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The autonomous operation of turbojets requires reliable, accurate, and manageable dynamical models for several key processes. This article describes a practical robust method for obtaining turbojet thrust and shaft speed models from experimental data. The proposed methodology combines several data mining tools with the intention of handling typical difficulties present during experimental turbojet modeling, such as high noise levels and uncertainty in the plant dynamics. The resulting shaft speed and thrust models achieved a percentage error of 0.8561% and 3.3081%, respectively, for the whole operating range. The predictive power of the resulting models is also assessed in the frequency domain. The turbojet cut frequencies are experimentally determined and were found to match those predicted by the identified models. Finally, the proposed strategy is systematically tested with respect to popular aeroengine models, outperforming them both in the time and frequency domains. These results allow us to conclude that the proposed modeling method improves current modeling approaches in both manageability and predictive power. [ABSTRACT FROM AUTHOR]

Rigorous testing of automotive sensors for accuracy and precision is essential before finalizing designs and proceeding with mass production. This testing process plays a crucial role in identifying these sensors’ potential shortcomings, which are essential components in the Advanced Driver Assistance Systems (ADAS). A virtual environment for sensor model simulation and testing provides a significant advantage in experimenting with the sensor in a vast array of realistic scenarios, which may be cost-prohibitive in physical testing. Thus, radar sensors, a commonly used sensor in ADAS, have been modeled using various methodologies in virtual environments. However, there is a need for a radar model that is both computationally efficient and accurate under different combinations of radar reflectivity, directivity, and signal-to-noise ratio (SNR). In this study, we propose a model of a Delphi-ESR 2.5 medium-range automotive radar in the Unreal Engine environment that leverages grid-based DBSCAN for point-cloud clustering and radar equations to calculate the SNR of grid points, facilitating object detection and tracking. The proposed model incorporates the reflectivity and directivity concept of Radar Cross-Section (RCS) to emulate the behavior of an actual radar. The sensor model exhibits superior accuracy and low computational requirement, with a mean error rate of 10.8% and an output rate of 8.7 Hz. This approach provides a reliable means of simulating and testing automotive radar sensors in a virtual environment, contributing to the further advancements of the ADAS systems.

The autonomous operation of turbojets requires reliable, accurate, and manageable dynamical models for several key processes. This article describes a practical robust method for obtaining turbojet thrust and shaft speed models from experimental data. The proposed methodology combines several data mining tools with the intention of handling typical difficulties present during experimental turbojet modeling, such as high noise levels and uncertainty in the plant dynamics. The resulting shaft speed and thrust models achieved a percentage error of 0.8561% and 3.3081%, respectively, for the whole operating range. The predictive power of the resulting models is also assessed in the frequency domain. The turbojet cut frequencies are experimentally determined and were found to match those predicted by the identified models. Finally, the proposed strategy is systematically tested with respect to popular aeroengine models, outperforming them both in the time and frequency domains. These results allow us to conclude that the proposed modeling method improves current modeling approaches in both manageability and predictive power.

Drone simulations offer a safe environment for collecting data and testing algorithms. However, the depth camera sensor in the simulation provides exact depth values without error, which can result in variations in algorithm behavior, especially in the case of SLAM, when transitioning to real-world environments. The aerodynamic model in the simulation also differs from reality, leading to larger errors in drag force calculations at high speeds. This disparity between simulation and real-world conditions poses challenges when attempting to transfer high-speed drone algorithms developed in the simulated environment to actual operational settings. In this paper, we propose a more realistic simulation by implementing a novel depth camera noise model and an improved aerodynamic drag force model. Through experimental validation, we demonstrate the suitability of our models for simulating real-depth cameras and air drag forces. Our depth camera noise model can replicate the values of a real depth camera sensor with a coefficient of determination ( R 2 ) value of 0.62, and our air drag force model improves accuracy by 51% compared to the Airsim simulation air drag force model in outdoor flying experiments at 10 m/s. [ABSTRACT FROM AUTHOR]

With the wide application of intelligent and network technologies in the transportation industry, intelligent driving of mine truck has become an inevitable trend in the development of mining industry. Simulation test is one of the important means to accelerate the development and implementation of intelligent driving technology. Different from the intelligent single-vehicle solution of passenger car autonomous driving, the intelligent mining truck system takes the ground management and supervision system as the center for comprehensive dispatch and command, and the mining environment is complex and time-varying, which brings great challenges to the safe operation of intelligent driving of mine truck. Therefore, according to the characteristics of multi-vehicle collaborative control and complex and changeable environment of mine trucks, this paper builds a set of mine trucks intelligent multi-vehicle collaborative simulation platform that includes ground management and supervision system, vehicle intelligent driving computing platform, mining scene model, vehicle sensor model, and multi-vehicle collaborative simulation communication interface. This simulation platform has been successfully applied to the testing of on-board decision control algorithms and ground dispatch planning algorithms for a mining truck intelligent driving project, and can provide test support for the research and development of multi-vehicle intelligent technology.

Drone simulations offer a safe environment for collecting data and testing algorithms. However, the depth camera sensor in the simulation provides exact depth values without error, which can result in variations in algorithm behavior, especially in the case of SLAM, when transitioning to real-world environments. The aerodynamic model in the simulation also differs from reality, leading to larger errors in drag force calculations at high speeds. This disparity between simulation and real-world conditions poses challenges when attempting to transfer high-speed drone algorithms developed in the simulated environment to actual operational settings. In this paper, we propose a more realistic simulation by implementing a novel depth camera noise model and an improved aerodynamic drag force model. Through experimental validation, we demonstrate the suitability of our models for simulating real-depth cameras and air drag forces. Our depth camera noise model can replicate the values of a real depth camera sensor with a coefficient of determination (R2) value of 0.62, and our air drag force model improves accuracy by 51% compared to the Airsim simulation air drag force model in outdoor flying experiments at 10 m/s.

Isolated current sensing is fundamental in several contexts, including power electronics, automotive, and smart buildings. In order to meet the requirements of modern applications, current sensors should feature ever larger bandwidth and dynamic range, as well as reduced power consumption and dimension. Among the available current sensing technologies, Hall-based current sensors have gained an increased popularity owing to their advantages in terms of size, economic feasibility, low power consumption, high dynamic range, and integrability with standard CMOS technologies. This tutorial aims at providing a comprehensive insight into the interdisciplinary world of Hall-effect current sensors, encompassing the fundamental principles of operation, the design of the semiconductor device, the implementation techniques, as well as the methods for sensor modeling and characterization. In particular, this manuscript focuses on Hall-effect sensors realized on standard silicon technologies, and it reviews some typical architectures for the transduction of the measurand current into a magnetic field, as well as the electronic front-end. While this tutorial is mainly addressed to students and non-expert readers, specific design aspects and dispersion effects due to temperature and other external phenomena are also discussed. [ABSTRACT FROM AUTHOR]

The rational polynomial function is widely accepted as the preferred sensor model for high-resolution satellite imagery (HRSI). However, satellite images and their associated rational polynomial coefficients (RPCs) often suffer from nonlinear systematic errors, which were caused by attitude oscillation, sensor deformation, and many other imperfect calibration errors, thus affecting the geo-referencing accuracy. Instead of modeling the biases by polynomials in the image space or refining RPCs directly, this study proposes an approach of going back to the physical model and correcting the local distortions in a self-calibration block adjustment. The algorithm of an equivalent geometric sensor model (EGSM) recovery from RPCs is described in detail. As an equivalent form of the physical sensor models, EGSM reflects the complete viewing geometry of push-broom HRSI. The interior and exterior orientation parameters of EGSM can be stably recovered from RPCs without using any metadata. An approach of RPCs refinement by self-calibration block adjustment based on EGSM is introduced. This approach can effectively compensate for the nonlinear systematic errors caused by platforms and sensors similar to the approach of a rigorous sensor model. The performance of EGSM-based block adjustment is compared with the RFM-based bias compensation method. Experiments using ZY-3 images show the EGSM-based approach can effectively eliminate the nonlinear distortions in satellite images caused by sensor deformation and attitude vibrations. Furthermore, experiments using images from various satellites show that the original RFM can be well fitted with the EGSM and the residuals are smaller than 0.1 pixels for all test images.

In the dynamic techniques for calibration of triaxial accelerometers, the sensor is held in motion during data acquisition; the acquired time-varying acceleration is in general more informative than the constant acceleration acquired in static calibration approaches. Dynamic methods, however, typically require complex and expensive calibration setups based on high-precision moving benches, to guarantee accurate a-priori knowledge of the acceleration applied to the sensors under test. This article presents a dynamic calibration procedure working with a very simple and inexpensive setup: a tilted, freely-rotating bench, made of a simple wheel mounted on a static support. The accelerometer is fastened to the bench, which is then manually set into rotation; the acceleration signal is then acquired while the bench is freely rotating. No a-priori knowledge is required about the bench rotation: the proposed calibration procedure estimates both the calibration parameters and the bench motion. To keep the setup as simple as possible, an effort has been made to minimize also the necessity of prior knowledge of the bench geometry: only the distance of the sensor from the rotation axis needs to be known, which can be easily obtained through direct measurement on the bench. The proposed calibration has been tested both on synthetic data, to prove the absence of estimation biases and to evaluate the potential accuracy of the estimated parameters, and on real data from a MEMS triaxial accelerometer, to assess the practical usability and measure the actual precision of the procedure. [ABSTRACT FROM AUTHOR]

Traditional Potentiometric Ion-selective Electrodes (ISE) are widely used in industrial and clinical settings. The simplicity and small footprint of ISE have encouraged their recent adoption as wearable/implantable sensors for personalized healthcare and precision agriculture, creating a new set of unique challenges absent in traditional ISE. In this paper, we develop a fundamental physics-based model to describe both steady-state and transient responses of ISE relevant for wearable/implantable sensors. The model is encapsulated in a “generalized Nernst formula” that explicitly accounts for the analyte density, time-dynamics of signal transduction, ion-selective membrane thickness, and other sensor parameters. The formula is validated numerically by self-consistent modeling of multispecies ion-transport and experimentally by interpreting the time dynamics and thickness dependence of thin-film solid-contact and graphene-based ISE sensors for measuring soil nitrate concentration. These fundamental results will support the accelerated development of ISE for wearable/implantable applications. [ABSTRACT FROM AUTHOR]

Featured Application: The proposed method can generate a mosaic image at the product level that is corrected only for radiometric and sensor distortions. High-resolution satellite images such as KOMPSAT-3 data provide detailed geospatial information over interest areas that are evenly located in an inaccessible area. The high-resolution satellite cameras are designed with a long focal length and a narrow field of view to increase spatial resolution. Thus, images show relatively narrow swath widths (10–15 km) compared to dozens or hundreds of kilometers in mid-/low-resolution satellite data. Therefore, users often face obstacles to orthorectify and mosaic a bundle of delivered images to create a complete image map. With a single mosaicked image at the sensor level delivered only with radiometric correction, users can process and manage simplified data more efficiently. Thus, we propose sensor-level mosaicking to generate a seamless image product with geometric accuracy to meet mapping requirements. Among adjacent image data with some overlaps, one image is the reference, whereas the others are projected using the sensor model information with shuttle radar topography mission. In the overlapped area, the geometric discrepancy between the data is modeled in spline along the image line based on image matching with outlier removals. The new sensor model information for the mosaicked image is generated by extending that of the reference image. Three strips of KOMPSAT-3 data were tested for the experiment. The data showed that irregular image discrepancies between the adjacent data were observed along the image line. This indicated that the proposed method successfully identified and removed these discrepancies. Additionally, sensor modeling information of the resulted mosaic could be improved by using the averaging effects of input data. [ABSTRACT FROM AUTHOR]

This article shows the feasibility of a high-frequency RFID passive sensor tag equipped with sensors based on a thin-film circuit on a Si substrate for the measurement of soil moisture. Three tags embedding different sensing systems compliant with the energy harvesting requirements were developed. The used sensors were previously characterized with the aim of tailoring an adequate electronic front-end. All the typical solutions for soil moisture detection were implemented: a fringe capacitance sensor, a soil impedance sensor, and finally a humidity sensor based on functionalization with TiO2 nano particles. The obtained results prove that the conceived solution is satisfactory and that the sensors have good sensing properties. The proposed system can be applied within the Internet of Things (IoT) framework in manifold monitoring scenarios of soil moisture sensing. [ABSTRACT FROM AUTHOR]

Metal-oxide gas sensors are widely used for detection of detrimental H2S gas. Among them, SnO2-CuO system has been proved to be an excellent candidate. The previous theoretical 2D thin bilayer model was able to explain some aspects of H2S sensing performance of this promising system. However, experimental researches have indicated that response values for this system are very diverse and various SnO2-CuO multicomponent heterostructures such as rods, wires and particles have much higher response than their thin bilayer counterparts. Understanding the reason behind this differences would help fabrication of optimized sensor element. However, the previous model cannot address this issue mainly because it doesn’t consider intrinsic n-p-n junctions which exist in SnO2-CuO multicomponent heterostructures. In current research, by implementing an n-p-n junction in a 3D “SnO2 wire/CuO shell” model, that issue has been originally addressed. For this purpose, Poisson, Laplace and Continuity equations were solved in cylindrical coordinate to obtain H2S response of “mono-wire” and “two-wires” configurations. Results showed that while these two configurations have almost equal electrical resistances in presence of H2S gas, the n-p-n junction makes the “two-wires” system much more resistive in air and consequently much more sensitive toward H2S gas comparing to “mono-wire” and thin bilayer configurations. Depending on the geometrical parameters, response of the “two-wires” configuration changed between 4000 to 35000 at 150°C and 1ppm H2S gas. Theoretical results are in part consistent with previous experimental reports. [ABSTRACT FROM AUTHOR]

In this article, we present a method to estimate high-frequency rotary motion of a highly compact electrostatic microscanner using the same electrodes for both actuation and sensing. The accuracy of estimated rotary motion is critical for reducing blur and distortion in image reconstruction applications with the microscanner given its changing dynamics due to perturbations such as temperature. To overcome the limitation that no dedicated sensing electrodes are available in the proposed applications due to size constraints, the method adopts electromechanical amplitude modulation to separate motion signal from parasitic capacitance feedthrough, and a novel nonlinear measurement model is derived to characterize the relationship between large out-of-plane angular motion and circuit output. To estimate motion, an extended Kalman filter and an unscented Kalman filter are implemented, incorporating a process model based on the microscanner's parametric resonant dynamics and the measurement model. Experimental results show that compared to estimation without using the measurement model, our method can improve the rotary motion estimation accuracy of the microscanner significantly, with a reduction of root-mean-square error (RMSE) in phase shift of 86.1%, and a reduction of RMSE in angular position error of 78.5%. [ABSTRACT FROM AUTHOR]

High-resolution satellite images such as KOMPSAT-3 data provide detailed geospatial information over interest areas that are evenly located in an inaccessible area. The high-resolution satellite cameras are designed with a long focal length and a narrow field of view to increase spatial resolution. Thus, images show relatively narrow swath widths (10–15 km) compared to dozens or hundreds of kilometers in mid-/low-resolution satellite data. Therefore, users often face obstacles to orthorectify and mosaic a bundle of delivered images to create a complete image map. With a single mosaicked image at the sensor level delivered only with radiometric correction, users can process and manage simplified data more efficiently. Thus, we propose sensor-level mosaicking to generate a seamless image product with geometric accuracy to meet mapping requirements. Among adjacent image data with some overlaps, one image is the reference, whereas the others are projected using the sensor model information with shuttle radar topography mission. In the overlapped area, the geometric discrepancy between the data is modeled in spline along the image line based on image matching with outlier removals. The new sensor model information for the mosaicked image is generated by extending that of the reference image. Three strips of KOMPSAT-3 data were tested for the experiment. The data showed that irregular image discrepancies between the adjacent data were observed along the image line. This indicated that the proposed method successfully identified and removed these discrepancies. Additionally, sensor modeling information of the resulted mosaic could be improved by using the averaging effects of input data.

In this paper, a system for the measurement of the instantaneous speed of rotating parts in turbines is proposed and modeled. The system is based on traditional and widely used variable reluctance sensors often mounted on-board for other purposes. By coupling this sensors with ad hoc rotating target (novel design) and front-end circuit, it is possible to extend the application of this sensor to accurate instantaneous angular speed monitoring, and to exploit it for torsional vibration monitoring or, in general, for the measurement of any other meaningful quantity related to the variation of the angular speed, useful for machine health monitoring and incipient fault diagnosis. The proposed rotating target produces a signal close to the pure sine wave, with the specific purpose of improving accuracy in torsional vibration monitoring. The complete system was modeled, also taking into account the effect of nonidealities in order to evaluate its performance. Experimental results confirm the satisfactory results obtained from simulations, and show the reduction of the magnitude of the higher order harmonics in the output signal spectrum ensuring improved performance for any torsional vibration monitoring assembly employing the proposed system. [ABSTRACT FROM AUTHOR]

In-depth analysis and performance evaluation of sensor fusion-based estimators may be critical when performed using real-world sensor data. For this reason, simulation is widely recognized as one of the most powerful tools for algorithm benchmarking. In this paper, we present a simulation framework suitable for assessing the performance of sensor fusion-based pose estimators. The systems used for implementing the framework were magnetic/inertial measurement units (MIMUs) and a camera, although the addition of further sensing modalities is straightforward. Typical nuisance factors were also included for each sensor. The proposed simulation environment was validated using real-life sensor data employed for motion tracking. The higher mismatch between real and simulated sensors was about 5% of the measured quantity (for the camera simulation), whereas a lower correlation was found for an axis of the gyroscope (0.90). In addition, a real benchmarking example of an extended Kalman filter for pose estimation from MIMU and camera data is presented.

Inductive Loop Detectors (ILDs) are the most commonly used sensors in traffic management systems. This paper shows that some spectral features extracted from the Fourier Transform (FT) of inductive signatures do not depend on the vehicle speed. Such a property is used to propose a novel method for vehicle classification based on only one signature acquired from a sensor single-loop, in contrast to standard methods using two sensor loops. Our proposal will be evaluated by means of real inductive signatures captured with our hardware prototype.

Terrestrial laser scanning typically requires the use of artificial targets for registration and georeferencing the data. This equipment can be burdensome to transport and set up, representing expense in both time and labor. Environmental factors such as terrain can sometimes make target placement dangerous or impossible, or lead to weak network geometry and therefore degraded product accuracy. The use of additional sensors can help reduce the required number of artificial targets and, in some cases, eliminate the need for them altogether. The research presented here extends methods for direct georeferencing of terrestrial laser scanner data using a dual GNSS antenna apparatus with additional photogrammetric observations from a scanner-mounted camera. Novel combinations of observations and processing methods were tested on data collected at two disparate sites in order to find the best method in terms of processing efficiency and product quality. In addition, a general model for the scanner and auxiliary data is given which can be used for least-squares adjustment and uncertainty estimation in similar systems with varied and diverse configurations. We found that the dual-antenna system resulted in cm-level accuracy practical for many applications and superior to conventional one-antenna systems, and that auxiliary photogrammetric observation significantly increased accuracy of the dual-antenna solution.

Signal strength-based positioning in wireless sensor networks is a key technology for seamless, ubiquitous localization, especially in areas where Global Navigation Satellite System (GNSS) signals propagate poorly. To enable wireless local area network (WLAN) location fingerprinting in larger areas while maintaining accuracy, methods to reduce the effort of radio map creation must be consolidated and automatized. Gaussian process regression has been applied to overcome this issue, also with auspicious results, but the fit of the model was never thoroughly assessed. Instead, most studies trained a readily available model, relying on the zero mean and squared exponential covariance function, without further scrutinization. This paper studies the Gaussian process regression model selection for WLAN fingerprinting in indoor and outdoor environments. We train several models for indoor/outdoor- and combined areas; we evaluate them quantitatively and compare them by means of adequate model measures, hence assessing the fit of these models directly. To illuminate the quality of the model fit, the residuals of the proposed model are investigated, as well. Comparative experiments on the positioning performance verify and conclude the model selection. In this way, we show that the standard model is not the most appropriate, discuss alternatives and present our best candidate.

Passive infrared (PIR) sensors are sensitive to infrared (IR) rays and are mostly used for motion detection. In this paper we propose a mathematical model that enables better understanding of the analog response of the PIR sensor, the Fresnel lens, and the associated electronic circuitry. The proposed model relates the sensor's analog output waveform to the width of sensing element, and speed of movement and distance of the subject from the PIR sensor. Experiments were performed to capture the sensor's analog output waveform corresponding to the subject's movement through the sensor's field of view (FoV) at different distances, and the relationship of the sensor's peak-to-peak output voltage with distance and speed of the subject. The simulation results are shown to match reasonably well with the experimental results. [ABSTRACT FROM AUTHOR]

In this paper the authors propose the design of a complete measurement system of clearance based on an ‘on-component’ triaxial capacitive probe for static and dynamic measurement of distance. The capacitive probe is designed to be deposited onto machinery components or complex structures and is aimed at reducing the impact of probe mounting on the monitored component. The problem is discussed from a theoretical point of view and then supported by the realization of a sensor prototype in screen printed technology and of two possible and alternative conditioning systems. The results prove the feasibility of this innovative measurement system. [ABSTRACT FROM AUTHOR]

The positional inaccuracies associated with the GPS/INS measurements make the terminal phase of the normal GPS/INS landing system imprecise. To solve this problem, an adaptive fuzzy data fusion algorithm is developed to obtain more accurate state estimates while the vehicle approaches the landing surface. This algorithm takes the translational displacements in x and y from the mounted Optical Flow (OF) sensor and fuses them with the INS attitude measurements and the altimeter measurements. This low cost adaptive algorithm can be used for vertical landings in areas where GPS outages might happen or in GPS denied areas. The adaptation is governed by imposing appropriate assumptions under which the filter measurement noise matrix R is predicted. The R matrix is continuously adjusted through a fuzzy inference system (FIS) based on the Kalman innovative sequence and the applied covariance-matching technique. This adaptive fuzzy Kalman fusion algorithm (AFKF) is used to estimate the vehicle’s states while landing is being commanded. AFKF results are compared with these obtained using a classical Kalman estimation technique. The AFKF algorithm shows better states estimates than its conventional counterpart does. Compared to prior landing systems, the proposed low cost AFKF has achieved a precision landing with less than 10 cm maximum estimated position error. Real precision landing flights were conducted to demonstrate the validity of the proposed intelligent estimation method. [ABSTRACT FROM AUTHOR]

Owing to the large surface area, 2-D materials are being used for sensing gas molecules, which are important for environmental protection and human health. Using first principle calculations, adsorption of CO and NO gas molecules on Cu-substituted monolayer MoS2 is studied in terms of energy, charge transfer, and density of states. Further, the behavior of CO and NO on Cu-substituted monolayer MoS2-based single electron transistor (SET) is explored. Strong interaction between Cu metal and MoS2 sheet suggests the stability of the Cu-doped MoS2 system at ambient conditions. It is further found that on doping Cu into MoS2 sheet, the adsorption strength of CO and NO molecules got enhanced as compared with the pristine MoS2 sheet and, hence, possesses better sensing capability. The sensing response of the Cu-doped MoS2 sheet in the SET environment toward these molecules is studied from the calculated charge stability diagram that serves as a unique fingerprint of each adsorbed molecule. The charging energy is reduced when Cu impurity is added to MoS2 for CO/NO adsorption, which makes such system more suitable in low-powered SET devices. The results show that SET based on Cu-doped MoS2 can essentially detect hazardous molecules and is proved to have potential application in gas sensors. [ABSTRACT FROM PUBLISHER]

There are numerous magnetic field sensors available, but no simple, robust, sensitive sensor for biomedical applications that does not require cryogenic cooling or shielding has yet been developed. In this contribution, a new approach for building a magnetoelectric field sensor is presented, which has the potential to fill this gap. The sensor is based on a resonant cantilever with a piezoelectric readout layer and a pair of opposing permanent magnets. One is attached to the cantilever, and the other one is fixed to a sample holder below. This new concept can be deduced from the most basic composite-based sensor [1], where the magnets interact analog to two particles in a polymer matrix. The bias-free, empirical measurements show a limit-of-detection of 46 pT/√Hz with a sensitivity of 2170 V/T using the sensor's resonance frequency of 223.5 Hz under ambient conditions. The sensor fabrication is based on low resolution silicon technology, which promises high compatibility and the possibility to be integrated into MEMS devices. The design of this new sensor can be easily altered and adjusted according to the requirements of the specific sensor application. For example, tuning of the operating resonance frequency cannot solely be modified in the production of the cantilever but also by the arrangement of the permanent magnets. In addition, the concept can also be applied to energy harvesters. Beside possible mechanical excitation, the presence of a magnetic stray field alone allows the sensor to convert 20 µT into a power of 1.31 µW/cm³ · Oe². The fact that the device does not require any DC bias field makes it very attractive for energy harvesting applications since this allows a purely passive operation. In this manuscript, the sensor assembly, measurements of directional sensitivity, noise level, limit-of-detection, evaluation for energy harvesting applications from magnetic fields and a quantitative sensor model are presented. [Display omitted] • new concept for magnetic field sensors using opposing permanent magnets. • low field detection of 46 pT/√Hz at 223.5 Hz possible. • potential energy harvester using magnetic field and acoustics (0.178 W/cm3 @ 20 µT). • Very robust against external dc fields (tested up to 10 mT). • No external powering required. [ABSTRACT FROM AUTHOR]

The article extends the cycle of papers dedicated to programming and verificatoin of PLC-programs by LTL-specification. This approach provides the availability of correctness analysis of PLC-programs by the model checking method.The model checking method needs to construct a finite model of a PLC program. For successful verification of required properties it is important to take into consideration that not all combinations of input signals from the sensors can occur while PLC works with a control object. This fact requires more advertence to the construction of the PLC-program model.In this paper we propose to describe a consistent behavior of sensors by three groups of LTL-formulas. They will affect the program model, approximating it to the actual behavior of the PLC program. The idea of LTL-requirements is shown by an example.A PLC program is a description of reactions on input signals from sensors, switches and buttons. In constructing a PLC-program model, the approach to modeling a consistent behavior of PLC sensors allows to focus on modeling precisely these reactions without an extension of the program model by additional structures for realization of a realistic behavior of sensors. The consistent behavior of sensors is taken into account only at the stage of checking a conformity of the programming model to required properties, i. e. a property satisfaction proof for the constructed model occurs with the condition that the model contains only such executions of the program that comply with the consistent behavior of sensors.

Phase fraction of oil–water two-phase flow is important to the process control and optimization. In this paper, a prediction model based on the mechanism of ultrasound attenuations in oil–water mixture is presented to estimate the phase fraction. This model consists of physical parameters of the two-phase flow and ultrasound frequency, by incorporating the scattering and attenuation of ultrasound in different flow patterns of the oil–water two-phase flow. The nonlinear relationship between ultrasound attenuation coefficient and phase fraction is corrected. An array of 16 ultrasonic transducers with 1-MHz center frequency and 20-Vpp sine acoustic signal is presented to implement this method, and a measuring system was designed and tested on a multiphase flow loop with actual flow experiments. The proposed prediction model showed a good agreement with the online phase fraction calibration in the flow experiments. The average error is 2.3% in water continuous flow, while in 2.8% in oil continuous flow. These findings and the mechanism prediction model provide basic foundations for the phase fraction estimation of oil–water two-phase flow by using the ultrasound attenuation technique. [ABSTRACT FROM PUBLISHER]

This paper presents a very accurate sensor for the measurement of the moisture of undisturbed soil samples. The sensor relies on accurate estimation of the permittivity which is performed independently of the soil type, and a subsequent calibration. The sensor is designed as an upgrade of the conventional soil sampling equipment used in agriculture—the Kopecky cylinder. The detailed description of the device is given, and the method for determining soil moisture is explained in detail. Soil moisture of unknown test samples was measured with an absolute error below 0.0057 g/g, which is only 2.24% of the full scale output, illustrating the high accuracy of the sensor.

The Ocean Bottom Seismometer (OBS) is a key instrument for the geophysical study of sea sub-bottom layers. At present, more reliable autonomous instruments capable of recording underwater for long periods of time and therefore handling large data storage are needed. This paper presents a new Ocean Bottom Seismometer designed to be used in long duration seismic surveys. Power consumption and noise level of the acquisition system are the key points to optimize the autonomy and the data quality. To achieve our goals, a new low power data logger with high resolution and Signal–to-Noise Ratio (SNR) based on Compact Flash memory card is designed to enable continuous data acquisition. The equipment represents the achievement of joint work from different scientific and technological disciplines as electronics, mechanics, acoustics, communications, information technology, marine geophysics, etc. This easy to handle and sophisticated equipment allows the recording of useful controlled source and passive seismic data, as well as other time varying data, with multiple applications in marine environment research. We have been working on a series of prototypes for ten years to improve many of the aspects that make the equipment easy to handle and useful to work in deep-water areas. Ocean Bottom Seismometers (OBS) have received growing attention from the geoscience community during the last forty years. OBS sensors recording motion of the ocean floor hold key information in order to study offshore seismicity and to explore the Earth’s crust. In a seismic survey, a series of OBSs are placed on the seabed of the area under study, where they record either natural seismic activity or acoustic signals generated by compressed air-guns on the ocean surface. The resulting data sets are subsequently used to model both the earthquake locations and the crustal structure.

Nanostructured Indium(III) oxide (In2O3) films deposited by low temperature pulsed electron deposition (LPED) technique on customized alumina printed circuit boards have been manufactured and characterized as gas sensing devices. Their electrical properties have monitored directly during deposition to optimize their sensing performance. Experimental results with oxidizing (NO2) as well as reducing (CO) gases in both air and inert gas carriers are discussed and modeled.

Geometric correction is fundamental in producing high quality satellite data products. However, the geometric correction for ocean color sensors, e.g., Geostationary Ocean Color Imager (GOCI), is challenging because the traditional method based on ground control points (GCPs) cannot be applied when the shoreline is absent. In this study, we develop a hybrid geometric correction method, which applies shoreline matching and frequency matching on slots with shorelines and without shorelines, respectively. Frequency matching has been proposed to estimate the relative orientation between GOCI slots without a shoreline. In this paper, we extend our earlier research for absolute orientation and geometric correction by combining frequency matching results with shoreline matching ones. The proposed method consists of four parts: Initial sensor modeling of slots without shorelines, precise sensor modeling through shoreline matching, relative orientation modeling by frequency matching, and generation of geometric correction results using a combination of the two matching procedures. Initial sensor modeling uses the sensor model equation for GOCI and metadata in order to remove geometric distortion due to the Earth’s rotation and curvature in the slots without shorelines. Precise sensor modeling is performed with shoreline matching and random sample consensus (RANSAC) in the slots with shorelines. Frequency matching computes position shifts for slots without shorelines with respect to the precisely corrected slots with shorelines. GOCI Level 1B scenes are generated by combining the results from shoreline matching and frequency matching. We analyzed the accuracy of shoreline matching alone against that of the combination of shoreline matching and frequency matching. Both methods yielded a similar accuracy of 1.2 km, which supports the idea that frequency matching can replace traditional shoreline matching for slots without visible shorelines.

This paper proposes an App Visualization (AppV) based on IoT Self-diagnosis Micro Control Unit (ISMCU) for accident prevention. It collects a current status of a vehicle through a sensor, visualizes it on a smart phone and prevents vehicles from accident. The AppV consists of 5 components. First, a Sensor Layer (SL) judges noxious gas from a current vehicle and a driver's driving habit by collecting data from various sensors such as an Accelerator Position Sensor, an O2 sensor, an Oil Pressure Sensor, etc. and computing the concentration of the CO collected by a semiconductor gas sensor. Second, a Wireless Sensor Communication Layer (WSCL) supports Zigbee, Wi-Fi, and Bluetooth protocol so that it may transfer the sensor data collected in the SL to ISMCU and the data in the ISMCU to a Mobile. Third, an ISMCU integrates the transferred sensor information and transfers the integrated result to a Mobile. Fourth, a Mobile App Block Programming Tool (MABPT) is an independent App generation tool that changes to visual data just the vehicle information which drivers want from a smart phone. Fifth, an Embedded Module (EM) records the data collected through a Smart Phone real time in a Cloud Server. Therefore, because the AppV checks a vehicle' fault and bad driving habits that are not known from sensors and performs self-diagnosis through a mobile, it can reduce time and cost spending on accidents caused by a vehicle's fault and noxious gas emitted to the outside. [ABSTRACT FROM AUTHOR]

The sensing mechanism of resistive metal oxide gas sensors is quite complicated. Many factors determine the sensitivity and the response of these devices. In the past, many research works pointed out that an improved sensitivity and faster responses can be obtained, given a certain material, by using nano-grained or nanostructured metal oxide materials. This is due to the obvious reason that the grain size reductions imply the surface to volume ratio enhancement, and, consequently, the sensor response enhancement, being the sensing determined by surface phenomena. Actually, this intuitive explanation is not exhaustive, so in this paper the authors present a novel model for nano-grained metal oxide gas sensors, that is used to highlight the main factors influencing their sensing properties. The developed model, describing spherical grains, is applied to WO3 for NO2 detection, and is compared with different models already in the literature. What emerges as an interesting result is that the usual model of large grained films can give a rough but meaningful idea of the sensor sensitivity magnitude, whereas more fine models have to be used in order to predict some peculiar behaviors related to nano-sized grains. [ABSTRACT FROM PUBLISHER]

Infrared imaging simulation technology can provide infrared data sources for the development, improvement and evaluation of infrared imaging systems under different environment, status and weather conditions, which is reusable and more economic than physical experiments. A real-time infrared imaging simulation process is established to reproduce a complete physical imaging process. Our emphasis is put on the modeling of infrared sensors, involving physical effects of both spatial domain and frequency domain. An improved image convolution method is proposed based on GPU parallel processing to enhance the real-time simulation ability with ensuring its simulation accuracy at the same time. Finally the effectiveness of the above methods is validated by simulation analysis and result comparison. [ABSTRACT FROM AUTHOR]

In-depth analysis and performance evaluation of sensor fusion-based estimators may be critical when performed using real-world sensor data. For this reason, simulation is widely recognized as one of the most powerful tools for algorithm benchmarking. In this paper, we present a simulation framework suitable for assessing the performance of sensor fusion-based pose estimators. The systems used for implementing the framework were magnetic/inertial measurement units (MIMUs) and a camera, although the addition of further sensing modalities is straightforward. Typical nuisance factors were also included for each sensor. The proposed simulation environment was validated using real-life sensor data employed for motion tracking. The higher mismatch between real and simulated sensors was about 5% of the measured quantity (for the camera simulation), whereas a lower correlation was found for an axis of the gyroscope (0.90). In addition, a real benchmarking example of an extended Kalman filter for pose estimation from MIMU and camera data is presented. [ABSTRACT FROM AUTHOR]

An approach to multiple gas sensing on decorated porous silicon (PS) substrates is presented. The simple microelectromechanical systems/nanoelectromechanical systems platform that we have developed facilitates the modeling of the interaction of nanostructured metal oxide islands with the analytes of interest, which are exemplified by NO and NH3. These conductometric sensors operate at room temperature and atmospheric pressure and, as they are forgiving, do not require film-based technology or lithography for their construction. We show that diffusion dominates the conductometric response. The direct response and the derivatives of this response are considered. The first derivative allows a quick evaluation of sensor response and the derivative is linear with concentration. The spectral simulations have been refined to include adsorption/desorption effects of the analyte gas and assess subsequent non-linear interface sensitivities. By including the physics of adsorption/desorption, the simulated sensor response is now a non-linear function of concentration. We model the absorption/diffusion through the use of the Langmuir absorption isotherm and find substantial agreement with experiment for the mixed analyte interactions of NH3 and NO combinations on several metal oxide decorated PS interfaces. [ABSTRACT FROM PUBLISHER]

Inductive Loop Detectors (ILDs) are the most commonly used sensors in traffic management systems. This paper shows that some spectral features extracted from the Fourier Transform (FT) of inductive signatures do not depend on the vehicle speed. Such a property is used to propose a novel method for vehicle classification based on only one signature acquired from a sensor single-loop, in contrast to standard methods using two sensor loops. Our proposal will be evaluated by means of real inductive signatures captured with our hardware prototype. [ABSTRACT FROM AUTHOR]

Recent robots target safety, reconfigurability and interactivity by addressing the “softness” of the hardware either by endowing additional degrees-of-freedom or through inherent compliancy. These robots require distributed sensing with flexibility and softness that would not interfere with the robot's agility. There have been various sensing solutions using soft conductive materials including conductive silicone, liquid metal-filled micro-channels, and conductive-ink based sensors. However, we still lack a comprehensive study on their potentials, drawbacks, and the different parameters that affect their response. We present our design, fabrication process and characterization results for conductive silicone polymer and carbon ink-based curvature sensors. These sensors are flexible, mechanically robust under large strains, scalable, and easy to fabricate in large numbers. We propose an equivalent mechanical system to model sensors’ response. This model is unique for its extensive characterization of these polymer based sensors. Based on the characterization results, we systematically categorize and compare the performance of conductive silicone and carbon ink-based sensors with different design parameters. [ABSTRACT FROM AUTHOR]

Design of an intelligent flow measurement technique by ultrasonic transducers using an Optimized Artificial Neural Network (OANN) is discussed in this paper. The objectives of the present work are (i) to extend the linearity range of flow measurement to 100% of full scale input range, (ii) to make the flow measurement technique adaptive to variations in (a) pipe diameter, (b) liquid density, (c) liquid temperature, and (iii) to achieve objectives (i) and (ii) by using an optimized artificial neural network. The output of an ultrasonic transducer is frequency. It is converted to voltage by using a suitable data conversion circuit. A suitable optimal ANN is added, in place of conventional calibration circuit, in cascade to data conversion circuit. ANN is trained, and tested with simulated data considering various values of pipe diameter, liquid density, and liquid temperature. The proposed technique is then subjected to practical data for validation. Results show that the proposed technique has fulfilled the desired objectives. [ABSTRACT FROM AUTHOR]

Signal strength-based positioning in wireless sensor networks is a key technology for seamless, ubiquitous localization, especially in areas where Global Navigation Satellite System (GNSS) signals propagate poorly. To enable wireless local area network (WLAN) location fingerprinting in larger areas while maintaining accuracy, methods to reduce the effort of radio map creation must be consolidated and automatized. Gaussian process regression has been applied to overcome this issue, also with auspicious results, but the fit of the model was never thoroughly assessed. Instead, most studies trained a readily available model, relying on the zero mean and squared exponential covariance function, without further scrutinization. This paper studies the Gaussian process regression model selection for WLAN fingerprinting in indoor and outdoor environments. We train several models for indoor/outdoor- and combined areas; we evaluate them quantitatively and compare them by means of adequate model measures, hence assessing the fit of these models directly. To illuminate the quality of the model fit, the residuals of the proposed model are investigated, as well. Comparative experiments on the positioning performance verify and conclude the model selection. In this way, we show that the standard model is not the most appropriate, discuss alternatives and present our best candidate. [ABSTRACT FROM AUTHOR]

Terrestrial laser scanning typically requires the use of artificial targets for registration and georeferencing the data. This equipment can be burdensome to transport and set up, representing expense in both time and labor. Environmental factors such as terrain can sometimes make target placement dangerous or impossible, or lead to weak network geometry and therefore degraded product accuracy. The use of additional sensors can help reduce the required number of artificial targets and, in some cases, eliminate the need for them altogether. The research presented here extends methods for direct georeferencing of terrestrial laser scanner data using a dual GNSS antenna apparatus with additional photogrammetric observations from a scanner-mounted camera. Novel combinations of observations and processing methods were tested on data collected at two disparate sites in order to find the best method in terms of processing efficiency and product quality. In addition, a general model for the scanner and auxiliary data is given which can be used for least-squares adjustment and uncertainty estimation in similar systems with varied and diverse configurations. We found that the dual-antenna system resulted in cm-level accuracy practical for many applications and superior to conventional one-antenna systems, and that auxiliary photogrammetric observation significantly increased accuracy of the dual-antenna solution. [ABSTRACT FROM AUTHOR]

This paper reports on the absolute 3-D localization of radar corner reflectors and persistent scatterers (PSs) by stereo synthetic aperture radar (SAR) carried out with TerraSAR-X and TanDEM-X. The novel combination of rigorously linearized range-Doppler equations with thorough sensor modeling, consideration of atmospheric delays, geodynamic signals, satellite dynamics, and geometrical calibration allows a direct target localization at the centimeter level. Therefore, there is no need for the application of geocoding since our approach delivers 3-D positions directly in the International Terrestrial Reference Frame (ITRF) 2008. Four radar corner reflectors located at the Wettzell Geodetic Observatory, Metsähovi Geodetic Observatory, and German Antarctic Receiving Station O'Higgins were localized in 3-D with a precision better than 4 cm. By introducing an updated geometrical calibration of TerraSAR-X and TanDEM-X, the absolute accuracy of the same level becomes possible, which was demonstrated by externally validating the reflector positions at Metsähovi and Wettzell against results of terrestrial geodetic surveying. Furthermore, PSs located in Berlin were retrieved with a precision of 10 cm, making the method a suitable tool for radar “positioning” in urban environments. Potential fields of application for our approach are the joint analysis with phase-based methods, geometrical calibration of radar satellites, and direct integration of SAR observations into Global Navigation Satellite System networks. [ABSTRACT FROM PUBLISHER]

In this paper, a mathematical model is required to describe the nanowires and nanodevices by software is Designing, modeled and implemented . In graphs obtained from the simulation of neural network based on its model reference nano-sensorsm, the current during the reaction and the surface concentration of corresponding tumor cells, identical to the obtained graphs from the test setup, is obtained that based on, a mathematical model based on MATLAB for nanodevices developed and in Designing a nano-bio sensors are used to detect disease. Arrays reference model in this paper consists of 64 nanowire sensor that it s used to detect prostate cancer. In this paper, a modified method based on principal component analysis (PCA) and neural network design is presented when compared to other existing methods that have almost accuracy 94%, the highest accuracy correctly of 99.28% is capable. [ABSTRACT FROM AUTHOR]