Sensor Precision Research Articles

Evaluation of Occupancy Grid Resolution through a Novel Approach for Inverse Sensor Modeling

Several robotic applications imply motion in complex and dynamic environments. Occupancy Grids model the surrounding environment by a grid composed of a finite number of cells. The probability whether a cell is occupied or empty is computed and updated iteratively based on sensor measurements by considering their uncertainty through probabilistic models. Even if Occupancy Grids have been widely used in the state-of-the-art, the relation between the cell size, the sensor precision and the inverse sensor model is usually neglected. In this paper, we propose a methodology to build the inverse probabilistic model for single-target sensors. The proposed approach is then applied to a LiDAR in order to evaluate the impact of the variation in the sensor precision and the grid resolution on the inverse sensor model. Based on this study, we finally propose a procedure that allows to choose the suitable grid resolution for obtaining a desired maximum occupancy probability in the inverse sensor model.

Multi-sensor data fusion for low power transmission of wireless sensor network in a greenhouse

The Wireless Sensor Network (WSN) is widely used for the acquisition of distributed greenhouse micro-climate data, such as temperature, humidity, CO2 concentration and solar radiation. In this paper the communication reliability of a WSN was evaluated based on experimental data within the greenhouse. In order to reduce redundant and invalid data to achieve a low power transmission, this paper proposes two kinds of multi-sensor data fusion scheme depending on the characteristics of the parameters. For parameters that change slowly (such as temperature, humidity, and CO2), a time fusion algorithm which can detect the time consistency of data is used to reduce the amount of data transmitted. The spatial fusion based on the supporting vector is used to give the final decision variable for the control system. For rapidly-changing parameters such as solar radiation, this paper proposes the Dual Prediction Scheme (DPS) based on an adaptive model selection to reduce the amount of data transmitted, with a spatial fusion algorithm based on GIA (Grey Incidence Analysis) for the aggregator. The data fusion schemes were implemented and tested in a greenhouse at the Tongji University Jiading Campus. The implementation results showed that the data fusion schemes can guarantee the sensor precision, and also significantly reduce the amount of data transmitted, which means prolonging the lifetime of the network.

A Dynamic Precision Evaluation Method for the Star Sensor in the Stellar-Inertial Navigation System

Integrating the advantages of INS (inertial navigation system) and the star sensor, the stellar-inertial navigation system has been used for a wide variety of applications. The star sensor is a high-precision attitude measurement instrument; therefore, determining how to validate its accuracy is critical in guaranteeing its practical precision. The dynamic precision evaluation of the star sensor is more difficult than a static precision evaluation because of dynamic reference values and other impacts. This paper proposes a dynamic precision verification method of star sensor with the aid of inertial navigation device to realize real-time attitude accuracy measurement. Based on the gold-standard reference generated by the star simulator, the altitude and azimuth angle errors of the star sensor are calculated for evaluation criteria. With the goal of diminishing the impacts of factors such as the sensors’ drift and devices, the innovative aspect of this method is to employ static accuracy for comparison. If the dynamic results are as good as the static results, which have accuracy comparable to the single star sensor’s precision, the practical precision of the star sensor is sufficiently high to meet the requirements of the system specification. The experiments demonstrate the feasibility and effectiveness of the proposed method.

Multi-wavelength spectrophotometric determination of pH using phenol red–doped sol–gel film sensors

ABSTRACTDye leakage and the nonlinear relationship between absorbance and pH affect the pH measurement precision of absorption-based optic sensors and limit their application. Based on the preparation of a transparent sol–gel thin film with a phenol red indicator, this work assumed that the absorbancies had an exponential relationship to the concentrations of the indicator, and the multi-wavelength equation for the pH was derived as follows:where n is the exponential coefficient, e1, e2, and e3 are the extinction coefficient ratios, and R is the ratio of the absorbancies. The calculation parameters for an immobilized phenol red film were pKa = 8.577, e1 = 0.0191, e2 = 1.530, e3 = 0.2992, and n = 0.6322, while those for an immobilized phenol red/Triton X-100 film were pKa = 8.612, e1 = 0.0507, e2 = 1.501, e3 = 0.3687, and n = 0.3931. The correspondence between the calculated pKa and the pKa from the calibration curve verifies the equation. The calculation parameters remain stable as the indicator content is increased, but change with an increase in the Triton X-100 content. Using the equation, the calculated pH shows stable behavior over a long duration regardless of dye leakage, and the calculated precision is 0.0053. The method provides drift-free and precise water pH measurements for R that is not dependent on the total concentration of the indicator, and can be applied to online monitoring.

Measurement precision improvement for excimer laser pulse energy sensor based on a special finite impulse response filter

In deep ultraviolet lithographic tools, precise measurement of excimer laser pulse energy is essential. However, system noise of the energy sensor, which is used to monitor the laser pulse during exposure dose control, complicate pulse energy measurement. To improve measurement precision, a specific finite impulse response filter is proposed for signal denoising. Particularly, the convolution kernel of the specific finite impulse response filter is derived from the nonlinear fitting of the energy sensor output. Experimental results show that the measurement precision of the energy sensor improves 2-3 times with the specific finite impulse response filter. To verify the effectiveness of the energy sensor, it is used for transmittance measurement; the measurement result of a Corning 7980 witness sample is consistent with that measured by a commercial ultraviolet spectrophotometer and the measurement accuracy is 0.11% (k = 2).

Development of Magnetic Levitation System for Science and Technology Education: Magnetic Levitation Control by Using a Hall Element Displacement Sensor with Neural Network

SUMMARYIn this study, we developed a magnetic levitation system using a Hall element displacement sensor with neural network for science and technology education. The sensor configured with three Hall elements was devised in order to measure displacement from an electromagnet to a levitated object with a permanent magnet. Use of the Hall element displacement sensor achieves a lower cost magnetic levitation system. Furthermore, three‐layered feedforward neural network was utilized in order to improve the precision of the Hall element displacement sensor. Finally, operation verification of the developed magnetic levitation system was conducted by designing state feedback regulator with observer.

Managing and Analyzing the Constructive and Functional Parameters on Fiberglass Custom Sensor Design for an Aerodynamic Balance

An external three component strain gauge balance, designed, analyzed and manufactured in the Military Technical Academy is presented. The balance is a multi-piece type and employs the use of six custom-made load cells (sensors) to transfer the aerodynamic forces and moments from the wing to the computer software environment for data processing and analysis. The relations between the loads acting on the wing model and the measured forces are also underlined. The placement of the strain gauge on the composite laminate is an important issue for increased sensor precision; therefore, it is given a thorough analysis. The positioning of the force vector and its influence on the strain distribution over the lift sensor is also numerically and experimentally analyzed.

Robust double gain unscented Kalman filter for small satellite attitude estimation

Limited by the low precision of small satellite sensors, the estimation theories with high performance remains the most popular research topic for the attitude estimation. The Kalman filter (KF) and its extensions have been widely applied in the satellite attitude estimation and achieved plenty of achievements. However, most of the existing methods just take use of the current time-step’s priori measurement residuals to complete the measurement update and state estimation, which always ignores the extraction and utilization of the previous time-step’s posteriori measurement residuals. In addition, the uncertainty model errors always exist in the attitude dynamic system, which also put forward the higher performance requirements for the classical KF in attitude estimation problem. Therefore, the novel robust double gain unscented Kalman filter (RDG-UKF) is presented in this paper to satisfy the above requirements for the small satellite attitude estimation with the low precision sensors. It is assumed that the system state estimation errors can be exhibited in the measurement residual; therefore, the new method is to derive the second Kalman gain Kk2 for making full use of the previous time-step’s measurement residual to improve the utilization efficiency of the measurement data. Moreover, the sequence orthogonal principle and unscented transform (UT) strategy are introduced to robust and enhance the performance of the novel Kalman Filter in order to reduce the influence of existing uncertainty model errors. Numerical simulations show that the proposed RDG-UKF is more effective and robustness in dealing with the model errors and low precision sensors for the attitude estimation of small satellite by comparing with the classical unscented Kalman Filter (UKF).

Improvement for the stability of an air-lubricated six-axis force/moment sensor

The air-lubricated six-axis force/moment sensor, a novel sensor developed using the gas lubrication principle, could avoid the coupling phenomenon and increase its precision to 0.2% full scale. During the sensor development, the stability of the nozzle-floating plate mechanism is a key issue, which is mainly caused by the “hammer phenomenon” that is affected by the size of the nozzle’s orifice. In this study, an air hammer stability criterion for the sensor was proposed and the design of the nozzle was optimized to avoid the air hammer phenomenon. The stable characteristics of the optimized nozzle were further verified with experiments through comparison with characteristics of the normal nozzle. In conclusion, the hammer instability phenomenon was averted through the optimal design of the nozzle and the precision of the air-lubricated six-axis sensor was improved, which can be applied in calibrating flexible tactile sensors with high-precision requirement.

An Accelerated Motion Blurred Star Restoration Based on Single Image

The accuracy of star centroid extraction and identified star number is both crucial features for star sensor precision. Motion blur and fracture are introduced when star sensor works under high dynamic conditions, which affect the accuracy of star centroid extraction and further reduce the precision of the star sensor. To improve the precision of star sensor, this paper proposes a star image restoration algorithm, including blur kernel estimation as well as an accelerated Richardson–Lucy (RL) reconstruction for motion blur star image under high dynamic conditions. First, an improved Radon transform method is presented by introducing a combination of Z-function and double threshold mask that have considerable anti-noise performance. Based on this improved method, the blur kernel of the degraded star image can be obtained by only utilizing a single blurred star image. Furthermore, as the traditional RL is time-consuming, to overcome this shortcoming, an accelerated algorithm based on the second-order vector extrapolation is proposed, offering speedup of 20 times over the original RL and five times over the existing acceleration algorithms. Finally, experiments on simulated as well as real star images demonstrate that the proposed algorithm is effective in improving the dynamic performance of star sensor even under low signal-to-noise ratio conditions, which is of great importance for the further applications of star sensor.

Comparison of three-dimensional scanner systems for craniomaxillofacial imaging

Two-dimensional photographs are the standard for assessing craniofacial surgery clinical outcomes despite lacking three-dimensional (3D) depth and shape. Therefore, 3D scanners have been gaining popularity in various fields of plastic and reconstructive surgery, including craniomaxillofacial surgery. Head shapes of eight adult volunteers were acquired using four 3D scanners: 1.5T Avanto MRI, Siemens; 3dMDface System, 3dMD Inc.; M4D Scan, Rodin4D; and Structure Sensor, Occipital Inc. Accuracy was evaluated as percentage of data within a range of 2mm from the 3DMDface System reconstruction, by surface-to-surface root mean square (RMS) distances, and with facial distance maps. Precision was determined by RMS. Relative to the 3dMDface System, accuracy was the highest for M4D Scan (90% within 2mm; RMS of 0.71mm±0.28mm), followed by Avanto MRI (86%; 1.11mm±0.33mm) and Structure Sensor (80%; 1.33mm±0.46). M4D Scan and Structure Sensor precision were 0.50±0.04mm and 0.51±0.03mm, respectively. Clinical and technical requirements govern scanner choice; however, 3dMDface System and M4D Scan provide high-quality results. It is foreseeable that compact, handheld systems will become more popular in the near future.

Minimally Invasive Microneedle Array Electrodes Employing Direct Electron Transfer Type Glucose Dehydrogenase for the Development of Continuous Glucose Monitoring Sensors

Closed loop systems hinge on the accuracy and precision of the continuous glucose monitoring sensors. Most of the commercially available continuous glucose monitoring sensors is implanted subcutaneously for a period of 7-14 days. The subsequent biofouling effects have implications on the performance of the sensors over time especially at low glucose concentrations. In addition, the commercially available sensors are sensitive to the presence of interfering species such as acetaminophen in the skin compartment. We report here on the marriage of minimally invasive, continuous glucose sensors and a direct electron transfer type glucose dehydrogenase enzymatic system. Whilst the microneedles here are designed to sit in the dermal interstitial fluid over a 24-48 hour period to minimize the biofouling effect, the direct electron transfer enzyme allows operation of the electrochemical sensor at lower potentials to minimize the effect of interference. The microneedle structure design also enables the use of compensation electrodes for background subtraction to further nullify the effects of interference.

Mathematical Modelling and Parameter Optimization of Fine Particle Sensors based on Laser Light Scattering

Fine particles have promoted air quality monitoring need of humankind after their harmful effects on human health are pathologically verified. To monitor fine particle concentrations at a low cost, laser light scattering technique is favored by many researchers. While several cheap and interesting sensors are developed, little attention is paid to the measurement model, which in turn bound the sensor precision. In order to alleviate this situation, a simplified model is proposed, which can describe the nonlinear relationship between particle volume and single particle angular light flux by considering the effect of Rayleigh scattering and Fraunhofer diffraction simultaneously. The model can be employed in the signal processing of commercial low-cost particle sensors based on laser light scattering. A data-driven approach is adopted to find the optimum parameters of this model by considering different kinds of fine particles. Based on Trust-Region and Levenberg-Marquardt methods combining with robust methods, the optimum parameters of this model are obtained. To verify the effectiveness of the proposed model, comparative experiments are conducted on: a particle sensor with and without parameter optimization, a DustTrak II 8530 monitor and a reference TEOM PM2.5 monitor. The results indicated that the optimal model improves the measurement accuracy of the fine particle mass concentration by 10%.

Star centroiding error compensation for intensified star sensors.

A star sensor provides high-precision attitude information by capturing a stellar image; however, the traditional star sensor has poor dynamic performance, which is attributed to its low sensitivity. Regarding the intensified star sensor, the image intensifier is utilized to improve the sensitivity, thereby further improving the dynamic performance of the star sensor. However, the introduction of image intensifier results in star centroiding accuracy decrease, further influencing the attitude measurement precision of the star sensor. A star centroiding error compensation method for intensified star sensors is proposed in this paper to reduce the influences. First, the imaging model of the intensified detector, which includes the deformation parameter of the optical fiber panel, is established based on the orthographic projection through the analysis of errors introduced by the image intensifier. Thereafter, the position errors at the target points based on the model are obtained by using the Levenberg-Marquardt (LM) optimization method. Last, the nearest trigonometric interpolation method is presented to compensate for the arbitrary centroiding error of the image plane. Laboratory calibration result and night sky experiment result show that the compensation method effectively eliminates the error introduced by the image intensifier, thus remarkably improving the precision of the intensified star sensors.

Gas sensor measurements during the initial action period of duty-cycling for power saving

One of the main problems of working with metal oxide semiconductor gas sensors in wireless sensor networks is power consumption. Duty-cycling, i.e., switching off the power of the gas sensor and minimizing the node’s active time is a well-known power saving technique. But the initial action period of the gas sensor after it is switched back on imposes a lower limit on the active time, and therefore a limit on the power savings. The length of a sensor’s initial action period depends on atmospheric conditions, but values of several tens of seconds or even minutes are common. In this paper a correction technique is presented which enables measurements taken during the sensor’s initial action period to be used, thus allowing the node’s active time to be reduced to less than that period. To check the validity of the proposed correction, the responses of two sensors are compared, one continuously powered and the other switched on and off. When the correction is applied to the switched sensor’s response, there appears a trade-off between sensor precision and power consumption. The measurements taken with the two sensors after 5s of power-on time are strongly correlated (r2=0.95).

Soil Water Content Sensor Response to Organic Matter Content under Laboratory Conditions.

Studies show that the performance of soil water content monitoring (SWCM) sensors is affected by soil physical and chemical properties. However, the effect of organic matter on SWCM sensor responses remains less understood. Therefore, the objectives of this study are to (i) assess the effect of organic matter on the accuracy and precision of SWCM sensors using a commercially available soil water content monitoring sensor; and (ii) account for the organic matter effect on the sensor’s accuracy. Sand columns with seven rates of oven-dried sawdust (2%, 4%, 6%, 8%, 10%, 12% and 18% v/v, used as an organic matter amendment), thoroughly mixed with quartz sand, and a control without sawdust were prepared by packing quartz sand in two-liter glass containers. Sand was purposely chosen because of the absence of any organic matter or salinity, and also because sand has a relatively low cation exchange capacity that will not interfere with the treatment effect of the current work. Sensor readings (raw counts) were monitored at seven water content levels (0, 0.02, 0.04, 0.08, 0.12, 0.18, 0.24, and 0.30 cm3 cm−3) by uniformly adding the corresponding volumes of deionized water in addition to the oven-dry one. Sensor readings were significantly (p < 0.05) affected by the organic matter level and water content. Sensor readings were strongly correlated with the organic matter level (R2 = 0.92). In addition, the default calibration equation underestimated the water content readings at the lower water content range (<0.05 cm3 cm−3), while it overestimated the water content at the higher water content range (>0.05 cm3 cm−3). A new polynomial calibration equation that uses raw count and organic matter content as covariates improved the accuracy of the sensor (RMSE = 0.01 cm3 cm−3). Overall, findings of this study highlight the need to account for the effect of soil organic matter content to improve the accuracy and precision of the tested sensor under different soils and environmental conditions.

Interval-Based Celestial Geolocation Using a Camera Array

To replicate classical celestial navigation algorithms with modern technologies, a hemispherical camera array is used to capture panoramic images of the sun for the purpose of obtaining azimuth and zenith angles. A novel geolocation algorithm is presented to use these angles to determine the absolute location without aid of satellites. It derives both the geolocation estimates and the error intervals based on measurement noise levels. Hough Transform, charge-coupled device blooming, and interval analysis are employed to improve accuracy even with lower quality sensors. The proofs of our theoretical results based on an interval analysis handle arbitrary celestial references. Calibration of and geolocation from our experimental prototype demonstrates the feasibility and potential of our system. We obtain geolocation to within a rectangular area of 0.533 km × 1.6 km using an array of inexpensive cameras with 640 × 480 pixels resolution. Our simulation result shows that the improvement of sensor precision with technology already available can reduce the area of uncertainty to less than 200 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .

Context-Aware Abnormality Monitoring Service for Care-Needing Persons Using a Probabilistic Model

Background/Objectives: Care-needing persons usually have routine life patterns in their visiting places and routes. Probabilistic models are useful tools to capture the inherent patterns for care-needing persons from a collection of data and to make inference for uncertain situation. Methods/Statistical Analysis: A dynamic Bayesian network model is proposed to capture the traveling patterns for care-needing person. A collection of traveling data having location, date, and battery charge state information is used to train the dynamic Bayesian network model, in which the training travel data are preprocessed to filter out noises using a smoothing filter. The model makes probabilistic inference for the latent random variables on the fly with respect to the arriving captured sensor data. To conduct the probabilistic inference, the Rao-Blackwellized particle filter is used for fast execution. To take protective steps at potentially abnormal situations, the context-based service rules are defined that specify what to do in which situations the care-needing person is. Findings: The situations of interest are evaluated based on the deviation of the inferred probability distribution from the trained ones regarding the latent random variables. The service rules enable to realize value-added services such abnormal state alerting, abnormality notification to stakeholders, and situational information collection at abnormal states for future forensic or tracking. A real data set has been collected from a young student and the proposed method has been trained and tested in an implemented prototype system for young child care. In the experiments for a 6 weeks data set, the method detected all new test routes successfully when the deviation distance is greater than about 20 meters due to the limited precision of the GPS sensors. Improvements/Applications: The proposed model can be applied to context-aware abnormality service for care-needing persons like young children, mentally ill or disabled persons, and elderly persons are exposed to various dangers in daily life.

SU‐G‐JeP4‐01: An Assessment of a Microsoft Kinect V2 Sensor for Voluntary Breath‐Hold Monitoring in Radiotherapy

Purpose:To determine whether the Microsoft Kinect Version 2 (Kinect v2), a commercial off‐the‐shelf (COTS) depth sensors designed for entertainment purposes, were robust to the radiotherapy treatment environment and could be suitable for monitoring of voluntary breath‐hold compliance. This could complement current visual monitoring techniques, and be useful for heart sparing left breast radiotherapy.Methods:In‐house software to control Kinect v2 sensors, and capture output information, was developed using the free Microsoft software development kit, and the Cinder creative coding C++ library. Each sensor was used with a 12m USB 3.0 active cable. A solid water block was used as the object. The depth accuracy and precision of the sensors was evaluated by comparing Kinect reported distance to the object with a precision laser measurement across a distance range of 0.6m to 2.0 m. The object was positioned on a high‐precision programmable motion platform and moved in two programmed motion patterns and Kinect reported distance logged. Robustness to the radiation environment was tested by repeating all measurements with a linear accelerator operating over a range of pulse repetition frequencies (6Hz to 400Hz) and dose rates 50 to 1500 monitor units (MU) per minute.Results:The complex, consistent relationship between true and measured distance was unaffected by the radiation environment, as was the ability to detect motion. Sensor precision was &lt; 1 mm and the accuracy between 1.3 mm and 1.8 mm when a distance correction was applied. Both motion patterns were tracked successfully with a root mean squared error (RMSE) of 1.4 and 1.1 mm respectively.Conclusion:Kinect v2 sensors are capable of tracking pre‐programmed motion patterns with an accuracy &lt;2 mm and appear robust to the radiotherapy treatment environment. A clinical trial using Kinect v2 sensor for monitoring voluntary breath hold has ethical approval and is open to recruitment.The authors are supported by a National Institute of Health Research (NIHR) Career Development Fellowship (CDF‐2013‐06‐005). Microsoft Corporation donated three sensors. The views expressed in this publication are those of the author(s) and not necessarily those of the NHS, the National Institute for Health Research or the Department of Health.

Investigating the cross-interference effects of alumina refinery process gas species on a SAW based mercury vapor sensor

In this study, the effect of some common alumina refinery process gas species on the response of a surface acoustic wave (SAW) based elemental mercury (Hg0) vapor microsensor was investigated. The developed sensor was based on a SAW delay line, fabricated on a ST-cut quartz substrate with nickel (Ni) interdigitated transducer (IDT) electrodes and gold (Au) film sensitive layer. Ni was chosen as IDT material due to its immiscibility in mercury and thus allowing the sensor to operate for a longer time, as required by the industries. The sensor was exposed to mercury vapor concentrations between 24 and 365ppbv without/with the presence of ammonia, acetaldehyde, ethyl mercaptan, dimethyl disulphide, methyl ethyl ketone and humidity. The detection limit of the sensor toward Hg0 vapor was calculated to be 1.4ppbv and 4.0ppbv at operating temperatures of 35 and 75°C, respectively. The coefficient of variance of the sensor response magnitude was found to be within ±1.86 and ±5.73% at 75°C when exposed to repeated pulses of 365ppbv of Hg0 vapor without and with the presence of interfering gas species, respectively. This indicates high precision of sensor while measuring low concentrations of Hg0 vapor. Results showed that when any one of the interfering gas species was exposed along with Hg0 vapor, the response magnitude of the sensor deviated up- to 7% (93% selectivity) at an operating temperature of 75°C. Furthermore, the sensor showed a selectivity of 87% when Hg0 vapor was tested along with a mixture of all interfering gases. There was insignificant cross-sensitivity effect when the humidity content was increased within the range of 15,300 to 30,600ppmv (equivalent to 50 to 100% relative humidity at room temperature). The overall results indicate that the developed sensor is able to detect Hg0 vapor selectively even in simulated industrial conditions and can be a potentially cheaper and more reliable alternative to the current Hg0 monitoring methods used in alumina refineries.