systems for quality assessment of different sorts of seeds. Thence, the review comprehensively highlights research attempts devoted to real implementations of only fully-operated multispectral imaging systems and does not consider those ones that just utilized some key wavelengths extracted from hyperspectral data analyses without building independent multispectral imaging systems. This makes this article the first attempt in briefing all published papers in multispectral imaging applications in seed phenotyping and quality monitoring by providing some examples and research results in characterizing physicochemical quality traits, predicting physiological parameters, detection of defect, pest infestation and seed health. Full article
(This article belongs to the Special Issue Optical Sensing and Imaging, from UV to THz Range)
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<p>Schematic representation of a computer-aided image analysis system for seed quality evaluation based on computer-vision and multispectral imaging techniques.</p>
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<p>Schematic representation of three approaches used for constructing spectral image cubes. The blue dashed arrows indicate scanning directions in each approach for sequential acquisitions to complete the volume of spatial and spectral 3D data cube.</p>
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<p>An illumination-based multispectral imaging system supported with an array of 12 LEDs with different nominal emission wavelengths. Each LED is sequentially switched on and synchronized with the camera trigger for image acquisition using a pre-programmed microcontroller.</p>
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<p>Images generated using nCDA algorithm for: (<b>a</b>) 100% <span class="html-italic">T. durum</span> wheat grains; (<b>b</b>) 100% <span class="html-italic">T. aestivum</span> wheat grains; (<b>c</b>) 10% adulteration of <span class="html-italic">T. durum</span> wheat grains with <span class="html-italic">T. aestivum</span> wheat grains [<a href="#B99-sensors-19-01090" class="html-bibr">99</a>].</p>
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<p>Probability images (represented in pseudo-colors) associated with a multispectral image of wheat kernel. High probability percentage indicates the region of the grain from which a given milled product was extracted. CB: coarse bran, SE: pure semolina and BF: break flours (Modified from Jaillais et al. [<a href="#B73-sensors-19-01090" class="html-bibr">73</a>]).</p>
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<p>Classification of castor seeds into three classes based on visual colour of seed coat (yellow, grey and black). (<b>a</b>) RGB images of the intact seeds; (<b>b</b>) transformed images of intact seeds by nCDA; (<b>c</b>) RGB images of cut seeds; (<b>d</b>) transformed images of cut seeds by nCDA and (<b>e</b>) RGB images of cut seeds after immersion in tetrazolium solution (adapted from Olesen et al. [<a href="#B102-sensors-19-01090" class="html-bibr">102</a>]).</p>
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<p>Difference between healthy and infected seeds by black point disease (<span class="html-italic">Alternaria</span> sp.) and <span class="html-italic">Fusarium</span> sp. (<b>A</b>) RGB-captured images, (<b>B</b>) nCDA-transformed images, and (<b>C</b>) RGB images after seed incubation (adapted from Vrešak et al. [<a href="#B81-sensors-19-01090" class="html-bibr">81</a>]).</p>
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<p>Defect detection on maize kernels (<b>a</b>) PCA scores image showing the clear separation between heat damage class and sound class maize, (<b>b</b>) classification image resulting form PLS-DA for discrimination between heat damage class and sound class maize kernels (adapted from Sendin et al. [<a href="#B43-sensors-19-01090" class="html-bibr">43</a>]).</p>
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<p>PCA score-images issued from PCA on whole images for the (<b>a</b>) resistant and (<b>b</b>) susceptible wheat genotypes; and the predicted contamination images resulting from SMLR model in the (<b>c</b>) resistant and (<b>d</b>) susceptible wheat genotypes kernels in which a kernel highly contaminated have a lot of red pixels (adapted from Jaillais et al. [<a href="#B74-sensors-19-01090" class="html-bibr">74</a>]).</p>
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