Single Optical Sensor Research Articles

Harmonized Landsat and Sentinel-2 Data with Google Earth Engine

Continuous and dense time series of satellite remote sensing data are needed for several land monitoring applications, including vegetation phenology, in-season crop assessments, and improving land use and land cover classification. Supporting such applications at medium to high spatial resolution may be challenging with a single optical satellite sensor, as the frequency of good-quality observations can be low. To optimize good-quality data availability, some studies propose harmonized databases. This work aims at developing an ‘all-in-one’ Google Earth Engine (GEE) web-based workflow to produce harmonized surface reflectance data from Landsat-7 (L7) ETM+, Landsat-8 (L8) OLI, and Sentinel-2 (S2) MSI top of atmosphere (TOA) reflectance data. Six major processing steps to generate a new source of near-daily Harmonized Landsat and Sentinel (HLS) reflectance observations at 30 m spatial resolution are proposed and described: band adjustment, atmospheric correction, cloud and cloud shadow masking, view and illumination angle adjustment, co-registration, and reprojection and resampling. The HLS is applied to six equivalent spectral bands, resulting in a surface nadir BRDF-adjusted reflectance (NBAR) time series gridded to a common pixel resolution, map projection, and spatial extent. The spectrally corresponding bands and derived Normalized Difference Vegetation Index (NDVI) were compared, and their sensor differences were quantified by regression analyses. Examples of HLS time series are presented for two potential applications: agricultural and forest phenology. The HLS product is also validated against ground measurements of NDVI, achieving very similar temporal trajectories and magnitude of values (R2 = 0.98). The workflow and script presented in this work may be useful for the scientific community aiming at taking advantage of multi-sensor harmonized time series of optical data.

A novel vision-based multi-functional sensor for normality and position measurements in precise robotic manufacturing

Cobots play an essential role in the fourth industrial revolution and the automation of complex manufacturing processes. However, cobots still face challenges in achieving high precision, which obstructs their usage in precise applications such as the aerospace industry. Nonetheless, advances in perception systems unlock new cobot manufacturing capabilities. This paper presents a novel multi-functional sensor that combines visual and tactile feedback using a single optical sensor, featuring a moving gate mechanism. This work also marks the first integration of Vision-Based Tactile Sensing (VBTS) into a robotic machining end-effector. The sensor provides vision-based tactile perception capabilities for precise normality control and exteroceptive perception for robot localization and positioning. Its performance is experimentally demonstrated in a precise robotic deburring application, where the sensor achieves the high-precision requirements of the aerospace industry with a mean normality error of 0.13° and a mean positioning error of 0.2 mm. These results open a new paradigm for using vision-based sensing for precise robotic manufacturing, which surpasses conventional approaches in terms of precision, weight, size, and cost-effectiveness.

STF-Trans: A two-stream spatiotemporal fusion transformer for very high resolution satellites images

Spatiotemporal satellite image fusion is regarded as an effective approach to address the limitations of a single optical sensor and generate data with high spatial and temporal resolutions. Thanks to the recent advances of Deep Learning, spatiotemporal fusion has gained a significant performance improvement. However, most models require at least three inputs, including a high resolution one at a prior date, to make the prediction at the desired date. Such a requirement is not always guaranteed in practice, in particular due to the high cost of high resolution image and bad atmospheric conditions. In the last few years, Transformers have achieved tremendous performance on several computer vision tasks compared to convolutional neural networks. Inspired by the new trend, we proposed an end-to-end two-stream SpatioTemporal fusion technique based on encoder–decoder transformer architecture, termed STF-Trans. To deal with a complexity and high-cost of data preparation, the proposed approach aims to fuse a coarse-resolution image with high temporal resolution, and a very high resolution Google Earth image, to produce a very high resolution image at the desired date. The proposed technique employs a two-stream convolutional network to capture both temporal and spatial features from the inputs, and then the encoder–decoder transformer captures long-range dependency of the latter and generates a more efficient image representation from the shallow features. Finally, a shallow network maps the features into the desired fused image. The experimental results show that the proposed method outperforms the current state-of-the-art techniques at both quantitative and qualitative evaluations.

Computer vision, machine learning based monocular biomechanical and security analysis

Modern computer vision technologies have served to bridge the gap between contemporary scientific analysis and machine learning assisted digital processing. Within the field of biomechanics, applied strategies incorporating both conventional and machine assisted means have shown great success in augmenting the observations of electromyogram graphical sensors; albeit within the constraints of specialized, multiple-source arrays. The ongoing study represents an endeavor to utilize several distinct technologies to achieve similar results with the application of a single optical sensor. This paper documents the research, development, and implementation of a monocular feature extraction pipeline, designed for the intended use of supplementing modern athletic biomechanical analysis and the security of the data. We will examine the techniques presented by related works, and how we have implemented these strategies into our framework.

Heart rate variability based physical exertion monitoring for manual material handling tasks

Physical exertion monitoring has been strongly emphasized to avert the ill-effects of physically demanding nature of many industries such as construction. Recently, several sensors-based approaches have been suggested as an alternative to traditional subjective feedback-based methods. Although the proposed sensor-based approaches have laid the foundation for automated physical exertion monitoring, they require multiple on-body and/or off-body sensors to collect psychological, physiological, acceleration/posture or weather-related data. As such, multiple on-body sensors may instigate irritation and discomfort whereas other off-body sensors require additional resources for handling and managing them. To address these limitations, taking a minimalistic approach, this study explored the use of heart rate variability (HRV) metrics which could be computed from a single electrocardiogram or optical sensor (often found in fitness wrist bands and smart watches). For this purpose, manual material handling experiments were conducted while state-of-the-art HRV features were used to perform physical exertion monitoring with ensemble classifiers and artificial neural network (ANN) based regression analysis. The results indicate that ensemble classifiers achieved accuracies from 64.2% to 81.2%, depending on the number of levels in which physical exertion data was divided, whereas ANN regression achieved the least root mean square error of 1.651. Given the wide availability of HRV sensors in fitness bands and wrist watches, this study highlights the usability and limitations of HRV based physical exertion monitoring which could help make informed decisions related to its adoption in physically demanding industries such as construction.

Advances in Optical Sensors for Persistent Organic Pollutant Environmental Monitoring.

Optical chemical sensors are widely applied in many fields of modern analytical practice, due to their simplicity in preparation and signal acquisition, low costs, and fast response time. Moreover, the construction of most modern optical sensors requires neither wire connections with the detector nor sophisticated and energy-consuming hardware, enabling wireless sensor development for a fast, in-field and online analysis. In this review, the last five years of progress (from 2017 to 2021) in the field of optical chemical sensors development for persistent organic pollutants (POPs) is provided. The operating mechanisms, the transduction principles and the types of sensing materials employed in single selective optical sensors and in multisensory systems are reviewed. The selected examples of optical sensors applications are reported to demonstrate the benefits and drawbacks of optical chemical sensor use for POPs assessment.

A single three-parameter tilted fibre Bragg grating sensor to monitor the thermosetting composite curing process

The unique sensing features of the tilted Fibre Bragg Grating (TFBG) as a single three-parameter optical sensor are demonstrated in this work, to monitor the manufacturing process of composite materials produced using Vacuum Assisted Resin Transfer Moulding (VARTM) process. Each TFBG sensor can measure simultaneously and separately strain, temperature and refractive index (RI) of the material where the optical fibre is embedded. A TFBG embedded in a 2 mm glass-fibre/epoxy composite plate was used to measure the thermomechanical variations induced during the curing process. At the same time, the RI measurements, performed with the same TFBG sensor, can estimate the degree of cure of the resin. The TFBG sensor shows to be a valid and promising technology to improve the state of art of sensing and monitoring in composite material manufacturing.

Single sensor measurement of heel-height during the push-off phase of gait

Objective. In healthy gait a forceful push-off is needed to get an efficient leg swing and propulsion, and a high heel lift makes a forceful push-off possible. The power of the push-off is decreased with increased age and in persons with impaired balance and gait. The aim of this study was to evaluate whether a wearable equipment (Striton) and algorithms to estimate vertical heel-height during gait from a single optical distance sensor is reliable and feasible for clinical applications. Approach. To assess heel-height with the Striton system an optical distance sensor was used to measure the distance to the floor along the shank. An algorithm was created to transform this measure to a vertical distance. The heel-height was validated in an experimental setup, against a 3D motion capture system (MCS), and test-retest and day-to-day tests were performed on 10 elderly persons. As a reference material 83 elderly persons were included, and heel-height was measured before and after surgery in four patients with the neurological disorder idiopathic normal pressure hydrocephalus (iNPH). Main results. In the experimental setup the accuracy was high with a maximum error of 2% at all distances, target colours and inclination angles, and the correlation to the MCS was R = 0.94. Test-retest and day-to-day tests were equal within ±1.2 cm. Mean heel-height of the elderly persons was 16.5 ± 0.6 cm and in the patients with iNPH heel-height was increased from 11.2 cm at baseline to 15.3 cm after surgery. Significance. Striton can reliably measure heel-height during gait, with low test-retest and day-to-day variability. The system was easy to attach, and simple to use, which makes it suitable for clinical applications.

Dynamic back face deformation measurement with a single optical fibre

A single optical fibre sensor is used to measure the dynamics of an impact. The method consists of sewing the optical fibre onto a woven Kevlar layer and placing it between the shoot pack and backing material. The measurement is accomplished by using the friction between the layer and the optical fibre to relate the optical fibre strain to impact deformation. Tests are done using a backing material of Roma Plastilina No.1 clay, and transparent ballistics gel with independent high-speed imaging. A final calculated BFD average error of 7.75% is presented as well as a timing error of 15.5% between the imaged dynamic BFD and the dynamic BFD determined by the FBG. This method is also tested at the U.S. Army Aberdeen Test Center in Maryland with a final calculated error of 7%.

Single optical sensor to multiple functions: Ratiometric sensing for SO32− and dual signal determination for copper (II)

Polymer dots possess superior emissive features, but most of them give rise to luminescence bands in the blue region. In addition, blue or green emissions have difficulty in penetrating tissue deeply. Therefore, long wavelength emissive signals are welcome for the development and application of polymeric dots towards sensing and bio-analysis. Herein, the color-tunable fluorescence polymer nanoparticles (F-PNPs) have been synthesized via one-step strategy based on the employment of hydroquinone and polyethyleneimine as precursors at low temperature. Moreover, its emission peak can be shifted from 523 nm to 612 nm by varying the excitation wavelength in the range of 380 nm to 480 nm. In view of sensing assessment, F-PNPs enable the quantitative determination of trace amount of SO32− and Cu2+. In the presence of SO32−, the polymer dots exhibit ratiometric fluorescence signals in deionized water and the color change from green to blue has been clearly observed by naked eyes (detection limit = 59 nM). In addition, two emission bands at 545 nm (green) and 601 nm (red) are observed to be responsive to the exposure of Cu2+. The entire dual sensing system for the detection of Cu2+ will be more accurate and reliable. The evaluation results reveal their optical signals are improved linearly due to the addition of Cu2+ at increasing concentrations and the detection limits are calculated to be 76 nM (green) and 41 nM (red), respectively. Such polymeric network will provide a new dynamic platform for sensing purposes in biomedicine study, environmental protection, and food safety.

Development of an optical sensor for measuring opacity changes in polyvinyl toluene scintillators

Polyvinyl toluene (PVT) based detectors are used in radiation portal monitors (RPMs) throughout the world to detect the trafficking of illicit nuclear material. PVT scintillators, which are normally optically clear, have been observed to suffer internal fogging throughout their volume due to prolonged exposure to varying environmental conditions. These changes could lead to reduced performance for RPMs that utilize plastic scintillators. In this research, a proof of concept system, consisting of different color light emitting diodes (LEDs) and a single optical sensor (OS), were used to examine the change in light transmission through a PVT scintillator. This Optical Monitoring System (OMS), coupled with an environmentally exposed PVT detector, was tested in an environmental chamber where it was subjected to changes in temperature and humidity ranging from 55 °C at 100% relative humidity to −20 °C at 40% relative humidity. At temperatures below 10 °C, light transmission was reduced by 81% ± 8% for blue LEDs, 84% ± 5% for yellow LEDs, and 49% ± 4% for green LEDs. Similar reductions in detected light were not recorded when the OMS was tested with only air between the LEDs and OS. Therefore, the significant reductions in transmitted light were attributed to changes occurring within the PVT scintillator. These results indicate that a significant reduction in PVT opacity occurs due to wide environmental changes. A device such as the OMS could be used to track these changes and provide users an early indication that a portal monitor is suffering from reduced performance.

Performance evaluation of a single mode optical fiber tip sensor for glucose detection

<p>Glucose must be carefully regulated to ensure its safety and quality. Nowadays, chemists often face difficulty to measure glucose concentrations as the current methods for the analysis are expensive and require complex procedures which have reduced the interest among chemists to use these techniques. This study evaluated the performance of a single mode optical fiber tip sensor for glucose detection. The sensing mechanism mainly depends on the Fresnel’s reflection that occurs at the flat fiber tip region while the sensor performance was evaluated based on its sensitivity and linearity. The sensor was tested in glucose solution at different concentration range; high range (0% - 14%), medium range (0% - 1.4%) and low range (0% - 0.14%). The sensor showed a good performance when sensing the glucose solution at high and medium concentration range with strong linearity of more than 90% and an average sensitivity of 0.0979 dBm/% and 0.1019 dBm/%, respectively. However, the low concentration range showed no significant changes during sensing. Based on the findings obtained in this study, the optical fiber tip sensor has the potential to be utilized for glucose detection and suitable for high and medium concentration range measurement.</p>

Multisensor temporal approach for transplanted paddy fields mapping using fuzzy-based classifiers

Paddy transplantation rate mapping can provide important information regarding water demand for its transplantation. It is a time-tagged activity that cannot be accurately mapped using a single date image. Further, many times, single sensor data may not have enough temporal resolution required to map it. Furthermore, due to cloud cover, a single optical sensor is not enough to provide required temporal scenes for mapping the transplanted paddy fields. Therefore, to have a finer temporal resolution, a multisensor approach was applied in our research work by integrating an optical sensor data with a synthetic aperture radar data to map transplanted paddy fields at a frequency of 5- to 10-day intervals. In our research, the transplanted paddy fields were mapped as a specific class of interest using suitable fuzzy-based classifiers. The specific fuzzy-based classifiers explored here were modified possibilistic c-mean (MPCM) and noise clustering (NC). For evaluating the results, mean membership difference (MMD) and entropy values were computed within transplanted paddy fields and for noninterest classes. Moreover, an error matrix criterion was also used to validate the results. The computed MMD and entropy values were found to be lower within transplanted paddy fields in comparison to the other classes of noninterest, indicating accurate classification. NC algorithm outperformed the MPCM algorithm while observing MMD and entropy values for assessment. However, using the fuzzy error matrix criterion, the performance of both algorithms was identical with an average overall accuracy and kappa coefficient of 90.88% and 0.82 for MPCM and 90.66% and 0.81 for NC, respectively.

Water soluble porphyrin as optical sensor for the toxic heavy metal ions in an aqueous medium.

Here we report the photophysical and sensing properties of the aqueous solution of meso-tetra(N-methyl-4-pyridyl)porphyrin toluene sulfonate (TMPP) for simultaneous detection of toxic metal ions in an aqueous medium by using different physiochemical methods such as UV-vis absorption, steady state and time resolved fluorescence, stopped flow, and cyclic voltammetry. The steady-state absorption and fluorescence spectra in organic solvents (EtOH, DMSO, DMF, MeOH and ACN) showed the formation of monomer form (λmaxabs=426nm and λmaxflu=654 and 715nm). In THF and water, different spectral features were recorded suggested the formation of aggregated forms in both solvents. The formation of aggregated form in water was confirmed by recording the remarkable fluorescence quenching of the singlet excited TMPP with increasing the concentrations of TMPP. In cationic micelles (CTAB), both the absorption and fluorescence spectra were significantly decreased with increasing the concentrations of CTAB with a break at CMC value at 6.0×10-5M. In an anionic micelle (SDS), the CMC value was found to be 1.0×10-4M. Upon interacting with different metal ions, the absorption and fluorescence spectra of TMPP showed different features depending on the metal ions. While the optical studies of TMPP showed no significant interaction in the presence of Mn+2, Co+2, Ba2+, and Ni+2, TMPP showed that it can function as a single optical chemical sensor for the toxic metal ions in water, particularly Hg+2, Pb+2, Cu+2, and Cd+2 ions.

Non-contact, bi-directional tool tip vibration measurement in CNC milling machines with a single optical sensor

Vibration measurements of tools are significant for machine modelling, error prone state identification and machine operation optimization. In the milling process, forced vibrations are the main factor resulting in shape deviations as well surface roughness of machined work pieces and depend on the spindle speed and tool properties. Numerical models for predicting the dynamic behavior of machine systems can help to reduce vibrations. For an optimization, the model parameters need to be evaluated by simultaneously measuring the vibration of the tool tip in force direction and its vertical direction under different rotational speeds with high spatio-temporal resolution. In this work, a non-contact, single-sensor system and a signal processing algorithm are presented for measuring the vibrational behavior inside of a CNC milling machine with a known force. It enables in situ, simultaneous bi-directional vibration measurements directly at the tool tip with measurement rates up to 50 kHz, a circumferential resolution below 230 μm and a displacement uncertainty down to 40 nm, at rotational speeds up to 300 Hz. The dynamic behavior parameters of the tool are evaluated depending on the measured vibration spectrum with relative uncertainties of 10-2, enabling model optimizations. The measurements show a decrease of the natural frequency with increasing spindle speed. While the laser-based measurement principle does not bias the vibrational behavior it inherently guarantees orthogonality of the sensing axes, as well.

On the equivalence of near-surface methods to determine the water-leaving radiance.

The equivalence of two radiometric methods relying on a single nadir-view optical sensor to determine the water-leaving radiance LW, namely the Single Depth Approach (SDA) and the Sky-Blocked Approach (SBA), was investigated applying identical hyperspectral radiometers operated on the same deployment platform. Values of LW from SDA and SBA measurements performed in the Black Sea across a variety of waters during ideal illumination conditions and with low-to-slight sea state, exhibited mean absolute differences within 0.5% in the blue-green spectral region and 2% in the red. This result, benefitting of a comprehensive parameterization of optical processes in combination with the characterization of sensors non-linearity, in-water response and reproducibility of absolute radiometric calibrations, indicated ample equivalence of the two near-surface methods in terms of performance and data reduction needs.

Semantic segmentation of multisensor remote sensing imagery with deep ConvNets and higher-order conditional random fields

Aerial images acquired by multiple sensors provide comprehensive and diverse information of materials and objects within a surveyed area. The current use of pretrained deep convolutional neural networks (DCNNs) is usually constrained to three-band images (i.e., RGB) obtained from a single optical sensor. Additional spectral bands from a multiple sensor setup introduce challenges for the use of DCNN. We fuse the RGB feature information obtained from a deep learning framework with light detection and ranging (LiDAR) features to obtain semantic labeling. Specifically, we propose a decision-level multisensor fusion technique for semantic labeling of the very-high-resolution optical imagery and LiDAR data. Our approach first obtains initial probabilistic predictions from two different sources: one from a pretrained neural network fine-tuned on a three-band optical image, and another from a probabilistic classifier trained on LiDAR data. These two predictions are then combined as the unary potential using a higher-order conditional random field (CRF) framework, which resolves fusion ambiguities by exploiting the spatial–contextual information. We utilize graph cut to efficiently infer the final semantic labeling for our proposed higher-order CRF framework. Experiments performed on three benchmarking multisensor datasets demonstrate the performance advantages of our proposed method.

Daily River Discharge Estimates by Merging Satellite Optical Sensors and Radar Altimetry Through Artificial Neural Network

Thanks to the large number of satellites, the multimission approach is becoming a viable method to integrate measurements and intensify the number of samples in space and time for monitoring the earth system. In this paper, we merged data from different satellite missions, optical sensors, and altimetry, for estimating daily river discharge through the application of the artificial neural network (ANN) technique. ANN was selected among other retrieval techniques because it offers an easy but effective way of combining input data from different sources into the same retrieval algorithm. The network is trained in a calibration period and validated in an independent period against in situ observations of river discharge for two gauging sites: Lokoja along the Niger River and Pontelagoscuro along the Po River. For optical sensors, we found that the temporal resolution is more important than the spatial resolution for obtaining accurate discharge estimates. Our results show that Landsat fails in the estimation of extreme events by missing most of the peak values due to its long revisit time (14–16 days). Better performances are obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) and Medium Resolution Imaging Spectrometer. Radar altimetry provides results in between MODIS-TERRA and MODIS-AQUA at Lokoja, whereas it outperforms all single optical sensors at Pontelagoscuro. The multimission approach, involving optical sensors and altimetry, is found the most reliable tool to estimate river discharge with a relative root-mean-square error of 0.12% and 0.27% and Nash–Sutcliffe coefficient of 0.98 and 0.83 for the Niger and Po rivers, respectively.

Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land cover mapping

Many applications that target dynamic land surface processes require a temporal observation frequency that is not easily satisfied using data from a single optical sensor. Sentinel-2 and Landsat provide observations of similar nature and offer the opportunity to combine both data sources to increase time-series temporal frequency at high spatial resolution. Multi-sensor image compositing is one way for performing pixel-level data integration and has many advantages for processing frameworks, especially if analyses over larger areas are targeted. Our compositing approach is optimized for narrow temporal-intervals and allows the derivation of time-series of consistent reflectance composites that capture field level phenologies. We processed more than a year's worth of imagery acquired by Sentinel-2A MSI and Landsat-8 OLI as available from the NASA Harmonized Landsat-Sentinel dataset. We used all data acquired over Germany and integrated observations into composites for three defined temporal intervals (10-day, monthly and seasonal). Our processing approach includes generation of proxy values for OLI in the MSI red edge bands and temporal gap filling on the 10-day time-series. We then derive a national scale crop type and land cover map and compare our results to spatially explicit agricultural reference data available for three federal states and to the results of a recent agricultural census for the entire country. The resulting map successfully captures the crop type distribution across Germany at 30 m resolution and achieves 81% overall accuracy for 12 classes in three states for which reference data was available. The mapping performance for most classes was highest for the 10-day composites and many classes are discriminated with class specific accuracies >80%. For several crops, such as cereals, maize and rapeseed our mapped acreages compare very well with the official census data with average differences between mapped and census area of 11%, 2% and 3%, respectively. Other classes (grapevine and forest classes) perform slightly less well, likely, because the available reference data does not fully capture the variability of these classes across Germany. The inclusion of the red edge bands slightly improved overall accuracies in all cases and improved class specific accuracies for most crop classes. Similarly, our gap filling procedure led to improved mapping accuracies when compared to nongap-filled 10-day features. Overall, our results demonstrate the valuable potential of approaches that utilize data from Sentinel-2 and Landsat which allows for detailed assessments of agricultural and other land-uses over large areas.

Stretchable Optical Sensing Patch System Integrated Heart Rate, Pulse Oxygen Saturation, and Sweat pH Detection

Continuous measurement of key physiological parameters, such as heart rate (HR), pulse oxygen saturation (SpO2), and sweat pH value, has very broad applications in healthcare, disease surveillance, and fitness and sports training. In this study, a stretchable optical sensing patch system was developed for real-time continuous noninvasive monitoring of the HR, SpO2, and sweat pH, and the sensing data were transmitted to a smartphone through Bluetooth. The sensor patch system adopted serpentine stretchable interconnects between the optical sensor and microcontroller chip with wireless function on a flexible substrate. The pH sensing function was implemented by coating a pH sensitive organically modified silicate film on the surface of a commercial blood oxygen sensor, achieving simultaneous measurement of HR, SpO2, and sweat pH with a single sensor. Real-time on-body assessment was carried out to evaluate the sensor patch system, showing its excellent repeatability and applicability. The sensor patch system could withstand up to 35% extension and exhibited a pH sensitivity of 4.42mV/pH from 4.0 to 8.0, while the accuracy of HR from 25 to 250 b/min and SpO2 from 70% to 100% sensing were ±1b/min and ±2%, respectively. The triple sensing functions was achieved through a single optical sensor on a flexible substrate while holding excellent contact with the body. The sensor patch system can be used for fitness guidance, skin disease detection, and wound monitoring and management by replacing related sensitive films.