Application Of Sensing Technology Research Articles

Surface Acoustic Wave Strain Sensor With Ultra-Thin Langasite

Langasite (LGS) surface acoustic wave (SAW) sensor is considered as an ideal wireless passive sensing technology application in complex environments. However, existing LGS-based SAW strain sensor has low strain range and its sensing accuracy is affected by temperature greatly as well as strain, which can not meet the application requirements. By exploiting ultra-thin substrate and a custom calibration algorithm, this paper proposes an ultra-thin 100 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> LGS SAW with (0°, 138.5°, 72°) Euler angle to extend strain range to 1200 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \varepsilon $ </tex-math></inline-formula> at <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$500^{\circ }\text{C}$ </tex-math></inline-formula> , which is twice higher than the state-of-the-art. The ultra-thin SAW sensor has lower temperature coefficient and hysteresis loop effect, compared with thick LGS SAW. Aiming to improve sensing accuracy, the mechanism of temperature effects on strain sensitivity is investigated in different temperature stages, including thermal expansion mismatch effect and high-temperature glue strain transfer ratio effect. The mechanism can explain strain sensitivity curve under various temperature nicely. Based on the mechanism, a new calibration method is also developed to eliminate temperature effects. The testing results show that this calibration method can improve the measured strain accuracy effectively.

Mixed reality environment for learning sensing technology applications in Construction: A usability study

Mixed reality is gradually becoming ubiquitous and significant in education owning to the inherent benefits of active participation and tacit knowledge development in a safe and engaging environment. However, limited studies have explored design features that facilitate its use as a pedagogical tool in construction education, particularly in equipping students with experiential skills that are otherwise challenging to obtain due to resource constraints and limited access to construction sites. By evaluating eye tracking, usability questions, and think-aloud protocol data and verbal feedback, this study investigated the usability of a mixed reality environment designed for equipping construction engineering students with competencies for deploying sensing technologies on construction projects. Results revealed features such as accuracy of represented construction activities, quality of animations, and easy access to information and resources as important for designing efficient mixed reality learning environments. While the usability data suggested that the user interface was usable, eye tracking provided profundity on encountered difficulties with the user interface. Through concise outline and sequential design of the user interface, this study revealed that knowledge scaffolding can improve task performance in a mixed reality learning environment. This study adds value to existing literature, in particular by providing insights into the affordances of mixed reality environments that address the technological gap between the construction industry and construction engineering education. The mixed reality learning environment contributes to the cognitive apprenticeship theory through the use of game objects to develop procedural knowledge for addressing construction industry challenges with sensing technologies.

Real‐time equilibrium moisture content monitoring to predict grain quality of corn stored in silo and raffia bags

AbstractThe use of silo and raffia bags for the temporary grain storage has been increasing in recent years. However, the methods for monitoring a stored product are limited to visual inspections and sampling. Thus, this research aimed to real‐time equilibrium moisture content monitoring to predict grain quality of corn stored in different conditions in silo and raffia bags using wireless sensor network prototype, Internet of Things (IoT) platform, and neural network algorithms. Experiments were conducted using corn grain with two initial water contents of 13% and 18% (w.b.), three storage environments with temperatures of 30, 23, and 17°C, and two types of packaging, that is, silo and raffia bags, for a 3‐month storage evaluation. During the monitoring of stored grain, variations in equilibrium moisture hygroscopic content were observed, which inferred changes in the corn quality. Water contents of 13% under a storage condition of 17°C showed the highest quality results, whereas storage in silo bags with water contents of 13% and 18% showed no differences at 23°C; however, at a temperature of 30°C, the grain suffered a high level of deterioration. The storage time influenced the reduction of grain quality for all factors. The physicochemical quality prediction results indicated a high coefficient of determination of the trained models, presenting itself as a promising perspective, mainly in developing embedded technologies for monitoring and predicting the qualitative variables of grain stored in silo and raffia bags.Practical ApplicationsThe application of sensor technology and the Internet of Thing (IoT) to monitor the temperature and relative humidity of intergranular air in real time for the determination of equilibrium moisture content became possible to predict the physical and physicochemical quality of grains stored in bag silos and raffia bags using artificial neural networks (ANN) algorithms. The results obtained were satisfactory and can replace the punctual sampling of the grains mass stored in hermetic packages. The application of a set of technologies possible to monitor the grain quality in real time and predict the grain storage time in bag and raffia silos to reduce losses.

Investigating the Impact of Cu2+ Doping on the Morphological, Structural, Optical, and Electrical Properties of CoFe2O4 Nanoparticles for Use in Electrical Devices.

This study investigated the production of Cu2+-doped CoFe2O4 nanoparticles (CFO NPs) using a facile sol−gel technique. The impact of Cu2+ doping on the lattice parameters, morphology, optical properties, and electrical properties of CFO NPs was investigated for applications in electrical devices. The XRD analysis revealed the formation of spinel-phased crystalline structures of the specimens with no impurity phases. The average grain size, lattice constant, cell volume, and porosity were measured in the range of 4.55–7.07 nm, 8.1770–8.1097 Å, 546.7414–533.3525 Å3, and 8.77–6.93%, respectively. The SEM analysis revealed a change in morphology of the specimens with a rise in Cu2+ content. The particles started gaining a defined shape and size with a rise in Cu2+ doping. The Cu0.12Co0.88Fe2O4 NPs revealed clear grain boundaries with the least agglomeration. The energy band gap declined from 3.98 eV to 3.21 eV with a shift in Cu2+ concentration from 0.4 to 0.12. The electrical studies showed that doping a trace amount of Cu2+ improved the electrical properties of the CFO NPs without producing any structural distortions. The conductivity of the Cu2+-doped CFO NPs increased from 6.66 × 10−10 to 5.26 × 10−6 ℧ cm−1 with a rise in Cu2+ concentration. The improved structural and electrical characteristics of the prepared Cu2+-doped CFO NPs made them a suitable candidate for electrical devices, diodes, and sensor technology applications.

Advancement in Silicon Integrated Photonics Technologies for Sensing Applications in Near-Infrared and Mid-Infrared Region: A Review

Exploration and implementation of silicon (Si) photonics has surged in recent years since both photonic component performance and photonic integration complexity have considerably improved. It supports a wide range of datacom and telecom applications, as well as sensors, including light detection and ranging, gyroscopes, biosensors, and spectrometers. The advantages of low-loss Si WGs with compact size and excellent uniformity, resulting from the high quality and maturity of the Si complementary metal oxide semiconductor (CMOS) environment, are major drivers for using Si in photonics. Moreover, it has a high refractive index and a reasonably large mid-infrared (MIR) transparency window, up to roughly 7 μm wavelength, making it beneficial as a passive mid-IR optical material. Several gases and compounds with high absorption properties in the MIR spectral region are of prodigious curiosity for industrial, medicinal, and environmental applications. In comparison to current bulky systems, the implementation of Si photonics devices in this wavelength range might allow inexpensive and small optical sensing devices with greater sensitivity (S), power usage, and mobility. In this review, recent advances in Si integrated photonic sensors working in both near-infrared (NIR) and MIR wavelength ranges are discussed. We believe that this paper will be valuable for the scientific community working on Si photonic sensing devices.

How Far Are We From Data-Driven and Animal-Based Welfare Assessment? A Critical Analysis of European Quality Schemes

Within the European Union, there is no harmonization of farm animal welfare quality schemes for meat and dairy products. Instead, there are several industry-driven initiatives and voluntary schemes that seek to provide information on animal welfare for attentive consumers. This study had two aims. First, we quantified how selected industry-wide quality schemes cover the welfare of pigs and dairy cattle on farms by comparing the evaluation criteria selected by schemes with the animal-, resource- and management-based measures defined in the Welfare Quality protocol (WQ®). Second, we identified how these quality schemes use the data generated along the value chain (sensors, breeding, production, and health recordings) for animal welfare assessments. A total of 12 quality schemes, paying attention to animal welfare but not necessarily limited to welfare, were selected for the analysis. The schemes originated from eight European countries: Finland, Sweden, Denmark, Ireland, the Netherlands, Germany, Austria, and Spain. Among the studied quality schemes, we have identified 19 standards for certification: nine for dairy and 10 for pig production. Most of the analyzed standards were comprehensive in welfare assessment. In total, 15 out of 19 standards corresponded to WQ® in more than 70%. However, this high correspondence was obtained when allowing for different information sources (environment instead of animal) than defined in WQ®. Compared to WQ®, the investigated schemes were lagging in terms of the number of measures evaluated based on the animals, with only five standards, out of 19, using predominantly animal-based measures. The quality schemes mostly applied resource-based instead of animal-based measures while assessing good health and appropriate behavior. The utilization of data generated along the value chain by the quality schemes remains insignificant as only one quality scheme allowed the direct application of sensor technologies for providing information on animal welfare. Nevertheless, several schemes used data from farm recording systems, mostly on animal health. The quality schemes rely mostly on resource-based indicators taken during inspection visits, which reduce the relevance of the welfare assessment. Our results suggest that the quality schemes could be enhanced in terms of data collection by the broader utilization of data generated along the value chain.

Discussion on Application of Sensor Technology in Mechatronics System

Discussion on Application of Sensor Technology in Mechatronics System

Research on Embedded Detection System Based on Intelligent Pressure Transmitter

Abstract A new development scheme of check equipment for recoil device is designed. In view of the existing reverse recoil device check there is a long time to prepare, data interpretation and difficult problems, the application of advanced touch technology and sensor technology, completed the embedded test system scheme based on intelligent pressure transmitter design, can quickly test reverse recoil device to check the key parameters, and be able to compare the interpretation of data in real-time, Shorten the inspection time of recoil device and improve the accuracy of test data. The simulation research of the scheme design system is carried out in the computer of LINUX system. Through the simulation, it is found that each system module can work correctly and normally, with higher generality and artificial intelligence.

136 Application of Ear-attached Sensor Technology (CowManager) to Detect Estrus in a Commercial Beef Cow AI Program

Abstract The CowManager (Agis, Harmelen, the Netherlands) ear sensor technology was developed for dairy cows in confinement and has recently been adopted for pastured beef cows. The sensors fit around the ‘840’ RFID button and houses a 3-dimensional accelerometer that registers movement of the cow’s ear. Each sensor stores up to 7 d of internal data at a time and is transmitted by solar powered routers in the pasture. The objective was to determine the effectiveness of the CowManager system’s estrus detection (ED) feature in beef cows during estrous synchronization (ES), AI, and during exposure to bulls for 60 d. Fifty 6-8 yr multiparous commercial Angus-crossbred cows with calves, were fitted with CowManager ear sensors for 14 d to allow for acclimation. Cows then underwent ES using a 6-d CO-Synch + controlled internal drug release (CIDR) protocol with AI at 72 h post CIDR + prostaglandin F2α with sexed female semen. Estrus detection was determined using breeding patches (Estrotect™, Rockway Inc., Spring Valley, WI), with estrus classified as greater than 50% of color rubbed off, and CowManager sensors with a notification to a smartphone and/or computer. The CowManager sensors were effective in determining estrus in 92% (46/50) of the synchronized cows, and the first service conception rate was 74% (37/50). CowManager sensors detected 100% (37/37), and ESTROTECT patches detected 91% (34/37) of the cows in estrus that conceived by AI. During bull exposure, CowManager detected 100% (13/13) of estrus and the natural breeding dates were confirmed by ultrasound fetal aging. ESTROTECT patches were not used during exposure to bulls. However, CowManager detected 5 of the AI pregnant cows as in estrus during the exposure to bulls. CowManager’s ED feature was highly effective in determining estrus for beef cattle and was similar to breeding patches for ED.

Sensing Technology Applications in the Mining Industry-A Systematic Review.

Introduction Industry 4.0 has enhanced technological development in all fields. Currently, one can analyse, treat, and model completely different variables in real time; these include production, environmental, and occupational variables. Resultingly, there has been a significant improvement in the quality of life of workers, the environment, and in businesses in general, encouraging the implementation of continuous improvement measures. However, it is not entirely clear how the mining industry is evolving alongside this industrial evolution. With this in mind, this systematic review aimed to find sensing technology applications within this sector, in order to assist the mining industry in its goal to evolve digitally. Methodology: The research and reporting of this article were carried out by means of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Results and discussion: A total of 29 papers were included in the study, with sensors being applied in several fields, namely safety, management, and localisation. Three different implementation phases were identified regarding its execution: prototype, trial, and (already) implemented. The overall results highlighted that many mechanisms are in need of improvement in underground settings. This might be due to the fact that underground mining has particular safety challenges. Conclusions: Ventilation and mapping are primary issues to be solved in the underground setting. With regard to the surface setting, the focus is directed toward slope stability and ways of improving it regarding monitoring and prevention. The literature screening revealed a tendency in these systems to keep advancing in technologically, becoming increasingly more intelligent. In the near future, it is expected that a more technologically advanced mining industry will arise, and this will be created and sustained by the optimisation of processes, equipment, and work practices, in order to improve both the quality of life of people and the health of the environment.

Application of Smart Fiber Optic Sensor Technology in Feature Extraction, Recognition, and Detection

To improve the signal recognition effect of the security system, this paper studies the security system based on intelligent Fiber Optic Sensor (FOS) technology. Firstly, the research background of intelligent FOS is introduced, and its current situation in feature extraction, recognition, and detection is introduced. Secondly, the double Mach–Zehnder (M-Z) Optical Fiber- (OF-) based interferometer model is implemented, and the Wavelet Analysis (WA) theory is introduced to analyze the characteristic threshold and Frequency (F) characteristics of intrusion signal. Finally, the distributed intelligent FOS-based perimeter security system is constructed, and an empirical study is conducted to verify its performance. The results show that the intruder knocking-induced signal F, intruder climbing-induced signal F, noiseless environment-induced signal F, and rainy environment-induced signal F are 0–250 kHz, 0–25 kHz, 0–1.5 kHz, and 0–3.5 kHz, respectively; in all the four cases, excellent results have been obtained after wavelet threshold denoising. Meanwhile, the received signal is decomposed into seven layers through multiscale WA theory. The signal feature classification is based on WA and takes variance as the representation, and the clear classification results are obtained; when the False Alarm Rate (FAR) = 1%, the detection probability of the proposed system is about 99%, while the detection probability of the traditional system is about 3%. The reference arm and sensing arm of the distributed OF-based perimeter security system can be laid in the same optical cable. Therefore, the designed wavelet threshold filtering method is feasible, and the detection probability of the designed WA-based system is higher than that of the traditional security system. The research content provides a reference for the development of intelligent FOS technology in the field of security.

High-resolution pressure transducer design and associated circuitry to build a network-ready smart sensor for distributed measurement in oil and gas production wells

Miniaturized single-mode thickness-shear pressure transducer combined with high-temperature SOI, silicon on insulator, integrated circuit technology is proposed as network-ready high-pressure high-resolution smart sensor for distributed data acquisition in oil and gas production wells. The transducer miniaturization is investigated with a full 3D computer model previously developed by the authors to assess the impact of intrinsic losses and various geometrical features on transducer performance. Over the last decades there has been a trend toward size reduction of high-resolution pressure transducer. The implemented model provides insight into the evolution of high-resolution pressure transducers from Hewlett-Packard™ to Quartzdyne™ and beyond. Distributed measurement in production oil wells in extreme harsh environment, such as found in the pre-salt layer, is an unsolved problem. The industry move toward electrified wells offers an opportunity for application of smart sensor technology and power line communications to achieve distributed high-resolution data acquisition.

Applications of sensor technology and mobile health in mental health research

Applications of sensor technology and mobile health in mental health research

Applications of Sensor Technology and mobile Health in Mental Health Research

Applications of Sensor Technology and mobile Health in Mental Health Research

Growth of thick ε(kappa)-Ga-=SUB=-2-=/SUB=-O-=SUB=-3-=/SUB=- films by halide vapor phase epitaxy

High crystalline quality epitaxial films of orthorhombic gallium oxide ε(kappa)-Ga2O3 with a thickness of more 20 μm have been grown for the first time using halide vapor phase epitaxy. Sapphire wafers with preliminarily deposited GaN layers were used as substrates. The properties of the produced films are studied by X-ray diffraction and electron microscopy. The results are considered an important step towards obtaining thick layers and quasi-bulk ε(kappa)-Ga2O3 crystals for practical applications in electronics and sensor technology. Keywords: gallium oxide, halide vapor phase epitaxy, polymorph, X-ray diffraction

Получение толстых слоев &amp;epsilon;(kappa)-Ga-=SUB=-2-=/SUB=-O-=SUB=-3-=/SUB=- методом хлоридной эпитаксии

High crystalline quality epitaxial films of orthorhombic gallium oxide ε(κ)-Ga2O3 with a thickness of more than 20 μm have been grown by halide vapor phase epitaxy for the first time. Sapphire wafers with preliminarily deposited GaN layers were used as substrates. The properties of the produced films are studied by X-ray diffraction and electron microscopy. The results obtained are considered as an important step in obtaining thick layers and quasi-bulk ε(κ)-Ga2O3 crystals for practical applications in electronic and sensor technology.

Enhanced electromechanical conversion via in situ grown CsPbBr3 nanoparticles/poly(vinylidene fluoride) fibers for physiological signal monitoring

Mechanical energy conversion based on piezoelectric principle has received much attention due to its promising applications in sustainable power supply systems and sensor technology. Ferroelectric poly(vinylidene fluoride) (PVDF) combines the advantages of both good electromechanical coupling and easy processability, yet the low piezoelectric coefficient limits its output performances thus cannot meet the increasing requirements for power generation and sensing. Here, inorganic metal halide perovskite CsPbBr3 (CPB) nanoparticles have been incorporated into the PVDF fibers via electrospinning technique, where an in situ crystallization and growth process of CPB nanoparticles have been established. Meanwhile, both the CPB nanoparticles and PVDF fibers are poled by the electric field during electrospinning process, which promotes the formation of polar phase of PVDF and the distortion of CPB lattice, resulting in greatly enhanced piezoelectric performances of CPB/PVDF composites. The output performances under external force of the flexible generator developed from electrospun CPB/PVDF films are significantly enhanced compared with neat PVDF film, with the maximum Voc value 8.4 times higher; while the measurements on the microscopic piezoelectric responses unambiguously reveal that the increased polar phase mainly contributes to the enhanced electromechanical coupling. The functions of CPB/PVDF film as physiological signals monitoring sensor have been performed, demonstrating its potential applications as flexible piezoelectric generator and wearable health monitoring electronics.

A Data-driven Integrated Safety Risk Warning Model based on Deep Learning for Civil Aircraft

With the extensive application of sensor technology, airlines accumulate a lot of flight data during fleet operations. Quick Access Recorder (QAR) is an important basis for aircraft state estimation and safety assessment, it records the operating status of various aircraft systems. However, the current application of QAR is mainly reflected in the investigation and verification of accidents, but it fails to apply such continuous real-time data to safety monitoring, risk prediction, and early warning. Therefore, this paper proposes a data-driven integrated safety risk warning model based on deep learning. Combined with the characteristics of QAR data and aircraft system failures, the failure mode and impact analysis (FMEA), cause chain analysis (CCA), long and short-term memory (LSTM), and other methods are used to describe the development of risks caused by system failures and predict the trend of flight parameters of each system, improve the ability to predict the risk and risk severity of the aviation system. Taking the Aircraft Refrigeration System (ARS) as the case object, the safety risk warning model is validated to calculate the risk level may face and find out the hidden hazard sources of civil aircraft operation in advance, to improve the safety level of civil aircraft operation

Significant capabilities of SMART sensor technology and their applications for Industry 4.0 in trauma and orthopaedics

The Industry 4.0 revolution has made it possible for the manufacturing of newly customised implants, tools, devices instruments for the medical field. Sensor technology uses embedded or surface devices that detect physical, chemical, biological signals and provide a way of interpretation of these signals to be measured and recorded. Sensor technology products have been established in various branches of medicine. In healthcare these applications have been utilized in the diagnosis, treatment and monitoring of diseases. Innovations in computer and design technology have allowed researchers to expand sensor technology in the field of orthopaedic surgery. SMART (Self- Monitoring Analysis and Reporting Technology) implants along with Sensor technology have been developed to provide improved diagnostic and therapeutic functions in the management of a spectrum of musculoskeletal conditions. SMART implants have evolved due to the advances in microchip technologies. In this article we explore the role of Sensor technology in the branch of trauma and orthopaedics. Experimental studies, in-vivo and current clinical applications of Sensor technology in total hip replacement, total knee replacement, spine surgery, fracture healing assessment is highlighted. Overall the significant capabilities of Sensor technology can be utilized to improve patient satisfaction and functional scores whilst managing their orthopaedic conditions.

A review of land observation satellite remote sensing application technology with new generation artificial intelligence

A review of land observation satellite remote sensing application technology with new generation artificial intelligence