Current Sensor Technology Research Articles

Sensing System Based on FBG for Corrosion Monitoring in Metallic Structures

As corrosion has slow development, its detection at an early age could be an alternative for reducing costs of structural rehabilitation. Therefore, the employment of structural health monitoring (SHM) systems, sensing configurations collecting data over time allowing for observing changes in the properties of the materials and damage emergence, for monitoring corrosion can be a good strategy to measure the damage and to decide the better moment for intervention. Nonetheless, the current corrosion sensor technology and the high costs of the sensing system implementation are limiting this application in the field. In this work, an optical fiber Bragg grating (FBG)-based sensing system is proposed for monitoring the thickness loss of a 1020 carbon steel metal plate subjected to controlled corrosion. The natural frequency of the plate was collected as a function of the corrosion time over 3744 h. To validate the experimental results, ultrasound measures and electrochemical tests were also carried out under similar conditions. The experimental results show adequate reliability, indicating the suitable functionality of the proposed system for monitoring the thickness loss caused by corrosion in metallic structures, in comparison with traditional methods, as ultrasonic and electrochemical measures.

Ambient Parameter Monitoring in Fresh Fruit and Vegetable Supply Chains Using Internet of Things-Enabled Sensor and Communication Technology.

Up to half of the global fruit and vegetable production is wasted or lost along the supply chain, causing wastage of resources and economic losses. Ambient parameters strongly influence quality and shelf life of fresh fruit and vegetables. Monitoring these parameters by using Internet of things (IoT)-enabled sensor and communication technology in supply chains can help to optimize product qualities and hence reduce product rejections and losses. Various corresponding technical solutions are available, but the diverse characteristics of fresh plant-based produce impede establishing valuable applications. Therefore, the aim of this review is to give an overview of IoT-enabled sensor and communication technology in relation to the specific quality and spoilage characteristics of fresh fruit and vegetables. Temperature, relative humidity (RH), O2, CO2 and vibration/shock are ambient parameters that provide most added value regarding product quality optimization, and can be monitored by current IoT-enabled sensor technology. Several wireless communication technologies are available for real-time data exchange and subsequent data processing and usage. Although many studies investigate the general possibility of monitoring systems using IoT-enabled technology, large-scale implementation in fresh fruit and vegetable supply chains is still hindered by unsolved challenges.

Intein-Mediated Protein Engineering for Cell-Based Biosensors.

Cell-based sensors provide a flexible platform for screening biologically active targets and for monitoring their interactions in live cells. Their applicability extends across a vast array of biological research and clinical applications. Particularly, cell-based sensors are becoming a potent tool in drug discovery and cell-signaling studies by allowing function-based screening of targets in biologically relevant environments and enabling the in vivo visualization of cellular signals in real-time with an outstanding spatiotemporal resolution. In this review, we aim to provide a clear view of current cell-based sensor technologies, their limitations, and how the recent improvements were using intein-mediated protein engineering. We first discuss the characteristics of cell-based sensors and present several representative examples with a focus on their design strategies, which differentiate cell-based sensors from in vitro analytical biosensors. We then describe the application of intein-mediated protein engineering technology for cell-based sensor fabrication. Finally, we explain the characteristics of intein-mediated reactions and present examples of how the intein-mediated reactions are used to improve existing methods and develop new approaches in sensor cell fabrication to address the limitations of current technologies.

Reprint of: Vibration sensing-based human and infrastructure safety/health monitoring: A survey

Current sensor technologies enable the passive and continuous monitoring of human behaviors as well as infrastructures to ensure personal safety and assess individual health state. One passive technology that has the potential of gathering personal data is the vibration sensor. In this paper, we carry out an extensive survey of the current vibration-based sensing technologies for human and infrastructure safety as well as health monitoring. These technologies utilize structural and bodies vibration as a source of data, and they can be incorporated in wearable or non-wearable devices. Furthermore, the vibration sensing technology utilizes low-cost and low-power sensors, which make it attractive for indoor and outdoor monitoring. We have classified the technologies into five categories: vibration-based sensing for assessing human health, recognizing personal behavior, inferring occupancy information, evaluating personal safety, and monitoring infrastructure health. In each category, we also classify the approaches that utilize single and multiple sensors. Moreover, we discuss the different types of signal processing and machine learning techniques that are applied to each approach.

Multiple Sensors Data Integration for Traffic Incident Detection Using the Quadrant Scan.

Non-recurrent congestion disrupts normal traffic operations and lowers travel time (TT) reliability, which leads to many negative consequences such as difficulties in trip planning, missed appointments, loss in productivity, and driver frustration. Traffic incidents are one of the six causes of non-recurrent congestion. Early and accurate detection helps reduce incident duration, but it remains a challenge due to the limitation of current sensor technologies. In this paper, we employ a recurrence-based technique, the Quadrant Scan, to analyse time series traffic volume data for incident detection. The data is recorded by multiple sensors along a section of urban highway. The results show that the proposed method can detect incidents better by integrating data from the multiple sensors in each direction, compared to using them individually. It can also distinguish non-recurrent traffic congestion caused by incidents from recurrent congestion. The results show that the Quadrant Scan is a promising algorithm for real-time traffic incident detection with a short delay. It could also be extended to other non-recurrent congestion types.

Flexible MXene/rGO/CuO hybrid aerogels for high performance acetone sensing at room temperature

The design and construction of high-performance room temperature gas-sensing materials have attracted great attention in the field of current gas sensor technology. Herein, we report an efficient approach for the production of ultrafine CuO nanoparticles-decorated three-dimensional (3D) hybrid aerogel by employing MXene (Ti3C2Tx) and reduced graphene oxide (rGO) nanosheets as cobuilding blocks. Benefiting from the synergistic effects of highly interconnected porous networks, large specific surface areas, homogeneous CuO nanoparticles dispersion, and good electron conductivity, the resulting 3D MXene/rGO/CuO aerogel expresses high acetone-sensing performance at room temperature (RT). The sensor response toward 100 ppm acetone is 52.09 % at RT with the fast response time (∼6.5 s) and recovery time (∼7.5 s), excellent reproducibility and selectivity. The strategy of compositing 3D graphene/MXene architectures with metal oxide semiconductor provides a novel pathway for the future development of RT gas sensors.

Three-dimensional spatiotemporal wind field reconstruction based on physics-informed deep learning

In this work, a physics-informed deep learning model is developed to achieve the reconstruction of the three-dimensional (3-D) spatiotemporal wind field in front of a wind turbine, by combining the 3-D Navier–Stokes equations and the scanning LIDAR measurements. To the best of the authors’ knowledge, this is for the first time that the full 3-D spatiotemporal wind field reconstruction is achieved based on real-time measurements and flow physics. The proposed method is evaluated using high-fidelity large eddy simulations. The results show that the wind vector field in the whole 3-D domain is predicted very accurately based on only scalar line-of-sight LIDAR measurements at sparse locations. Specifically, at the baseline case, the prediction errors for the streamwise, spanwise and vertical velocity fields are 0.263 m/s, 0.397 m/s and 0.361 m/s, respectively. The prediction errors for the horizontal and vertical direction fields are 2.84° and 2.58° which are important in tackling yaw misalignment and turbine tilt control, respectively. Further analysis shows that the 3-D wind features are captured clearly, including the evolutions of flow structures, the wind shear in vertical direction, the blade-level speed variations due to turbine rotation, and the speed variations modulated by the turbulent wind. Also, the developed model achieves short-term wind forecasting without the commonly-used Taylor’s frozen turbulence hypothesis. Furthermore it is very useful in advancing other wind energy research fields e.g. wind turbine control & monitoring, power forecasting, and resource assessments because the 3-D spatiotemporal information is important for them but not available with current sensor and prediction technologies.

Single Camera Object Detection for Self-Driving Vehicle: A Review


 
 
 The development of technologies for autonomous vehicle (AV) have seen rapid achievement in the recent years. Commercial carmakers are actively embedding this system in their production and are undergoing tremendous testing in the real world traffic environment. It is one of today’s most challenging topics in the intelligent transportation system (ITS) field in term of reliability as well as accelerating the world’s transition to a sustainable future. The utilization of current sensor technology however indicates some drawbacks where the complexity is high and the cost is extremely huge. This paper reviews the recent sensor technologies and their contributions in becoming part of the autonomous self-driving vehicle system. The ultimate focus is toward reducing the sensor count to just a single camera based on the single modality model. The capability of the sensor to detect and recognize on-the-road obstacles such as overtaking vehicle, pedestrians, signboards, bicycle, road lane marker and road curvature will be discussed. Different feature extraction approach will be reviewed further with the selection of the recent Artificial Intelligent (AI) methods that are being implemented. At the end of this review, the optimal techniques of processing information from single camera system will be discussed and summarized.
 
 

Laser Tuning in Layered h-BN Crystals.

Integrated optics shows great potential in the current optical communication systems, sensor technology, optical computers, and other fields. Tunable laser technology within a certain range is the key to achieving on-chip optical integration; to realize which, Raman scattering is a competitive method that can effectively transfer incident laser energy to optical phonons due to the photon-phonon interaction. Here, we take hexagonal boron nitride as the energy conversion medium, and based on the angle-resolved polarized Raman spectroscopy, it is found that when laser polarization vector ei ⊥ c axis, the spectrum obtains maximal scattering across the cross section and a minimal depolarization ratio. At room temperature, h-BN obtains an output signal with a wavelength of 522.8 nm and a full-width at half-maximum of 0.24 nm under the excitation of 488 nm pump laser, and the depolarization ratio is 0.09 (theoretically, it is 0, and this difference is due to experimental errors). And then, within the temperature range of 80∼420 K, the scattered light wavelength shows a high-precision shift of 0.006 nm/25 K, indicating that continuous wavelength tuning has been successfully achieved in h-BN.

Vibration sensing-based human and infrastructure safety/health monitoring: A survey

Current sensor technologies enable the passive and continuous monitoring of human behaviors as well as infrastructures to ensure personal safety and assess individual health state. One passive technology that has the potential of gathering personal data is the vibration sensor. In this paper, we carry out an extensive survey of the current vibration-based sensing technologies for human and infrastructure safety as well as health monitoring. These technologies utilize structural and bodies vibration as a source of data, and they can be incorporated in wearable or non-wearable devices. Furthermore, the vibration sensing technology utilizes low-cost and low-power sensors, which make it attractive for indoor and outdoor monitoring. We have classified the technologies into five categories: vibration-based sensing for assessing human health, recognizing personal behavior, inferring occupancy information, evaluating personal safety, and monitoring infrastructure health. In each category, we also classify the approaches that utilize single and multiple sensors. Moreover, we discuss the different types of signal processing and machine learning techniques that are applied to each approach.

Reliability and Validity of Clinically Accessible Smart Glove Technologies to Measure Joint Range of Motion.

Capturing hand motions for hand function evaluations is essential in the medical field. For many allied health professionals, measuring joint range of motion (ROM) is an important skill. While the universal goniometer (UG) is the most used clinical tool for measuring joint ROM, developments in current sensor technology are providing clinicians with more measurement possibilities than ever. For rehabilitation and manual dexterity evaluations, different data gloves have been developed. However, the reliability and validity of sensor technologies when used within a smart device remain somewhat unclear. This study proposes a novel electronically controlled sensor monitoring system (ECSMS) to obtain the static and dynamic parameters of various sensor technologies for both data gloves and individual sensor evaluation. Similarly, the ECSMS was designed to closely mimic a human finger joint, to have total control over the joint, and to have an exceptionally high precision. In addition, the ECSMS device can closely mimic the movements of the finger from hyperextension to a maximum ROM beyond any person’s finger joint. Due to the modular design, the ECSMS’s sensor monitoring board is independent and extensible to include various technologies for examination. Additionally, by putting these sensory devices through multiple tests, the system accurately measures the characteristics of any rotary/linear sensor in and out of a glove. Moreover, the ECSMS tracks the movement of all types of sensors with respect to the angle values of finger joints. In order to demonstrate the effectiveness of sensory devices, the ECSMS was first validated against a recognised secondary device with an accuracy and resolution of 0.1°. Once validated, the system simultaneously determines real angles alongside the hand monitoring device or sensor. Due to its unique design, the system is independent of the gloves/sensors that were tested and can be used as a gold standard to realise more medical equipment/applications in the future. Consequently, this design greatly enhances testing measures within research contact and even non-contact systems. In conclusion, the ECSMS will benefit in the design of data glove technologies in the future because it provides crucial evidence of sensor characteristics. Similarly, this design greatly enhances the stability and maintainability of sensor assessments by eliminating unwanted errors. These findings provide ample evidence for clinicians to support the use of sensory devices that can calculate joint motion in place of goniometers.

Review of Wearable Sensor-Based Health Monitoring Glove Devices for Rheumatoid Arthritis.

Early detection of Rheumatoid Arthritis (RA) and other neurological conditions is vital for effective treatment. Existing methods of detecting RA rely on observation, questionnaires, and physical measurement, each with their own weaknesses. Pharmaceutical medications and procedures aim to reduce the debilitating effect, preventing the progression of the illness and bringing the condition into remission. There is still a great deal of ambiguity around patient diagnosis, as the difficulty of measurement has reduced the importance that joint stiffness plays as an RA identifier. The research areas of medical rehabilitation and clinical assessment indicate high impact applications for wearable sensing devices. As a result, the overall aim of this research is to review current sensor technologies that could be used to measure an individual’s RA severity. Other research teams within RA have previously developed objective measuring devices to assess the physical symptoms of hand steadiness through to joint stiffness. Unfamiliar physical effects of these sensory devices restricted their introduction into clinical practice. This paper provides an updated review among the sensor and glove types proposed in the literature to assist with the diagnosis and rehabilitation activities of RA. Consequently, the main goal of this paper is to review contact systems and to outline their potentialities and limitations. Considerable attention has been paid to gloved based devices as they have been extensively researched for medical practice in recent years. Such technologies are reviewed to determine whether they are suitable measuring tools.

Spatiotemporal wind field prediction based on physics-informed deep learning and LIDAR measurements

Spatiotemporal wind field information is of great interest in wind industry e.g. for wind resource assessment and wind turbine/farm monitoring & control. However, its measurement is not feasible because only sparse point measurements are available with the current sensor technology such as LIDAR. This work fills the gap by developing a method that can achieve spatiotemporal wind field predictions by combining LIDAR measurements and flow physics. Specifically, a deep neural network is constructed and the Navier–Stokes equations, which provide a good description of atmospheric flows, are incorporated in the deep neural network by employing the physics-informed deep learning technique. The training of this physics-incorporated deep learning model only requires the sparse LIDAR measurement data while the spatiotemporal wind field in the whole domain (which cannot be measured) can be predicted after training. This study, which can discover complex wind patterns that do not present in the training dataset, is totally distinct from previous machine learning based wind prediction studies which treat machine learning models as “black-box” and require the corresponding input and target values to learn complex relations. The numerical results on the prediction of the wind field in front of a wind turbine show that the proposed method predicts the spatiotemporal flow velocity (including both downwind and crosswind components) in the whole domain very well for a wide range of scenarios (including various measurement noises, resolutions, LIDAR look directions, and turbulence levels), which is promising given that only line-of-sight wind speed measurements at sparse locations are used.

Unobtrusive Health Monitoring in Private Spaces: The Smart Home.

With the advances in sensor technology, big data, and artificial intelligence, unobtrusive in-home health monitoring has been a research focus for decades. Following up our research on smart vehicles, within the framework of unobtrusive health monitoring in private spaces, this work attempts to provide a guide to current sensor technology for unobtrusive in-home monitoring by a literature review of the state of the art and to answer, in particular, the questions: (1) What types of sensors can be used for unobtrusive in-home health data acquisition? (2) Where should the sensors be placed? (3) What data can be monitored in a smart home? (4) How can the obtained data support the monitoring functions? We conducted a retrospective literature review and summarized the state-of-the-art research on leveraging sensor technology for unobtrusive in-home health monitoring. For structured analysis, we developed a four-category terminology (location, unobtrusive sensor, data, and monitoring functions). We acquired 912 unique articles from four relevant databases (ACM Digital Lib, IEEE Xplore, PubMed, and Scopus) and screened them for relevance, resulting in papers analyzed in a structured manner using the terminology. The results delivered 25 types of sensors (motion sensor, contact sensor, pressure sensor, electrical current sensor, etc.) that can be deployed within rooms, static facilities, or electric appliances in an ambient way. While behavioral data (e.g., presence (), time spent on activities ()) can be acquired effortlessly, physiological parameters (e.g., heart rate, respiratory rate) are measurable on a limited scale (). Behavioral data contribute to functional monitoring. Emergency monitoring can be built up on behavioral and environmental data. Acquired physiological parameters allow reasonable monitoring of physiological functions to a limited extent. Environmental data and behavioral data also detect safety and security abnormalities. Social interaction monitoring relies mainly on direct monitoring of tools of communication (smartphone; computer). In summary, convincing proof of a clear effect of these monitoring functions on clinical outcome with a large sample size and long-term monitoring is still lacking.

Evaluation of a novel video- and laser-based displacement sensor prototype for civil infrastructure applications

Deflection measurements on structures continue to be a challenge with current sensor technologies. Material degradation and changes in the mechanical properties over time (e.g. creep and shrinkage in concrete bridges) directly impact the deflections exhibited by a structure. In this article, we introduce and discuss the evaluation of a novel laser- and video-based displacement sensor prototype to monitor displacements and rotations on structures remotely. The sensor is inexpensive, using off-the shelf components, but also accurate and practical for situations that do not allow the use of conventional displacement sensors, which require a reference base. In contrast to other image-based approaches such as digital image correlation (DIC) or Eulerian-based virtual video sensors (VVS), the digital camera of our proposed solution is located at the measurement location on the structure. The sensor was evaluated using laboratory tests to determine the practicality, accuracy, and sensitivity to lighting conditions. The accuracy of the sensor was found to be approximately ± 0.9 mm (± 0.035 in) (95% prediction limits) for a 30.5 m (100 ft) measurement distance under laboratory conditions. Finally, we applied and evaluated the sensor under real-world conditions on a concrete deck/single steel box girder pedestrian bridge under static and dynamic loading conditions as well as on a five-story steel moment-frame building under ambient conditions. Essential for field applications, the results demonstrate the prototype offers an inexpensive yet practical and accurate solution for monitoring displacements and rotations remotely.

Monitoring the Growth and Yield of Fruit Vegetables in a Greenhouse Using a Three-Dimensional Scanner.

Monitoring the growth of fruit vegetables is essential for the automation of cultivation management, and harvest. The objective of this study is to demonstrate that the current sensor technology can monitor the growth and yield of fruit vegetables such as tomato, cucumber, and paprika. We estimated leaf area, leaf area index (LAI), and plant height using coordinates of polygon vertices from plant and canopy surface models constructed using a three-dimensional (3D) scanner. A significant correlation was observed between the measured and estimated leaf area, LAI, and plant height (R2 > 0.8, except for tomato LAI). The canopy structure of each fruit vegetable was predicted by integrating the estimated leaf area at each height of the canopy surface models. A linear relationship was observed between the measured total leaf area and the total dry weight of each fruit vegetable; thus, the dry weight of the plant can be predicted using the estimated leaf area. The fruit weights of tomato and paprika were estimated using the fruit solid model constructed by the fruit point cloud data extracted using the RGB value. A significant correlation was observed between the measured and estimated fruit weights (tomato: R2 = 0.739, paprika: R2 = 0.888). Therefore, it was possible to estimate the growth parameters (leaf area, plant height, canopy structure, and yield) of different fruit vegetables non-destructively using a 3D scanner.

Label-Free, Real-Time Phospholipase-A Isoform Assay.

Phospholipase-A (PLA) enzymes catalyze the hydrolysis of ester bonds in select glycerophospholipids. Sensors for rapidly measuring the PLA activity in biological samples have relevance in the study of venom compositions and in medical diagnostics for the diagnosis of diseases such as acute pancreatitis. Current PLA sensor technologies are often restricted by the time it takes to prepare an assay, the necessity of using fluorescent labels, or the fact they might require strict pH control of the buffer vehicles used. Here we present a tethered bilayer lipid membrane (tBLM) impedance sensor array for the rapid and real-time detection of PLA, which includes the ability to selectively detect phospholipase-A2 (PLA2) from phospholipase-A1 (PLA1) isoforms. Comparing the activity of PLA1 and PLA2 in an array of tBLMs composed of ether phospholipids, ester phospholipids or ether-ester phospholipids allows for the rapid and reliable distinction between the isoforms, as measured using swept-frequency electrical impedance spectroscopy. After testing the assay using pure enzymes, we demonstrate the capacity of the sensor to identify specific PLA2-type, calcium-dependent activity from the venom of the South American bullet ant, Paraponera clavata, at a concentration of 1 μg/mL. The specificity of the phospholipase activity was corroborated using matrix-assisted laser-desorption/ionization time-of-flight mass spectrometry. As further validation, we tested the activities of a PLA1 isoform in the presence of different buffers commonly used in biology and biochemistry experiments. Sensitivity testing shows that PLA1 can be detected at an activity as low as 0.06 U/mL. The rapid and reliable detection of phospholipases presented in this study has potential applications in the study of animal venoms as well as in lipase bioreactors and point-of-care devices.

Challenges on detection, identification and monitoring of indoor airborne chemical-biological agents

Potential chemical and biological (CB) attacks in indoor spaces pose specific challenges for prevention and preparedness. This paper summarises the conclusions obtained from the work conducted in the framework of the ERNCIP11The European Reference Network for Critical Infrastructure Protection (ERNCIP) is the Commission co-financed project coordinated by Joint Research Centre aiming at providing a framework within which experimental facilities and laboratories will share knowledge and expertise in order to harmonize test protocols throughout Europe, leading to better protection of critical infrastructures against all type of threats and hazards and to the creation of a single market for security solutions. Three of the working groups deal with CBRN threats: chemical and biological risks to drinking water; detection of indoor airborne chemical-biological agents and radiological and nuclear threats to critical infrastructure.. A thorough review of the existing and emerging technologies for CB detection, coupled with simulations of CB airborne dispersion in (critical) infrastructures to investigate the implementation of such sensors is presented. The conclusions include: (a) limitations of current sensor technology, (b) the effect of spatial variability of contamination within a building envelop on the effectiveness of such technology (c) the importance of early identification of both CB compounds for post-event mitigation.

Bayesian degradation modelling for spare parts inventory management

AbstractDecision makers in various sectors, such as manufacturing and transportation, strive to minimize downtime costs. Often, brief-planned stoppage times allow for changes in shifts and line configurations and longer periods are scheduled for major repairs. It is quite important to proactively make use of these downtimes to reduce the costs of unexpected downtimes due to failures. Among many aspects, the availability of spare parts significantly affects the operational costs of such systems. Current sensor technologies enable the condition monitoring of critical components and degradation-based spare parts management. This paper focuses on Bayesian degradation modelling for spare parts inventory management for a new system. We propose a stochastic dynamic program to minimize the expected spare parts inventory cost for a fixed planning horizon. A numerical example illustrates the value of Bayesian analysis in this management setting. The proposed methodology finds the optimal time between long stoppages and optimal spare parts order quantity when the prior information about the degradation process is accurate. The methodology can be used to analyse the sensitivity of the optimal solution to changes in the accuracy and bias of the prior distributions of the model parameters, the cost structure and the number of machines in the system.

Microplasma emission spectroscopy of carbon dioxide using the carbon monoxide Ångström system

The present paper describes a system and method for indirect emission spectroscopy of CO2 in the visible spectrum. This is achieved by using a microplasma spectrometer that first converts CO2 into CO and then measures emissions from the CO Ångström system (B1Σ + → A1Π) at 560 nm. The experiments were performed on gaseous samples of CO2, mixed in both N2 and air, to concentrations between 0.01% and 100%. In addition to the microplasma spectrometer, the process was monitored by mass spectrometry with a residual gas analyzer. The CO2 to CO conversion efficiency was found to be very high, reaching a maximum of 41% at close to 100% selectivity. Furthermore, the CO Ångström system was shown to facilitate excellent spectroscopic measurement of CO2 concentrations below 10%, with a linearity of R2 > 0.99 and an expected limit of detection in the parts-per-thousands range. The most promising aspect of the results was that the analysis was performed on extremely small total sample amounts where the gas flow through the systems was in the 0.1 µmole/s range. Hence, the present system has the prospect of filling a void in current sensor technology, where inexpensive and easy-to-use optical systems, such as nondispersive infrared sensors, cannot handle small sample amounts, while mass spectrometers, which can handle such samples, still are expensive, complex, and bulky.