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In this study, the uncertainty of measurement paths was estimated using selected statistical methods. Specifically, temperature measurements obtained from contact temperature sensors used in a heat transfer test section were investigated. The experiments utilized a drywell temperature calibrator, thermoelements (types K, J, N, and T), and a data acquisition station. Additionally, a certified Pt-100 resistance sensor connected to the temperature meter was considered during measurements. The temperature range for the selected measurement points was 0.3 to 100 °C, covering both increasing and decreasing temperatures. To calculate the expanded uncertainty, both the uncertainty propagation method and the Monte Carlo method were employed. The results were analyzed and found to be similar.

Abstract Target localization for distributed radars requires precise knowledge of the calibration errors in time, frequency, and space. Previous studies focus on the sensor location error and sensor directional error in space. This paper concentrates on the system errors in time and frequency which directly affect the targets estimation of range and velocity. Based on linear frequency modulation waveform, the impact of the system errors including sample interval, pulse repetition interval, and centre frequency are analysed. An alternating optimization algorithm is proposed to achieve the target localization, velocity estimation and system errors estimation simultaneously. Simulation shows the effectiveness of the algorithm and the influence on localization due to varying number of targets and radars.

Currently, blood pressure assessment is on the verge of shifting from classical cuff-based measurements to continuous estimation using smart devices based on the absorption and reflection characteristics of light in tissue. This type of blood pressure estimation has been known for a long time and depends on calibration with conventional blood pressure measurement systems. Products from well-known manufacturers in this market already perform calibration with automatic cuffs using an oscillometric estimation. The main aim is to show that classical oscillometric blood pressure estimation is not suitable for calibration, which allows a burden-free and continuous estimation by today’s approaches. This study identifies the reason and also the solution for this in the second-order blood pressure variation. The current approach to oscillometric estimation of blood pressure has an uncorrectable systematic error of the order of the second-order blood pressure variation. Therefore the current method has too much error to be used as a basis for calibration to allow continuous estimation of blood pressure based on another vital parameter. It is shown that when using a measurement of the second-order blood pressure variation as basis for such calibration this problem can be solved.

In literature covering laboratories for switch-mode power supplies (SMPS) the focus was on university ones and learning outcome of their students. And there were almost no publications by authors from industry. Moreover, in a small number of older papers, description of the lab setup or equipment was provided. This paper addressed missing gaps by presenting an industrial laboratory for SMPS. Different aspects like lab structure, organization, procedures, equipment and software resources, calibration and safety, and typical standards used for hardware development, are covered comprehensively. The SMPS lab serves for development of dc-dc converters for commercial project(s) and for research. The developed dc-dc converters are used as auxiliary power supplies (APS) of inductive charging systems. Additional investments in the lab, SMPS related, during period of 1.5 years, were paid-off quickly since the APS was project blocker. Side benefits and outcomes were created by publishing research papers that targeted industry and academia thus bridging the gap between the two. The publications increased company’s visibility in academia, created an opportunity for a cooperation project, and served as partial documentation for engineers. It was also shown that investments in equipment and software are not enough. And that one has to invest in knowledge of engineers through buying books, attending seminars, visiting trade fairs, memberships, and subscriptions. Hence, notes on learning of power electronics were given too. Several results of applied research activities are presented to demonstrate lab capabilities. They were characterization of custom-designed magnetic parts at high operating temperatures for operating current range, and solutions for the APS with different flyback topologies. A comparison with few university labs revealed large difference in resources and outcomes in favor of the university ones. But differentiation of our industrial lab to them was covered as well.

This paper presents a simple, lightweight, automatic calibration method for low-cost triaxial accelerometers, utilizing the Earth’s gravitational constant in various orientations. It can be easily implemented using only fixed-point arithmetic and can run on low-power microcontrollers for real-time measurements, making it practical for scenarios with limited data storage and computational power, such as actigraphy or IoT applications. The method offers ease of use by automatically detecting motionless intervals, eliminating the need for complex positioning techniques. The procedure detects resting states and calculates the corresponding three-dimensional mean acceleration values during the measurement. After appropriately selecting these mean values, a set of calibration points is formed and passed to a gradient-based optimization algorithm for iterative estimation of the calibration coefficients. Different metrics were used for verification and comparison with other methods, which were calculated through simulations and tests based on real measurements. The results show that, despite its lightweight nature, the method performs equally to more complex solutions. This article provides a thorough explanation of a novel method for collecting calibration points, the optimization algorithm, and the methods used for performance evaluation in a reproducible manner.

We present a Vehicle Model (VM) that has 17 degrees of freedom and includes nonlinear tire and powertrain subsystems. Implemented as a relatively small piece of C++ code, the model runs vehicle dynamics 2000 times faster than real time at a simulation time step of $1 \times 10^{-3}, \text {s}$ on a single core of a commodity CPU. When executed on the GPU, one can perform 300000 vehicle simulations in real-time. These properties make the model a good candidate for reinforcement learning, model predictive control, model predictive path integral control, path planning, state estimation, and traffic simulation tasks. The model is expressive in that it can capture the dynamics of vastly different vehicles. This is demonstrated by first calibrating the model to mimic the dynamics of a 1/ $6^{th}$ scale vehicle called the Autonomy Research Testbed (ART) vehicle, which has a mass of approximately 5.8 kg. Subsequently, the model is calibrated to mimic the dynamics of a heavy-duty High Mobility Multipurpose Wheeled Vehicle (HMMWV), which has a mass of 2097 kg. The Bayesian calibration process discussed can $(i)$ handle real-life measurement noise, and $(ii)$ provide model parameter probability distributions. The vehicle model, which is open source and freely available in a public repository, can also be imported into Python owing to SWIG wrapping.

Calibration is essential to optimize the performance of any radio transceiver during its development and manufacturing processes. This paper proposes over-the-air (OTA) calibration methods based on measuring and calibrating the phases of phased array transceiver paths. The phase responses of each signal path with all phase shifter states were characterized using an OTA radio link in the transmission and reception modes. Scattering parameter measurements give amplitude and phase responses over the frequency range, which can be converted to the delay domain with the Fourier transform. In this paper, two new delay domain phase calibration methods are presented and compared with a previously presented frequency domain method. The best performance was achieved with the relative phase calibration method in the delay domain which uses all measurement data to find the calibration values. The other two methods, i.e., the absolute phase calibration method in the delay domain and in the frequency domain, use the average phase as the calibration value. OTA radiation pattern measurements from two prototype 5G millimeter wave transceivers were used to compare the RF performances of the calibration methods, and measurements were performed with a 100 MHz 5G new radio waveform. The radiation patterns were analyzed based on direction accuracy, power level, and side lobe level, to give a few examples. The transceiver units were calibrated using the three studied methods, and while having significantly different uncalibrated performances, after calibration, both transceivers were tuned to similar performance levels.

We recently established an impedance standard for the D-band, one of the 6G candidate frequencies. However, primary standards are difficult to use for routine calibration because they require multiple impedance standards and a lot of time to calibrate the vector network analyzer (VNA). Therefore, a transfer standard is needed to efficiently apply the calibration value and to propagate the uncertainty of the primary standard to a device under test (DUT). In this paper, we describe a design method for a transfer standard with small uncertainty even when an arbitrary DUT is measured. This is achieved by propagating the uncertainty of the primary standard to the transfer standard and then propagating it again to the uncertainty of the DUT. We developed a calibration kit that has low uncertainty over a wide frequency band, from 110 GHz to 170 GHz, and consists of waveguide offset shorts for ease of production. We also propose a method to minimize DUT uncertainty and a method to minimize “phase distance” to find the optimal length of the offset short. When using three offset shorts, an uncertainty similar to that of the short-open-load-thru (SOLT) calibration kit was obtained, and when using four offset shorts, an uncertainty comparable to the primary standard was obtained. Lastly, this paper examines the repeatability of measurements and reproducibility during the production process.

Abstract The de‐embedding calibration method has been proposed to achieve high‐precision calibration for a single port electric field or magnetic field probe, which can effectively eliminate the calibration ripple. However, the method's effectiveness for a four‐port calibration system has not been verified yet. In this paper, a four‐port de‐embedding calibration method with a differential magnetic field probe is proposed, and its effectiveness is proved. Two symmetric grounded coplanar waveguide transmission lines are applied in the proposed method to solve the ABCD‐matrix of the embedded part of the calibrator. The de‐embedded S‐parameter model of the four‐port calibration system for differential magnetic field probe can be obtained. The calibration results indicate that the proposed method can also reduce the calibration ripple and compensate for the attenuation caused by the calibrator. Compared with the traditional calibration method using a microstrip line calibrator, the ripples of the proposed method can be reduced by 34%. The analysis results of the frequency interval of the ripple (FIR) in different methods show that the de‐embedding method can reduce the FIRs (except around 1.2 GHz) caused by the reflection of the calibrator and retain the FIR (about 1.2 GHz) caused by the reflection of the probe itself.

It is well known that errors are inevitable in experimental observations, but it is equally unavoidable to eliminate errors in modeling the process leading to the experimental observations. If estimation and prediction are to be done with reasonable accuracy, the accumulated errors must be adequately managed. Research in fatigue is challenging because modeling can be quite complex. Furthermore, experimentation is time-consuming, which frequently yields limited data. Both of these exacerbate the magnitude of the potential error. The purpose of this paper is to demonstrate a procedure that combines modeling with independent experimental data to improve the estimation of the cumulative distribution function (cdf) for fatigue life. Subsequently, the effect of intrinsic error will be minimized. Herein, a simplified fatigue crack growth modeling is used. The data considered are a well-known collection of fatigue lives for an aluminum alloy. For lower applied stresses, the fatigue lives can range over an order of magnitude and up to 107 cycles. For larger applied stresses, the scatter in the lives is considerably reduced. Consequently, modeling must encompass a variety of conditions. The primary conclusion of the effort is that merging independent experimental data with a reasonably acceptable model vastly improves the accuracy of the calibrated cdfs for fatigue life, given the loading conditions. This allows for improved life estimation and prediction. For the aluminum data, the calibrated cdfs are shown to be quite good by using statistical goodness-of-fit tests, stress-life (S-N) analysis, and confidence bounds estimated using the mean square error (MSE) method. A short investigation into the effect of sample size is also included. Thus, the proposed methodology is warranted.

Abstract The capacitance mismatch problem limits the accuracy improvement of high‐precision SAR ADCs (Successive Approximation Register Analog‐to‐Digital Converters). To address the capacitance array mismatch in SAR ADCs, this paper proposes a novel capacitor calibration scheme based on the Time‐to‐Digital Converter (TDC). This scheme achieves calibration accuracy as high as 0.01% and can be flexibly designed to meet the accuracy requirements of SAR ADCs. Simulation results indicate that the capacitance mismatch issue of a redundant capacitor 13‐bit SAR ADC can be completely eliminated, and the effective number of bits (ENOB) approach the ideal value of 13.18 bits. Additionally, the analog component of this scheme utilizes four inverter chains, two D flip‐flops, and four counters, without requiring a large area for auxiliary calibration capacitors.

Abstract In this paper, an improved two‐step method is presented for the sensitivity analysis of vector network analyzer (VNA) S‐parameter measurements due to the non‐ideal line‐reflect‐match (LRM) calibration standards. This improved method is based on the indirect uncertainty propagation mechanism, which is especially suitable for the S‐parameter measurements applying the self‐calibration technique. To further simplify the formula derivation, the deviation matrices [δA] and [δB] are newly defined to represent the uncertainties of the T‐matrices of error boxes. With this definition, formulas for the deviations of device under test (DUT) S‐parameters can be concluded as functions of [δA] and [δB] in a concise form. Eventually, by solving only three linear combinations of the elements from [δA] and [δB], the sensitivity coefficients of DUT S‐parameters due to non‐ideal LRM can be conveniently deduced in an analytical form. Finally, experiments are performed to verify the proposed method.

Fiber-reinforced cementitious matrix (FRCM) composites have been largely used to strengthen existing concrete and masonry structures in the last decade. To design FRCM-strengthened members, the provisions of the Italian CNR-DT 215 (2018) or the American ACI 549.4R and 6R (2020) guidelines can be adopted. According to the former, the FRCM effective strain, i.e., the composite strain associated with the loss of composite action, can be obtained by combining the results of direct shear tests on FRCM–substrate joints and of tensile tests on the bare reinforcing textile. According to the latter, the effective strain can be obtained by testing FRCM coupons in tension, using the so-called clevis-grip test set-up. However, the complex bond behavior of the FRCM cannot be fully captured by considering only the effective strain. Thus, a cohesive approach has been used to describe the stress transfer between the composite and the substrate and cohesive material laws (CMLs) with different shapes have been proposed. The determination of the CML associated with a specific FRCM–substrate joint is fundamental to capture the behavior of the FRCM-strengthened member and should be determined based on the results of experimental bond tests. In this paper, a procedure previously proposed by the authors to calibrate the CML from the load response obtained by direct shear tests of FRCM–substrate joints is applied to different FRCM composites. Namely, carbon, AR glass, and PBO FRCMs are considered. The results obtained prove that the procedure allows to estimate the CML and to associate the idealized load response of a specific type of FRCM to the corresponding CML. The estimated CML can be used to determine the onset of debonding in FRCM–substrate joints, the crack number and spacing in FRCM coupons, and the locations where debonding occurs in FRCM-strengthened members.

Abstract A self‐calibrating circuit system for use within active phased array antennas is presented. The system addresses the issue of unwanted channel phase imbalances and random phase errors which can deteriorate array performance. Basically, unwanted insertion phase offsets that can originate within each RF channel, due to the supporting and active circuit components and possible temperature variations, can be eliminated by our designed, simulated, and experimentally verified self‐calibrating circuit. With existing technologies and calibration approaches, it is practically difficult and time consuming to check the phase feeding accuracy, as typically a large number of data sets are required to achieve similar channel corrections. As an alternative, our proposed circuit system allows for the consecutive phase shift between RF channels to be inspected and self‐corrected within a feedback loop managed by a MATLAB‐Arduino program. The active S‐band feed system can self‐calibrate in about 25 s (or less), using commercially available phase sensors and voltage controlled phase shifters. To the best knowledge of the authors, no similar computer controlled feed‐back circuit to support the self‐correction of active phased arrays has been previously reported.

This article presents a new technique for evaluating the consistency of the reference impedance in multiline thru–reflect–line (TRL) calibration. During the calibration process, it is assumed that all transmission line standards have the same characteristic impedance. However, these assumptions are prone to errors due to imperfections, which can affect the validity of the reference impedance after calibration. Our proposed method involves using multiple stepped impedance lines of different lengths to extract the broadband reflection coefficient of the impedance transition. This reflection coefficient can be used to validate the reference impedance experimentally without requiring fully defined standards. We demonstrate this method using multiline TRL based on microstrip structures on a printed circuit board (PCB) with an on-wafer probing setup.

Advances in CMOS technology have enabled MOSFET with cutoff and maximum oscillation frequencies (ft and fmax) in the 400 GHz range, thus opening the path to CMOS-based applications at millimeter-wave (mm-wave) and sub-THz frequencies. Accurate compact models and therefore on-wafer MOSFET measurements at mm-wave frequencies and beyond become crucial for IC design at such high frequencies. However, accurate on-wafer measurement at these frequencies is a complex task requiring dedicating special care to calibration kit (calkit) design and characterization. This paper presents a complete and detailed parasitic correction procedure approach that demonstrates accurate corrected MOSFET measurements up to 110 GHz. It describes the custom calkit designed to perform ‘in-situ’ multiline Thru-Reflect-Line (mTRL) calibration. In this work, we compare different methods to evaluate the transmission line standards characteristic impedance and identify the best one by comparing the extracted series resistances of a MOSFET. The best method features frequency variations as low as

It is well-known that the availability of transmit and receive arrays in colocated multiple-input multiple-output radar systems can be exploited to detect multiple targets, and estimate their range and angular coordinates. Unluckily, the accuracy achieved in the estimation of target parameters can be severely affected by hardware nonidealities. The impact of these nonidealities can be mitigated through the adoption of specific methods explicitly developed to estimate and compensate for them, i.e., briefly, of calibration methods. As far as we know, until now, most of the calibration techniques proposed in the technical literature for colocated multiple-input multiple-output radars are based on the idea of placing one or more reference targets in front of the considered radar device at perfectly known and fixed locations, and quantifying the difference between the expected radar image and the measured one. In this manuscript, two novel calibration methods are proposed. Both exploit the measurements captured by a colocated multiple-input multiple-output radar device at multiple positions; for this reason, the acquisition of such measurements involves the movement of the radar. The first one does not require any knowledge about the nature, number, position or radar cross section of the reference targets, provided that all these are in far-field; the second one, instead, needs a single reference target in near-field conditions, with the only constraints for it to be pointwise and isolated from stray targets in the range domain. This drastically simplifies the experimental setup to be employed for radar calibration. The proposed methods are tested on a commercial radar device and compared with a traditional method for radar calibration.

The application of robots in high-precision automated machining is constrained by their limited multi-directional repeatability. In contrast, robots demonstrate superior levels of unidirectional repeatability, implying the potential for enhancing their precision. To further improve the positioning accuracy of robots, backlash error induced by rotating direction is considered, the concept of robot joint extended space is proposed, and the robot kinematic model is used to analyze the spatial similarity of robot error in the extended joint space. The dynamic Kriging method based on the optimization of basis functions is proposed to avoid overfitting of the surrogate model, and a model for estimating the robot’s positioning error in the joint extended space is constructed. Based on the estimated positioning error, the proposed calibration method is finally experimentally validated by error feedforward compensation. The results indicate that after Kriging interpolation in the robot joint space and feedforward compensation, the maximum/average positioning error of the robot is improved from 1.5157 mm and 0.8562 mm before compensation to 0.3471 mm and 0.1856 mm after compensation, and then further improved to 0.1848 mm and 0.1197 mm after adopting joint expansion space and dynamic Kriging interpolation, which decreases by 46.7% and 33.5%, respectively. This method effectively compensates the multi-directional repeatability error introduced by the joint backlash and improves the robot’s positioning accuracy.

Calibration performed by a robotic manipulator is crucial in the field of industrial intelligent production, as it ensures precise and accurate measurements. In this paper, we present a new method for addressing the hand-to-eye calibration problem using deep reinforcement learning. Our proposed algorithm utilizes an actor-critic framework and incorporates neurodynamics adaptive reward and action functions, which allows for better convergence, reduces the dependence on the initial value, and overcomes the local convergence issues of traditional deep reinforcement learning method. Additionally, we introduce a step-wise mechanism under the guidance of the attention mechanism, and zero stability to handle the complexity of the calibration task in challenging environments. A number of experiments were conducted to demonstrate the validity of the proposed algorithm. The experimental results show that our proposed algorithm can achieve a nearly 100% success rate after training phase. Additionally, we compared our proposed algorithm with other widely used methods, such as deterministic deep policy gradient (DDPG) and soft actor-critic (SAC) to further demonstrate its effectiveness.

Temperature is known to have a significant impact on the performance and ageing of Li-ion batteries. Because of it, their temperature is monitored and controlled implementing a Battery Thermal Management System (BTMS) in which lumped-parameter thermal models are commonly used to estimate the cells’ thermal behavior, due to their low computation effort. In this regard, it is important to plan a proper calibration procedure able to accurately assess the cell’s thermal properties. While literature presents several methodologies with different levels of accuracy and complexity, there is a lack of a systematic approach. Furthermore, only a few works take into account the heat lost through the cell holder by conduction. In this work, a re-designed cell holder is introduced to minimize its thermal interaction with the cell during testing, then it provides a better understanding of thermal models calibration by comparing two simple, but effective, testing procedures. The specific heat capacity of a cylindrical cell is evaluated by carrying out two testing procedures in which heat flow direction is inverted. In the first procedure, heat is actively generated from the cell, while in the second procedure it is externally provided using a flexible polyimide heater. This has led to two different formulations of the energy balance equation that differ from the internal thermal dissipation modeling. At the end, a validation has been carried out implementing a current profile generated from the UDDS drive cycle. Both formulations have shown to have the similar temperature prediction with a RMSE of 0.484 °C.

Pan-tilt (PT) camera is an indispensable part of the video surveillance systems due to its rotatable property and low cost. As the primitive output of the PT camera limits its practical applications, an accurate calibration method is required. Previous single point calibration method (SPCM) was presented to estimate angles Pan and Tilt via single control point. For the more intuitive geometric interpretation and more robust performance, we propose a novel single point calibration method (novel SPCM). In this scheme, a nonlinear PT camera function (PT function) is established via a normalization approach. With PT function, calibration problem is converted as the intersection situation of two circles formed by Pan and Tilt. Solutions can be regarded as the intersection points of two circles in 3D space. Theoretical analysis shows that novel SPCM is stable to measurement noise, for it still finds the least-square solutions even if two circles have no intersection. In the simulation experiments, reprojection error of novel SPCM is 32.4% smaller than SPCM for the large noise situation. It is 25.1% faster than SPCM. With the angle smooth strategy, novel SPCM achieves accurate and stable performance in the real data experiment.

Abstract This letter presents an 11 b 200 MS/s 28 nm CMOS 2b/cycle successive‐approximation register (SAR) analogue‐to‐digital converter (ADC). The offset calibration technique is proposed to reduce the comparator offset mismatch that degrades the linearity of the high‐resolution 2b/cycle SAR ADC. The offset mismatch is reduced to within 0.25 least significant bit (LSB) by generating a compensation voltage from capacitor‐resistor (C‐R) hybrid digital‐to‐analogue converters (DACs). The prototype ADC implemented in a 28‐nm CMOS process demonstrates measured differential and integral non‐linearities within 0.6 LSB and 1.73 LSB at 11 b resolution, respectively. The measured signal‐to‐noise‐and‐distortion ratio (SNDR) and spurious‐free dynamic range (SFDR) are 50.9 dB and 66.2 dB at Nyquist, respectively. The prototype ADC occupies an active die area of 0.115 mm2 and consumes 3.98 mW at a 1.1‐V supply voltage.

Abstract The tracking and data relay satellite (TDRS) in geosynchronous orbit (GEO) can provide ranging and communication services for user spacecraft in low Earth orbit (LEO) through the inter‐satellite link (ISL). The time delay of TDRS ISL transponder in orbit differs from its pre‐launch state and changes due to the complex space environment, adding significant errors to the user spacecraft's orbit determination based on four‐way ranging data. The authors have developed a calibration system and proposed an efficient on‐orbit calibration method for the ISL transponder's time delay in TDRS. Experimental results demonstrate the effectiveness of the authors’ method, and the RMS of calibration residuals is less than 0.5 m.

Increasing exhaust gas temperature (EGT) and providing hydrocarbon (HC) reactants by in-cylinder post injection are effective measures to assist diesel engine exhaust aftertreatment operation for efficient pollution control. The aim of this study is to explore calibration guidance of post injection for base engine performance and aftertreatment requirements. Therefore, the effect of start of post injection (SOPI) timing on engine performance and emissions, especially the relationship between the variations of the EGT and HC emissions caused by different post injection timings were experimentally studied with a 2.0 L high-pressure common-rail diesel engine at different loads. The results showed that the maximum torque contribution was produced by post injection at the earliest SOPI timing at all load levels. When the post injection timing was retarded, the engine brake torque decreased gradually, and more energy of the post injected fuel raised the EGT. The EGT decreased and the HC emissions increased significantly as the SOPI timing was further retarded. At the 20%, 50%, and 80% loads, the maximum EGTs were 357.7, 465.4, and 615.8 °C, increased by 11.6%, 8.6%, and 7%, respectively. The maximum HC emissions were 4932, 3929, and 2746 ppm, increased by 12.44, 1.8, and 5.18 times, respectively. Moreover, a tradeoff relationship between the EGT and the HC emissions was observed at different SOPI timings by a normalized analysis. However, no clear tradeoff relationship between the soot and the NOxemissions was found in the SOPI timing sweep. Consequently, the appropriate SOPI timing range should be determined based on the needs of the aftertreatment system, and multiple post injections may be required to simultaneously increase the EGT and provide the HC reactants for aftertreatment. The optimal SOPI timing also needs to be selected based on impacts on soot and NOxat different loads.

Material characterization requires the proper calibration of a material measurement system. This paper describes a free-space unknown thru measurement method using three independent planar metal offset shorts for calibrating a free-space material measurement system. This method is validated by comparing the measurement results with those of the TRL (thru-reflect-line) measurement method for two glass plates of 2.780 mm and 4.775 mm thickness in W-band (75–110 GHz). This can be an affordable and effective alternative to conventional free-space material measurement methods because the precision fabrication of a planar offset short is more feasible and inexpensive than building a precise positioning system in a free-space material measurement system. One can use this measurement method up to a high-frequency range that the fabrication accuracy of a planar offset short is acceptable.

Abstract Low‐cost air pollutant sensors suffer several interferences due to the variation of climatic elements. Recent studies look for calibration solutions based on different regression and classification machine learning algorithms. The present work brings together the implementation and extraction of performance metrics from these algorithms in a single open‐source tool. Both the input data and parameters for each algorithm are automatically configured. This feature makes the tool compatible with any input dataset and removes the need to interact with complex codes.

Abstract Capacitor mismatch problem due to process variation causes weight error, which deteriorates the linearity of SAR ADC. In this paper, a novel calibration scheme based on genetic algorithm(GA) combined with a radix‐less‐than‐2 SAR ADC is proposed to extract the weight error caused by capacitor mismatch. This is a foreground calibration scheme and no extra injections are added. The proposed GA‐based calibration scheme is simulated based on 40 nm CMOS technology. After calibration, ENOB increases from 10.19 bits to 11.46 bits, INL changes from +2.22/−2.12 LSB to +0.81/−0.80 LSB, and DNL changes from +1.79/−1.00 LSB to +0.99/−1.00LSB. As can be seen from the simulation results, the proposed calibration scheme can effectively improve the linearity deterioration caused by capacitor mismatch.

Abstract The problem of high‐resolution direction finding is considered using a distributed array network with array position errors, called partly calibrated array. These errors are common in practice, and significantly affect high angular resolution. Existing calibration methods rely on centralized data transmission, which requires all the measurements to be transmitted to a computation center. This centralized mode leads to communication bottlenecks and system inflexibility. Here, a decentralized mode is designed, where each sensor only communicates locally with their neighboring sensors. Based on this mode, a corresponding direction‐finding approach is proposed using sequential methods that process data one by one, which corresponds to the decentralized signal processing sensor by sensor. Simulations verify that the proposed method achieves decentralization with minor cost in estimation accuracy.

With the rapid growth of self-driving vehicles, automobiles demand diverse data from multiple sensors to perceive the surrounding environment. Calibrating preprocessing between multiple sensors is necessary to utilize the data effectively. In particular, the LiDAR-camera pair, a suitable complement with 2D-3D information for each other, has been widely used in autonomous vehicles. Most traditional calibration methods require specific calibration targets set up under complicated environmental conditions, which require expensive human manual work. In this study, we propose a deep neural network that does not require any specific targets and offline setup to find the six degrees of freedom (6 DoF) transformation between LiDAR and the camera. Unlike previous deep learning CNN-based methods, which use raw 3D point clouds and 2D images frame by frame, CalibBD utilizes Bi-LSTM for sequence data to extract temporal features between consecutive frames. It not only predicts the calibration parameters by minimizing both transformation and depth losses but also calibrates the camera parameters by using temporal loss to refine the calibration parameters. The proposed model achieves a steady performance under various deviations of mis-calibration parameters and achieved higher results in terms of accuracy than the state-of-the-art CNN-based method on the KITTI datasets.

The Capacity Drop (CD) is a sustained drop in a freeway bottleneck outflow when its upstream section becomes congested. Understanding how to replicate CD with microscopic models can help us to better understand its properties and mechanism. However, there is no published study replicating an observed occurrence of CD using microscopic models. In this study, we combine a lane-changing model with two widely known car-following models and calibrate the combined model parameters against field data, to replicate the CD in a merge bottleneck. We confirm that both car-following models can reproduce the CD. Moreover, we show that including the cooperative lane-changing behavior in the model is critical for the replication success. Further, we perform a sensitivity analysis with respect to the ramp demand and maximum acceleration parameters, showing: (i) slower acceleration rate leads to a lower bottleneck throughput, and (ii) higher ramp demand leads to decreased bottleneck throughput.

This paper explores the challenges of radar cross section (RCS) measurements at W-band and proposes several techniques to overcome these challenges to obtain an accurate RCS measurement. This paper combines $S_{11}$ measurements collected using traditional radio frequency (RF) hardware with post-processing algorithms to extract RCS values with a single antenna. Vector background subtraction and time-gating suppress unwanted reflections while increasing the system’s dynamic range capability. Experiments are conducted at the University of Oklahoma’s mm-Wave Laboratory with a Vector Network Analyzer (VNA) and frequency extender to evaluate the proposed measurement techniques. The setup is calibrated with a metal sphere, and the RCS of a smaller metal sphere and an arrangement of four spheres is extracted. The single sphere’s RCS is measured with a 0.304 dBsm average error, and the distributed object under test (OUT) RCS is measured with a 1.60 dBsm average error from 80-90 GHz. Each OUT is placed on a 3D-printed pedestal, and the remainder of the test hardware is part of an existing mm-Wave antenna measurement configuration. The post-processing clutter cancellation techniques enable a flexible test configuration to isolate the OUT RCS without purchasing specialized hardware.

A kinematic calibration approach is proposed for a triple-planar 3-PR(4R)R parallel mechanism whose attitude error cannot be corrected by motion. First, the kinematic principle of the 3-PR(4R)R parallel mechanism is presented. Then, an error mapping function model is designed based on the mechanism’s closed-loop vector equations. The model reveals the presence of coupling errors in the mechanism, i.e., the coupling of attitude and position errors, and is used to analyze error sensitivity as an indicator of component machining and assembly accuracy. A kinematic inverse solution is used to build a zero-point calibration and a full calibration method. Calibration is performed by a laser tracker-based calibration system, which generates much lower error values and delivers much improved accuracy. Experiments demonstrate that the proposed approach can potentially be used to calibrate parallel mechanisms with non-compensable error terms.

Precise sampling is a crucial part of the aerosol measurement processes that ideally requires perfectly isokinetic conditions in which particles in the sampling volume move exactly the same way as they would in an undisturbed flow. Such conditions might be difficult to achieve in practical measurement situations where the direction and speed of the air stream may change continuously. We propose a novel method avoiding sampling errors due to moderately disturbed particle flow in case of an imaging cloud droplet instrument. It is shown that despite the non-isokinetic and non-isoaxial conditions accurate droplet density can be obtained by rejecting part of the measurement volume in post processing. The adjustment of the sampling volume is easily applied using a holographic imaging method, which offers multiple well-defined image planes to accurately set the boundaries of the sampling volume. To verify the hypothesis, aerodynamic sampling effects of a holographic cloud droplet instrument are studied using computational fluid dynamics (CFD) and particle tracing simulations and by comparing them with wind tunnel experiments. We found out that changes in the airflow affected the particle density mostly near the walls of the probe. It was observed that the error in droplet density could be kept under 10 % by limiting the cross-channel depth of the measurement volume to two-thirds of the full wall-to-wall distance. Further improvement was achieved by using simulation results to formulate a relation between sampled and ambient droplet concentration as a function of droplet diameter and air speed. Less than 1 % deviation in droplet density was achieved in this case compared to simulated values. Orientation of the instrument’s inlet relative to the direction of airflow was found out to have the strongest effect on the achievable accuracy. Results show that the droplets can be reliably sampled also in a non-isoaxial case if the measurement volume was further reduced. Reasonable accuracy was achieved with 10-degree deviation within limited air speed and droplet diameter range.

We propose a new residual model to analyze the uncertainty of scattering parameters (S-parameters) calibrated by an electronic calibration unit (ECU). Residual errors are usually estimated from the observed ripple after connecting a load or a short at the end of an airline. Therefore, this ripple method can only be used in a frequency range where the airline loss was not large. We, however, obtained the residual error from the uncertainty of the calibration kit using a simple numerical approach. As a result, we can determine the correlations between real/imaginary and magnitude/phase uncertainties. The proposed residual model showed the same results as a VNA error model. We also added a new error term to account for the effect of temperature-dependent drift of the ECU. In addition, we analytically derived the sensitivity coefficients for a 2-port DUT based on the proposed residual model. The proposed residual model will be helpful for the uncertainty analysis of S-parameters calibrated using the ECU.

There has been a wide interest in high-resolution air quality monitoring with low-cost gas sensor systems in the last years. Such gas sensors, however, suffer from cross-sensitivities, interferences with environmental factors, unit-to-unit variability, aging, and concept drift. Therefore, reliability and trustworthiness of the measurements in the low parts-per-billion (ppb) range remain a concern, particularly over the course of the lifetime of a sensor network in urban environments. In this simulation study, the possibility to continuously recalibrate a wireless sensor network with mobile references and stochastic gradients, computed from encounters, is explored. By using data collected in field experiments, encounters between static and mobile nodes are modeled as a probabilistic process. Moreover, the influence of a collection of design parameters such as base calibration, initial recalibration, choice of optimization algorithm, as well as encounter frequency are analyzed and discussed. With an optimized protocol, it can be shown that long-term reliable measurements with absolute errors of about 50 ppb for CO, 3 ppb for NO2, and 4 ppb for O3 could be achievable with a few mobile references in urban environments.

The robot kinematic error model may include redundant parameters, which require removal prior to the identification of kinematic parameters to ensure the accuracy and stability of such. Traditionally, the redundant parameters are determined by the numerical analysis. This paper presents an analytical method to determine the identifiable kinematic parameters for serial-robot calibration under various identification conditions. Among various kinematic models of robots, the DH (Denavit-Hartenberg) model is the most commonly used, so the analysis of redundant parameters is based on the DH error model. The correlation between the columns of the Jacobian matrix is analyzed by theoretical methods, resulting in the linear relationship between these columns. The redundant error parameters and the linear independent parameters after removing redundancies from robots with different measurement conditions and different configurations are clearly given. Thus, the redundant parameters can be easily determined based on the values of kinematic parameters, which facilitates the determination of the non-redundant kinematic calibration model. In addition, the physical meanings of redundant parameters are also elaborated, which can reveal the inherent principle of the error parameters’ linear correlation. Finally, the parameter identification simulation experiment shows that the identification results, after removing the redundant parameters, all converge to the true values when the random noise is not considered, which verifies the correctness of the parameter redundancy analysis results. In the case of considering random noise, the non-redundant parameter identification has better convergence than the full parameter identification. This study can provide theoretical and application references for robot kinematic calibration.

The working environment of autonomous driving and robot navigation is so complex and dynamic that a single type of sensor is insufficient for performing object detection. Thus, in many perception schemes, the LiDAR-camera fusion strategy is preferred. However, the performance of a LiDAR-camera fusion heavily relies on a set of accurately calibrated extrinsic parameters. We propose PSNet, an end-to-end convolutional neural network (CNN) for calibration; this is the first calibration network to use parallel subnetworks to obtain multiresolution features and fuse them adaptively to encourage robustness against different initial error ranges. The method has three key characteristics: (i) Addition of a downsampling block to improve suitability for sparse projected depth maps; (ii) Connection of the high-to-low resolution convolution streams in parallel to obtain multiresolution features that are spatially more precise and contain richer semantic information; (iii) Fusion of multiresolution streams by the multiscale feature aggregation module. The network corrects errors from initial calibration to the ground truth online, rather than directly obtaining the accurate parameters. We evaluated our model on the KITTI datasets and it outperformed other CNN-based methods. In addition, extensive experiments evaluating the model with untrained and unfamiliar datasets demonstrated that our method exhibited good generalization ability.

For the problem that the self-error of industrial robot calibration device has influence on calibration accuracy, a calibration method of industrial robots based on the principle of Perigon Error Close is proposed. In the method, the theory that the sum of circular indexing interval errors around a circle is zero is applied to robot calibration to improve robot calibration accuracy. The calibration principle of the proposed method is provided in detail and the calibration equation is derived in this paper. The calibration system based on the proposed method was constructed with one semiconductor laser and two position sensing detectors (PSDs) fixed on a rotary table. Based on the position error data obtained from laser spot position on the PSDs, the robot kinematics parameter errors were identified by using Levenberg Marquardt (LM) algorithm. The robot calibration experimental setup was constructed and the related verification experiments were carried out. The model parameter identification experiment validates the feasibility of the proposed method for industrial robot calibration. The calibration compensation experiment results of industrial robots show that the maximum position error of the robot is reduced by 71.9% and the average position error is reduced by 77.8%, which validates the effectiveness of the proposed method for industrial robot calibration.

This study proposes a calibration method and analyses the effect of this calibration in lung measures, using minimally invasive electrical impedance spectroscopy with the 3-electrode method, for tissue differentiation between healthy and neoplasm lung tissue. Tissue measurements were performed in 99 patients [54 healthy tissue and 15 neoplastic tissue samples obtained] with an indicated bronchoscopy. Statistically significant difference (P < 0.001) were found between healthy lung tissue and neoplasm lung tissue in bioimpedance parameters. The calibration of the bioimpedance measures with respect to a measure performed in bronchi reduces the inter-patient dispersion, increasing the sensitivity, decreasing the specificity and increasing the area below the ROC curve for three out of four impedance-derived estimators. Results also show that there are no significant differences between healthy lung tissue among smoker, non-smoker and ex-smoker samples, which was initially stated as a possible cause of EIS measurement dispersion in lungs.

A liquid permittivity sensing method is proposed, which is based on measuring the S21 of an on-chip capacitor submerged in a material under test (MUT). The real part of permittivity can be estimated from ɛ′ – |S21| fitted relation in the frequency range of 100 MHz–5 GHz, which are pre-calibrated with four kinds of known materials. The on-chip capacitor is realized with a top metal in a complementary metal oxide semiconductor (CMOS) process, which has an inter-digitized structure featuring a small size of 150 μm × 195 μm. A simple circuit model of the capacitor is used to develop ɛ′ – |S21| relation with a fitting parameter. It shows less than 6.4% root-mean-square (rms) error of ɛ′ for propanol (C3H8O) at 100 MHz–5 GHz.

As an inverse problem, the parameter identification of manipulators is essential to in-situ calibration. Since the ill-posed inverse kinematic model is sensitive to the measurement value, even tiny errors will make the geometric model of the manipulator wrongly identified. To overcome this problem, a Regularized Parameter Identification Method (RPIM) is proposed to calibrate the geometric model of the manipulator. The inverse kinematic model of a 6 DOF manipulator is modified by a Tikhonov regularization to overcome its ill-posed problem. The regularization parameter is optimized by the improved L curve method to adjust the initial model to a well-posed one that approximates the real situation. A calibration system is designed to evaluate the effectiveness of the suggested method. The position of the selected targets is tested by using a laser tracker. The experimental result shows that the absolute position errors of the manipulator are 2.533mm without calibration, 0.472mm by RPIM, 1.445mm by Least Square Method (LSM), 1.353mm by Gradient Descent (GD), and 0.956mm by Gauss-Newton (GN). It shows that the absolute position error of RPIM is reduced by 81.331% after calibration, which is superior to other methods.

With increased pressures and tightening budgets within English Children’s Services in the UK, seeking more effective operational and financial management is becoming a more significant topic of discussion. In other sectors, complex data analysis methods provide the aforementioned management improvements through better understanding of current situations leading to better decision making. Currently, investment remains at a slow pace in English Local Authorities due to budget restrictions. In this paper, a potential opportunity is explored with existing publicly available data related to this area. With the help of industry experts, an Agent-Based Model is created to emulate basic Children’s Services operations and optimised to fit existing data using NSGA-III. With relatively close matches being achieved with sample authorities, this approach demonstrates promise in advancing analytics capabilities for Children’s Services and practical solutions are discussed. With this presented work, it is shown that further expansion and exploration into real-world applications is warranted.

Abstract To make accurate measurements, field mills must be calibrated in a known DC electric field space charge in which the quantities can be control1ed. A simulation model of the generation apparatus of the electric field with a space charge has been built. Based on the combination of the electric field and the transfer of a dilute substances model, the influence of openings in the metal screen was calculated. Simulation results showed that the opening size of the high‐voltage electrode has a great influence on the distribution of the electric field and ion density at the calibration zone. Next, a generation apparatus was designed and developed and ion mobility was researched. The experimental results showed that ion mobility decreased with an increase in electric field strength. However, ion mobility tends to be saturated when the electric field reaches a certain critical value. Two fitting formulas of ion mobility of positive and negative charge are proposed that can be used to predicate ion mobility in a high electric field.

Abstract The software-defined radio has been allowing engineers the possibility of implementing operations on software that were previously only possible to be performed through hardware. With the continuous increase in the processing capacity of computers, it is possible to modify in real time the operating parameters of new radios called Software Defined Radios (SDR) or use commercial devices for other purposes, in addition to those established in their design. By using a low-cost device RTL-SDR R820T2, controlled by a Raspberry Pi 3, with a free software, the present work proposes a characterization procedure in order to collect power levels measurements in a given frequency range with greater accuracy, acting as a spectrum analyzer. Preliminary measures were performed with the device embarked on a drone.

Abstract This article presents the development of an inclinometer device based on a Mach-Zehnder interferometer considering the effects caused by temperature variations. It was demonstrated the operation of a LPG-taper system for angle measurements. The practical results satisfactorily show reliable inclination measures, even with the cross sensibility effect caused by variable temperature.

A microwave sensor realized in monolithic technology, intended for permittivity estimation with the use of a highly sensitive coupled-line section, is presented in this paper. It also contains a dedicated measurement circuitry realized as a five-port correlator, therefore, a simple scalar power measurement can be utilized to obtain vector signal corresponding to the measured material without the usage of an external vector network analyzer. To suppress systematic measurement errors arising from the manufactured circuit's imperfections it is equipped with an integrated calibration block, the parameters of which can be simply tuned with two biasing voltages. The sensor is calibrated following a recently reported procedure, which requires neither known nor specific calibration values, in contrast to other methods with much stronger constraints. It is shown for the first time, that a measurement system incorporating a multiport correlator can be successfully calibrated with such arbitrarily chosen settings of the calibration block. To experimentally validate correctness of the calibration's convergence, the developed sensor is calibrated multiple times with different sets of the calibration block's settings, each time resulting in the same output values. It proves that the approach to calibration of measurement systems incorporating the multiport correlator can be significantly simplified with no impact on calibration accuracy. Finally, the measurement of the sensor's response for two dielectric samples clearly demonstrates the advantages of the utilized calibration.

In this paper, a phase based method to calibrate a binocular vision sensor is presented. In this method, only a surface plate is utilized. A series of phase shifting patterns are projected onto the surface plate. In this case, fundamental matrix can be calculated out from point correspondences on two image planes. As intrinsic parameters of each camera have been obtained according to related calibration method, essential matrix can be worked out. Then, rotation matrix and translation matrix with a coefficient are deduced based on singular value decomposition of the essential matrix. As size of the pattern can be confirmed from a look-up table, the coefficient is determined. So far, the binocular vision sensor is calibrated. In our proposed method, only one planar target without any pattern is necessary, which is with high precision and easy machined. As the calibration method is based on related phase algorithm, the calibration process is not affected by the ambient light which makes our calibration method robust. Experiment results show the precision of our proposed method. When we utilized a laser displacement sensor with a precision of 0.01 mm to determine size of the pattern, root mean square error of our calibration method is 0.103 mm with regard to the measurement area of about $100\times 80$ mm. Moreover, as planar features are widely existing, our proposed method is very suitable for on-site calibration and auto-calibration.

This paper proposes a linear compensator algorithm to improve the performance of an absolute multipolar magnetic encoder (AMPME). An AMPME is an absolute magnetic rotary encoder that uses a multipolar magnet (MPM) to increase the resolution. The resolution can be dramatically increased in proportion to the number of poles in the MPM. However, various hardware problems that occur during the encoder manufacturing process degrade the AMPME performance. Also, harmonic components occur in the raw data due to various problems, such as the resistance error of the analog circuit, magnetic field overlap between the magnets, position error between the sensor and magnet, and pole-pitch difference. In particular, during the magnetization process of the MPM, the pole-pitch difference becomes a problem when the sizes of each pole are not uniform. This problem causes harmonic components that reduce the absolute position accuracy. To solve these problems, this paper proposes a linear compensation method. The proposed linear compensator consists of two parts. The first part is the enhanced ratiometric linearization for phase calculation and calibration. The second is the phase compensator for removing the phase difference via the pole-pitch difference of the MPM. The linear compensator improves various parameters by precomputing the offset, amplitude, and phase corrections. After compensating for sinusoidal signals, the linear compensator applies appropriate parameters at the appropriate times. This method is faster, easier to set up, and more accurate than the conventional method. Furthermore, this method is experimentally verified against the existing harmonic rejection method. Experimental results are provided to verify the effectiveness of the proposed method.

Line structured light sensor has been applied in various three dimensional (3D) measurement scenes with the advantages of non-contact, low cost and high speed. Its accuracy, which is directly determined by the calibration method, needs to be further improved to fulfill the measuring tasks of precision parts. Here, we proposed a numerical method that can eliminate the errors of the model based methods through two strategies. One is the establishment of the numerical mapping relationship between stripe pixel coordinates and their world coordinates through piecewise cubic interpolation. Corner points of a checkerboard target are used to obtain sufficient interpolating nodes. This target can be manually aligned with the laser plane and the alignment error would be eliminated via the point projection. The other is the data-driven laser plane optimization. The data set of reference interval distance is computed based on the invariance of cross ratio. The optimization model is to minimize the root mean squared error of measured interval distances by adjusting the laser plane coefficients. After the optimization, a higher numerical mapping relationship can be achieved. It bypasses the camera and the distortion models and reaches a calibration error of only 0.005mm. The comparison studies and the measurement of the steps further validate the proposed method.

Till now, most calibration methods only compensate geometric error caused by inaccurate kinematic parameters, while the desired accuracy may still not be achieved when the robot is performing long-stroke, heavy-duty loading and unloading tasks. In this paper, a generalized semi-analytical beam deformation model is firstly proposed for the Cartesian robot to compensate the non-geometric error due to structural deformation under both distributed and concentrated loads. The adjustment factors are introduced in this model to deal with the over-constrained boundary conditions for both intermediate and side modules of the long-stroke Cartesian robot. This improves the fitness of the coming geometric error model, which assumes the beam to be rigid and straight. In addition, as the major error components which do not conform to the Gaussian distribution have been extracted in earlier steps, the Gaussian process regression model is imported to predict the residual error more accurately. In this way, comprehensive geometric and non-geometric error modeling and compensation procedures are formed for the multi-module, long-stroke Cartesian robot. Simulations and real-time experiments are conducted to validate the effectiveness of the proposed method.