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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.

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.

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.

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.

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.

The Field Programmable Gate Array (FPGA) structure poses several constraints that make the implementation of complex asynchronous circuits such as Time–Mode (TM) circuits almost unfeasible. In particular, in Programmable Logic (PL) devices, such as FPGAs, the operation of the logic is usually synchronous with the system clock. However, it can happen that a very high–performance specifications demands to abandon this paradigm and to follow an asynchronous implementative solution. The main driver forcing the use of programmable logic solutions instead of tailored Application Specific Integrated Circuits (ASIC), best suiting an asynchronous design, is the request coming from the research community and industrial R&D of fast–prototyping at low Non Recursive Engineering (NRE) costs. For instance in the case of a high–resolved Time–to–Digital Converter (TDC), a signal clocked at some hundreds of MHz implemented in FPGA allows implementing a TDC with resolution at ns. If a higher resolution is required, the signal frequency cannot be increased further and one of the aces up the designer’s sleeve is the propagation delay of the logic in order to quantize the time intervals by means of a so-called Tapped Delay–Line (TDL). This implementation of TDL–based TDC in FPGAs requires special attention by the designer both in making the best use of all available resources and in foreseeing how signals propagate inside these devices. In this paper, we investigate the implementation of a high–performance TDL–TDC addressed to 28–nm 7–Series Xilinx FPGA, taking into account the comparison between different technological nodes from 65–nm to 20–nm. In this context, the term high–performance means extended dynamic–range (up to 10.3 s), high–resolution and single–shot precision (up to 366 fs and 12 ps r.m.s respectively), low differential and integral non–linearity (up to 250 fs and 2.5 ps respectively), and multi–channel capability (up to 16).

According to the transmission mode of polarized light in Mueller ellipsometry, a characterization method for the thickness and optical constants of isotropic nano films based on Self-Adaptive Differential Evolution algorithm (SADE) is proposed. By establishing the least square model of the output light intensity with respect to the Mueller matrix of the standard sample to be measured, the elements of the Mueller matrix are solved by using the Sade algorithm, and the Mueller spectral curve obtained by fitting is compared with that measured by DRC-MME, and the film thickness is calculated by using the transfer matrix. The SiO2 / Si standard samples with calibration values of 100.4 nm and 121.56 nm are simulated and calculated. The experiment shows that: when numbers of iterations accumulated to 65 and 80 respectively, the residual square sum of the light intensity of the objective function converges to the minimum values of 1.24 and 1.02. The calculated film thickness values are 101.25nm and 120.53nm respectively, and the relative errors are both less than 1%. It proves the characteristics of simple calculation, fast convergence and accurate global optimal solution.

This work proposes a novel solution to relocalize the SLAM tracking based on spherical cameras. It focuses on the imaging method of spherical camera, the feature extracting algorithm and the relocalization of SLAM tracking based on the 3D reconstruction. In the imaging method, we design a new camera containing eight fish-eye lenses, and then we propose a calibration method to calibrate the eight fish-eye lenses spherical camera; To get the high-performance feature points of panoramic image, we propose a network based on a separate network to extract local feature accurately and quickly. With the correct key points obtained by the feature extracting method, we reoptimize the SLAM tracking after the maximum posteriori estimation usually applied in common back-end SLAM to relocalize the SLAM tracking. The experiment results show that the calibration method achieved 0.973 reprojection error, lower than the common methods like Zhang’s or DLT. The inlier rate and matching time of proposed SimpGeoDesc are all better than the reference models ContextDesc and GeoDesc. With the correct feature points, SLAM tracking is clearer and more steadily with our relocalization method. That is the solution of relocalization of SLAM tracking proposed in this work is effective. The AR application of the relocalization proves the feasibility of our propose relocalization method.

LUTI (Land-Use and Transportation Interaction) models are decision-making aid tools that simulate complex dynamic bilateral feedback between transportation and land-use models within a territory. Although calibration (parameter estimation) is a crucial requirement of LUTI models, fully automated approaches with the usage of multi-objective functions have not been fully addressed. To address this limitation, a generic calibration approach is proposed for the parameters of the land-use model using a differential evolution algorithm. A global sensitivity analysis was performed to identify the most important land-use model parameters. These parameters were then calibrated using the differential evolution algorithm with the Root Mean Square Error (RMSE) and Mean Absolute Normalized Error (MANE) as multi-objective functions. Five key capabilities are provided in the suggested technique for calibration of LUTI models including 1) global estimation rather than local estimation, 2) consideration of multi-objective functions, 3) continuously improving the results, 4) easily adaptability, and 5) involving multi parameters in the calibration process. The TRANUS land-use model was used to test the performance of the suggested calibration technique. The validation and consolidation of the approach were tested based on convergence, minimization of errors, and modeled/observed data ratio by comparing with the genetic algorithm and particle swarm optimization techniques. Using the deferential evaluation algorithm, the suggested approach outperformed both genetic and particle swarm optimization techniques and provided the most stable and diverse solutions.

There are usually non-equilibrium transmission characteristic among the measurement channels of a photoelectric testing system with intersecting detection areas, and it will lead to a poorer measurement result provided that they are not properly calibrated and equalized. To improve the measurement accuracy of the system, this article presents an original calibration & equalization method to effectively restrain the effect of the non-equilibrium transmission characteristics. The measurement principle of this system is described in detail. Then, main transmission characteristic of the measurement channel is analyzed, and a calibration model is established and the requirements of its calibration signal are considered. Next, the equalization principle of measurement channel is studied, and then a compensating network is developed to perform an iterated algorithm to equalize the measurement channels. Through semi-physical simulation, the results show that an experiment platform is developed to generate several parallel calibrated signals with quasi–delta, and this can obtain the key characteristic (i.e., delay time) of the measurement channels. Furthermore, the transmission delay difference between any two measurement channels is below $0.5~\mu \text{s}$ through iterated equalization. Consequently, the proposed calibration and equalization method can effectively decrease the systematic error of the testing system.

The pollution of the air constitutes an environmental risk to health, crops, animals, forests and water. There are several policies for reducing air pollution regarding industry, energy, transportation, and agriculture. Unfortunately, there is limited monitoring of the air quality in cities and rural areas for supervising the accomplishment of these policies. Reliable monitoring of air pollutants is, typically, based on expensive fixed stations, which constitutes a barrier to tackle. This research presents the design, implementation and evaluation of a small, low-cost, station for monitoring atmospheric pollution. The prototype registers ozone (O3) and carbon monoxide (CO) using inexpensive sensors. To assure high reliability of the measurements obtained by the sensors installed in this station, it is proposed a calibration procedure based on the selection of the best performance analysis of the following machine learning techniques: multiple linear regression, artificial neural networks, and random forest. Additionally, a decision rule is implemented to select an optimal combination of sensors for the estimation models, while the sample timestamp is considered as a temporal heuristic at the input of the system, assuming similarities in the daily environmental dynamics. In order to test the station in a realistic scenario, the calibration and evaluation sets were taken in two different time frames of one and two months, respectively. The overall process was implemented with reference data coming from a certified air quality fixed station in the city of Cuenca - Ecuador. Experimental results showed that the real-time reports of ozone provided by the prototype are quite similar to the fixed station during the evaluation period, with a resulting correlation of up to r = 0.92 and r = 0.91 in the calibration and evaluation set, respectively. However, signal drift and aging in COx sensors diminished the accuracy of carbon monoxide calibration models, resulting in lower correlation (r ≤ 0.76) with the evaluation set.

Thermal images produced by the long-wavelength infrared (LWIR) camera are robust and independent from environmental illumination change. They can help the standard visible light camera working under the complicated environmental condition. Breaking through the traditional stereo multi-spectral sensor consisting of a visible-light camera and a LWIR camera, a novel architecture of large field of view (FOV) cooperated infrared and visible spectral sensor for visual odometry is proposed. The novel sensor is equipped with two visible cameras, four infrared cameras covering 120 degrees FOV in horizontal under both bands. Distribution of cameras and related peripheral devices are specifically designed which makes the sensor's volume less than 100 cm (length) × 10 cm (height) × 10 cm (width). The sensor's cameras calibration, distortion correction and measurement principle are elaborated. Feature-based method for visible and multi-windowed optimization-based image alignment for infrared is designed for the visual odometer based on the different imaging mechanism and distribution of cameras in the sensor. The frames and estimated poses management from both bands are proposed. Moreover, all proposed methodologies can be implemented in the sensor's embedded processor. The electrical power consumption is only 12W. Experiments of the sensor's evaluation are performed, experimental results show that large FOV cooperated multi-spectral cameras can efficiently improve the robustness of visual odometry. The real-time performance of the sensor is higher than 10fps with disparity map construction under both bands.

The calibration of traffic-flow simulation models continues to be a significant problem without a generalized, comprehensive, and low-cost solution. Existing calibration approaches either have not explicitly addressed the multi-objective characteristics of the problem or determining their hyperparameters requires significant effort. In addition, statistical evaluation of alternative solution algorithms is not performed to ensure dominance and stability. This study proposes an adaptation and advanced implementation of the Multi-Objective Global-Best Harmony Search (MOGBHS) algorithm for calibrating microscopic traffic-flow simulation models. The adapted MOGBHS provides five key capabilities for solving the proposed problem including 1) consideration of multiple objectives, 2) easily extendable to memetic versions, 3) simultaneous consideration of continuous and discrete variables, 4) efficient ordering of no dominated solutions, 5) relatively easy tuning of hyperparameters, and 6) easily parallelization to maximize exploration and exploitation without increasing computing time. Three traffic flow models of different dimensionality and complexity were used to test the performance of seventeen metaheuristics for solving the calibration problem. The efficiency and effectiveness of the algorithms were tested based on convergence, minimization of errors, calibration criterion, and two statistical nonparametric tests. The proposed approach dominated all alternative algorithms in all cases and provided the most stable and diverse solutions.

An inertial navigation system is commonly used in various marine platforms above and below the sea surface to calculate the position, velocity and orientation of its carrier platform. Such systems contain an inertial measurement unit (IMU) to measure the specific force and angular velocity which in turn are integrated to obtain the navigation state. Due to sensor noises and other error terms, the navigation solution drifts in time. In situations of pure inertial navigation (no external aiding), multiple IMUs (MIMU) can be used to improve the performance of a single unit. In this paper, we explore the benefits of using a MIMU system for common navigation operations. To that end, a 32 MIMU architecture (192 inertial sensors) was designed and constructed for the experimental evaluations. Utilizing this system we examined the effect of the number of sensors in the architecture versus position accuracy, stationary calibration, coarse alignment and gyro free design. We derive closed form empirical expressions enabling insight to the connection between number of IMUs to the expected performance.

In this paper, a calibration approach based on transfer function extraction for the Cartesian vector modulator (VM) is presented. Three kinds of VM models-the ideal VM model, the frequency-dependent VM model and the modified frequency-dependent VM model, are introduced in the proposed calibration approach. The calibration approach starts with an initialization of the transfer function of the modified frequency-dependent VM model. Then, the parameters of the transfer function are modified and extracted from the data of the measured transmission state (transmission amplitude and phase) of the actual VM by iteration, until the transmission state predicted by the extracted transfer function agrees well with the measured transmission state. Subsequently, the extracted transfer function of the modified frequency-dependent VM model is capable of describing the transmission characteristics of the actual VM, and the calibrated baseband control voltages for the desired transmission amplitude and phase of the actual VM are able to be obtained by using the extracted transfer function. An actual VM is used as an example to verify this method. By adopting the proposed method, the maximum amplitude and phase errors at different complex gain setpoints are reduced to 0.05 dB and 0.3° respectively after only two iteration steps. Since the actual VM is able to be accurately calibrated in only a few iteration steps, the results reveal that high accuracy and efficiency can be obtained in this calibration technique, which is well suited for applications involving high-accuracy calibration, real-time calibration and multichannel VM system calibration.

This paper presents experimental results of an initial and in-situ mutual coupling calibration techniques of an active phased array antenna. The antenna is a dual-polarized 64-element C-band subarray panel equipped with an RF beamformer that enables mutual coupling measurements. Both techniques were implemented, tested and validated using a custom anechoic chamber and a radar backend. The implementation of a radar backend enables the in-situ validation of both methods. A conventional near-field calibration procedure, also known as park and probe, was used as a reference, in order to validate the proposed method. To quantify the calibration effectiveness of the mutual coupling-based techniques, the results are translated into phase and amplitude errors. It is found that the amplitude estimation of the tested initial technique is affected by edge effects, resulting in errors larger than 1 dB, whereas the phase estimation is less sensitive, yielding an overall root mean square error (RMSE) of 2.5°. In contrast, the proposed in-situ technique is not affected by edge effects, and its estimation RMSE is less than 0.12 dB in amplitude and less than 0.75° in phase. Mutual coupling-based calibration techniques are demonstrated to be very versatile, as they provide calibration methods for environments outside an anechoic chamber, and they also enable immediate feedback on the health of the system.

Calibration of aerial mapping camera has an important influence on the applications of earth observation. However, the traditional aerial mapping cameras calibrations depend on large-scale calibration target or collimated light, moreover, it is difficult to build the large-scale calibration targets and the collimated light method requires an accurate turntable and a high-precision goniometer, which is a kind of expensive instrument. To solve this problem, this paper proposes a novel high-precision calibration method which is not restricted by the rigorous conditions, such as small aperture, unevenly energy distribution and expensive equipment. Specifically, a collimator and an elaborately designed multiple pinhole mask are firstly used to generate the collimated light of a large aperture with known directions to simulate the calibration targets at infinity. Then, the camera takes pictures for the aperture of the multiple pinhole collimator at multiple angles to ensure that the image points cover the entire detector. Thirdly, the final calibrated results are obtained by solving the data acquired from multiple angles. Finally, the proposed method is verified by Monte-Carlo simulation and real experimental data, whose results indicate that our method can reach the same accuracy performance as the existing methods at lower cost and faster speed, and thus is practical for engineering application.

As a key module for self-driving cars and advanced driver assistant systems (ADAS), frequency modulation continuous wave (FMCW) automotive millimeter-wave (mmwave) radar has a good market prospect. Due to the relative motion of target and radar in the detection window, the motional frequency spread is inevitable especially for wide-band FMCW automotive mmwave radar systems with large bandwidth and possible high-speed motion. In this paper, for wide-band FMCW automotive mmwave radar systems, we first derive analytical expressions of the motional frequency interference term and phase interference term in the intermediate frequency (IF) signal. Then, the effect of frequency spread is comprehensively analyzed with the help of numerical results. Motivated by the mechanism and influence of frequency spread, we propose three schemes for motional frequency spread calibration with different performance-complexity tradeoff. Finally, simulations verify that the proposed schemes can effectively compensate the degradation of system resolution and parameter estimation accuracy due to frequency spread.

This paper presents the idea and experimental tests of a non-stationary stereovision system used for supervision of a loading crane. The system based on two independent gimbals, equipped with cameras, allows simultaneously to measure and track a group of individual spatial points. The four steps calibration procedure is required for each gimbal axis, camera optics, gimbals positions, and loading crane position. Modern camera systems have been widely applied in several fields of the industry allowing for accurate measurements and real-time quality control. The systems based on stereo camera models and appropriate image analysis algorithms can measure a group of spatial points, complex surfaces, small displacements, or vibrations. There are also several vision systems such as laser tracker, camera mounted on fully controlled rotating head (called gimbal), or TOF camera, which can be used to follow and measure real-time object displacements. Unfortunately, they all have significant individual limitations explained in this paper. Accuracy tests of 3D measurements including active tracking of length pattern and crane tip have been performed within a selected working area. Based on reconstructed 3D data points, different types of geometric errors have been presented. The results revealed effects of accuracy degradation on the workspace boundaries and for higher observation angles, as well as 3D data bias suggesting mechanical issues. Several improvements in calibration and gimbal construction might result in far enough accuracy for normal operations of the crane.

Wheeled mobile robots (WMRs) inevitably experience positional inaccuracy, which is caused by a number of factors including design imperfections, problems with component fabrication, sensor errors, and electromechanical malfunction. We have previously proposed an odometry-based technique that reduces positional inaccuracy in WMRs driven with standard wheels. This technique combines high sampling rates and short-term accuracy, and calculates necessary lateral and longitudinal corrections by using linear regression to model the relationship between positional inaccuracy and angular velocities of the robot's wheels. This technique can do so without the precision of measurement required by other techniques. In this paper, we discuss how a nonparametric bootstrap approach can be used to find both interval and point estimates of the modified angular velocities required to alleviate the positional inaccuracy of the WMR. First, the robot travels along a path recommended by the odometry-based error reduction technique. Then, the positional and angular errors of the robot at the stop point are measured. Next, these measurements are used to estimate angular velocities, providing the necessary confidence intervals (prediction). Results from these calculations could be incorporated into the robot program to modify the movement along a given path (validation). To show viability, the bootstrap technique was applied to a prototype mobile robot while the robot was programmed to move along an unseen trajectory. Results indicate that, for this typical unseen path, the bootstrap technique is capable of improving real-time positional systematic and non-systematic inaccuracy with acceptable levels of precision compared to the linear regression technique under the normality assumption. The bootstrap technique exhibited better efficacy than the linear regression, therefore, it may be a useful tool to conduct real-time calibration of differential drive WMRs.

We present a neural network architecture to determine the volume and complex permittivity of grain stored in metal bins. The neural networks output the grain height, cone angle and complex permittivity of the grain, using the input of experimental field data (S-parameters) from an electromagnetic imaging system consisting of 24 transceivers installed in the bin. Key for practical applications, the neural networks are trained on synthetic data sets but generate the parametric information using experimental data as input, without the use of calibration objects or open-short-load measurements. To accomplish this, we formulate a data normalization scheme that enables the use of a loss function that directly compares measured S-parameters and simulation model fields. The normalization strategy and the ability to train on synthetic data means we do not need to collect experimental training data. We demonstrate the applicability of this synthetically trained neural network to experimental data from two different bin geometries, and discuss the ability of these neural networks to successfully infer parameters that can be used for grain inventory management. Our neural-network-based approach enables rapid inference, providing a more cost-effective long-term solution than existing optimization-based parametric inversion methods.

Full-dimension MIMO (FD-MIMO) using planar active antenna systems (AAS) is considered a critical technology for fifth-generation (5G) cellular systems to improve network capacity. An AAS is typically subject to hardware impairments that negatively impact network capacity. Hence, this article focuses on impairments that cause phase and magnitude errors between radio frequency (RF) chains and shows why they are particularly difficult to avoid in practical AAS. Although previous investigations show these impairments to degrade performance, they are not useful in deriving measurable impairment margins for practical FD-MIMO deployments. Knowing impairment limits are critical for system designers to make hardware design tradeoffs such as AAS configuration, component selection, implementation complexity, and cost. Moreover, it also helps set conformance limits for critical lab verification. Therefore, the paper first investigates the impact of the impairments and derives their practical limits for FD-MIMO by explicitly considering the cumulative effects of the channel model, inter-cell interference, link adaptation, and channel aging due to feedback delays. It is shown that a lower number of digitized RF chains can be a better choice under lower impairments. Next, the sources of impairments are investigated by using measurements carried out in the lab and the field during live operation in a commercial LTE network. Phase drift from local oscillators (LO) and internal temperature variations are identified as two significant sources. The tradeoffs and shortcomings of some of the existing solutions in massive MIMO literature are discussed. Finally, in order to address the shortcomings, a novel and practical coherent LO distribution architecture and array calibration mechanism are proposed. This solution is shown to be applicable to both TDD and FDD FD-MIMO. Measurement results are provided to prove the high degree of coherency and stability achieved on a unique array architecture called high definition active antenna system (HDAAS).

In this paper, an adaptive image calibration technique suitable for cultural heritage surveillance in controlled environments is proposed. Heritage pieces, as objects important to conserve, are prone to degradation and thus in the need of a constant watch in order to assess the moment where maintenance is required, preferably in a non-invasive way. By means of a crowdsourcing approach, we take as surveillance cues the pictures visitors take of the piece under surveillance to avoid the need of a permanent human effort on monitoring. In this paper we propose a novel image calibration approach destined to monitor the state of a cultural heritage piece taking as information cues photographs of different sources. Our technique aims to surpass the colorimetry difference between pictures taken by different cameras, performing an adaptive linear interpolation having a given display with a set of colours as reference values, thus transforming the initial colour space of any picture to a fixed one. This is performed in the RGB domain. Lastly, the pictures are transformed to the CIELAB space to extract the colour coordinates and measure the overall objective quality of the calibration. Acting like this allows the heritage monitoring of its possible degradation along time in an effective way without incurring in expensive resources.

The rigid-body transformation between a LiDAR and monocular camera is required for sensor fusion tasks, such as SLAM. While determining such a transformation is not considered glamorous in any sense of the word, it is nonetheless crucial for many modern autonomous systems. Indeed, an error of a few degrees in rotation or a few percent in translation can lead to 20 cm reprojection errors at a distance of 5 m when overlaying a LiDAR image on a camera image. The biggest impediments to determining the transformation accurately are the relative sparsity of LiDAR point clouds and systematic errors in their distance measurements. This paper proposes (1) the use of targets of known dimension and geometry to ameliorate target pose estimation in face of the quantization and systematic errors inherent in a LiDAR image of a target, (2) a fitting method for the LiDAR to monocular camera transformation that avoids the tedious task of target edge extraction from the point cloud, and (3) a “cross-validation study” based on projection of the 3D LiDAR target vertices to the corresponding corners in the camera image. The end result is a 50% reduction in projection error and a 70% reduction in its variance with respect to baseline.

This paper studies the calibration of industrial robots. Aiming at the problems of cumbersome industrial robot modeling and poor calibration effects, this paper proposes a new calibration method for determining the DH frame and DH parameters of industrial robots by measuring first and modeling afterwards. Firstly, the theory of Fixed Axis-Invariant is proposed, the Axis-Invariant is determined based on dual-vectors, and the origin of Fixed Axis-Invariant is determined by intersection obtained through the projection from one measuring point to the Axis-Invariant. Secondly, a method for solving the D-H frame and D-H parameters of industrial robots based on Fixed Axis-Invariant is proposed. Then, combined with laser tracker and industrial robot control software for secondary development, the developed software can be used to automatically measure and obtain the D-H frame and D-H parameters of industrial robot. Finally, compared with other calibration methods, it can be seen that the calibration method based on Fixed Axis-Invariant proposed in this paper can obtain better results. It can improve the absolute positioning accuracy of industrial robots to less than 0.28 mm, which can solve the problems of cumbersome industrial robot modeling and poor calibration effects.

A multi-camera system combines features from different cameras to exploit a scene of an event to increase the output image quality. The combination of two or more cameras requires prior settings in terms of calibration and architecture. Therefore, this paper surveys the available literature in terms of multi-camera systems' physical arrangements, calibrations, algorithms, and their advantages and disadvantages. We also survey the recent developments and advancements in four areas of multi-camera system applications, which are surveillance, sports, education, and mobile phones. In the surveillance system, the combination of multiple heterogeneous cameras and the discovery of Pan-Tilt-Zoom (PTZ) and smart cameras have brought tremendous achievements in the area of multi-camera control and coordination. Different approaches have been proposed to facilitate effective collaboration and monitoring among the camera network. Furthermore, the application of multi-cameras in sports has made the games more interesting in the aspect of analyses and transparency. The application of the multi-camera system in education has taken education beyond the four walls of the class. The method of teaching, student attendance enrollment, determination of students' attention, teacher and student assessment can now be determined with ease, and all forms of proxy and manipulation in education can be reduced by using a multi-camera system. Besides, the number of cameras featuring on smartphones is gaining noticeable recognition. However, most of these cameras serve different purposes, from zooming, telephoto, and wider Field of View (FOV). Therefore, future smartphones should be expecting more cameras or the development would be in a different direction.