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Time-of-flight (ToF) range imaging cameras measure the distance from the camera to each position imaged by each respective pixel. This article summarizes the current state of ToF imaging range measurement. Covering the major forms of biasing error, we review the existing methods to correct them. These error correction methods comprise a mix of camera and data acquisition modifications, coupled with the appropriate signal processing. The pervasive influence of random noise is considered last, but not at all least, and jitter, or random timing error, is highlighted as a special cause of error in ToF imaging of great significance despite the limited attention given to it. [ABSTRACT FROM AUTHOR]

Uncertainty analysis can be viewed as the activity that quantifies the range where the true value of a physical quantity lies. This aspect of measurement has been in use and given a paramount importance for many decades primarily by metrologists, instrumentation engineers, and practitioners who deal with measurement devices in carrying out their professional duties. In recent years, however, it has become increasingly important among design engineers from diverse fields of engineering to achieve high-quality designs of complex engineering systems. This paper introduces recent works that apply the techniques developed for measurement uncertainty evaluation to the design of reliable and robust engineering systems. [ABSTRACT FROM AUTHOR]

The goal of Technical Committee on Fault Tolerant Measurement Systems (TC-32) is to promote the research, training and outreach activities that cover all aspects of theory and practices of ensuring trustworthy measurement results under every operating condition. This is especially important in critical infrastructures where the cost of failure is very high. In recent years, TC-32 promotes and supports technical activities in reliable computation, propagation of uncertainty in signal processing systems and architectures/infrastructures/workflows to ensure fault tolerant instrumentation and measurements. [ABSTRACT FROM AUTHOR]

This paper presents a novel parametric design analysis of the magnetic sensor module used to find mover positions in linear motors. The proposed framework utilizes a computationally inexpensive proper orthogonal decomposition-dynamic mode decomposition-based model order reduction (MOR), coupled with a multiparameter moment matching method. The model is then utilized by a moment-based probabilistic design optimization method that considers the manufacturing uncertainties to find the optimal sensor design. A magnetic sensor module is designed for employing the proposed framework, and the results show that: 1) the proposed MOR technique is accurate; 2) the reliability analysis performed during the probabilistic design optimization can help to reduce the excessive non-compliance costs for the manufacturers; and 3) the combined framework is able to significantly expedite the optimal design search. [ABSTRACT FROM AUTHOR]

An estimate of a measurand using a nonlinear function of uncorrelated input quantities can be done by either applying the nonlinear function to the means of the input quantities (Method 1) or calculating the mean of a set of values obtained from propagating individual measurement values through the nonlinear function (Method 2). This paper proposes an improvement over the standard Method 2 procedures when the input quantities are assumed to be statistically independent and the nonlinear function has a general sum-of-product form, which covers many common measurement models. This paper shows that the proposed new approach (called Method 2S), if applicable, always produces a mean-squared error smaller than that of the conventional Method 2 procedures. The proposed approach improves the efficiency of Type-A evaluation as well as the Monte Carlo method. It also well complements the mainstream practices in the measurement uncertainty evaluation. [ABSTRACT FROM AUTHOR]

Expanded uncertainty estimation is normally required for mission-critical applications, e.g., those involving health and safety. It helps to get a distribution range of the required confidence level for the uncertainty evaluation of a system. There are a number of available techniques to estimate the expanded uncertainty. However, there is currently no commonly accepted benchmark test distribution set adopted to compare the performances of different techniques when they are used to estimate the expanded uncertainty. Without such a common benchmarking platform, the relative reliability of a particular technique in comparison to other techniques can be untrustworthy. To address the shortcoming, this paper proposes a set of analytically derived benchmark test distributions. It goes on to show the benefits of using them by comparing the performance of existing distribution fitting techniques when applied to the moment-based expanded uncertainty evaluation. The most commonly used moment-based distribution fitting techniques, such as Pearson, Tukey’s gh, Cornish–Fisher expansion, and extended generalized lambda distributions, are employed as test cases in this paper. The test distribution set proposed in this paper provides a common benchmarking platform for metrologists intending to assess the performance of different expanded uncertainty estimation techniques. Results from the performance comparison would help practitioners to make a better choice of a distribution fitting technique that would best suit their respective systems. [ABSTRACT FROM AUTHOR]