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Using the barometer for height estimation often requires the use of external reference to correct biases in measurement. These biases are often caused by the change of the ambient pressure environment. The barometric height estimation is especially challenging in tactical and rescue applications where high temperatures or sudden large pressure shocks can change the pressure rapidly. We assess the suitability of barometers for infrastructure-free navigation in tactical applications. First, this paper investigates the effect of transition in seamless indoor/outdoor navigation. Second, we measure the effects of pressure shocks, caused by explosions or firearms, on low-cost and lightweight micro-electromechanical barometers to ensure that the sensors are capable of operating under these conditions. Finally, we investigate the use of sonar measurement to estimate the vertical speed as an alternative to the reference barometer for infrastructure-free navigation. The fusion of barometer and sonar achieved on average 0.46-m root-mean-square error (RMSE) while simple barometric height estimation had a RMSE of 0.65 m. The fusion method had no errors over 1.5 m during the test. This accuracy is generally sufficient to find the correct floor level which is crucial for tactical situational awareness. The goal of this paper is to develop the methods for seamless indoor/outdoor navigation, and therefore the most important result of this paper is that the error caused when transitioning between outdoor and indoor environments is visibly reduced. [ABSTRACT FROM AUTHOR]

While formal and rigorous procedures are adopted to ensure the reliability of measurement results, the decision-making activities based on the information returned by measurement are often poorly structured and based on vague assumptions and reasoning, thus negatively impacting the reliability of the achieved conclusion. To overcome this problem, a structured multicriteria decision-making approach based on the analytic hierarchy process was recently proposed in the literature to properly support decision-making activities by exploiting the information provided by measurement results related to heterogeneous properties and its application was demonstrated in a case study. Using the same case study, this paper analyses the sensitivity of the obtained conclusion to various uncertainty sources that are embedded in that approach. In particular, the impact of uncertainties on both measurement and pairwise comparisons of the criteria is analyzed and their combined effect is evaluated by considering a worst case situation. Moreover, the effect of the formula adopted to aggregate the information returned by the measurement of heterogeneous properties on the final decision is considered. The performed analysis contributes to support a disciplined and methodologically sound implementation of decision-making activities, significantly improving both the repeatability and reproducibility of the whole evaluation process, as well as the confidence in the achieved conclusions. [ABSTRACT FROM AUTHOR]

The interest of industry and academia in medical measurements continues to increase, due to technology advancements that allow new sensors, measurement systems, and methodologies, supporting a wide number of medical applications. [ABSTRACT FROM PUBLISHER]

Testability analysis is a fundamental prerequisite to any fault location or parameter identification technique (FLPIT) for analog circuits. However, differently from the linear time-invariant field, this topic has been poorly investigated with regard to nonlinear circuits. In this paper, for the first time, to the best of our knowledge, the question is addressed with reference to periodically switched networks with dc stimuli (PSNDSs), a class of inherently nonlinear time-varying circuits that subsumes dc-dc converters as noteworthy members. A rigorous testability measure at both a circuit and a component level is thereby obtained, which provides information as to how many and which components can be unambiguously diagnosed or estimated from time-domain measurements. Consequently, it yields an upper bound, independent of the parameter values and the test instants, to the performance of whatever method actually employed as an FLPIT. This theoretical testability metric has been converted into an efficient computer program for the fully automated testability analysis of PSNDSs presented herein. Such a computer program represents the first practical testability gauge for PSNDSs: it can be employed by both test engineers and circuit designers as a guide to properly selecting injection and measurement points as well as estimating the number of necessary measurement instants, so as to shape their own FLPITs. Several examples are provided for which an in-depth pencil-and-paper testability analysis is contrasted with its automated counterpart provided by the aforementioned software: in particular, popular dc-dc converter topologies are thoroughly analyzed. Comparisons with a previously proposed frequency-domain testability measure are also considered. [ABSTRACT FROM PUBLISHER]

This paper presents the analysis of all driving during 1 year by 14 older drivers from the Candrive study. Data analytic techniques have been applied to this unique big data set that includes global positioning system and geographic information system data with an average of 1567 trips and 20 200 km for each driver. The drivers have stable general, cognitive, and physical health over the year. This paper identifies trip features and tests their potential to be used to distinguish between driver pairs and shows that trip length and duration allow 90.1% and 90.1% of the 91 driver pairs (14 drivers) to be distinguished ( $p<5$ %). Driver velocity histograms are also shown to allow driver pairs to be distinguished. This paper explores the deceleration habits of the drivers by locating all deceleration events with a net velocity drop of >=4 km/h (average of 30 156 events per driver) and finds that the event mean and minimum deceleration values have different two-phase relationships for each driver. Trips taken by a driver are compared with the drivers’ two-phase relationships and this paper shows that the measures achieve their maximum performance for trips with >=8 events. The minimum measure is better (correlation of 0.74) than the mean measure (0.54). These relationships provide a measure of a driver’s unique behaviors and could allow more personalized autonomous driving systems and capture features of the deceleration habits for the driver that can be used to distinguish between drivers. [ABSTRACT FROM PUBLISHER]

In this paper, a new analytical model is proposed to describe the behavior of uncertainty for a generic vision-based measurement system applied to mechanical vibration measurement. In particular, this paper proposes a novel way to evaluate the effects of motion blur. The theoretical framework presented here takes into account the camera acquisition parameters, the dynamics of the measurand (in terms of instantaneous speed), and the image scaling factor to develop a physical model of uncertainty when measuring a mainly monomodal vibration. Uncertainty is evaluated starting from the normalized discrepancy between a measured position and the actual one. The analytical model proposed here has been validated extensively with the help of a test stand that can generate biaxial monomodal vibrations. The motion of a target mounted on the test stand has been simultaneously monitored by a stereo vision rig and a set of triangulation lasers. Model validation has been carried out using two measurement techniques: stereo vision blob analysis and digital image correlation. The experimental results show that the proposed model is able to represent the behavior of uncertainty for both the techniques. After the model has been validated, it is shown how it is possible to exploit it to estimate the uncertainty of a vision rig starting from the nominal characteristics of both the vision system and the target. This can be a tool to facilitate the design of vision-based measurement systems. [ABSTRACT FROM AUTHOR]

This paper presents the results from a one-year study of 12 patients with moderate dementia in an adult day program who played a novel whack-a-mole game-based measurement instrument for cognitive behavior and performance. The ongoing measurement of cognition and changes associated with dementia is a challenge for healthcare providers. Measurement methods based on a tablet-based instrument are proposed. Partnership with the adult day program greatly eased recruitment: all but 1 eligible participant joined our study, compared to one in five, or lower, for previous studies with similar populations. There are three unique aspects to the design of our game: first, it has two distinct targets requiring different actions, which increases the cognitive processing for the users; second, each level is systematically more difficult; third, it records and analyzes player performance. The results show that the patients’ game performance improves over the first few weeks; this indicates that they are learning the game and retaining ability gains from week-to-week, suggesting some procedural learning is still intact. Over the year, 4 participants showed cognitive decline, 4 were stable and 3 improved based on their Minimental State Exam (MMSE) score. Two measures are proposed based on level progression within the sessions and mole hit performance. The level progression measure identifies declining participants with one false negative (FN) and one false positive error. The mole hit performance measure identifies declining participants with one FN error. These results demonstrate the potential for the proposed instrument to provide an ongoing measurement as an alternative for the repeated application of the MMSE. [ABSTRACT FROM AUTHOR]

Accommodation of a situation when a prior map is not available in an indoor localization system is valuable to cost-effective operations by removing a need for map drawing and map updating. This paper suggests a trajectory learning method using crowdsourced measurements in order to support the absence of map. A localization framework based on a particle filter is formalized by machine-learning-based feature extraction and Gaussian process (GP) regression. The feature extraction algorithm reduces dimensionality of sparse measurement vector, and it is applied to detect floor level and designated landmarks. Also, the combination of the feature extraction and the GP regression is used for modeling nonlinear relationship between location and measurement. By this combination, locations of Wi-Fi access points are not required to be known. From the field experimental results, we confirm that the detections of floor level and landmarks are accurate, the learned trajectories are close to the true map, and positioning accuracy is improved thanks to the learning-aided localization. [ABSTRACT FROM PUBLISHER]

This paper presents a simultaneous localization and mapping (SLAM) method that utilizes the measurement of ambient magnetic fields present in all indoor environments. In this paper, an improved exponentially weighted particle filter was proposed to estimate the pose distribution of the object and a Kriging interpolation method was introduced to update the map of the magnetic fields. The performance and effectiveness of the proposed algorithms were evaluated by simulations on MATLAB based on a map with magnetic fields measured manually in an indoor environment and also by tests on the mobile devices in the same area. From the tests, two interesting phenomena have been discovered; one is the shift of location estimation after sharp turning and the other is the accumulated errors. While the latter has been confirmed and investigated by a few researchers, the reason for the first one still remains unknown. The tests also confirm that the interpolated map by using the proposed method improves the localization accuracy. [ABSTRACT FROM PUBLISHER]

Biosensors are prominent in several areas, such as medical diagnosis, food preparation, pharmaceutical industries, and clinical analysis; high performances are required in spite of attaining accuracy, sensitivity, low cost, easy handling, and portability, but the major parameter that is always representing a challenge for biosensors is specificity. High sensitivity and specificity can be obtained by combining appropriate transduction methods together with immunoassay techniques. In this paper, integrated inductive biosensors for the magnetic immunoassay process, which use magnetic beads as markers for biomolecules, are presented with potential applications to proteins and DNA measurements. The working principle and a dedicated fabrication technology, which also embeds thermal actuation and control, are described; in particular, an improved sensing architecture is proposed here, which allows one to expand the microsensor operative field toward very low bead concentrations. The sensor characterization results are presented together with the analytical model of the transduction principle; a detection limit of approximately 300 beads has been demonstrated. The microsensor is also capable of operating by measuring up to 450 000 beads with a good linearity. Therefore, the sensor architecture proposed here has been demonstrated for wide operating field with high resolution and good linearity; the results proposed confirm the suitability of the devices developed for magnetic immunoassay applications. [ABSTRACT FROM AUTHOR]

Smartphones have become an indispensable tool in the work and life of most people. Given the many kinds of sensors built into smartphones, solving indoor navigation problems using these phones has become feasible. This paper proposes such a solution, using a particle-filter-based indoor positioning method. This method improves the positioning accuracy and helps improve the user experience. The major features of the method are that it leverages and fuses the advantages of two different indoor positioning methods [pedestrian dead reckoning (PDR) and received-signal-strength-indicator-based indoor positioning], and considers the influence on positioning of the indoor spatial map. The smartphone built-in sensors are used as data sources and constitute a feasible method for PDR. The iBeacon devices are also used—because of the many advantages, they present in indoor environments, such as low energy consumption, convenient deployment, and popular applications—and their received signal strength indicator values are used as observations in the particle filter. The devices used in this paper are conventional, without any special equipment, and the experimental sites are common work environments. Therefore, we believe that the proposed method has a wide application potential for indoor navigation applications (e.g., museum guides), and is especially suitable for working with indoor navigation maps. [ABSTRACT FROM AUTHOR]

This paper presents a design in which a common, off-the-shelf, low-cost dust sensor, normally aimed at providing the measurements of the total dust concentration, is employed as the core of a more sophisticated instrument, object of this design, which is able to measure also the particle size of sampled airborne particulate, thus providing the particle size analysis of the sampled air. This result has been achieved by introducing some modifications to the sensor’s operating directions as specified by the manufacturer. These modifications mainly concern: a different geometry for the sampled air flow (to enable the detection of single particles), a more accurate processing of the output signal (to maximize the signal/noise ratio), and a simple yet an accurate metrological calibration, which exploits the statistical properties of commonly available polydispersed particulate (talc powder), instead of needing monodispersed material (expensive and difficult to employ). The experimental results show that, under the proper conditions, the calibrated instrument is indeed able to measure the optical size of the detected particles and therefore to determine the particle size distribution (i.e., the granulometric analysis) of the sampled particulate. [ABSTRACT FROM AUTHOR]

Several issues related to the practical synthesis of ternary sequences with specified spectra are addressed in this paper. Specifically, sequences with harmonic multiples of two and three suppressed are studied, given their relevance when testing and characterizing nonlinear systems. In particular, the effect of nonuniform digital-to-analog converter (DAC) levels on the spectral properties of the generated signal is analyzed. It is analytically shown that the DAC nonuniform levels result in degraded harmonic suppression performance. Moreover, a new approach is proposed for designing ternary sequences, which is flexible and can be adapted to suit different requirements. The resulting sequences, denoted as randomized constrained sequences, are characterized theoretically by deriving an analytical expression of the power spectral density. Furthermore, they are extensively compared with three synthesis approaches proposed in the literature. The approach is validated by numerical simulations and experimental results, showing the potential to achieve harmonic suppression performance of approximately 100 dB. [ABSTRACT FROM AUTHOR]

The quantification of muscle weakness is useful to evaluate the health status and performance of patients and athletes. In this paper, we proposed a novel methodology to investigate and to quantify the effects induced by inaccuracy sources occurring when using a hand-held dynamometer (HHD) for knee strength measurements. The validation methodology is based on the comparison between the output of a one-component commercial HHD and the outputs of a six-component load cell, comparable in dimension and mass. An optoelectronic system was used to measure HHD positioning angles and displacements. The setup allowed to investigate the effects induced by: 1) the operator’s ability to place and to hold still the HHD and 2) ignoring the transversal components of the force exchanged. The main finding was that the use of a single component HHD induced an overall inaccuracy of 5% in the strength measurements if the angular misplacements are kept within the values found in this paper (≤15°) and with a knee range of motion ≤22°. Extension trials were the most critical due to the higher force exerted, i.e., 249.4 ± 27.3 versus 146.4 ± 23.9 N of knee flexion. The most relevant source of inaccuracy was identified in the angular displacement on the horizontal plane. [ABSTRACT FROM PUBLISHER]

Optically trapped nanoparticles are used in various fields ranging from biophysics to precision sensing. An optically trapped nanoparticle can be regarded as a harmonic oscillator driven by the thermal fluctuations of its environment. Unlocking the potential of optically levitated systems for precision measurements in the classical and the quantum regime requires cooling of the particle motion. In parametric feedback cooling, the center-of-mass motion of a nanoparticle optically levitated in a vacuum is reduced by temporally modulating the optical trapping potential. This technique requires a precise measurement of the particle’s motion to derive the feedback signal and is prone to measurement noise and inevitable thermal process noise. In a state-of-the-art implementation, the feedback signal is derived from a simple phase-locked loop (PLL). Kalman filters are regularly deployed in a variety of application scenarios to improve system performance under noisy conditions. In this paper, we investigate theoretically and experimentally the performance of parametric feedback cooling, where the measurement signal is Kalman filtered before entering the PLL. Compared to sole PLL cooling, our numerical full-system simulations show a 20% reduction of the residual motional energy of a trapped nanoparticle in the presence of the Kalman filter. We detail a field-programmable gate array-based implementation of a Kalman filter and evaluate its performance in-field. [ABSTRACT FROM AUTHOR]

Precision measurement of the oscillation period of a beam balance oscillator has a fundamental role in the inertial mass measurement project. In order to improve the accuracy of oscillation period measurement, the nonlinear oscillation theory is used to study the periodic oscillation process of the beam balance oscillator in this paper, and the analytical solutions of the dynamic equations of the balance under free and forced conditions are deduced. Moreover, the effects of ground vibration, air turbulence, and temperature fluctuation on the accuracy of oscillation period measurement are analyzed in detail in both vacuum and atmospheric environments, and a method based on beam structural parameters adjustment and temperature coefficient correction is proposed to suppress these disturbances under air condition. Experimental results show that the relative standard uncertainty of the measured oscillation period can be reduced from $2.5\times 10^{-5}$ to $1.2\times 10^{-6}$ (Class A, $k =1$) after the above-mentioned method is adopted. [ABSTRACT FROM AUTHOR]

The measurement of NO2 abundance in the air is of interest for monitoring pollution sources and air quality. A new instrumental concept has been recently introduced, which makes use of an acoustooptical tunable filter (AOTF) as the main component of a spectral imager. With the capability of resolving the absorption spectrum of NO2, and a high spatial and temporal sampling, this instrument can be seen as a NO2 camera. In this instrument, the selection of different optical wavelengths by the AOTF is done by applying a radio frequency (RF) signal to the AOTF. The selected frequency and the applied amplitude are the key parameters for the quality of the measurements. The design and assembly of an RF chain, consisting of an RF generator and RF amplifier, will lead to an electronics bench capable of driving the NO2 camera. In view of a potential spaceborne application of this instrumental concept, the design of the RF generator is only making use of space-qualified components. This paper focuses on the development of this part of the electronics bench. [ABSTRACT FROM AUTHOR]

We present a measurement system capable to detect and reconstruct the trajectory of moving charged debris expelled with the exhaust gases or circulating in the lubricating oil of engines, aero-engines, or gas turbines. The measurement technique is based on a circular array of three electrostatic sensors used to estimate the trajectory, the charge, and the velocity of a moving charged particle. We discuss methods to process the information provided by the sensors, combining physical models and the geometrical characteristics of the sensor array, providing a theoretical characterization of the measurement accuracy, which is shown to be not dependent on the direction of the particle motion with respect to the sensor placement. The information obtained with the measurement system can be used to reject false positives when the sensors and the front-end electronics are used in noisy environments. [ABSTRACT FROM AUTHOR]

The droplet imaging instrument (DII) is a new instrument for cost-effective in situ measurements of the size and concentration of water droplets. The droplet size distribution and the concentration of atmospheric liquid water are important for the prediction of icing on structures, such as wind turbines. To improve the predictions of icing, there is a need to explore cost-effective working solutions. Through imaging, a wide range of droplet sizes can be measured. This paper describes a study of the atmospheric liquid water content and the median volume diameter using the DII and a commercial reference instrument—the cloud droplet probe 2 from Droplet Measurement Technologies Inc. The measurement is done at a weather measurement station in mid-Sweden. For a second validation, the result is compared with predictions using a numerical weather prediction model. The size measurement of the DII is verified using polymer microspheres of four known size distributions. The study shows that the DII measurement is precise, but there is a systematic difference between the two compared instruments. It also shows that droplets larger than $50~\mu \text{m}$ in diameter are occasionally measured, which we believe is important for the prediction of icing. [ABSTRACT FROM AUTHOR]

In this paper, a new approach is proposed to estimate the location of a sensor in a wireless sensor network. For the estimation, only a few anchor nodes with known locations and received signal strength (RSS) indicator (RSSI) are needed. It is well known that the RSS reduces with increasing distance between the transceivers following a nonlinear path loss model. Most published works determine these parameters offline. This often yields limited estimation accuracy due to high variance of RSSI measurements. To improve the estimation accuracy, the parameters are estimated together with the unknown node's location in real time in this paper. To optimize the results, a new particle swarm optimization with log-barrier approach is proposed. Both simulation and experimental results show that the proposed scheme performs well as compared with some existing schemes. [ABSTRACT FROM AUTHOR]

Decentralized state estimation of power systems is under investigation for the monitoring of modern power systems. In this framework, the identification of sensor failure is a critical issue. A novel method is proposed here to achieve this goal, yielding improved reliability of the decentralized power system monitoring. Particularly, the improved reliability could be regarded as the reliable uninterrupted state estimation of the power system, in the presence of failures of the sensors. Sensor measurements are locally validated before they are elaborated. The validation algorithm is based on reasonable thresholds of the measurand computed via polynomial chaos theory and determined based on the effect of the uncertainty in the system, particularly that of loads. These thresholds are dynamically computed; therefore, the criterion for acceptability of the measurements is always updated for the current operating conditions. In the presence of unacceptable data, the measurements are discarded and replaced with an estimate of the measured value. The application of this approach to decentralized state estimation is presented in this paper. Numerical results obtained from MATLAB/Simulink and real-time simulations of a shipboard direct-current zonal power system are used to demonstrate the effectiveness of the proposed method in addressing measurement failures in decentralized state estimation. [ABSTRACT FROM AUTHOR]

An on-chip instrument for measuring ex post the drug actually released by transdermal delivery is proposed. The volume of a drug conveyed through the skin is assessed by measuring the bioimpedance locally before and after delivery. In therapeutic applications, the instrument can provide a low-cost and clinically portable solution. In the first part of this paper, the concept and physical design of the instrument as well as details about its realization and operations are illustrated. Then, the metrological characterization, using injection as a reference subcutaneous transfer of a known volume of drug, is reported in laboratory, in vitro on eggplants and ex vivo on pig ears. Results of resolution, short-term stability, interindividual tissue reproducibility, sensitivity, repeatability, and linearity point out noteworthy scenarios of clinical applications. [ABSTRACT FROM AUTHOR]

In this paper, accurate argument calculation for sine-fitting algorithms is investigated, assuming floating-point (FP) arithmetic. An easy-to-implement incremental calculation technique is suggested. In order to decrease error propagation, the algorithm is complemented with an advanced summation technique. Theoretical and numerical analyses on computational demand are performed to highlight that incremental argument calculation outperforms the method proposed in former research. Furthermore, an algorithm is implemented to mitigate the effect of imprecise representation of frequency on FP arithmetic. Monte Carlo analyses are carried out to demonstrate the accuracy of the suggested algorithms. Results show that phase information can be evaluated precisely even with single-precision FP arithmetic, applying incremental argument calculation. By this means, the cost of equipment that is needed to perform sine fitting can be reduced significantly. Finally, possible application areas are shown to demonstrate the applicability of the suggested solutions in the state-of-art measurement procedures. [ABSTRACT FROM PUBLISHER]

State estimation and fusion is studied using Kalman filter (KF) when a slow-rate integrated measurement is available. Integrated measurement is common in industrial processes, when a sample of material is gradually collected over some period of time and then sent to a laboratory for analysis. In this case, the laboratory measurement will reflect the material properties that have been integrated over the sampling period. The goal is to estimate the fast-rate value of states that evolve with time. A modified KF is proposed to execute state estimation using a slow-rate integrated measurement. Fusion of the slow-rate state estimate and other fast-rate measurements can improve the final state estimation of the process. The performance of the proposed method is demonstrated through both simulation and experimental study in a laboratory scale hybrid tank pilot plant.

Note to Practitioners—When a sample of material is sent to laboratory for analysis, a common practice is to gradually collect a small amount of material over a period of time in a container and send the collection to the laboratory at the end of the sampling period. In this case, the laboratory measures the average of the variable during that period of time. Experienced operators can use this measurement to operate the process. However, including this integrated slow rate measurement into the automatic process control and monitoring system is a challenging problem. In this paper, we mathematically formulate the estimation of the real-time value of a variable using integrated laboratory measurement. The estimation is also improved through fusion of this measurement and other conventional measurements. Laboratory experimental result illustrates the feasibility of the proposed method. [ABSTRACT FROM PUBLISHER]

In this paper, the online measurement of particle concentrations in suspensions containing gas bubbles is studied using the ultrasonic spectra and the synergy interval partial least squares regression (Si-PLS) model in the solid–liquid mixing processes. At first, a comparison is made among the ultrasonic signals obtained from suspensions and pure water with and without gas bubbles and the gas bubble effects of high agitation speeds on ultrasonic measurement are evaluated. A moving average and standard deviation method is developed to deal with the real-time ultrasonic signals, aiming to decrease the fluctuations and noises of signals and improve the stability of signals. Then, based on the optimal spectral subintervals selected with the Si-PLS model, the prediction model for the particle concentrations is built, which considers the gas bubble effects as an interferent. The optimal model, of which the root-mean-square error of the prediction subset is 0.38 wt%, is successfully applied to measure the concentration of TiO2 suspensions in the range of 0–15.79 wt% when the agitation speed is less than 1200 r/min. The online measurement results suggest that the proposed method has the potential to be a useful tool for eliminating gas bubble effects and measuring particle concentrations in the solid–liquid mixing processes. [ABSTRACT FROM PUBLISHER]

This paper presents results of a trilateral comparison with a traveling ac voltage standard comprising a planar thin-film thermal converter. The ac–dc voltage transfer difference of the standard was measured at 1 V and at selected frequencies from 20 Hz to 1 MHz against primary thermal ac voltage standards at the Silesian University of Technology, Istituto Nazionale di Ricerca Metrologica, and Trescal. [ABSTRACT FROM AUTHOR]

This paper proposes an analysis of the quasi-stationary magnetic field generated by coils and its applicability to 3-D short-range positioning scenarios. Starting from a theoretical background, an approximation of the induced voltage in a sensor coil is developed and analyzed. Then, the design and performance of a short-range magnetic field-based positioning system is described. The system is realized by using three-axis field generating coils and a three-axis sensor coil. It uses resonance properties to decrease current and power consumption requirements. The system is targeted for simple and low-cost applications, requiring subdecimeter accuracies over short ranges of approximately a couple of meters. The realized prototype, validated through in-field measurements, features a positioning error in the order of 4–5 cm over an area of 1.15 m $\times $ 1.55 m $\times $ 0.8 m. [ABSTRACT FROM PUBLISHER]

In order to maintain safe and efficient operation of a fluidized bed, the electrostatic charges in the bed should be monitored continuously. Electrostatic sensors with wire-mesh electrodes are introduced in this paper to measure the charge distribution in the cross section of a fluidized bed. A finite element model is built to investigate the sensing characteristics of the wire-mesh sensors. In comparison with conventional electrostatic sensors, wire-mesh sensors have higher and more uniform sensitivity distribution. Based on the induced charges on the electrodes and the sensitivity distributions of the sensors, the charge distribution in the cross section of the fluidized bed is reconstructed. However, it is difficult to directly measure the induced charges on the electrodes. A charge calibration process is conducted to establish the relationship between the induced charge on the electrode and the electrostatic signal. Experimental studies of charge distribution measurement were conducted on a lab-scale bubbling fluidized bed. The electrostatic signals from the wire-mesh sensors in the dense phase and splash regions of the bed for different fluidization air flow rates were obtained. Based on the results obtained from the charge calibration process, the estimated induced charges on the electrodes are calculated from the root-mean-square values of the electrostatic signals. The characteristics of the induced charges on the electrodes and the charge distribution in the cross section under different flow conditions are investigated, which proves that wire-mesh electrostatic sensors are able to measure the charge distribution in the bubbling fluidized bed. [ABSTRACT FROM AUTHOR]

Distribution systems require ad hoc estimators, distribution system state estimation (DSSE) techniques, to acquire knowledge about the system status. An incorrect evaluation of the accuracy of the DSSE creates decision risks in network management. The possible variations in the network parameter values and the decays of the metrological characteristics of the measurement system elements are uncertainty sources very often not considered. Considering these possible lacks of accuracy, this paper focuses on the robustness of distributed measurement systems aimed to obtain accurate DSSE results. The problem of the proper assessment of the accuracy of the DSSE results obtained through a weighted least squares (WLS) approach is faced. A method capable of including different uncertainty sources in the uncertainty estimation of the WLS approach is presented. Furthermore, this paper proposes an optimal meter placement algorithm robust with respect to possible malfunctions in measurement system components. The results obtained on a portion of an Italian distribution network, along with their accuracy, are presented and discussed. [ABSTRACT FROM AUTHOR]

When the National Weather Radar Testbed (NWRT) was installed in 2004, a single-channel digital receiver was implemented so that the radar could mimic typical Weather Surveillance Radar (WSR) version 1988 Doppler (WSR-88D) capability. This, however, left unused eight other channels, built into the antenna. This paper describes the hardware instrumentation of a recently completed project that digitizes the radar signals produced by these channels. The NWRT is the nation's first phased array devoted to weather observations, and this testbed serves as an evaluation platform to test new hardware and signal processing concepts. The multichannel digital data will foster a new generation of adaptive/fast scanning techniques and space-antenna/interferometry measurements, which will then be used for improved weather forecasting via data assimilation. The multichannel receiver collects signals from the sum, azimuth-difference, elevation-difference, and five broad-beamed auxiliary channels. One of the major advantages of the NWRT is the capability to adaptively scan weather phenomena at a higher temporal resolution than is possible with the WSR-88D. Access to the auxiliary channels will enable clutter mitigation and advanced array processing for higher data quality with shorter dwell times. Potential benefits of higher quality and higher resolution data include: better understanding of storm dynamics and convective initiation; better detection of small-scale phenomena, including tornadoes and microbursts; and crossbeam wind, shear, and turbulence estimates. These items have the distinct possibility to ultimately render increased lead time for warnings and improved weather prediction. Finally, samples of recently collected data are presented in the results section of this paper. [ABSTRACT FROM PUBLISHER]

Sit-to-stand (SiSt) analysis has been widely used in clinical practice to assess the risk of falls in the older adult population. This paper proposes automated algorithms for the unobtrusive measurement of SiSt timing and symmetry using bed pressure sensors. An integrated signal comprising all of the sensor outputs was analyzed to measure both the bed-departure timing and the timing of three clinical phases within the transfer. Data collected in clinical trials, along with independent clinical video analysis, verified the success of the bed-departure timing algorithm with a mean error of 0.11 s. The phase measurement algorithm showed significant differences (p < 0.001) between younger and older adults in Phases II and III of the transfers, comparing well with studies found in recent clinical literature. The sensor outputs were then used to form sequences of pressure images, and an automated region of interest (ROI) detection algorithm was designed to extract regional signals from the hips and the hands. The final algorithm was designed to measure the symmetry of the body throughout the SiSt transfer from the extracted regional signals. A system accuracy of 93.0% was obtained for the automated symmetry classification of transfers. The techniques proposed in this paper can increase the precision and efficiency in clinical SiSt assessments. Their unobtrusive nature makes them particularly suitable for integration into a continuous monitoring system such as those required within the smart home environment. [ABSTRACT FROM AUTHOR]

This paper addresses the evaluation of dynamic uncertainty in dynamic measurements via polynomial chaos theory (PCT). In particular, this PCT analysis can be used in support of the design of a dynamic compensator for a given dynamic uncertainty target. Assuming that the filter that implements the dynamic compensator is designed based on the dynamic model of the transducer, then, the filter is affected by the parametric uncertainty with which the model is known. Furthermore, the order of the filter itself affects the propagation of uncertainty due to noise and uncertainty of the coefficients. In particular, in this paper, the inverse model filter is assumed to be built as the combination of elemental differentiator FIRs, each realizing numerically the first derivative. This analysis therefore provides support in finding a tradeoff between filter size, thus computational effort and uncertainty. [ABSTRACT FROM PUBLISHER]

In spectrum sensing and wireless communications analysis, signals of interest typically occupy only a few among several possible bands and do so for short-time bursts within a given observation interval. The frequency and time location of these signals may be known only approximately a priori (for instance, the nominal frequency of a wireless channel) or, in general, not accurately enough to set up more detailed measurements. In this paper, a compressive sensing (CS) algorithm is employed to provide accurate preliminary information and suitably preprocessed data for a vector signal analysis algorithm. The CS paradigm exploits sparsity, a feature common to several signals of interest, to allow the design of efficient data acquisition schemes. It is shown that the application of the sensing method called modulated wideband converter allows one to successfully extract specific signal bursts from a record of samples covering a longer time interval and a broader bandwidth. The accuracy of the extraction process is analyzed and the results referring to vector analysis are presented. This provides blind spectrum sensing and signal extraction capabilities that can effectively simplify the time-consuming process of setting up a spectrum analyzer for vector signal analysis. [ABSTRACT FROM PUBLISHER]

The ability to monitor the velocity and concentration profiles for the whole diameter of a pipe would allow the complex flow dynamics associated with particles in a pneumatic suspension to be measured. This paper presents a method of online monitoring of the particle velocity and particle concentration for the whole diameter of the pipe for a pneumatic bulk solid conveying system. This is achieved using an array structure of five electrostatic sensors across the whole diameter of the pipe to measure the particle velocity and concentration profiles. Experimental tests were carried out on a laboratory-scale test rig over a range of particle velocities. The results show that the electrostatic sensor array is capable of measuring the multiple velocities and concentrations that occur across the diameter of a pneumatic conveying pipe. Through the analysis of velocity and correlation coefficient data, flow across the diameter of the pipe including that along the pipe wall is determined to have more turbulence than the flow at the center of the pipe. [ABSTRACT FROM AUTHOR]

Fluidized beds are widely applied in a range of industrial processes. In order to maintain the efficient operation of a fluidized bed, the flow parameters in the bed should be monitored continuously. In this paper, electrostatic sensor arrays are used to measure the flow characteristics in a bubbling fluidized bed. In order to investigate the electrostatic charge distribution and the flow dynamics of solid particles in the dense region, time and frequency domain analysis of the electrostatic signals is conducted. In addition, the correlation velocities and weighted average velocity of Geldart A particles in the dense and transit regions are calculated, and the flow dynamics of Geldart A and D particles in the dense and transit regions are compared. Finally, the influence of liquid antistatic agents on the performance of the electrostatic sensor array is investigated. According to the experimental results, it is proved that the flow characteristics in the dense and transit regions of a bubbling fluidized bed can be measured using electrostatic sensor arrays. [ABSTRACT FROM PUBLISHER]

Crack detection and measurement in civil structures has been a constant field of research. Conventionally, a technician is responsible to detect and measure cracks in the field. In this paper, a system based on machine vision concepts has been developed with the goal to automate the crack measurement process. Using this method with only a single camera installed in a truck or even in a robot, a sequence of images is processed and the crack dimensions are estimated. The experimental results validate the application of the proposed method for real structures. [ABSTRACT FROM PUBLISHER]

A vision-based measurement (VBM) of dust concentration with high accuracy can be used to analyze dust pollution and reveal the distribution law of dust concentration in a production workshop. In conventional VBM, grayscale and morphological features of a dust image are commonly used. However, their measurement accuracy is limited by the atmospheric light scattering effect of surrounding light and the occlusion effect of dust particles. Thus, a high accuracy VBM system is investigated that is based on the relationship between dust concentration and image transmission (RCT) model. Meanwhile, a calculation method for image transmission that utilizes the atmospheric light scattering effect and the occlusion effect of dust particles is proposed in this paper. The experimental results show that in the proposed system, the relative error is ±3.0%, the relative uncertainty is 2.05%, the correlation is 0.99, the measurement range is 0.5–1000 mg/m3, and the measurement period is 2 s. The RCT tends to be a negative correlation, which is a quadratic polynomial. The impact of the atmospheric light scattering effect and the occlusion effect of dust particles on measurement accuracy can be eliminated by utilizing image transmission. The proposed system can be used as an alternative measurement of dust concentration in the workshop. [ABSTRACT FROM AUTHOR]

For the accurate determination of the Avogadro and Planck constants by the X-ray crystal density method, the mass of 1-kg silicon spheres must be measured precisely. This paper describes the mass measurement method and the evaluation of its uncertainty at the National Metrology Institute of Japan (NMIJ). For the precise mass measurement, the amount of the physical adsorption of water vapor on the surface of the silicon spheres, which could be a relative amount of about 10^-8 of the total mass, was evaluated by comparison weighings both in nitrogen gas and in water vapor at a pressure of about 1200 Pa. In addition, we compare the results of seven 1-kg mass measurements conducted at the NMIJ and the International Bureau of Weights and Measures from 1996 to 2011 to confirm the reliability of our mass measurement. [ABSTRACT FROM PUBLISHER]

Accurate measurement of the particle size distribution of pneumatically conveyed pulverized fuel is critical to achieving optimal combustion efficiency and minimum pollutant emissions at coal and biomass-fired power plants. This paper presents a prototype instrumentation system for the on-line continuous measurement of particle size distribution through acoustic emission (AE) detection. The proposed method extracts particle size information from the impulsive AE signals arising from impacts of particles with a metallic waveguide protruding into the flow. The relationship between the particle size and the peak AE voltage is established through mathematical modeling of the particle impact process. Identification of the peak AE voltage is achieved with an energy-based peak detection algorithm. Experimental results obtained with glass beads demonstrate the capability of the system to discriminate particles of different sizes from the recorded AE signals. The system performs better in terms of accuracy for higher speed, lower concentration particles as the impact signals are stronger and better separated in the time domain. [ABSTRACT FROM PUBLISHER]

In this paper, a ranging technique based on inductive coupling between resonating coils is presented. By exploiting resonance, both a high range and high signal-to-noise ratio at the receiver are achieved. The theoretical background is discussed; a theoretical model is presented; and a practical implementation is illustrated and experimentally validated. It is shown that the proposed technique, implemented using off-the-shelf components, is only moderately sensitive to the effect of conductive objects placed close to the receiver, whereas it proves its effectiveness in an ordinary laboratory setup, achieving a maximum error of <3~cm over a 5.8-m range. It is thus suitable for indoor positioning applications. [ABSTRACT FROM PUBLISHER]

In this paper, an iterative algorithm is proposed to retrieve the particle-size distributions via Fraunhofer diffraction. A dual integral inversion was proposed in our previous work, the inversion is robust and generates precise particle sizing, if the diffraction pattern can be accurately captured. In real applications, the pattern can only be partially detected, and the inversion fails to reconstruct the size distributions in detail. However, the results of the inversion can be used to produce an initial estimate. Then, a simulated diffraction pattern was generated from the estimated particle sizes. The deviation between the measured pattern and the simulated one was deduced to correct the results of particle sizing. The corrections can be achieved in an iterative approach, and the particle-size distribution was updated subsequently. The iteration stopped once the deviation was below the target value. Both simulation and experiment were conducted to validate the feasibility and effectiveness of the proposed algorithm. The results demonstrate that the size distribution from the proposed algorithm agrees well with the original phantoms for both noise-free and noise-contaminated data. [ABSTRACT FROM AUTHOR]

In this paper, we present a fast method for classification of defects detected by eddy current testing (ECT). This is done by using defects derived by lab experiments. For any defect, the ECT magnetic field response for different EC-probe’s paths is represented on a complex plane to obtain Lissajous’ figures. Their shapes are described through the use of few geometrical parameters forming a feature vector. Such vectors are used as signatures of the defects detected by the probe at different crossing angles and distances from the defect. The effectiveness of the proposed approach is evaluated by measuring the performances of three machine learning-based classifiers (Naïve Bayes, C4.5/J48 Decision Tree, and Multilayer Perceptron neural network), through the following metrics: area under ROC curve, the Matthews correlation coefficient, and F-Measure. The results confirm the usefulness of the proposed approach to defects detection and classification without the need of an overall scanning of the faulty area. So, it is able to minimize the efforts, and, consequently, the cost of an ECT test. [ABSTRACT FROM PUBLISHER]

An \ell1-norm-based reconstruction algorithm for particle sizing by using \ell1-regularization is introduced in this paper. Both simulation and experiment were conducted by using a photodiode array detector to evaluate the performance of the algorithm. Particle size distributions retrieved by using Chahine, truncated singular value decomposition (TSVD), and Tikhonov algorithms were also obtained to compare with that obtained by the \ell1-norm-based algorithm. In computer simulation, Rosin–Rammler, normal, and lognormal distributions of spherical particles from 7.6 to 98 \mu\m in diameter were created. The measurement data of the photodiode array detector were generated based on Fraunhofer diffraction theory. Simulation results show that the \ell1-norm-based algorithm not only performs better than Chahine algorithm but also performs similar to the TSVD and Tikhonov algorithms for noise-free data and is less sensitive to the noise than the TSVD and Tikhonov algorithms for noise-contaminated data. In experiment, a standard particle plate covered by particles with known size distribution, i.e., Rosin–Rammler distribution, was used. The experimental results validated the effectiveness of the \ell1-norm-based algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a new version of the field-mill sensor structure for atmospheric electric field measurements is presented. Both the hardware components (i.e., the mechanical structure, the electronic front end, and the acquisition and control systems) and the data processing software are designed in order to reduce power consumption and enhance the instrument metrological performance in terms of accuracy, sensitivity, and frequency band. [ABSTRACT FROM PUBLISHER]

Flaw profile characterization from nondestructive evaluation (NDE) measurements is a typical inverse problem. A novel transformation of this inverse problem into a tracking problem and subsequent application of a sequential Monte Carlo method called particle filtering has been proposed by the authors in an earlier publication. In this paper, the problem of flaw characterization from multisensor data is considered. The NDE inverse problem is posed as a statistical inverse problem, and particle filtering is modified to handle data from multiple measurement modes. The measurement modes are assumed to be independent of each other with principal component analysis used to legitimize the assumption of independence. The proposed particle-filter-based data fusion algorithm is applied to experimental low-frequency NDE data to investigate its feasibility. [ABSTRACT FROM AUTHOR]

Deregulation and privatization actions are creating new problems of control, management and reliability, because of new players and new technologies spreading in distribution systems. Such new scenarios require more comprehensive and accurate knowledge of the system to make control actions efficient and reliable. In particular, attention must be paid to phase angles estimation to avoid critical situations. In this context, the use of phasor measurement units (PMUs) looks promising. This paper presents an efficient branch-current-based distribution systems state estimation. The estimator allows synchronized phasor measurements provided by PMUs to be included. In addition, the branch current state model is extended so that the knowledge of the voltage profile is significantly improved. The estimator is expressed both in polar and rectangular coordinates and a comparison between the obtainable accuracy and computational efficiency is presented. Furthermore, the possibility to treat radial and weakly meshed topology, also in presence of dispersed generation, is analyzed. The results obtained on different distribution networks are presented and discussed. [ABSTRACT FROM AUTHOR]

In order to characterize turbulent flows, correlation measurements are a common procedure. Laser Doppler sensors are often applied for this task, but in the case of high seeding concentrations, the so-called dual-burst signals occur. These are caused by multiple particles passing the measurement volume at the same time. In this paper, a model-based signal processing approach is presented which is able to evaluate such distorted signals. In order to assess the performance of dual-burst algorithms, the Cramér–Rao lower bound is calculated for the first time. A comparison of this new technique with conventional dual-burst algorithms reveals a systematic error which is at least seven times lower and even makes an evaluation of the complete signal in some cases possible in the first place. As a result, the total frequency estimation uncertainty of dual-burst signals, which is directly linked to the velocity uncertainty, is improved by a factor of five and more. Hence, this new technique enables the precise evaluation of correlation functions and instantaneous velocity gradients of complex flows. [ABSTRACT FROM PUBLISHER]

Measurements are more and more required in an increasing variety of human activities to acquire reliable information useful for effectively supporting decision-making processes. Furthermore, the entities whose properties are to be measured and the measuring systems are becoming increasingly complex, hardly to be modeled and managed. In this rapidly evolving scenario, several issues concerning the fundamentals of measurement science and technology arise. The purpose of this paper is to discuss some aspects of the crucial question about which evaluation processes can be considered measurements. Rather than focusing on formal conditions or technological constraints, we propose a pragmatic characterization of measurement, under the assumption that a better comprehension of the concept can be achieved by identifying and discussing the basic features which justify the reliability attributed to measurement results. With such an interdisciplinary approach, this work aims at promoting a broad discussion among interested researchers, even working in different scientific disciplines, so to increase the synergies among different research areas and to improve the body of knowledge about measurement fundamentals. [ABSTRACT FROM AUTHOR]

A method is proposed for analyzing key comparison data. It is based on the assumption that each laboratory participating in the comparison exercise obtains independent and consistent estimates of the measurand and that, in addition, each laboratory provides an estimate of the quantity that collects all systematic effects that the laboratory took into account. The unknown value of the latter quantity, subtracted from its estimate, is defined as the laboratory's bias. The uncertainties associated with the estimates of the measurand and with the vanishing biases' estimates are also assumed to be reported. In this paper, we show that the information provided in this way may be of help for judging the performances of the laboratories in their correction of systematic effects. This is done by developing formulas for the final (consensus) estimates and uncertainties of the measurand and of the biases. Formulas for the final estimates and uncertainties of the pairwise differences between the biases are also developed. An example involving simulated key comparison data makes apparent the benefits of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper describes the instrumentation, design, and implementation of an inexpensive nearly all-digital field-programmable gate-array (FPGA)-based radar receiver useful in a variety of applications including single-/dual-polarization weather radar, sidelobe cancellation, subarray modules for a digital beam-forming phased-array radar, and other applications where a compact, low-power, and low-cost receiver is needed. The design of the receiver includes a minimal analog radio-frequency front-end followed by an analog-to-digital converter utilizing a bandpass sampling technique which allows the FPGA to produce baseband in-phase (I) and quadrature (Q) signals without the use of multipliers or lookup tables. [ABSTRACT FROM AUTHOR]