Sensor Precision Research Articles

Inductive Sensor Design for Electromagnetic Tracking in Image Guided Interventions

As the importance and prevalence of electromagnetic tracking in medical and industrial applications increases, the need for customized sensor design has escalated. This work focuses on AC-based electromagnetic tracking systems where off-the-shelf inductive sensors may not be optimal for many medical instruments or tracking applications. We present a repeatable approach for the design, optimisation and implementation of air-core and ferrite-core inductive sensors suitable for AC-based electromagnetic tracking. Coil-based sensors were designed and tested to investigate the effect of the usual coil parameters such as turn count, geometry and core material on sensor tracking accuracy and precision. Our methodologies were experimentally validated using the Anser EMT system which enabled rapid experimental deployment. Experimental performance is reported compared to off-the-shelf sensors. Static tracking errors of less than 2mm were achieved and close correlation with theoretical design sensitivity and precision was observed. This work may represent a valuable tool in the design of bespoke sensors for electromagnetic tracking where customize sensitivity and form factor are critical.

Automatic Field Detection of Western Corn Rootworm (Diabrotica virgifera virgifera; Coleoptera: Chrysomelidae) with a New Probe.

The Western corn rootworm (WCR), Diabrotica virgifera virgifera LeConte (Coleoptera: Chrysomelidae), is a significant invasive pest of maize plantations in Europe. Integrated pest management demands an adequate monitoring system which detects the activity of insects with high accuracy in real-time. In this study, we show and test a new electronic device (ZooLog KLP), which was developed to detect WCR in the field. The ZooLog KLP consists of a trapping element that attracts insects with its color and species-specific sex pheromone. The other part is an opto-electronic sensor-ring which detects the specimens when they fall into the trap. At detection, the time of catch is recorded and sent to a web interface. In this study, we followed WCR flight patterns for six weeks in two locations, using ZooLog KLP probes. We investigated sensor precision by comparing the number of catches to the number of detections. The tool reached high accuracy (95.84%) in recording WCR. We found a peak in flight activity in August and a bimodal daily pattern. This method may be beneficial in detecting the WCR during their activity, and this new device may serve as a prototype for real-time monitoring systems and improve the management of this pest.

Feeling Uncertain—Effects of a Vibrotactile Belt that Communicates Vehicle Sensor Uncertainty

With the rise of partially automated cars, drivers are more and more required to judge the degree of responsibility that can be delegated to vehicle assistant systems. This can be supported by utilizing interfaces that intuitively convey real-time reliabilities of system functions such as environment sensing. We designed a vibrotactile interface that communicates spatiotemporal information about surrounding vehicles and encodes a representation of spatial uncertainty in a novel way. We evaluated this interface in a driving simulator experiment with high and low levels of human and machine confidence respectively caused by simulated degraded vehicle sensor precision and limited human visibility range. Thereby we were interested in whether drivers (i) could perceive and understand the vibrotactile encoding of spatial uncertainty, (ii) would subjectively benefit from the encoded information, (iii) would be disturbed in cases of information redundancy, and (iv) would gain objective safety benefits from the encoded information. To measure subjective understanding and benefit, a custom questionnaire, Van der Laan acceptance ratings and NASA TLX scores were used. To measure the objective benefit, we computed the minimum time-to-contact as a measure of safety and gaze distributions as an indicator for attention guidance. Results indicate that participants were able to understand the encoded uncertainty and spatiotemporal information and purposefully utilized it when needed. The tactile interface provided meaningful support despite sensory restrictions. By encoding spatial uncertainties, it successfully extended the operating range of the assistance system.

Method to Determine the Closed-Loop Precision of Resonant Sensors From Open-Loop Measurements

Resonant sensors determine a sensed parameter by measuring the resonance frequency of a resonator. For fast continuous sensing, it is desirable to operate resonant sensors in a closed-loop configuration, where a feedback loop ensures that the resonator is always actuated near its resonance frequency, so that the precision is maximized even in the presence of drifts or fluctuations of the resonance frequency. However, in a closed-loop configuration, the precision is not only determined by the resonator itself, but also by the feedback loop, even if the feedback circuit is noiseless. Therefore, to characterize the intrinsic precision of resonant sensors, the open-loop configuration is often employed. To link these measurements to the actual closed-loop performance of the resonator, it is desirable to have a relation that determines the closed-loop precision of the resonator from open-loop characterisation data. In this work, we present a methodology to estimate the closed-loop resonant sensor precision by relying only on an open-loop characterization of the resonator. The procedure is beneficial for fast performance estimation and benchmarking of resonant sensors, because it does not require actual closed-loop sensor operation, thus being independent on the particular implementation of the feedback loop. We validate the methodology experimentally by determining the closed-loop precision of a mechanical resonator from an open-loop measurement and comparing this to an actual closed-loop measurement.

The Selection of the Correct Accuracy Level Using The Optimal Precision and Accuracy Method

This research discusses the importance of selecting the correct value of sensor precision and computational accuracy in production equipment in coffee farmer cooperatives. Accuracy levels that are too low or too high will reduce the effectiveness of mechanization and automation in this field. This research used The Optimal Precision or Accuracy method which based on MSB-First Interval Bounded Variable Precision (MFIBVP) technique. Based on data obtained during the survey in this study, the optimal precision level curve is obtained, so that the optimal precision level can be determined in sub processes that are naturally difficult to determine. In conclusion, the Optimal Precision and Accuracy Method can be used to determine the right accuracy level needed at each stage of the process in a production system

Sparse Sensing and Optimal Precision: An Integrated Framework for H2/H∞ Optimal Observer Design

In this paper, we simultaneously determine the optimal sensor precision and the observer gain, which achieves the specified accuracy in the state estimates. Along with the unknown observer gain, the formulation parameterizes the scaling of the exogenous inputs that correspond to the sensor noise. Reciprocal of this scaling is defined as the sensor precision, and sparseness is achieved by minimizing the $l_1$ norm of the precision vector. The optimization is performed with constraints guaranteeing specified accuracy in state estimates, which are defined in terms of $\mathcal{H}_2$ or $\mathcal{H}_{\infty}$ norms of the error dynamics. The results presented in this paper are applied to the linearized longitudinal model of an F-16 aircraft.

Terrestrial SfM-MVS photogrammetry from smartphone sensors

Smartphones can be regarded as cameras, natively equipped with geolocation and orientation sensors, making them powerful, portable, user-friendly and inexpensive tools for terrestrial structure from motion/multiview stereo photogrammetry (SfM-MVS) surveys. Camera extrinsic parameters (i.e. camera position and orientation), required to produce fully georeferenced SfM-MVS 3D models are available for the majority of smartphone images via inbuilt magnetometer, accelerometer/gyroscope, and global navigation satellite system (GNSS) sensors. The precision of these internal sensors is not yet sufficient to directly use them as input to SfM-MVS photogrammetric reconstructions. However, when the reconstructed scene is significantly greater than the positional error, camera extrinsic parameters can be successfully used to register 3D models during post-processing. We present a survey of a 400 m wide vertical cliff to illustrate a workflow that enables the use of smartphone cameras to generate and fully georeference photogrammetric models without employing ground control points. Survey images were acquired at a distance of ~350 m to the mapped scene using a consumer-grade smartphone. This survey image dataset was subsequently used to build an unreferenced 3D model, which was registered during post-processing using orientation and position metadata tagged to each photograph.

Process adapted calibration improves fluorometric pH sensor precision in sophisticated fermentation processes.

Miniaturization and automation have become increasingly popular in bioprocess development in recent years, enabling rapid high-throughput screening and optimization of process conditions. In addition, advances in the bioprocessing industry have led to increasingly complex process designs, such as pH and temperature shifts, in microbial fed-batch fermentations for optimal soluble protein expression in a range of hosts. However, in order to develop an accurate scale-down model for bioprocess screening and optimization, small-scale bioreactors must be able to accurately reproduce these complex process designs. Monitoring methods, such as fluorometric-based pH sensors, provide elegant solutions for the miniaturization of bioreactors, however, previous research suggests that the intrinsic fluorescence of biomass alters the sigmoidal calibration curve of fluorometric pH sensors, leading to inaccurate pH control. In this article, we present results investigating the impact of biomass on the accuracy of a commercially available fluorometric pH sensor. Subsequently, we present our calibration methodology for more precise online measurement and provide recommendations for improved pH control in sophisticated fermentation processes.

Measurements of juvenile Atlantic bluefin tuna (Thunnus thynnus) size using an unmanned aerial system

An APH-22 vertical-takeoff-and-landing hexacopter was used to collect aerial images of schools and individuals of juvenile Atlantic bluefin tuna (ABFT; Thunnus thynnus) at the sea surface in the southern Gulf of Maine. Quantitative measures of fish length, width, and inter-fish spacing were obtained from these images by applying calibration settings and performance measures from calibrating, testing, and evaluating the onboard motion and altimeter sensors and the digital camera and lenses. The accuracy and precision of the onboard motion sensors, camera, and lens calibrations were sufficient to provide length measurements to sub-centimeter precision, but the altimeter performance was least reliable and required additional information, such as images of known-sized objects during each flight, to provide measurements at the accuracy and precision needed for data to be incorporated in fisheries management. The APH-22 was ideal for acquiring images of ABFT individuals and schools and may be a useful tool for remotely monitoring the behavior and body condition of these elusive animals.

A Biomimetric Lactate Imprinted Smart Polymers as Capacitive Sweat Sensors

This paper demonstrates a non-enzymatic biomimetric flexible sweat sensor. Fabricated by multi-layered assembly, the layers comprised a conductive nanoporous carbon nanotube-cellulose nanocrystals (CNC/CNT) porous films entrained with poly (N-isopropylacrylamide) (pNIPAM) based microgel. The pNIPAM microgels based sensor films are effective for detection of ionic strength. The pNIPAM microgels coated with lactate imprinted poly (aniline/phenylboronic acid) (pANI/PBA) moieties switch off their responsivity to NaCl and instead effectively respond to lactate. As such, the printed pNIPAM microgels have been demonstrated as flexible capacitive sensor for multiplex detection of electrolyte (e.g. NaCl) and lactate levels in sweat analysis. The linear concentration range obtained for pNIPAM @CNC/CNT sensor in response to NaCl was 3–80 mM, with a limit of detection (LOD) of 0.23 mM NaCl. The lactate imprinted pANI/PBA- pNIPAM @CNC/CNT sensor resulted in a linear range of 1 mM-25 mM, and a LOD of 0.10 mM, an order of magnitude higher than the non-imprinted form. For both analytes, the sensor precision ranged from 0.5-5.0%, indicative of the overall reliability and validity. The fabricated flexible sensors were demonstrated to be effective in quantitation of electrolytes (principally NaCl) and lactate in a real sweat sample.

A Software Improvement Technique for Platinum Resistance Thermometers

Temperature measurement is essential in industries. The advantages of resistance temperature detectors (RTDs) are high sensitivity, repeatability, and long-term stability. The measurement performance of this thermometer is of concern. The connection between RTDs and a novel microprocessor system provides a new method to improve the performance of RTDs. In this study, the adequate piecewise sections and the order of polynomial calibration equations were evaluated. Systematic errors were found when the relationship between temperature and resistance for PT-1000 data was expressed using the inverse Callendar-Van Dusen equation. The accuracy of these calibration equations can be improved significantly with two piecewise equations in different temperature ranges. Two datasets of the resistance of PT-1000 sensors in the range from 0 °C to 50 °C were measured. The first dataset was used to establish adequate calibration equations with regression analysis. In the second dataset, the prediction temperatures were calculated by these previously established calibration equations. The difference between prediction temperatures and the standard temperature was used as a criterion to evaluate the prediction performance. The accuracy and precision of PT-1000 sensors could be improved significantly with adequate calibration equations. The accuracy and precision were 0.027 °C and 0.126 °C, respectively. The technique developed in this study could be used for other RTD sensors and/or different temperature ranges.

Luenberger-based State-Of-Charge evaluation and experimental validation with lithium cells

This paper presents a technique to estimate the State-Of-Charge of lithium cells, based on a Luenberger state estimator. Although this approach is not new, in this paper several practical implementation details are discussed, including techniques to find numerical parameters of the required cell model with tests that give enough precision with limited testing effort. Additionally, the effects of limited sensor precision on the final estimated State-Of-Charge are discussed. The paper also provides guidelines on how to keep the cell model (needed for the estimator to operate satisfactorily) updated during normal operations. The results achieved with the proposed approach show very interesting results, also when compared to other techniques described in the recent literature.

Mobile Application Development and Testing for Work Zone Activity Real-Time Data Collection

Work zones are prevalent in the United States as the infrastructure is increasingly in need of maintenance. Lack of reliable data is one of the main obstacles in work zone research. Reliability suffers because of underreporting of crashes and inclusion in the analysis of irrelevant activities that are not attributable to work zones. In addition, the work zone environment is very dynamic, resulting in differing reasons for crashes. These are barriers to gaining an accurate understanding of safety in work zones. The objective of this paper is to design, develop, and deploy a mobile application (app) for real-time work zone data collection to address these issues. The development process consisted of the following steps. First, a user interface was designed to enable users to collect various work zone activity information. Second, taking advantage of recent advances in cloud computing, a real-time database was designed for efficient storage and instantaneous communication of work zone activity data. Field tests were then conducted at 13 work zone sites in Columbia, Missouri. Finally, the performance of the app was evaluated based on scalability, precision, and user friendliness. The app was able to respond to queries at real-time speeds even as the size of the database and the number of users increased. The precision of sensors was within appreciable accuracy for the geolocation. The app’s user friendliness was acknowledged by the users. The successful deployment of this mobile app would lead to accurate work zone data which is very useful for work zone management, traveler information, contract monitoring, safety analysis, and project coordination.

Research on Sensor Correction Method for Monitoring in Early Warning System of Coal Spontaneous Combustion

According to statistics, there are as many as 4000 coal spontaneous combustion accidents every year, causing heavy casualties and property losses. By accurately detecting the change of index gas concentration, it can provide reliable criteria for identifying and warning the early hidden dangers of coal spontaneous combustion. The improvement of sensor precision is the basis of intelligent monitoring. Modification of wireless multi-parameter sensor used in coal mine is of great significance for accurate monitoring of gas concentration in coal mine. In order to modify the wireless multi-parameter sensor, the high and low temperature experiments of the sensor were carried out at -5∼45°C, and the real values of O2 concentration at 15%, 21%, 25% and CO concentration at 175ppm, 250ppm and 375ppm were measured. Measurement data was Non-linear fitting and correction by using SVM (Support Vector Machine), BP neural network and Elastic Network regression method. The experimental results show that the Elastic Network regression compensation reduces the O2 sensor from the original maximum mean absolute percentage error (MAPE) of 18.4% to 0.52%, and the average MAPE decreases from 13.89% to 0.21%. Using the SVM nonlinear compensation, the CO sensor is reduced from the original maximum MAPE of 43.2% to 1.6%, and the average MAPE is reduced from 21.38% to 1.4%. Elastic Network regression compensation excellent achieves nonlinear compensation of O2 sensor. The SVM better realizes the nonlinear compensation of the CO sensor. Through non-linear compensation, wireless multi-parameter sensor can better meet the requirements of coal mine gas concentration monitoring.

Encoding multiple digital DNA signals in a single analog channel.

For many analytic and biomedical applications, the presence of an analyte above or below a critical concentration is more informative for decision making than the actual concentration value. Straightforward analog-to-digital signal conversion does not take full advantage of the precision and dynamic range of modern sensors. Here, we present and experimentally demonstrate an analog-to-multiple-digital signal conversion, reporting digital signals that indicate whether the concentrations of specific DNA sequences exceed respective threshold values. These threshold values can be individually programmed for each target sequence. Experimentally, we showed representation of four DNA targets’ information in a single fluorescence channel.

SPARC: Wireless, Continuous Monitoring of Bowel State and Function

Introduction and ObjectiveThe neural control of bowel function during content storage, motility, and defecation is not well understood. A more complete understanding may be possible with electrophysiology or pharmacology studies that include functional measures of the bowel in conscious subjects. To overcome this, we developed a wireless colonic pressure and volume monitoring sensor for use anaesthetized pigs. This study validated sensor function and physiologic outcomes in 2 acute animal studies.MethodsThe device measured 80 mm long and 7 mm wide and consisted of a flexible printed circuit board housed in a silicone‐gel filled, thin‐walled silicone tube to maintain flexibility when implanted in the colon. The fully packaged device contained 5 sensors: two pressure sensors to measure circumferential and longitudinal bowel contractions, and three ring electrodes to measure conductance and conductivity of bowel contents near the device. An additional ring electrode was used as a common return for the 3 ring electrodes. The device transmitted radio data at 10 Hz and included an inductive energy interface antenna for battery recharging.Accuracy and precision of sensors were first characterized through benchtop testing in a benchtop colon phantom against a reference pressure sensor, and with artificial stools of varying water content. The functionality of the sensors was further evaluated in 2 preliminary acute in vivo experiments with the device powered through a power source.Yorkshire pigs, approximately 4–5 months old, weighing 40–60 kg were used for the in vivo experiment. Each pig was anaesthetized and the sensor was affixed to the rectum first and then to the colon using a mucosal clip. Recording was done for 5 hours and data was transmitted wirelessly to the recording device. Artificial stool of different saline concentration was inserted into the rectum to simulate different conductivity and conductance of the stool. Artificial stool was also mixed with radio contrast to take CT images of the stool location.ResultsPower consumed by the device was 140 μA when on and 2 μA when off, with an expected battery lifetime between charges of 40 hours. Benchtop results showed that the pressure sensors detected controlled pressure changes with a root mean square error (RMSE) of 3.7 cmH20. Regression analysis for the conductivity measurements showed a maximum error of 19% and a RMSE of 675 μS/cm. For volume estimation, there was a maximum error of 26% and a RMSE of 4.5 mm for the diameter of a stool. In vivo conductivity measurements were qualitatively sensitive to stools with different conductivities, and to increasing stool volume. The pressure sensors recorded changes in bowel pressure in response to abdominal compressions and slow‐wave bowel contractions.ConclusionSmall wireless, bowel sensors can help us understand the physiologic functions of the bowel and its connection with the nervous system. Initial testing indicated the feasibility of this device in pigs in measuring pressure and volume inside the bowel.Support or Funding InformationThis work was funded by the NIH Stimulating Peripheral Activity to Relieve Conditions (SPARC) program, NIH grant number OT2OD023873.

Real-time measurement of CO2 isotopologue ratios in exhaled breath by a hollow waveguide based mid-infrared gas sensor

A hollow waveguide (HWG) based mid-infrared gas sensor using a 2.73 µm distributed feedback (DFB) laser was developed for simultaneously measuring the concentration changes of the three isotopologues 13CO2, 12CO2, and 18OC16O in exhaled breath by direct absorption spectroscopy, and then determining the 13CO2/12CO2 isotope ratio (δ13C) and 18OC16O/12CO2 isotope ratio (δ18O). The HWG sensor showed a fast response time of 3 s. Continuous measurement of δ13C and δ18O in the standard CO2 sample with known isotopic ratios for ∼2 h was performed. Precisions of 2.20‰ and 1.98‰ for δ13C and δ18O respectively at optimal integration time of 734 s were estimated from Allan variance analysis. Accuracy of -0.49‰ and -1.20‰ for δ13C and δ18O, respectively, were obtained with comparison to the values of the reference standard. The Kalman filtering method was employed to improve the precision and accuracy of the HWG sensor while maintaining high time resolution. Precision of 5.45‰ and 4.88‰ and the accuracy of 0.21‰ and -1.13‰ for δ13C and δ18O, respectively, were obtained at the integration time of 0.54 s with the application of Kalman filtering. The concentrations of 12CO2, 13CO2 and 18OC16O in breath cycles were measured and processed by Kalman filtering in real time. The measured values of δ18O and δ13C in exhaled breath were estimated to be -21.35‰ and -33.64‰, respectively, with the integration time of 1 s. This study demonstrates the ability of the HWG sensor to obtain δ13C and δ18O values in breath samples and its potential for immediate respiratory monitoring and disease diagnosis.

Improved monitoring of adherence with patching treatment using a microsensor and Eye Patch Assistant

Use of a microsensor has been suggested to monitor patching adherence. Application has been limited because the microsensor's small size makes it easy to lose and a swallowing risk. We designed the Eye Patch Assistant (EPA) to hold the small microsensor in place and reduce the risk of loss or swallowing. This study reports the accuracy, precision, ease of use, and comfort for patching with EPA (patch+EPA) to monitor adherence. Adults (N=13) wore an adhesive patch alone or a patch+EPA for 2hours each, recorded wear time, and completed an ease of use/comfort questionnaire; 30 children wore a patch or patch+EPA and completed the questionnaire. Sensor sampling interval was every 5minutes or every 1minute. Sensor accuracy and precision were evaluated by Bland-Altman analysis and 95% limits of agreement, and questionnaire scores compared by Wilcoxon tests. With 5-minute sampling, we found excellent accuracy for adults (mean actual vs recorded time difference, 1.4minutes) and children (mean difference, -0.9min). We found high precision for both adults and children (95% limits of agreement half widths of 6.4minutes and 1.9minutes, respectively). In adults, the ease of use score for the patch+EPA was lower than the patch (P<0.01), but the comfort score for the patch+EPA was higher (P<0.01). For children, scores did not differ significantly. The patch+EPA functioned well between 45° and 82°F. The patch+EPA was well accepted and monitored adherence accurately and precisely.

Star Sensor Layout Design Based on PSO

Incident light, such as sunlight and atmosphere-earth light, will interfere with the viewing field of star sensor, and the accuracy and precision of star sensor will decrease. A design method based on PSO algorithm and STK software to solve layout problems efficiently is applied. The star sensor layout problem is described as the problem of finding the global optimal solution in three-dimensional space. The results show that this method can solve the problem of satellite sensor layout quickly and effectively and the method has certain engineering application value.

Multimodal Biometric Recognition: Fusion of Modified Adaptive Bilinear Interpolation Data Samples of Face and Signature using Local Binary Pattern Features

Biometric based authentication systems use particular person characteristics which might be based on either behavior like voice, signature etc. or body structure like face, iris, palm print, fingerprint, etc. The performance of any unimodal biometric arrangement is depending on elements like surroundings, atmosphere, and sensor precision. Also, there are numerous trait unique demanding situations which include pose, expression, growing old and so forth for face reputation, occlusion and acquisition related problems for iris and terrible high-quality and social popularity related troubles for fingerprint. Hence, fusion of more than one biometric samples, traits or algorithms to achieve quality performance is another way to reap the better overall performance. In current scenario many researchers concentrating on Multimodal Biometrics with new fusion techniques ideas. We propose a new method of feature level fusion which uses Modified Adaptive Bilinear Interpolation (MABI) method to increase the resolution of data sample, which gives better features for fusion which gives more accurate results. In this work, experiment is done on AT&amp;T face Cambridge University Computer Laboratory and MCYT signature Biometric Recognition Group datasets with combination of both unimodal and multimodal traits. K Nearest Neighbor (KNN) and Ensemble methods are used for classification. The proposed biometric system can be used in biometric surveillance, biometric screening for secured places, forensic applications etc.