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Advanced Sensors and Signal Processing of Sensor Data: Recent Advances and Applications

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

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 108991

Special Issue Editor

Dr. Radek Martinek

E-Mail Website
Guest Editor
VSB—Technical University of Ostrava, Ostrava, Czech Republic
Interests: Smart Sensors; Smart Sensing Technology; Signal Processing of Sensor Data; Digital Signal Processing; Biomedical Signal Processing; Fiber-Optic and Acoustic Sensors; Visible Light Communication; Fetal Monitoring; Virtual Instrumentation; Digital Processing of Speech Signals; Noise Reduction; Software-Defined Radio; Shunt Active Performance Filters; Artificial Intelligence Techniques; Adaptive Filtering; Soft Computing - Artificial Intelligence

Special Issue Information

Dear Colleagues,

This Special Issue of the Sensors journal entitled “Advanced Sensors and Signal Processing of Sensor Data: Recent Advances and Applications” will focus on all aspects of research and development related to these areas. Modern sensor systems use a wide range of techniques to pull the desired signals out of noise and interference. Advanced signal processing methods are slowly spreading to almost every area of research, and this trend tends to accelerate even more, since it is mainly influenced by practical implementations of mentioned algorithms. To develop new signal processing methods, it is necessary to fully cover the theoretical and practical background of both existing and currently explored solutions (e.g., adaptive filtration, hybrid algorithms).

The application of these new methods to smart sensing technologies and/or sensor data signal processing is not thoroughly explored, and there are significant gaps to fill with the help of newly developed technologies.

We invite submissions on a wide range of smart sensing research and signal processing of sensor data, including but not limited to:

  • Fiber-optic and acoustic sensors in the field of civil engineering;
  • Biomedical sensors data processing (ECG, PCG, SCG, BCG, etc.);
  • Fiber-optic and acoustic sensors in the field of road and rail traffic;
  • Intelligent sensor systems for Industry 4.0 and SMART technologies;
  • Fiber-optic and acoustic sensors in the field of seismicity of drilling and blasting operations;
  • Temperature measurement based on optical methods;
  • Visible light communication—transmitters and receivers (channel equalization);
  • Fiber-optics and acoustic sensors in biomedical engineering (measurements in magnetic resonance);
  • Sensor-based signal processing in energetics;
  • Sensory systems for SMART cities.

Original papers that describe new research or innovative approaches are welcome. We look forward to your participation in this Special Issue.

Dr. Radek Martinek
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Smart sensors
  • Smart sensing technology
  • Signal processing of sensor data
  • Digital signal processing
  • Biomedical signal processing
  • Fiber-optic and acoustic sensors

Published Papers (30 papers)

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9 pages, 25357 KiB  
Communication
Pipeline Monitoring Using Highly Sensitive Vibration Sensor Based on Fiber Ring Cavity Laser
by Nageswara Lalam, Ping Lu, Abhishek Venketeswaran and Michael P. Buric
Sensors 2021, 21(6), 2078; https://0-doi-org.brum.beds.ac.uk/10.3390/s21062078 - 16 Mar 2021
Cited by 11 | Viewed by 3792
Abstract
A vibration fiber sensor based on a fiber ring cavity laser and an interferometer based single-mode-multimode-single-mode (SMS) fiber structure is proposed and experimentally demonstrated. The SMS fiber sensor is positioned within the laser cavity, where the ring laser lasing wavelength can be swept [...] Read more.
A vibration fiber sensor based on a fiber ring cavity laser and an interferometer based single-mode-multimode-single-mode (SMS) fiber structure is proposed and experimentally demonstrated. The SMS fiber sensor is positioned within the laser cavity, where the ring laser lasing wavelength can be swept to an optimized wavelength using a simple fiber loop design. To obtain a better signal-to-noise ratio, the ring laser lasing wavelength is tuned to the maximum gain region biasing point of the SMS transmission spectrum. A wide range of vibration frequencies from 10 Hz to 400 kHz are experimentally demonstrated. In addition, the proposed highly sensitive vibration sensor system was deployed in a field-test scenario for pipeline acoustic emission monitoring. An SMS fiber sensor is mounted on an 18” diameter pipeline, and vibrations were induced at different locations using a piezoelectric transducer. The proposed method was shown to be capable of real-time pipeline vibration monitoring. Full article
(This article belongs to the Special Issue Advanced Sensors and Signal Processing of Sensor Data: Recent Advances and Applications)
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21 pages, 4258 KiB  
Article
An Improved Algorithm for Measuring Nitrate Concentrations in Seawater Based on Deep-Ultraviolet Spectrophotometry: A Case Study of the Aoshan Bay Seawater and Western Pacific Seawater
by Xingyue Zhu, Kaixiong Yu, Xiaofan Zhu, Juan Su and Chi Wu
Sensors 2021, 21(3), 965; https://0-doi-org.brum.beds.ac.uk/10.3390/s21030965 - 01 Feb 2021
Cited by 6 | Viewed by 2756
Abstract
Nowadays, it is still a challenge for commercial nitrate sensors to meet the requirement of high accuracy in a complex water. Based on deep-ultraviolet spectral analysis and a regression algorithm, a different measuring method for obtaining the concentration of nitrate in seawater is [...] Read more.
Nowadays, it is still a challenge for commercial nitrate sensors to meet the requirement of high accuracy in a complex water. Based on deep-ultraviolet spectral analysis and a regression algorithm, a different measuring method for obtaining the concentration of nitrate in seawater is proposed in this paper. The system consists of a deuterium lamp, an optical fiber splitter module, a reflection probe, temperature and salinity sensors, and a deep-ultraviolet spectrometer. The regression model based on weighted average kernel partial least squares (WA-KPLS) algorithm together with corrections for temperature and salinity (TSC) is established. After that, the seawater samples from Western Pacific and Aoshan Bay in Qingdao, China with the addition of various nitrate concentrations are studied to verify the reliability and accuracy of the method. The results show that the TSC-WA-KPLS algorithm shows the best results when compared against the multiple linear regression (MLR) and ISUS (in situ ultraviolet spectrophotometer) algorithms in the temperatures range of 4–25 °C, with RMSEP of 0.67 µmol/L for Aoshan Bay seawater and 1.08 µmol/L for Western Pacific seawater. The method proposed in this paper is suitable for measuring the nitrate concentration in seawater with higher accuracy, which could find application in the development of in-situ and real-time nitrate sensors. Full article
(This article belongs to the Special Issue Advanced Sensors and Signal Processing of Sensor Data: Recent Advances and Applications)
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13 pages, 880 KiB  
Article
A Radiometric Technique for Monitoring the Desulfurization Process of Blister Copper
by Alejandro Vásquez, Francisco Pérez, Maximiliano Roa, Ignacio Sanhueza, Hugo Rojas, Victor Parra, Eduardo Balladares, Roberto Parra and Sergio Torres
Sensors 2021, 21(3), 842; https://0-doi-org.brum.beds.ac.uk/10.3390/s21030842 - 27 Jan 2021
Cited by 3 | Viewed by 2445
Abstract
In this paper, a novel optical technique for following the progress of the blister copper desulfurization process is presented. The technique is based on the changes observed in the continuous spectrum of the visible–near-infrared (VIS–NIR) radiation that the blister melt emits while the [...] Read more.
In this paper, a novel optical technique for following the progress of the blister copper desulfurization process is presented. The technique is based on the changes observed in the continuous spectrum of the visible–near-infrared (VIS–NIR) radiation that the blister melt emits while the chemical reactions of the sulfur elimination process are taking place. Specifically, the proposed technique uses an optical probe composed of an optical fiber, a collimating lens, and a quartz tube, which is immersed in the melt. This optical probe provides a field of view of the blowing zone where the desulfurization reaction occurs. The experimental results show that the melt VIS–NIR total irradiance evolves inversely to the SO2 concentration reported by a gas analyzer based on differential optical absorption spectroscopy. Furthermore, the blister copper spectral emissivity as well as the total emissivity observed throughout the process show strong correlation with the sulfur content during desulfurization reaction. Full article
(This article belongs to the Special Issue Advanced Sensors and Signal Processing of Sensor Data: Recent Advances and Applications)
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22 pages, 8118 KiB  
Article
Using an Optimization Algorithm to Detect Hidden Waveforms of Signals
by Yen-Ching Chang and Chin-Chen Chang
Sensors 2021, 21(2), 588; https://0-doi-org.brum.beds.ac.uk/10.3390/s21020588 - 15 Jan 2021
Viewed by 2056
Abstract
Source signals often contain various hidden waveforms, which further provide precious information. Therefore, detecting and capturing these waveforms is very important. For signal decomposition (SD), discrete Fourier transform (DFT) and empirical mode decomposition (EMD) are two main tools. They both can easily decompose [...] Read more.
Source signals often contain various hidden waveforms, which further provide precious information. Therefore, detecting and capturing these waveforms is very important. For signal decomposition (SD), discrete Fourier transform (DFT) and empirical mode decomposition (EMD) are two main tools. They both can easily decompose any source signal into different components. DFT is based on Cosine functions; EMD is based on a collection of intrinsic mode functions (IMFs). With the help of Cosine functions and IMFs respectively, DFT and EMD can extract additional information from sensed signals. However, due to a considerably finite frequency resolution, EMD easily causes frequency mixing. Although DFT has a larger frequency resolution than EMD, its resolution is also finite. To effectively detect and capture hidden waveforms, we use an optimization algorithm, differential evolution (DE), to decompose. The technique is called SD by DE (SDDE). In contrast, SDDE has an infinite frequency resolution, and hence it has the opportunity to exactly decompose. Our proposed SDDE approach is the first tool of directly applying an optimization algorithm to signal decomposition in which the main components of source signals can be determined. For source signals from four combinations of three periodic waves, our experimental results in the absence of noise show that the proposed SDDE approach can exactly or almost exactly determine their corresponding separate components. Even in the presence of white noise, our proposed SDDE approach is still able to determine the main components. However, DFT usually generates spurious main components; EMD cannot decompose well and is easily affected by white noise. According to the superior experimental performance, our proposed SDDE approach can be widely used in the future to explore various signals for more valuable information. Full article
(This article belongs to the Special Issue Advanced Sensors and Signal Processing of Sensor Data: Recent Advances and Applications)
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18 pages, 4862 KiB  
Article
Demodulation Method of F-P Sensor Based on Wavelet Transform and Polarization Low Coherence Interferometry
by Jiwen Cui, Yizhao Niu, Hong Dang, Kunpeng Feng, Xun Sun and Jiubin Tan
Sensors 2020, 20(15), 4249; https://0-doi-org.brum.beds.ac.uk/10.3390/s20154249 - 30 Jul 2020
Cited by 11 | Viewed by 2440
Abstract
Polarized low-coherence interferometry (PLCI) is widely used for the demodulation of Fabry–Perot (F-P) sensors. To avoid the influence of noise and dispersion on interference fringes, this paper proposes a data processing method in which the wavelet tools are applied to extract useful information [...] Read more.
Polarized low-coherence interferometry (PLCI) is widely used for the demodulation of Fabry–Perot (F-P) sensors. To avoid the influence of noise and dispersion on interference fringes, this paper proposes a data processing method in which the wavelet tools are applied to extract useful information from the extremum locations and envelope center of the fringes. Firstly, the wavelet threshold denoising (WTD) algorithm is used to remove electrical noise, and the complex Morlet wavelet is used to extract the fringe envelope. Based on this, the envelope center is used to predict the extremum locations of the specified order in its adjacent interval, the predicted locations are used as references to track the exact extremum locations, and the middle location of the peak and valley values is obtained to demodulate the F-P cavity accurately. The validity of this demodulation theory is verified by an air F-P cavity whose cavity length varies from 17 to 20 μm. With a sampling interval of 30 nm, the experimental results indicate that the repeatability accuracy is higher than 6.04 nm, and the resolution is better than 4.0 nm. Full article
(This article belongs to the Special Issue Advanced Sensors and Signal Processing of Sensor Data: Recent Advances and Applications)
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41 pages, 3949 KiB  
Article
A System for the Detection of Persons in Intelligent Buildings Using Camera Systems—A Comparative Study
by Miroslav Schneider, Zdenek Machacek, Radek Martinek, Jiri Koziorek and Rene Jaros
Sensors 2020, 20(12), 3558; https://0-doi-org.brum.beds.ac.uk/10.3390/s20123558 - 23 Jun 2020
Cited by 7 | Viewed by 2261
Abstract
This article deals with the design and implementation of a prototype of an efficient Low-Cost, Low-Power, Low Complexity–hereinafter (L-CPC) an image recognition system for person detection. The developed and presented methods for processing, analyzing and recognition are designed exactly for inbuilt devices (e.g., [...] Read more.
This article deals with the design and implementation of a prototype of an efficient Low-Cost, Low-Power, Low Complexity–hereinafter (L-CPC) an image recognition system for person detection. The developed and presented methods for processing, analyzing and recognition are designed exactly for inbuilt devices (e.g., motion sensor, identification of property and other specific applications), which will comply with the requirements of intelligent building technologies. The paper describes detection methods using a static background, where, during the search for people, the background image field being compared does not change, and a dynamic background, where the background image field is continually adjusted or complemented by objects merging into the background. The results are compared with the output of the Horn-Schunck algorithm applied using the principle of optical flow. The possible objects detected are subsequently stored and evaluated in the actual algorithm described. The detection results, using the change detection methods, are then evaluated using the Saaty method in order to determine the most successful configuration of the entire detection system. Each of the configurations used was also tested on a video sequence divided into a total of 12 story sections, in which the normal activities of people inside the intelligent building were simulated. Full article
(This article belongs to the Special Issue Advanced Sensors and Signal Processing of Sensor Data: Recent Advances and Applications)
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24 pages, 3335 KiB  
Article
BM-IQE: An Image Quality Evaluator with Block-Matching for Both Real-Life Scenes and Remote Sensing Scenes
by Ningshan Xu, Dongao Ma, Guoqiang Ren and Yongmei Huang
Sensors 2020, 20(12), 3472; https://0-doi-org.brum.beds.ac.uk/10.3390/s20123472 - 19 Jun 2020
Cited by 3 | Viewed by 2574
Abstract
Like natural images, remote sensing scene images; of which the quality represents the imaging performance of the remote sensor, also suffer from the degradation caused by imaging system. However, current methods measuring the imaging performance in engineering applications require for particular image patterns [...] Read more.
Like natural images, remote sensing scene images; of which the quality represents the imaging performance of the remote sensor, also suffer from the degradation caused by imaging system. However, current methods measuring the imaging performance in engineering applications require for particular image patterns and lack generality. Therefore, a more universal approach is demanded to assess the imaging performance of remote sensor without constraints of land cover. Due to the fact that existing general-purpose blind image quality assessment (BIQA) methods cannot obtain satisfying results on remote sensing scene images; in this work, we propose a BIQA model of improved performance for natural images as well as remote sensing scene images namely BM-IQE. We employ a novel block-matching strategy called Structural Similarity Block-Matching (SSIM-BM) to match and group similar image patches. In this way, the potential local information among different patches can get expressed; thus, the validity of natural scene statistics (NSS) feature modeling is enhanced. At the same time, we introduce several features to better characterize and express remote sensing images. The NSS features are extracted from each group and the feature vectors are then fitted to a multivariate Gaussian (MVG) model. This MVG model is therefore used against a reference MVG model learned from a corpus of high-quality natural images to produce a basic quality estimation of each patch (centroid of each group). The further quality estimation of each patch is obtained by weighting averaging of its similar patches’ basic quality estimations. The overall quality score of the test image is then computed through average pooling of the patch estimations. Extensive experiments demonstrate that the proposed BM-IQE method can not only outperforms other BIQA methods on remote sensing scene image datasets but also achieve competitive performance on general-purpose natural image datasets as compared to existing state-of-the-art FR/NR-IQA methods. Full article
(This article belongs to the Special Issue Advanced Sensors and Signal Processing of Sensor Data: Recent Advances and Applications)
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16 pages, 1510 KiB  
Article
MVDT-SI: A Multi-View Double-Triangle Algorithm for Star Identification
by Lijian Sun and Yun Zhou
Sensors 2020, 20(11), 3027; https://0-doi-org.brum.beds.ac.uk/10.3390/s20113027 - 27 May 2020
Cited by 6 | Viewed by 2875
Abstract
Recently, the triangle algorithm has become the most widely used star identification algorithm because of its simplicity and convenience, where the magnitude information plays a key role in the construction of star map features. However, in practice, the magnitude information of the observed [...] Read more.
Recently, the triangle algorithm has become the most widely used star identification algorithm because of its simplicity and convenience, where the magnitude information plays a key role in the construction of star map features. However, in practice, the magnitude information of the observed star map is often difficult to use, because they might contain errors or be lost in some worst cases. To solve this problem, we proposed a multi-view double-triangle algorithm for star identification in this paper. This algorithm constructs double-triangle features of stars with the angle and distance information of star points. Moreover, to reduce the influence of noise interference on the identification accuracy of the model, we built multi-view double-triangle features for the observed star map to improve the robustness of the algorithm. Synthetic and real experiments show that our algorithm has a high identification accuracy of more than 98.4% in face of “false star” noises and “missing star” noises, and our algorithm is not affected by the focal length and the shooting angle of the star sensor. Moreover, the results also show that our algorithm has good robustness, short identification time and reduced storage costs, which could be beneficial in practice. Full article
(This article belongs to the Special Issue Advanced Sensors and Signal Processing of Sensor Data: Recent Advances and Applications)
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31 pages, 11404 KiB  
Article
Design of a Measuring System for Electricity Quality Monitoring within the SMART Street Lighting Test Polygon: Pilot Study on Adaptive Current Control Strategy for Three-Phase Shunt Active Power Filters
by Radek Martinek, Petr Bilik, Jan Baros, Jindrich Brablik, Radana Kahankova, Rene Jaros, Lukas Danys, Jaroslav Rzidky and He Wen
Sensors 2020, 20(6), 1718; https://0-doi-org.brum.beds.ac.uk/10.3390/s20061718 - 19 Mar 2020
Cited by 21 | Viewed by 4030
Abstract
This study focuses on the design of a measuring system for monitoring the power quality within the SMART street lighting test polygon at university campuses with relation to testing an adaptive current control strategy for three-phase shunt active power filters. Unlike conventional street [...] Read more.
This study focuses on the design of a measuring system for monitoring the power quality within the SMART street lighting test polygon at university campuses with relation to testing an adaptive current control strategy for three-phase shunt active power filters. Unlike conventional street lighting, SMART elements are powered 24/7. Due to the electronic character of the power part of such mass appliances, there are increased problems with the power quality of the electric energy. Compared to the current concept of street lighting, there is a significant increase in the content of higher current harmonic components, which cause several problems in the distribution system. The test polygon contains 16 luminaires made by various manufacturers and mounted with various SMART components. Using the polygon control and monitoring system, dynamic load scenarios were selected. These scenarios tested the possibilities of different adaptive current control strategies for three-phase shunt active power filters to improve the power quality of electricity. This study focuses on three adaptive algorithms that respond to dynamic changes of current harmonics level in real-time. The possibility of active filter control was tested using FPGA, mainly due to the low latency of the filter control part. Full article
(This article belongs to the Special Issue Advanced Sensors and Signal Processing of Sensor Data: Recent Advances and Applications)
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9 pages, 2754 KiB  
Article
Sinusoidal Single-Pixel Imaging Based on Fourier Positive–Negative Intensity Correlation
by Ling-Tong Meng, Ping Jia, Hong-Hai Shen, Ming-Jie Sun, Dong Yao, Han-Yu Wang and Chun-Hui Yan
Sensors 2020, 20(6), 1674; https://0-doi-org.brum.beds.ac.uk/10.3390/s20061674 - 17 Mar 2020
Cited by 6 | Viewed by 3193
Abstract
Single-pixel imaging techniques extend the time dimension to reconstruct a target scene in the spatial domain based on single-pixel detectors. Structured light illumination modulates the target scene by utilizing multi-pattern projection, and the reflected or transmitted light is measured by a single-pixel detector [...] Read more.
Single-pixel imaging techniques extend the time dimension to reconstruct a target scene in the spatial domain based on single-pixel detectors. Structured light illumination modulates the target scene by utilizing multi-pattern projection, and the reflected or transmitted light is measured by a single-pixel detector as total intensity. To reduce the imaging time and capture high-quality images with a single-pixel imaging technique, orthogonal patterns have been used instead of random patterns in recent years. The most representative among them are Hadamard patterns and Fourier sinusoidal patterns. Here, we present an alternative Fourier single-pixel imaging technique that can reconstruct high-quality images with an intensity correlation algorithm using acquired Fourier positive–negative images. We use the Fourier matrix to generate sinusoidal and phase-shifting sinusoid-modulated structural illumination patterns, which correspond to Fourier negative imaging and positive imaging, respectively. The proposed technique can obtain two centrosymmetric images in the intermediate imaging course. A high-quality image is reconstructed by applying intensity correlation to the negative and positive images for phase compensation. We performed simulations and experiments, which obtained high-quality images, demonstrating the feasibility of the methods. The proposed technique has the potential to image under sub-sampling conditions. Full article
(This article belongs to the Special Issue Advanced Sensors and Signal Processing of Sensor Data: Recent Advances and Applications)
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23 pages, 924 KiB  
Article
Adaptive Software Defined Equalization Techniques for Indoor Visible Light Communication
by Radek Martinek, Lukas Danys and Rene Jaros
Sensors 2020, 20(6), 1618; https://0-doi-org.brum.beds.ac.uk/10.3390/s20061618 - 14 Mar 2020
Cited by 10 | Viewed by 3738
Abstract
This paper focuses on a channel feed-forward software defined equalization (FSDE) of visible light communication (VLC) multistate quadrature amplitude modulation (M-QAM) based system, implemented in the LabVIEW programming environment. A highly modular platform is introduced; the whole experiment is simulated in software and [...] Read more.
This paper focuses on a channel feed-forward software defined equalization (FSDE) of visible light communication (VLC) multistate quadrature amplitude modulation (M-QAM) based system, implemented in the LabVIEW programming environment. A highly modular platform is introduced; the whole experiment is simulated in software and then thoroughly explored and analyzed during practical measurements in the laboratory, simulating real-world situations. The whole platform is based on modified National Instruments software defined radios (NI SDR) and a commercially available Philips light source, often used in Czech government institutions. Three FSDE algorithms were tested: least mean squares (LMS), normalized least mean squares (NLMS), and QR decomposition based RLS (QR-RLS). Based on measurements, QR-RLS provides the best results, improving measured values by up to 10%. The experiments also show that the simulated results are very similar to real measurements, thus proving the validity of the chosen approach. The whole platform manages to improve measured data simply by making changes to the software side of the testing prototype. Full article
(This article belongs to the Special Issue Advanced Sensors and Signal Processing of Sensor Data: Recent Advances and Applications)
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28 pages, 2917 KiB  
Article
Case Studies on The Use of LiveLink for MATLAB for Evaluation and Optimization of The Heat Sources in Experimental Borehole
by Stepan Ozana, Radovan Hajovsky, Martin Pies and Radek Martinek
Sensors 2020, 20(5), 1297; https://0-doi-org.brum.beds.ac.uk/10.3390/s20051297 - 27 Feb 2020
Cited by 1 | Viewed by 2337
Abstract
In the Czech part of the Upper Silesian Coal Basin (Moravian-Silesian region, Czech Republic), there are many deposits of endogenous combustion (e.g., localized burning soil bodies, landfills containing industrial waste, or slag rocks caused by mining processes). The Hedwig mining dump represents such [...] Read more.
In the Czech part of the Upper Silesian Coal Basin (Moravian-Silesian region, Czech Republic), there are many deposits of endogenous combustion (e.g., localized burning soil bodies, landfills containing industrial waste, or slag rocks caused by mining processes). The Hedwig mining dump represents such an example of these sites where, besides the temperature and the concentrations of toxic gases, electric and non-electric quantities are also monitored within the frame of experimentally proposed and patented technology for heat collection (the so-called “Pershing” system). Based on these quantities, this paper deals with the determination and evaluation of negative heat sources and the optimization of the positive heat source dependent on measured temperatures within evaluation points or on a thermal profile. The optimization problem is defined based on a balance of the heat sources in the steady state while searching for a local minimum of the objective function for the heat source. From an implementation point of view, it is the interconnection of the numerical model of the heat collector in COMSOL with a user optimization algorithm in MATLAB using the LiveLink for MATLAB. The results are elaborated in five case studies based on the susceptibility testing of the numerical model by input data from the evaluation points. The tests were focused on the model behavior in terms of preprocessing for measurement data from each chamber of the heat collector and for the estimated value of temperature differences at 90% and 110% of the nominal value. It turned out that the numerical model is more sensitive to the estimates in comparison with the measured data of the chambers, and this finding does not depend on the type optimization algorithm. The validation of the model by the use of the mean-square error led to the finding of optimal value, also valid with respect to the other evaluation. Full article
(This article belongs to the Special Issue Advanced Sensors and Signal Processing of Sensor Data: Recent Advances and Applications)
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15 pages, 3986 KiB  
Article
On the Detection of Spectral Emissions of Iron Oxides in Combustion Experiments of Pyrite Concentrates
by Carlos Toro, Sergio Torres, Víctor Parra, Rodrigo Fuentes, Rosario Castillo, Walter Díaz, Gonzalo Reyes, Eduardo Balladares and Roberto Parra
Sensors 2020, 20(5), 1284; https://0-doi-org.brum.beds.ac.uk/10.3390/s20051284 - 27 Feb 2020
Cited by 16 | Viewed by 3259
Abstract
In this paper, we report on the spectral detection of wustite, Fe(II) oxide (FeO), and magnetite, Fe(II, III) oxide (Fe3O4), molecular emissions during the combustion of pyrite (FeS2), in a laboratory-scale furnace operating at high temperatures. These [...] Read more.
In this paper, we report on the spectral detection of wustite, Fe(II) oxide (FeO), and magnetite, Fe(II, III) oxide (Fe3O4), molecular emissions during the combustion of pyrite (FeS2), in a laboratory-scale furnace operating at high temperatures. These species are typically generated by reactions occurring during the combustion (oxidation) of this iron sulfide mineral. Two detection schemes are addressed: the first consisting of measurements with a built-in developed spectrometer with a high sensitivity and a high spectral resolution. The second one consisting of spectra measured with a low spectral resolution and a low sensitivity commercial spectrometer, but enhanced and analyzed with post signal processing and multivariate data analysis such as principal component analysis (PCA) and a multivariate curve resolution—the alternating least squares method (MCR-ALS). A non-linear model is also proposed to reconstruct spectral signals measured during pyrite combustion. Different combustion conditions were studied to evaluate the capacity of the detection schemes to follow the spectral emissions of iron oxides. The results show a direct correlation between FeO and Fe3O4 spectral features intensity, and non-linear relations with key combustion variables such as flame temperature, and the combusted sulfide mineral particle size. Full article
(This article belongs to the Special Issue Advanced Sensors and Signal Processing of Sensor Data: Recent Advances and Applications)
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13 pages, 6563 KiB  
Article
Online Correction Method for the Registration Error between TSMFTIS Detector and Interferogram
by Jun Cao, Yan Yuan, Lijuan Su, Conghui Zhu and Qiangqiang Yan
Sensors 2020, 20(4), 1195; https://0-doi-org.brum.beds.ac.uk/10.3390/s20041195 - 21 Feb 2020
Cited by 2 | Viewed by 2148
Abstract
Temporally-spatially modulated Fourier transform imaging spectrometers (TSMFTISs) provide high-throughout-type push-broom spectrometry with both temporal and spatial modulation features. The system requires strict registration between the detector and the interferogram. However, registration errors are unavoidable and directly change the corresponding optical path difference values [...] Read more.
Temporally-spatially modulated Fourier transform imaging spectrometers (TSMFTISs) provide high-throughout-type push-broom spectrometry with both temporal and spatial modulation features. The system requires strict registration between the detector and the interferogram. However, registration errors are unavoidable and directly change the corresponding optical path difference values of the interferogram. As a result, the interferogram should be corrected before restoring the spectrum. In order to obtain the correct optical path difference (OPD) values, an online registration error correction method based on robust least-square linear fitting is presented. The model of the registration error was constructed to analyze its effect on the reconstructed spectra. Fitting methods were used to obtain correct optical path difference information. Simulations based on the proposed method were performed to determine the influence of the registration error on the restored spectra and the effectiveness of the proposed correction method. The simulation results prove that the accuracy of the recovered spectrum can be improved after correcting the interferogram deviation caused by the registration error. The experimental data were also corrected using the proposed methods. Full article
(This article belongs to the Special Issue Advanced Sensors and Signal Processing of Sensor Data: Recent Advances and Applications)
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26 pages, 6272 KiB  
Article
Improved ORB Algorithm Using Three-Patch Method and Local Gray Difference
by Chaoqun Ma, Xiaoguang Hu, Jin Xiao, Huanchao Du and Guofeng Zhang
Sensors 2020, 20(4), 975; https://0-doi-org.brum.beds.ac.uk/10.3390/s20040975 - 12 Feb 2020
Cited by 22 | Viewed by 4802
Abstract
This paper presents an improved Oriented Features from Accelerated Segment Test (FAST) and Rotated BRIEF (ORB) algorithm named ORB using three-patch and local gray difference (ORB-TPLGD). ORB takes a breakthrough in real-time aspect. However, subtle changes of the image may greatly affect its [...] Read more.
This paper presents an improved Oriented Features from Accelerated Segment Test (FAST) and Rotated BRIEF (ORB) algorithm named ORB using three-patch and local gray difference (ORB-TPLGD). ORB takes a breakthrough in real-time aspect. However, subtle changes of the image may greatly affect its final binary description. In this paper, the feature description generation is focused. On one hand, instead of pixel patch pairs comparison method used in present ORB algorithm, a three-pixel patch group comparison method is adopted to generate the binary string. In each group, the gray value of the main patch is compared with that of the other two companion patches to determine the corresponding bit of the binary description. On the other hand, the present ORB algorithm simply uses the gray size comparison between pixel patch pairs, while ignoring the information of the gray difference value. In this paper, another binary string based on the gray difference information mentioned above is generated. Finally, the feature fusion method is adopted to combine the binary strings generated in the above two steps to generate a new feature description. Experiment results indicate that our improved ORB algorithm can achieve greater performance than ORB and some other related algorithms. Full article
(This article belongs to the Special Issue Advanced Sensors and Signal Processing of Sensor Data: Recent Advances and Applications)
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19 pages, 6919 KiB  
Article
Comparison of Smoothing Filters in Analysis of EEG Data for the Medical Diagnostics Purposes
by Aleksandra Kawala-Sterniuk, Michal Podpora, Mariusz Pelc, Monika Blaszczyszyn, Edward Jacek Gorzelanczyk, Radek Martinek and Stepan Ozana
Sensors 2020, 20(3), 807; https://0-doi-org.brum.beds.ac.uk/10.3390/s20030807 - 02 Feb 2020
Cited by 54 | Viewed by 6370
Abstract
This paper covers a brief review of both the advantages and disadvantages of the implementation of various smoothing filters in the analysis of electroencephalography (EEG) data for the purpose of potential medical diagnostics. The EEG data are very prone to the occurrence of [...] Read more.
This paper covers a brief review of both the advantages and disadvantages of the implementation of various smoothing filters in the analysis of electroencephalography (EEG) data for the purpose of potential medical diagnostics. The EEG data are very prone to the occurrence of various internal and external artifacts and signal distortions. In this paper, three types of smoothing filters were compared: smooth filter, median filter and Savitzky–Golay filter. The authors of this paper compared those filters and proved their usefulness, as they made the analyzed data more legible for diagnostic purposes. The obtained results were promising, however, the studies on finding perfect filtering methods are still in progress. Full article
(This article belongs to the Special Issue Advanced Sensors and Signal Processing of Sensor Data: Recent Advances and Applications)
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24 pages, 1716 KiB  
Article
Detection of Atrial Fibrillation Episodes in Long-Term Heart Rhythm Signals Using a Support Vector Machine
by Robert Czabanski, Krzysztof Horoba, Janusz Wrobel, Adam Matonia, Radek Martinek, Tomasz Kupka, Michal Jezewski, Radana Kahankova, Janusz Jezewski and Jacek M. Leski
Sensors 2020, 20(3), 765; https://0-doi-org.brum.beds.ac.uk/10.3390/s20030765 - 30 Jan 2020
Cited by 46 | Viewed by 10170
Abstract
Atrial fibrillation (AF) is a serious heart arrhythmia leading to a significant increase of the risk for occurrence of ischemic stroke. Clinically, the AF episode is recognized in an electrocardiogram. However, detection of asymptomatic AF, which requires a long-term monitoring, is more efficient [...] Read more.
Atrial fibrillation (AF) is a serious heart arrhythmia leading to a significant increase of the risk for occurrence of ischemic stroke. Clinically, the AF episode is recognized in an electrocardiogram. However, detection of asymptomatic AF, which requires a long-term monitoring, is more efficient when based on irregularity of beat-to-beat intervals estimated by the heart rate (HR) features. Automated classification of heartbeats into AF and non-AF by means of the Lagrangian Support Vector Machine has been proposed. The classifier input vector consisted of sixteen features, including four coefficients very sensitive to beat-to-beat heart changes, taken from the fetal heart rate analysis in perinatal medicine. Effectiveness of the proposed classifier has been verified on the MIT-BIH Atrial Fibrillation Database. Designing of the LSVM classifier using very large number of feature vectors requires extreme computational efforts. Therefore, an original approach has been proposed to determine a training set of the smallest possible size that still would guarantee a high quality of AF detection. It enables to obtain satisfactory results using only 1.39% of all heartbeats as the training data. Post-processing stage based on aggregation of classified heartbeats into AF episodes has been applied to provide more reliable information on patient risk. Results obtained during the testing phase showed the sensitivity of 98.94%, positive predictive value of 98.39%, and classification accuracy of 98.86%. Full article
(This article belongs to the Special Issue Advanced Sensors and Signal Processing of Sensor Data: Recent Advances and Applications)
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10 pages, 5215 KiB  
Article
Analysis of Vibration and Acoustic Signals for Noncontact Measurement of Engine Rotation Speed
by Xuansheng Shan, Lu Tang, He Wen, Radek Martinek and Janusz Smulko
Sensors 2020, 20(3), 683; https://0-doi-org.brum.beds.ac.uk/10.3390/s20030683 - 26 Jan 2020
Cited by 10 | Viewed by 3305
Abstract
The non-contact measurement of engine speed can be realized by analyzing engine vibration frequency. However, the vibration signal is distorted by harmonics and noise in the measurement. This paper presents a novel method for the measurement of engine rotation speed by using the [...] Read more.
The non-contact measurement of engine speed can be realized by analyzing engine vibration frequency. However, the vibration signal is distorted by harmonics and noise in the measurement. This paper presents a novel method for the measurement of engine rotation speed by using the cross-correlation of vibration and acoustic signals. This method can enhance the same frequency components in engine vibration and acoustic signal. After cross-correlation processing, the energy centrobaric correction method is applied to estimate the accurate frequency of the engine’s vibration. This method can be implemented with a low-cost embedded system estimating the cross-correlation. Test results showed that this method outperformed the traditional vibration-based measurement method. Full article
(This article belongs to the Special Issue Advanced Sensors and Signal Processing of Sensor Data: Recent Advances and Applications)
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19 pages, 1999 KiB  
Article
Predictive Maintenance of Boiler Feed Water Pumps Using SCADA Data
by Marek Moleda, Alina Momot and Dariusz Mrozek
Sensors 2020, 20(2), 571; https://0-doi-org.brum.beds.ac.uk/10.3390/s20020571 - 20 Jan 2020
Cited by 29 | Viewed by 10742
Abstract
IoT enabled predictive maintenance allows companies in the energy sector to identify potential problems in the production devices far before the failure occurs. In this paper, we propose a method for early detection of faults in boiler feed pumps using existing measurements currently [...] Read more.
IoT enabled predictive maintenance allows companies in the energy sector to identify potential problems in the production devices far before the failure occurs. In this paper, we propose a method for early detection of faults in boiler feed pumps using existing measurements currently captured by control devices. In the experimental part, we work on real measurement data and events from a coal fired power plant. The main research objective is to implement a model that detects deviations from the normal operation state based on regression and to check which events or failures can be detected by it. The presented technique allows the creation of a predictive system working on the basis of the available data with a minimal requirement of expert knowledge, in particular the knowledge related to the categorization of failures and the exact time of their occurrence, which is sometimes difficult to identify. The paper shows that with modern technologies, such as the Internet of Things, big data, and cloud computing, it is possible to integrate automation systems, designed in the past only to control the production process, with IT systems that make all processes more efficient through the use of advanced analytic tools. Full article
(This article belongs to the Special Issue Advanced Sensors and Signal Processing of Sensor Data: Recent Advances and Applications)
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Graphical abstract

15 pages, 2764 KiB  
Article
A Weather Forecast Model Accuracy Analysis and ECMWF Enhancement Proposal by Neural Network
by Jaroslav Frnda, Marek Durica, Jan Nedoma, Stanislav Zabka, Radek Martinek and Michal Kostelansky
Sensors 2019, 19(23), 5144; https://0-doi-org.brum.beds.ac.uk/10.3390/s19235144 - 24 Nov 2019
Cited by 19 | Viewed by 5226
Abstract
This paper presents a neural network approach for weather forecast improvement. Predicted parameters, such as air temperature or precipitation, play a crucial role not only in the transportation sector but they also influence people’s everyday activities. Numerical weather models require real measured data [...] Read more.
This paper presents a neural network approach for weather forecast improvement. Predicted parameters, such as air temperature or precipitation, play a crucial role not only in the transportation sector but they also influence people’s everyday activities. Numerical weather models require real measured data for the correct forecast run. This data is obtained from automatic weather stations by intelligent sensors. Sensor data collection and its processing is a necessity for finding the optimal weather conditions estimation. The European Centre for Medium-Range Weather Forecasts (ECMWF) model serves as the main base for medium-range predictions among the European countries. This model is capable of providing forecast up to 10 days with horizontal resolution of 9 km. Although ECMWF is currently the global weather system with the highest horizontal resolution, this resolution is still two times worse than the one offered by limited area (regional) numeric models (e.g., ALADIN that is used in many European and north African countries). They use global forecasting model and sensor-based weather monitoring network as the input parameters (global atmospheric situation at regional model geographic boundaries, description of atmospheric condition in numerical form), and because the analysed area is much smaller (typically one country), computing power allows them to use even higher resolution for key meteorological parameters prediction. However, the forecast data obtained from regional models are available only for a specific country, and end-users cannot find them all in one place. Furthermore, not all members provide open access to these data. Since the ECMWF model is commercial, several web services offer it free of charge. Additionally, because this model delivers forecast prediction for the whole of Europe (and for the whole world, too), this attitude is more user-friendly and attractive for potential customers. Therefore, the proposed novel hybrid method based on machine learning is capable of increasing ECMWF forecast outputs accuracy to the same level as limited area models provide, and it can deliver a more accurate forecast in real-time. Full article
(This article belongs to the Special Issue Advanced Sensors and Signal Processing of Sensor Data: Recent Advances and Applications)
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22 pages, 6889 KiB  
Article
A Laser-Based On-Machine Measuring System for Profile Accuracy of Double-Headed Screw Rotor
by Zhixu Dong, Fangsu Xu, Xingwei Sun and Weijun Liu
Sensors 2019, 19(23), 5059; https://0-doi-org.brum.beds.ac.uk/10.3390/s19235059 - 20 Nov 2019
Cited by 6 | Viewed by 2749
Abstract
Great length, large weight and other factors may cause difficulty in measuring the profile accuracy of the double-headed screw rotor. To solve this problem, an on-machine measuring system based on a laser-displacement sensor (LDS) was designed and implemented in this paper by taking [...] Read more.
Great length, large weight and other factors may cause difficulty in measuring the profile accuracy of the double-headed screw rotor. To solve this problem, an on-machine measuring system based on a laser-displacement sensor (LDS) was designed and implemented in this paper by taking an LXK100 four-axis whirlwind milling machine as the carrier. To improve the measurement accuracy of the system, the generalized variable-structural-element morphological method, polynomial interpolation algorithm and ellipse fitting method were first combined to realize the rapid subpixel centroid extraction from a noise-containing spot image, thus improving the data acquisition accuracy of the LDS, and then the hybrid method was experimentally verified. Next, a wavelet threshold function with high-order differentiability and adaptive wavelet coefficient contractility was constructed based on the hyperbolic tangent function, so as to inhibit the disturbance from random errors and preserve real profile information, and this method was simulated and verified. Subsequently, a smoothing algorithm for point cloud data was proposed based on the Lagrange multiplier method to avoid the defect of the piecewise curve-fitting method, that is, function continuity and differentiability could not be satisfied at piecewise points. Finally, the profile accuracy was calculated in real time according to the data reconstruction result and the machining quality was judged. The measurement experiment of the double-headed screw rotor indicates that the proposed on-machine measuring system can complete the profile accuracy measurement for a screw pitch within 39.7 s with measurement accuracy reaching ±8 μm, and the measurement uncertainties of the major axis, minor axis and screw pitch are 0.72 μm, 0.69 μm and 1.24 μm, respectively. Therefore, the measurement accuracy and efficiency are both remarkably improved. Full article
(This article belongs to the Special Issue Advanced Sensors and Signal Processing of Sensor Data: Recent Advances and Applications)
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16 pages, 3509 KiB  
Article
Calibration Method of Orthogonally Splitting Imaging Pose Sensor Based on KDFcmPUM
by Na Zhao, Changku Sun and Peng Wang
Sensors 2019, 19(22), 4991; https://0-doi-org.brum.beds.ac.uk/10.3390/s19224991 - 16 Nov 2019
Viewed by 1706
Abstract
This paper proposes a partition of unity method (PUM) based on KDFCM (KDFcmPUM) that can be implemented to solve the dense matrix problem that occurs when the radial basis function (RBF) interpolation method deals with a large amount of scattered data. This method [...] Read more.
This paper proposes a partition of unity method (PUM) based on KDFCM (KDFcmPUM) that can be implemented to solve the dense matrix problem that occurs when the radial basis function (RBF) interpolation method deals with a large amount of scattered data. This method introduces a kernel fuzzy clustering algorithm to improve clustering accuracy and achieve the partition of unity. The local compact support RBF is used to construct the weight function, and local expression is obtained from the interpolation of the global RBF. Finally, the global expression is constructed by the weight function and local expression. In this paper, the method is applied to the orthogonally splitting imaging pose sensor to establish the mathematical model and the calibration and test experiments are carried out. The calibration and test accuracy both reached ±0.1 mm, and the number of operations was reduced by 4% at least. The experimental results show that KDFcmPUM is effective. Full article
(This article belongs to the Special Issue Advanced Sensors and Signal Processing of Sensor Data: Recent Advances and Applications)
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15 pages, 2812 KiB  
Article
Edible Gelatin Diagnosis Using Laser-Induced Breakdown Spectroscopy and Partial Least Square Assisted Support Vector Machine
by Hao Zhang, Shun Wang, Dongxian Li, Yanyan Zhang, Jiandong Hu and Ling Wang
Sensors 2019, 19(19), 4225; https://0-doi-org.brum.beds.ac.uk/10.3390/s19194225 - 28 Sep 2019
Cited by 10 | Viewed by 3525
Abstract
Edible gelatin has been widely used as a food additive in the food industry, and illegal adulteration with industrial gelatin will cause serious harm to human health. The present work used laser-induced breakdown spectroscopy (LIBS) coupled with the partial least square–support vector machine [...] Read more.
Edible gelatin has been widely used as a food additive in the food industry, and illegal adulteration with industrial gelatin will cause serious harm to human health. The present work used laser-induced breakdown spectroscopy (LIBS) coupled with the partial least square–support vector machine (PLS-SVM) method for the fast and accurate estimation of edible gelatin adulteration. Gelatin samples with 11 different adulteration ratios were prepared by mixing pure edible gelatin with industrial gelatin, and the LIBS spectra were recorded to analyze their elemental composition differences. The PLS, SVM, and PLS-SVM models were separately built for the prediction of gelatin adulteration ratios, and the hybrid PLS-SVM model yielded a better performance than only the PLS and SVM models. Besides, four different variable selection methods, including competitive adaptive reweighted sampling (CARS), Monte Carlo uninformative variable elimination (MC-UVE), random frog (RF), and principal component analysis (PCA), were adopted to combine with the SVM model for comparative study; the results further demonstrated that the PLS-SVM model was superior to the other SVM models. This study reveals that the hybrid PLS-SVM model, with the advantages of low computational time and high prediction accuracy, can be employed as a preferred method for the accurate estimation of edible gelatin adulteration. Full article
(This article belongs to the Special Issue Advanced Sensors and Signal Processing of Sensor Data: Recent Advances and Applications)
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18 pages, 8187 KiB  
Article
Restoration Method of a Blurred Star Image for a Star Sensor Under Dynamic Conditions
by Zhiya Mu, Jun Wang, Xin He, Zhonghui Wei, Jiawei He, Lei Zhang, You Lv and Dinglong He
Sensors 2019, 19(19), 4127; https://0-doi-org.brum.beds.ac.uk/10.3390/s19194127 - 24 Sep 2019
Cited by 9 | Viewed by 2569
Abstract
Under the dynamic working conditions of a star sensor, motion blur of the star will appear due to its energy dispersion during imaging, leading to the degradation of the star centroid accuracy and attitude accuracy of the star sensor. To address this, a [...] Read more.
Under the dynamic working conditions of a star sensor, motion blur of the star will appear due to its energy dispersion during imaging, leading to the degradation of the star centroid accuracy and attitude accuracy of the star sensor. To address this, a restoration method of a blurred star image for a star sensor under dynamic conditions is presented in this paper. First, a kinematic model of the star centroid and the degradation function of blurred star image under different conditions are analyzed. Then, an improved curvature filtering method based on energy function is proposed to remove the noise and improve the signal-to-noise ratio of the star image. Finally, the Richardson Lucy algorithm is used and the termination condition of the iterative equation is established by using the star centroid coordinates in three consecutive frames of restored images to ensure the restoration effect of the blurred star image and the accuracy of the star centroid coordinates. Under the dynamic condition of 0~4°/s, the proposed algorithm can effectively improve the signal-to-noise ratio of a blurred star image and maintain an error of the star centroid coordinates that is less than 0.1 pixels, which meets the requirement for high centroid accuracy. Full article
(This article belongs to the Special Issue Advanced Sensors and Signal Processing of Sensor Data: Recent Advances and Applications)
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24 pages, 13092 KiB  
Article
Alternative Approaches to Vibration Measurement Due to the Blasting Operation: A Pilot Study
by Stanislav Kepak, Martin Stolarik, Jan Nedoma, Radek Martinek, Jakub Kolarik and Miroslav Pinka
Sensors 2019, 19(19), 4084; https://0-doi-org.brum.beds.ac.uk/10.3390/s19194084 - 21 Sep 2019
Cited by 6 | Viewed by 3229
Abstract
As the infrastructure grows, space on the surface in the urban area is diminishing, and the view of the builders is increasingly moving underground. Implementation of underground structures, however, presents a number of problems during construction. One of the primary side effects of [...] Read more.
As the infrastructure grows, space on the surface in the urban area is diminishing, and the view of the builders is increasingly moving underground. Implementation of underground structures, however, presents a number of problems during construction. One of the primary side effects of tunnel excavation is vibration. These vibrations need to be monitored for potential damage to structures on the surface, and this monitoring is an integral part of any such structure. This paper brings an original pilot comparative study of standard seismic instrumentation with experimentally developed fiber-optic interferometric and acoustic systems for the purpose of monitoring vibration caused by the blasting operation. The results presented show that systems operating on physical principles (other than those previously used) have the potential to be an alternative that will replace the existing costly seismic equipment. The paper presents waveform images and frequency spectra from experimental measurements of the dynamic response of the rock environment, due to blasting operation performed shallowly during the tunnel excavation of a sewer collector. In the time and frequency domain, there is, by comparison, significant agreement both in the character of the waveform images (recording length, blasting operation timing) and in the spectra (bandwidth, dominant maxima). Full article
(This article belongs to the Special Issue Advanced Sensors and Signal Processing of Sensor Data: Recent Advances and Applications)
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10 pages, 4078 KiB  
Letter
High-Precision Single-Photon Laser Time Transfer with Temperature Drift Post-Compensation
by Wendong Meng, Yurong Wang, Kai Tang, Zhijie Zhang, Shuanggen Jin, Ivan Procházka, Zhongping Zhang and Guang Wu
Sensors 2020, 20(22), 6655; https://0-doi-org.brum.beds.ac.uk/10.3390/s20226655 - 20 Nov 2020
Cited by 4 | Viewed by 1901
Abstract
Laser time transfer is of great significance in timing and global time synchronization. However, the temperature drift may occur and affect the delay of the electronics system, optic generation and detection system. This paper proposes a post-processing method for the compensation of temperature-induced [...] Read more.
Laser time transfer is of great significance in timing and global time synchronization. However, the temperature drift may occur and affect the delay of the electronics system, optic generation and detection system. This paper proposes a post-processing method for the compensation of temperature-induced system delay, which does not require any changes to the hardware setup. The temperature drift and time stability of the whole system are compared with and without compensation. The results show that the propagation delay drift as high as 240 ps caused by temperature changes is compensated. The temperature drift coefficient was diminished down to ~0.05 ps/°C from ~20.0 ps/°C. The system precision was promoted to ~2 ps from ~11 ps over a time period of 80,000 s. This method performs significant compensation of single-photon laser time transfer system propagation drift and will help to establish an ultra-stable laser time transfer link in space applications. Full article
(This article belongs to the Special Issue Advanced Sensors and Signal Processing of Sensor Data: Recent Advances and Applications)
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1 pages, 187 KiB  
Erratum
Erratum: Toro, C. et al. On the Detection of Spectral Emissions of Iron Oxides in Combustion Experiments of Pyrite Concentrates. Sensors 2020, 20, 1284
by Carlos Toro, Sergio Torres, Víctor Parra, Rodrigo Fuentes, Rosario Castillo, Walter Díaz, Gonzalo Reyes, Eduardo Balladares and Roberto Parra
Sensors 2020, 20(21), 6141; https://0-doi-org.brum.beds.ac.uk/10.3390/s20216141 - 28 Oct 2020
Cited by 1 | Viewed by 1181
Abstract
The authors wish to make the following corrections to this paper [...] Full article
(This article belongs to the Special Issue Advanced Sensors and Signal Processing of Sensor Data: Recent Advances and Applications)
12 pages, 2688 KiB  
Letter
Effective Three-Stage Demosaicking Method for RGBW CFA Images Using The Iterative Error-Compensation Based Approach
by Kuo-Liang Chung, Tzu-Hsien Chan and Szu-Ni Chen
Sensors 2020, 20(14), 3908; https://0-doi-org.brum.beds.ac.uk/10.3390/s20143908 - 14 Jul 2020
Cited by 6 | Viewed by 2843
Abstract
As the color filter array (CFA)2.0, the RGBW CFA pattern, in which each CFA pixel contains only one R, G, B, or W color value, provides more luminance information than the Bayer CFA pattern. Demosaicking RGBW CFA images [...] Read more.
As the color filter array (CFA)2.0, the RGBW CFA pattern, in which each CFA pixel contains only one R, G, B, or W color value, provides more luminance information than the Bayer CFA pattern. Demosaicking RGBW CFA images I R G B W is necessary in order to provide high-quality RGB full-color images as the target images for human perception. In this letter, we propose a three-stage demosaicking method for I R G B W . In the first-stage, a cross shape-based color difference approach is proposed in order to interpolate the missing W color pixels in the W color plane of I R G B W . In the second stage, an iterative error compensation-based demosaicking process is proposed to improve the quality of the demosaiced RGB full-color image. In the third stage, taking the input image I R G B W as the ground truth RGBW CFA image, an I R G B W -based refinement process is proposed to refine the quality of the demosaiced image obtained by the second stage. Based on the testing RGBW images that were collected from the Kodak and IMAX datasets, the comprehensive experimental results illustrated that the proposed three-stage demosaicking method achieves substantial quality and perceptual effect improvement relative to the previous method by Hamilton and Compton and the two state-of-the-art methods, Kwan et al.’s pansharpening-based method, and Kwan and Chou’s deep learning-based method. Full article
(This article belongs to the Special Issue Advanced Sensors and Signal Processing of Sensor Data: Recent Advances and Applications)
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12 pages, 2479 KiB  
Letter
Noise-Adaptive Visible Light Communications Receiver for Automotive Applications: A Step Toward Self-Awareness
by Alin-Mihai Căilean, Mihai Dimian and Valentin Popa
Sensors 2020, 20(13), 3764; https://0-doi-org.brum.beds.ac.uk/10.3390/s20133764 - 05 Jul 2020
Cited by 18 | Viewed by 3281
Abstract
Visible light communications are considered as a promising solution for inter-vehicle communications, which in turn can significantly enhance the traffic safety and efficiency. However, the vehicular visible light communications (VLC) channel is highly dynamic, very unpredictable, and subject to many noise sources. Enhancing [...] Read more.
Visible light communications are considered as a promising solution for inter-vehicle communications, which in turn can significantly enhance the traffic safety and efficiency. However, the vehicular visible light communications (VLC) channel is highly dynamic, very unpredictable, and subject to many noise sources. Enhancing VLC systems with self-aware capabilities would maximize the communication performances and efficiency, whatever the environmental conditions. Within this context, this letter proposes a novel signal to noise ratio (SNR)-adaptive visible light communication receiver architecture aimed for automotive applications. The novelty of this letter comes from an open loop signal processing technique in which the signal treatment complexity is established based on a real-time SNR analysis. So, the receiver evaluates the SNR, and based on this assessment, it reconfigures its structural design in order to ensure a proper signal treatment, while providing an optimal tradeoff between communication performances and computational resources usage. This approach based on software reconfiguration has the potential to provide the system with enhanced flexibility and enables its usage in resource sharing application. As far as we know, this approach has not been considered in vehicular VLC systems. The performances of the proposed architecture are demonstrated by simulations, which confirm the SNR-adaptive capacity and the optimized performances. Full article
(This article belongs to the Special Issue Advanced Sensors and Signal Processing of Sensor Data: Recent Advances and Applications)
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12 pages, 4028 KiB  
Letter
Simultaneous Measurement of 6DOF Motion Errors of Linear Guides of CNC Machine Tools Using Different Modes
by Peizhi Jia, Bin Zhang, Qibo Feng and Fajia Zheng
Sensors 2020, 20(12), 3439; https://0-doi-org.brum.beds.ac.uk/10.3390/s20123439 - 18 Jun 2020
Cited by 12 | Viewed by 2763
Abstract
Based on the prior work on the six degrees of freedom (6DOF) motion errors measurement system for linear axes, and for the different types of machine tools and different installation methods, this study used a ray tracing idea to establish the measurement models [...] Read more.
Based on the prior work on the six degrees of freedom (6DOF) motion errors measurement system for linear axes, and for the different types of machine tools and different installation methods, this study used a ray tracing idea to establish the measurement models for two different measurement modes: (1) the measurement head is fixed and the target mirror moves and (2) the target mirror is fixed and the measurement head moves. Several experiments were performed on the same linear guide using two different measurement modes. The comparative experiments show that the two measurement modes and their corresponding measurement models are correct and effective. In the actual measurement process, it is therefore possible to select the corresponding measurement model according to the measurement mode. Furthermore, the correct motion error evaluation results can be obtained. Full article
(This article belongs to the Special Issue Advanced Sensors and Signal Processing of Sensor Data: Recent Advances and Applications)
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