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Plant wearable sensors have shown exceptional promise in continuously monitoring plant health. However, the potential adverse effects of these sensors on plant growth remain unclear. This study systematically quantifies wearable sensors' interference with plant growth using two ornamental species, Peperomia tetraphylla and Epipremnum aureum. We evaluated the impacts of four common disturbances—mechanical pressure, hindrance of gas exchange, hindrance of light acquisition, and mechanical constraint—on leaf growth. Our results indicated that the combination of light hindrance and mechanical constraint demonstrated the most significant interference. When the sensor weight was no greater than 0.6 g and the coverage was no greater than 5% of the leaf area, these four disturbances resulted in slight impacts on leaf growth. Additionally, we fabricated a minimally interfering wearable sensor capable of measuring the air temperature of the microclimate of the plant while maintaining plant growth. This research provides valuable insights into optimizing plant wearable sensors, balancing functionality with minimal plant interference. [ABSTRACT FROM AUTHOR]

In this study, a polymer-based surface plasmon resonance (SPR) sensor for refractive index measurements was designed and manufactured via inkjet 3D printing; then, it was optically characterized. Next, it was investigated how the surface finish of the 3D printed optical waveguide affects the sensor performance, i.e., its sensitivity. More in detail, it was studied how the surface roughness changes with the placement of the 3D printed items on the building platform. To achieve this purpose, a Phase I distribution-free quality monitoring analysis of the selected manufacturing process was implemented for a small pilot production run. The aim was to check the stability of surface roughness versus the placement of the 3D printed parts on the building platform. The 3D printed sensor's surface roughness was assessed through a profilometry study. In particular, the surface roughness was determined for the core of the optical waveguide used to excite the SPR phenomena. Furthermore, the SPR sensors were optically characterized to find the existing relationship between their sensitivity and the considered quality of surface finish. In particular, by varying the surface roughness of the used waveguide, the light scattering in the waveguide changes, and the SPR sensitivity changes too, similarly to the light-diffusing fibers covered by gold nanofilms where the guided light is scattered through a plurality of voids distributed in the core. The procedure followed to investigate the sensor roughness, and establishing their performance enabled the optimal operative range for their application in practice to be identified. Finally, a better knowledge of the 3D printing manufacturing process has been achieved to improve quality of surface finish. [ABSTRACT FROM AUTHOR]

Using absorbent products to manage the urinary incontinence (UI) of dependent residents in care facilities (such as nursing homes, and hospitals) requires frequent routine checks throughout the day and night to see if products need changing. Timely changes of saturated products are necessary to avoid long-lasting skin exposure to wet absorbent products, unpleasant odor, leaking of such products and embarrassing moments for the users. Limited staffing, high workload, and peaks on the demand for caregiving are challenges that hamper swift support for the care dependent population. This paper describes novel sensing technology that has been developed for monitoring the wet state of absorbent products remotely. The Orizon system by Ontex enables caregivers to prioritize care routines, avoid sleep disturbance at night and achieve effective leakage prevention. Moreover, the monitoring data can be used to understand the individual product usage and incontinence pattern of individuals, helping incontinence specialists to choose the optimal product and implement appropriate toilet training for each user. [ABSTRACT FROM AUTHOR]

Tendon–sheath structures are commonly utilized to drive surgical robots due to their compact size, flexibility, and straightforward controllability. However, long-distance cable tension estimation poses a significant challenge due to its frictional characteristics affected by complicated factors. This paper proposes a miniature tension sensor array for an endoscopic cable-driven parallel robot, aiming to integrate sensors into the distal end of long and flexible surgical instruments to sense cable tension and alleviate friction between the tendon and sheath. The sensor array, mounted at the distal end of the robot, boasts the advantages of a small size (16 mm outer diameter) and reduced frictional impact. A force compensation strategy was presented and verified on a platform with a single cable and subsequently implemented on the robot. The robot demonstrated good performance in a series of palpation tests, exhibiting a 0.173 N average error in force estimation and a 0.213 N root-mean-square error. In blind tests, all ten participants were able to differentiate between silicone pads with varying hardness through force feedback provided by a haptic device. [ABSTRACT FROM AUTHOR]

Eddy current displacement sensors (ECDSs) are widely used for the noncontact position measurement of small displacements (lift-offs). Challenges arise with larger displacements as the sensitivity of the ECDSs decreases. This leads to a more pronounced impact of temperature variations on the inductance and, consequently, an increased position error. Design solutions often rely on multiple coils, suitable coil carrier materials, and compensation measures to address the challenges. This study presents a single-coil ECDS for large displacement ranges in environments with high temperatures and temperature variations. The analysis is based on a sensor model derived from an equivalent circuit model (ECM). We propose design measures for both the sensing coil and the target, focusing on material selection to handle the impact of temperature variations. A key part of improving performance under varying temperatures includes model-based temperature compensation for the inductance of the sensing coil. We introduce a method to calibrate the sensor for large displacements, using a modified coupling coefficient based on field simulation data. Our analysis shows that this single-coil ECDS design maintains a position error of less than 0.2% full-scale for a temperature variation of 100 K for the sensing coil and 110 K for the target. [ABSTRACT FROM AUTHOR]

Herein, short review paper investigated the rapidly progress in fluorescent biosensors field and their significant influences on analytical chemistry applications. An electrical signal can be produced from a biological reaction using an integrated receptor-transducer device called a biosensor. Because there are so many uses for biosensors in the medical field, including medication delivery, environmental monitoring, water and food quality monitoring, and illness detection, biosensor design and development have become a top priority for scientists and researchers in the last ten years. In the beginning, we started to explain the basic principles of fluorescence. Then, we moved to discuss about the current advancements, creative sensor designs, and the fusion of materials science and nanotechnology. The paper highlighted the sensitivity of fluorescent biosensors by addressing a wide range of applications, including biological research, environmental monitoring, and medical diagnostics. This review paper censoriously assessed the present difficulties, such as interference and constrained dynamic range, and provided explanations into ongoing research. Advanced material and nanotechnology integration is emerging as a force that improves biosensor performance and broadens their uses. This study also discussed the future possible breakthroughs in biosensor technology and its broad use in a variety of scientific and societal fields.

Identification and control of the pollutant load in sewer networks (SNs) are among the priorities for utilities to reduce the impact on water bodies and to individuate the presence of pathogens to prevent their further spread, sometimes through interventions on a social scale. This goal can be achieved essentially through the development of a monitoring system. This paper proposes a backtracking methodology for efficiently planning a monitoring system in SNs assuming steady-state conditions during the analysis. The methodology is based on the calculation of the impact coefficient, which is related to the dilution and decay of contaminants and pathogens in the network, to evaluate the impact of each possible contaminated node on a downstream one in terms of concentration. This information supports the identification of candidate monitoring points, i.e., where to place measurement sensors to ensure complete coverage and control of the network. Additional analysis has been performed considering unsteady conditions for comparing the impact coefficient values averaged over 24 h and those of the steady-state methodology. Results show a similar value between steady-state and unsteady conditions, thus justifying the use of steady-state conditions for the proposed methodology, and also for real practical applications, with a significant improvement in terms both of simplicity and computational time saving. [ABSTRACT FROM AUTHOR]

Tendon–sheath structures are commonly utilized to drive surgical robots due to their compact size, flexibility, and straightforward controllability. However, long-distance cable tension estimation poses a significant challenge due to its frictional characteristics affected by complicated factors. This paper proposes a miniature tension sensor array for an endoscopic cable-driven parallel robot, aiming to integrate sensors into the distal end of long and flexible surgical instruments to sense cable tension and alleviate friction between the tendon and sheath. The sensor array, mounted at the distal end of the robot, boasts the advantages of a small size (16 mm outer diameter) and reduced frictional impact. A force compensation strategy was presented and verified on a platform with a single cable and subsequently implemented on the robot. The robot demonstrated good performance in a series of palpation tests, exhibiting a 0.173 N average error in force estimation and a 0.213 N root-mean-square error. In blind tests, all ten participants were able to differentiate between silicone pads with varying hardness through force feedback provided by a haptic device.

We consider a convex set Ω and look for the optimal convex sensor ω ⊂ Ω of a given measure that minimizes the maximal distance to the points of Ω. This problem can be written as follows inf { d H (ω , Ω) | | ω | = c and ω ⊂ Ω } , where c ∈ (0 , | Ω |) , d H being the Hausdorff distance. We show that the parametrization via the support functions allows us to formulate the geometric optimal shape design problem as an analytic one. By proving a judicious equivalence result, the shape optimization problem is approximated by a simpler minimization problem of a quadratic function under linear constraints. We then present some numerical results and qualitative properties of the optimal sensors and exhibit an unexpected symmetry breaking phenomenon. [ABSTRACT FROM AUTHOR]

We designed an out-of-water radar water velocity and depth sensor, which is unique due to its low cost and low power consumption. The sensor is a first at a cost of less than USD 50, which is well suited to previously cost-prohibited high-resolution monitoring schemes. This use case is further supported by its out-of-water operation, which provides low-effort installations and longer maintenance-free intervals when compared with in-water sensors. The inclusion of both velocity and depth measurement capabilities allows the sensor to also be used as an all-in-one solution for flowrate measurement. We discuss the design of the sensor, which has been made freely available under open-hardware and open-source licenses. The design uses commonly available electronic components, and a 3D-printed casing makes the design easy to replicate and modify. Not before seen on a hydrology sensor, we include a 3D-printed radar lens in the casing, which boosts radar sensitivity by 21 dB. The velocity and depth-sensing performance were characterised in laboratory and in-field tests. The depth is accurate to within ±6% and ±7 mm and the uncertainty in the velocity measurements ranges from less than 30% to 36% in both laboratory and field conditions. Our sensor is demonstrated to be a feasible low-cost design which nears the uncertainty of current, yet more expensive, velocity sensors, especially when field performance is considered. [ABSTRACT FROM AUTHOR]

In this work we propose a method to detect turmeric adulteration using the Cavity Perturbation Technique (CPT) at 2.4GHz. Two different adulterants are examined (egg-yellow color and starch). We show that when a single adulterant is added, the resonant frequency and unloaded quality factor values follow clear trends as a function of added contaminant. Unfortunately, when the turmeric is adulterated with different concentrations of two adulterants (e.g., a 50% color/50% starch) CPT does not lead to good results. To address this, we also present an automated machine learning flow that dramatically enhances the adulteration detection. The proposed flow has the additional uniqueness that it optimizes the predictive model based on the selected target hardware platform doing technology independent as well as technology dependent model optimizations. Experimental results show that our predictive model can be optimized based on the accuracy required for different hardware platforms. In particular we target a microcontroller and a dedicated hardware implementation.

The reliable monitoring of heart rate during intense exercise is imperative to effectively manage training loads while providing insights from a healthcare perspective. However, current technologies perform poorly in contact sports settings. This study aims to evaluate the best approach for heart rate tracking using photoplethysmography sensors embedded into an instrumented mouthguard (iMG). Seven adults wore iMGs and a reference heart rate monitor. Several sensor placements, light sources and signal intensities were explored for the iMG. A novel metric related to the positioning of the sensor in the gum was introduced. The error between the iMG heart rate and the reference data was assessed to obtain insights into the effect of specific iMG configurations on measurement errors. Signal intensity was found to be the most important variable for error prediction, followed by the sensor light source, sensor placement and positioning. A generalized linear model combining an infrared light source, at an intensity of 5.08 mA, and a frontal placement high in the gum area resulted in a heart rate minimum error of 16.33%. This research shows promising preliminary results for the use of oral-based heart rate monitoring, but highlights the need for the careful consideration of sensor configurations within these systems. [ABSTRACT FROM AUTHOR]

Tracking of the sun, which increases the efficiency of solar energy production systems, has shown considerable development in recent years. This development has been achieved by custom-positioned light sensors, image cameras, sensorless chronological systems and intelligent controller supported systems or by synergetic use of these systems. This study contributes to this research area with a novel spherical-based sensor which measures spherical light source emittance and localizes the light source. This sensor was built by using miniature light sensors placed on a spherical shaped three-dimensional printed body with data acquisition electronic circuitry. Besides the developed sensor data acquisition embedded software, preprocessing and filtering processes were conducted on these measured data. In the study, the outputs of Moving Average, Savitzky-Golay, and Median filters were used for the localization of the light source. The center of gravity for each filter used was determined as a point, and the location of the light source was determined. The spherical sensor system obtained by this study is applicable for various solar tracking methods. The approach of the study also shows that this measurement system is applicable for obtaining the position of local light sources such as the ones placed on mobile or cooperative robots. [ABSTRACT FROM AUTHOR]

Ammonium (NH4+) ions are a primary contaminant in the river and along the waterside near an agricultural area, therefore, necessitating sensitive detection of pollutants before irreversibly damaging environment. Herein, a new approach of metal-organic framework-derived tungsten ethoxide/polypyrrole-reduced graphene oxide (MOFs-W(OCH2CH3)6/Ppy-rGO) electrochemical sensors are introduced. Through a simple hydrothermal process, Ppy-rGO is linked to tungsten ethoxide as an organic linker. This creates the MOFs-W(OCH2CH3)6/Ppy-rGO nanocrystal through hydrogen bonding. The synergistic combination of tungsten ethoxide and Ppy-rGO provides three-fold advantages: stabilization of Ppy-rGO for extended usage, enabling detection of analytes at ambient temperature, and availability of multiple pathways for effective detection of analytes. This is demonstrated through excellent detection of NH4+ ions over a dynamic concentration range of 0.85 to 3.35 µM with a ppb level detection limit of 0.278 µM (9.74 ppb) and a quantitation limit of 0.843 µM (29.54 ppb). The increment in the concentration of NH4+ ions contributes to the increment in proton (H+) concentration. The increment in proton concentration in the solution will increase the bonding activity and thus increase the conductivity. The cyclic voltammetry curves of all concentrations of NH4+ analytes at the operating potential window between −1.5 and 1.5 V exhibit a quasi-rectangular shape, indicating consistent electronic and ionic transport. The distinctive resistance changes of the MOFs-W(OCH2CH3)6/Ppy-rGO to various NH4+ ion concentrations and ultrasensitive detection provide an extraordinary platform for its application in the agriculture industry. [ABSTRACT FROM AUTHOR]

Dielectric elastomer sensors (DESs) have been known as highly stretchable strain sensors for about two decades. They are composite films consisting of alternating dielectric and electrode layers. Their electrical capacitance between the electrodes is enhanced upon stretching. In this paper, a variety of advanced designs of DESs is introduced. An explanation of how these sensors work and how they perform in terms of capacitance versus deformation or load force is provided. Moreover, the paper describes how the sensor design affects the sensor characteristics in order to achieve a high measuring sensitivity. The most relevant quantities to be measured are distance variations or elongations, forces and pressure loads. It is demonstrated that the sensor design can be supported by Finite Element Method (FEM) simulations. In the second part of the paper, possible applications of the advanced DESs are outlined. Pure sensor applications to detect or monitor pressure or deformation are distinguished from other applications, where sensors form a part of a human–machine interface (HMI). DESs are predestined to be used in contact with the human body due to their softness and flexibility. In the case of an HMI, a dosed load on a sensor by the user's hand enables the remote control of arbitrary technical functions. This can preferably be realized with an operating glove, which exhibits different categories of DESs. Possible applications of DESs are described with the support of functional demonstrators. [ABSTRACT FROM AUTHOR]

Thin-film heat flux sensor is suitable for the harsh environment in aerospace application. To further improve the accuracy of testing and to realize the measurement in two directions, this work proposes a new design of the thin-film heat flux sensor. Based on the numerical simulation method, this work investigates the influence of geometric parameters, physical property and working conditions on the sensor test error, and analyzes the testing performances under steady and transient states. Results indicate that the sensor test error is mainly related to the thermal resistance ratio of the sensor in the measurement system. The accuracy of the thin-film heat flux sensor increases with the increase of sensor size or local total thermal resistance. Besides, the newly designed sensor has satisfying steady and transient characteristics, including the small time constant, fast response, and good linear relationship between the measured temperature difference and the surface heat flux.

The growth of renewable energy sources presents a pressing challenge to the operation and maintenance of existing fossil fuel power plants, given that fossil fuel remains the predominant fuel source, responsible for over 60% of electricity generation in the United States. One of the main concerns within these fossil fuel power plants is the unpredictable failure of boiler tubes, resulting in emergency maintenance with significant economic and societal consequences. A reliable high-temperature sensor is necessary for in situ monitoring of boiler tubes and the safety of fossil fuel power plants. In this study, a comprehensive four-stage multi-physics computational framework is developed to assist the design, optimization installation, and operation of the high-temperature stainless-steel and quartz coaxial cable sensor (SSQ-CCS) for coal-fired boiler applications. With the consideration of various operation conditions, we predict the distributions of flue gas temperatures within coal-fired boilers, the temperature correlation between the boiler tube and SSQ-CCS, and the safety of SSQ-CCS. With the simulation-guided sensor installation plan, the newly designed SSQ-CCSs have been employed for field testing for more than 430 days. The computational framework developed in this work can guide the future operation of coal-fired plants and other power plants for the safety prediction of boiler operations.

In this work, a surface plasmon resonance (SPR) multiparameter sensor for simultaneous determination of refractive index and temperature was manufactured through a novel and low-cost approach. Monitoring these parameters is useful when biosensors are developed by exploiting SPR phenomena. A polymer planar optical structure was realized via inkjet 3D printing, by using photo-curable resins having tailored refractive index for device's core and cladding, respectively. The multiparameter sensor was fully designed, manufactured, and experimentally tested to check the numerical analyses run on a preliminary phase. In such a way, a temperature resolution equal to about 0.5 °C and a refractive index resolution equal to about 2 × 10−4 RIU (refractive index unit) were obtained. Next, even a quality control analysis of the 3D printed surface was carried out by following a novel approach that relies on the profile monitoring technique, with the aim to evaluate the suitability of the design and the geometric accuracy control. In addition, thanks to the cost analysis performed through a properly model, it was proved that the multiparameter sensor designed, manufactured, and tested satisfies the low-cost requirements, being the estimated cost ~ 23 €, which is an absolutely competitive cost if compared with other traditional sensors. In the end, even the performance of the sensor in terms of bulk sensitivity (equal to about 900 nm/RIU) resulted to be higher than similar devices already presented in the state-of-the-art, thus proving the validity of the developed SPR multiparameter sensor both in economic and performance terms. [ABSTRACT FROM AUTHOR]

Tea polyphenols, amino acids, soluble sugars, and other ingredients in fresh tea leaves are the key parameters of tea quality. In this research, a tea leaf ingredient estimation sensor was developed based on a multi-channel spectral sensor. The experiment showed that the device could effectively acquire 700–1000 nm spectral data of tea tree leaves and could display the ingredients of leaf samples in real time through the visual interactive interface. The spectral data of Fuding white tea tree leaves acquired by the detection device were used to build an ingredient content prediction model based on the ridge regression model and random forest algorithm. As a result, the prediction model based on the random forest algorithm with better prediction performance was loaded into the ingredient detection device. Verification experiment showed that the root mean square error (RMSE) and determination coefficient (R2) in the prediction were, respectively, as follows: moisture content (1.61 and 0.35), free amino acid content (0.16 and 0.79), tea polyphenol content (1.35 and 0.28), sugar content (0.14 and 0.33), nitrogen content (1.15 and 0.91), and chlorophyll content (0.02 and 0.97). As a result, the device can predict some parameters with high accuracy (nitrogen, chlorophyll, free amino acid) but some of them with lower accuracy (moisture, polyphenol, sugar) based on the R2 values. The tea leaf ingredient estimation sensor could realize rapid non-destructive detection of key ingredients affecting tea quality, which is conducive to real-time monitoring of the current quality of tea leaves, evaluating the status during tea tree growth, and improving the quality of tea production. The application of this research will be helpful for the automatic management of tea plantations. [ABSTRACT FROM AUTHOR]

• A physics-based multilayer ultrasonic wave propagation model is proposed. • Micro-sized ultrasonic fingerprint sensors with high performances are achieved. • The sensor exhibits broad bandwidth, high sensitivity, and high imaging resolution. • It provides valuable design insights for improving ultrasonic sensor performance. Under-display ultrasonic fingerprint imaging presents a promising approach for smart terminal device authentication, which requires ultrasonic waves to penetrate organic light-emitting diode (OLED) screen composed of multiple layers, necessitating ultrasonic sensors with high sensitivity. To address this issue, this study first establishes a physics-based multi-layer model that describes the ultrasonic wave propagation behavior in an OLED screen. The optimal design parameters of the ultrasonic fingerprint sensor are explored by using the multi-layer model and the Krimholtz-Leedom-Matthaei (KLM) model. A PIN-PMN-PT single crystal-based micro-sized ultrasonic sensor with a center frequency of 33 MHz is then successfully fabricated, simultaneously achieving small aperture size (300 µm × 300 µm) and high performance. The sensor exhibits a broad bandwidth (−6 dB: 77.01 %) and high sensitivity (−42.55 dB), as well as superior spatial resolution (lateral: 112.48 μm, axial: 45.06 μm). This study provides valuable design guidance for enhancing ultrasonic fingerprint sensor performances. [ABSTRACT FROM AUTHOR]

This paper presents a review of two-dimensional (2D) and three-dimensional (3D) electrical tomography (ET) hardware accelerators for real-time applications. While many recent review papers have discussed various algorithms for image reconstruction or acquisition systems, none of them has considered state-of-the-art hardware implementations of the associated image reconstruction algorithms to achieve real-time performance, especially for 3D ET where the computation requirement is excessively high. A 3D ET is useful in various applications such as robotics, autonomous vehicles, and process control, but it is computationally very expensive with respect to its 2D counterpart. Most implementations are based on single or multi-core CPUs and, to a lesser extent, on either graphics processing units (GPUs) or field programmable gate arrays (FPGAs). However, there is a clear gap between the currently available processors, whose computation power exceeds hundreds of teraflops per second (TOPS) at a reasonable low power consumption, and the ones recently used in ET systems. This gives great potential for next-generation ET systems to achieve real-time 2D and 3D ET reconstruction within a small form factor. The paper summarizes the most recent ET hardware systems with respect to their performance in terms of quality and processing frame rate, reconstruction methods, along with optimization and future directions.

Electrical impedance tomography (EIT) is a non-invasive detection technology that uses the electrical response value at the boundary of an observation field to image the conductivity changes in an area. When EIT is applied to the thoracic cavity of the human body, the conductivity change caused by the heartbeat will be concentrated in a sub-region of the thoracic cavity, that is, the heart region. In order to improve the spatial resolution of the target region, two sensor optimization methods based on conformal mapping theory were proposed in this study. The effectiveness of the proposed method was verified by simulation and phantom experiment. The qualitative analysis and quantitative index evaluation of the reconstructed image showed that the optimized model could achieve higher imaging accuracy of the heart region compared with the standard sensor. The reconstruction results could effectively reflect the periodic diastolic and systolic movements of the heart and had a better ability to recognize the position of the heart in the thoracic cavity. [ABSTRACT FROM AUTHOR]

By enabling the non-invasive monitoring and quantification of biomolecular processes, molecular imaging has dramatically improved our understanding of disease. In recent years, non-invasive access to the molecular drivers of health versus disease has emboldened the goal of precision health, which draws on concepts borrowed from process monitoring in engineering, wherein hundreds of sensors can be employed to develop a model which can be used to preventatively detect and diagnose problems. In translating this monitoring regime from inanimate machines to human beings, precision health posits that continual and on-the-spot monitoring are the next frontiers in molecular medicine. Early biomarker detection and clinical intervention improves individual outcomes and reduces the societal cost of treating chronic and late-stage diseases. However, in current clinical settings, methods of disease diagnoses and monitoring are typically intermittent, based on imprecise risk factors, or self-administered, making optimization of individual patient outcomes an ongoing challenge. Low-cost molecular monitoring devices capable of on-the-spot biomarker analysis at high frequencies, and even continuously, could alter this paradigm of therapy and disease prevention. When these devices are coupled with molecular imaging, they could work together to enable a complete picture of pathogenesis. To meet this need, an active area of research is the development of sensors capable of point-of-care diagnostic monitoring with an emphasis on clinical utility. However, a myriad of challenges must be met, foremost, an integration of the highly specialized molecular tools developed to understand and monitor the molecular causes of disease with clinically accessible techniques. Functioning on the principle of probe-analyte interactions yielding a transducible signal, probes enabling sensing and imaging significantly overlap in design considerations and targeting moieties, however differing in signal interpretation and readout. Integrating molecular sensors with molecular imaging can provide improved data on the personal biomarkers governing disease progression, furthering our understanding of pathogenesis, and providing a positive feedback loop toward identifying additional biomarkers and therapeutics. Coupling molecular imaging with molecular monitoring devices into the clinical paradigm is a key step toward achieving precision health. [ABSTRACT FROM AUTHOR]

Current commercial sensors to monitor water flow velocities are expensive, bulky, and require significant effort to install. Low-cost sensors open the possibility of monitoring storm and waste water systems at a much greater spatial and temporal resolution without prohibitive costs and resource investment. To aid in this, this work developed a low-cost, low-power velocity sensor based on acoustic Doppler velocimetry. The sensor, costing less than 50 USD is open-source, open-hardware, compact, and easily interfaceable to a wide range of data-logging systems. A freely available sensor design at this price point does not currently exist, and its novelty is in enabling high-resolution real-time monitoring schemes. The design is capable of measuring water velocities up to 1200 mm/s. The sensor is characterised and then verified in an in-field long-term test. Finally, the data from this test are then used to evaluate the performance of the sensor in a real-world scenario. The analysis concludes that the sensor is capable of effectively measuring water velocity. [ABSTRACT FROM AUTHOR]

Simultaneous estimation of thermal properties can be challenging, especially when the parameters are temperature-dependent. Previous research has shown that by using a complementary experiment, temperature-dependent thermal conductivity can be estimated using a single experiment. The objective of this study was to optimize the complementary experiments that can facilitate the simultaneous estimation of temperature-dependent thermal conductivity and volumetric heat capacity. A theoretical study was conducted with two experiments in a single trial with the sample being kept in a cylindrical sample holder, which had a thin film heater in the center. The first part of the experiment was conducted by keeping the external surface temperature at 50 °C for 300 s and allowing the center temperature to equilibrate with the boundary temperature. Then, the second part of the experiment followed, where the thin film heater was supplied with electrical power to increase the center temperate to 140 °C. Several heating profiles were studied to maximize the information obtained from the complementary experiments, and the best one was the power profile with a sinusoidal function. All four parameters of sweet potato puree temperature-dependent thermal conductivity (0.509 to 0.629 W/mK at 25 °C and 140 °C, respectively) and volumetric heat capacity (3.617 × 106 to 4.180 × 106 J/m3K at 25 °C and 140 °C, respectively) were estimated with low standard errors. [ABSTRACT FROM AUTHOR]

The manufacturing of flexible strain sensors for wearable electronics usually requires different conductive materials for the sensing part and the connection part. This increases the complexity, cost, and performance issues due to the mismatch of the thermo-electro-mechanical properties of the materials. Herein, a new design scheme using a single conductive material is presented for a low-cost mass-producible fabric strain sensor, where a carbon/silicone nanocomposite is screen-printed to make both parts. By exploring the dimension effect and modelling of the conductive tracks, and adopting a large difference of over 100 times in aspect ratio, this research makes the electrical response of the fabric strain sensor depend almost exclusively on the sensing part, while its connection part has a low resistance. The sensor exhibits outstanding performance with a wide working range (60% strain), adequate linearity, long fatigue life (∼50,000 cycles), and mechanical robustness, rendering it suitable for human body movement detection. Moreover, the manufacturing process is simple and low-cost ($11 per m2). Thus, the new design scheme overcomes the mismatch issue and provides an important reference value for the design of flexible resistive sensors working in a high resistance range, from ∼ 100 KΩ to several MΩ.

Due to the extremely small size and arbitrary orientation of the cracks, a highly sensitive sensor based on the balanced-field electromagnetic technique was designed for in-line inspection of oil and gas pipeline cracks. A balanced-field electromagnetic technique sensor mutual inductance model was established and used to theoretically analyze the parameters affecting sensitivity. Finite element simulation was used to analyze the specific effects of the magnetically conductive medium, the number of coil turns, and the sensor lift-off height on the sensor output, respectively, and the sensor parameters of high sensitivity were determined. The detection effect of the sensor on the pipeline crack was tested by the single-sensor experiment and the pulling test. The results show that the designed balanced-field electromagnetic technique sensor is effective in detecting both circumferential and axial cracks of 0.5 to 6 mm in depth. As the crack depth increases, the sensitivity decreases and the detection voltage amplitude increases linearly. The sensitivity of the sensor is highest when detecting circumferential and axial cracks of 1 mm in depth at 1.76 and 0.87 mV/mm, respectively. In addition, the amplitude of the circumferential crack signal at the same depth is approximately twice that of the axial crack signal. [ABSTRACT FROM AUTHOR]

Electrochemical Impedance Spectroscopy (EIS) is an analytical technique widely used in electrochemistry. EIS is being commonly used by researchers in biosensor development, corrosion study, battery, solar cell and fuel cell performance measurement etc. Due to the availability of high precision analog-to-digital and digital-to-analog converters available on microcontroller chips at low costs, EIS based purpose-built consumer electronic devices becoming more commonplace now. This tutorial paper presents the essential theoretical knowledge to understand the EIS and how the practical parameters are selected for an EIS measurement for a novice. The theoretical explanation of EIS is followed by a section with examples of the use of EIS in electrochemical biosensor development and performance measurement of batteries. This paper finally concludes on the advantages of the use of EIS as an analytical technique. [ABSTRACT FROM AUTHOR]

The reliable monitoring of heart rate during intense exercise is imperative to effectively manage training loads while providing insights from a healthcare perspective. However, current technologies perform poorly in contact sports settings. This study aims to evaluate the best approach for heart rate tracking using photoplethysmography sensors embedded into an instrumented mouthguard (iMG). Seven adults wore iMGs and a reference heart rate monitor. Several sensor placements, light sources and signal intensities were explored for the iMG. A novel metric related to the positioning of the sensor in the gum was introduced. The error between the iMG heart rate and the reference data was assessed to obtain insights into the effect of specific iMG configurations on measurement errors. Signal intensity was found to be the most important variable for error prediction, followed by the sensor light source, sensor placement and positioning. A generalized linear model combining an infrared light source, at an intensity of 5.08 mA, and a frontal placement high in the gum area resulted in a heart rate minimum error of 16.33%. This research shows promising preliminary results for the use of oral-based heart rate monitoring, but highlights the need for the careful consideration of sensor configurations within these systems.

• Non-destructive evaluation techniques in measurement applications require transferring complex signals to usable quantities. • Equivalent circuit models for eddy current sensors evaluated for their voltage transfer functions are those of lossy transformers. • Splitting transformer coupling and eddy-current losses enables simple models, which can be automatically constructed. • Implementation of parameter identification in a real-time soft sensor is possible. • This soft sensor can be used to measure material properties in a repeatable fashion. The use of eddy-current techniques as a measurement method for material characterization instead of simple fault detection requires transformation of the electrical response signals to quantifiable physical parameters that correlate with material properties. Apart from calibration-curve methods, exact approaches that correctly represent all interactions between excitation field and response field are computationally intensive and not well suited to real-time application. In this work, an equivalent circuit network model is constructed and its analytical equation for the voltage transfer function is derived. This model is then implemented in a soft sensor system. Finally, measurement results from various materials with different electromagnetic properties are presented. [ABSTRACT FROM AUTHOR]

Tracking of the sun, which increases the efficiency of solar energy production systems, has shown considerable development in recent years. This development has been achieved by custom-positioned light sensors, image cameras, sensorless chronological systems and intelligent controller supported systems or by synergetic use of these systems. This study contributes to this research area with a novel spherical-based sensor which measures spherical light source emittance and localizes the light source. This sensor was built by using miniature light sensors placed on a spherical shaped three-dimensional printed body with data acquisition electronic circuitry. Besides the developed sensor data acquisition embedded software, preprocessing and filtering processes were conducted on these measured data. In the study, the outputs of Moving Average, Savitzky-Golay, and Median filters were used for the localization of the light source. The center of gravity for each filter used was determined as a point, and the location of the light source was determined. The spherical sensor system obtained by this study is applicable for various solar tracking methods. The approach of the study also shows that this measurement system is applicable for obtaining the position of local light sources such as the ones placed on mobile or cooperative robots.

Dielectric elastomer sensors (DESs) have been known as highly stretchable strain sensors for about two decades. They are composite films consisting of alternating dielectric and electrode layers. Their electrical capacitance between the electrodes is enhanced upon stretching. In this paper, a variety of advanced designs of DESs is introduced. An explanation of how these sensors work and how they perform in terms of capacitance versus deformation or load force is provided. Moreover, the paper describes how the sensor design affects the sensor characteristics in order to achieve a high measuring sensitivity. The most relevant quantities to be measured are distance variations or elongations, forces and pressure loads. It is demonstrated that the sensor design can be supported by Finite Element Method (FEM) simulations. In the second part of the paper, possible applications of the advanced DESs are outlined. Pure sensor applications to detect or monitor pressure or deformation are distinguished from other applications, where sensors form a part of a human–machine interface (HMI). DESs are predestined to be used in contact with the human body due to their softness and flexibility. In the case of an HMI, a dosed load on a sensor by the user’s hand enables the remote control of arbitrary technical functions. This can preferably be realized with an operating glove, which exhibits different categories of DESs. Possible applications of DESs are described with the support of functional demonstrators.

Ammonium (NH4+) ions are a primary contaminant in the river and along the waterside near an agricultural area, therefore, necessitating sensitive detection of pollutants before irreversibly damaging environment. Herein, a new approach of metal-organic framework-derived tungsten ethoxide/polypyrrole-reduced graphene oxide (MOFs-W(OCH2CH3)6/Ppy-rGO) electrochemical sensors are introduced. Through a simple hydrothermal process, Ppy-rGO is linked to tungsten ethoxide as an organic linker. This creates the MOFs-W(OCH2CH3)6/Ppy-rGO nanocrystal through hydrogen bonding. The synergistic combination of tungsten ethoxide and Ppy-rGO provides three-fold advantages: stabilization of Ppy-rGO for extended usage, enabling detection of analytes at ambient temperature, and availability of multiple pathways for effective detection of analytes. This is demonstrated through excellent detection of NH4+ ions over a dynamic concentration range of 0.85 to 3.35 µM with a ppb level detection limit of 0.278 µM (9.74 ppb) and a quantitation limit of 0.843 µM (29.54 ppb). The increment in the concentration of NH4+ ions contributes to the increment in proton (H+) concentration. The increment in proton concentration in the solution will increase the bonding activity and thus increase the conductivity. The cyclic voltammetry curves of all concentrations of NH4+ analytes at the operating potential window between −1.5 and 1.5 V exhibit a quasi-rectangular shape, indicating consistent electronic and ionic transport. The distinctive resistance changes of the MOFs-W(OCH2CH3)6/Ppy-rGO to various NH4+ ion concentrations and ultrasensitive detection provide an extraordinary platform for its application in the agriculture industry.

The HyperSpectral Imager for Climate Science (HySICS) is the core instrument of the Climate Absolute Refractivity and Reflectance Observatory (CLARREO) Pathfinder (CPF) mission and is currently scheduled to be launched to the International Space Station (ISS) in 2023. HySICS is an Offner–Chrisp imaging spectrometer designed to meet an unprecedented radiometric uncertainty requirement of 0.3% (k = 1) over its entire spectral range of 350–2300 nm. The approach represents the need for significant improvement over the Radiometric Calibration (RadCal) of existing space-borne spectrometers. One strategy to demonstrate that HySICS achieves this level of accuracy is through an Independent Calibration (IndCal) effort that can provide an alternative referencing RadCal, which follows a traceability chain independent of the operational RadCal of ratioing approach. The IndCal relies on a pre-launch detector-based absolute RadCal of HySICS, using a tunable laser system as source, and the system planned for the HySICS absolute RadCal is the Goddard Laser for Absolute Measurement of Radiance (GLAMR). GLAMR was developed at NASA's Goddard Space Flight Center and has been used to calibrate multiple operational remote sensing instruments, as well as the SOlar, Lunar Absolute Reflectance Imaging Spectroradiometer (SOLARIS), a calibration demonstration system developed for the CLARREO mission. In this work, the data of SOLARIS GLAMR RadCal conducted in 2019 are processed to derive the Absolute Spectral Response (ASR) functions and other key characterization parameters of SOLARIS detectors. The results are further analyzed with the goals to plan the HySICS GLAMR RadCal, in particular to optimize its configuration, to demonstrate the traceability route to the NIST standard, and to develop the error budget of the calibration approach. The SOLARIS calibration is also compared with other source- and detector-based calibrations to validate the absolute radiometric accuracy achieved. [ABSTRACT FROM AUTHOR]

Untethered soft robots that locomote using electrothermally-responsive materials like shape memory alloy (SMA) face challenging design constraints for sensing actuator states. At the same time, modeling of actuator behaviors faces steep challenges, even with available sensor data, due to complex electrical-thermal-mechanical interactions and hysteresis. This article proposes a framework for in-situ sensing and dynamics modeling of actuator states, particularly temperature of SMA wires, which is used to predict robot motions. A planar soft limb is developed, actuated by a pair of SMA coils, that includes compact and robust sensors for temperature and angular deflection. Data from these sensors are used to train a neural network-based on the long short-term memory (LSTM) architecture to model both unidirectional (single SMA) and bidirectional (both SMAs) motion. Predictions from the model demonstrate that data from the temperature sensor, combined with control inputs, allow for dynamics predictions over extraordinarily long open-loop timescales (10 min) with little drift. Prediction errors are on the order of the soft deflection sensor’s accuracy. This architecture allows for compact designs of electrothermally-actuated soft robots that include sensing sufficient for motion predictions, helping to bring these robots into practical application.

Previously, a conductivity invariance phenomena (CIP) has been discovered - at a certain lift-off, the inductance change of the sensor due to a test sample is immune to conductivity variations, i.e. the inductance - lift-off curve passes through a common point at a certain lift-off, termed as conductivity invariance lift-off. However, this conductivity invariance lift-off is fixed for a particular sensor setup, which is not convenient for various sample conditions. In this paper, we propose using two parameters in the coil design - the horizontal and vertical distances between the transmitter and the receiver to control the conductivity invariance lift-off. The relationship between these two parameters and the conductivity invariance lift-off is investigated by simulation and experiments and it has been found that there is an approximate linear relationship between these two parameters and the conductivity invariance lift-off. This is useful for applications where the measurements have restrictions on lift-off, e.g. uneven coating thickness which limits the range of the lift-off of probe during the measurements. Therefore, based on this relationship, it can be easier to adjust the configuration of the probe for a better inspection of the test samples.

Tea polyphenols, amino acids, soluble sugars, and other ingredients in fresh tea leaves are the key parameters of tea quality. In this research, a tea leaf ingredient estimation sensor was developed based on a multi-channel spectral sensor. The experiment showed that the device could effectively acquire 700–1000 nm spectral data of tea tree leaves and could display the ingredients of leaf samples in real time through the visual interactive interface. The spectral data of Fuding white tea tree leaves acquired by the detection device were used to build an ingredient content prediction model based on the ridge regression model and random forest algorithm. As a result, the prediction model based on the random forest algorithm with better prediction performance was loaded into the ingredient detection device. Verification experiment showed that the root mean square error (RMSE) and determination coefficient (R2) in the prediction were, respectively, as follows: moisture content (1.61 and 0.35), free amino acid content (0.16 and 0.79), tea polyphenol content (1.35 and 0.28), sugar content (0.14 and 0.33), nitrogen content (1.15 and 0.91), and chlorophyll content (0.02 and 0.97). As a result, the device can predict some parameters with high accuracy (nitrogen, chlorophyll, free amino acid) but some of them with lower accuracy (moisture, polyphenol, sugar) based on the R2 values. The tea leaf ingredient estimation sensor could realize rapid non-destructive detection of key ingredients affecting tea quality, which is conducive to real-time monitoring of the current quality of tea leaves, evaluating the status during tea tree growth, and improving the quality of tea production. The application of this research will be helpful for the automatic management of tea plantations.

Basedon the Acoustoelectric (AE) effect, acoustoelectric brain imaging (ABI) is of potential for brain imaging with high spatial resolution. In this study, a symmetrical sensor configuration is designed for ABI. Taking four pairs of electrodes as an example, the electrodes are evenly distributed, and, for each electrode pair, corresponding recording electrode and reference electrode are symmetrical about the center of the measurement object. First, the influence of the angle between electrode sensor and dipole is investigated. Results show that the angle influences the image quality, and, when the angle is zero, the correlation coefficient is the largest, with a value of 0.89. Further, multi-electrode influence is also discussed. Compared with single electrode, multi-electrode behaves better in image reconstruction, with the correlation coefficient increased from 0.81(single) to 0.99(eight pairs). For multi-electrode, compared with that of uneven distribution, the average correlation coefficient of even distribution increases from 0.75 to 0.99. Finally, the performance of the proposed sensor configuration is tested with both simulation and phantom experiment. In simulation, compared with that of asymmetrical sensor, the average correlation coefficient of symmetrical sensor improves from 0.85 to 0.87. For phantom experiment, the AE signal SNR increases by 8.9% and correlation coefficient of reconstructed image increases from 0.63 to 0.65. Phantom experiment results validate that the symmetrical sensor is more accurate for ABI, which further verify the results in simulation. With high SNR of AE signal and more accurate reconstructed image, the proposed sensor configuration is of significance in the further research and application of ABI. [ABSTRACT FROM AUTHOR]