Related papers: Uncertainty Analysis for Material Measurements Usi…
Since S-parameter measurements without uncertainty cannot claim any credibility, the uncertainties in full two-port Vector Network Analyser (VNA) measurements were estimated using total complex differentials (Total Differential Errors). To…
Adversarial robustness remains a critical challenge in deploying neural network classifiers, particularly in real-time systems where ground-truth labels are unavailable during inference. This paper investigates \textit{Volatility in…
In the context of industrially mass-manufactured products, quality management is based on physically inspecting a small sample from a large batch and reasoning about the batch's quality conformance. When complementing physical inspections…
This paper presents a complex permittivity measurement method for low-dispersive materials as a function of frequency. The introduced method relies only on transmitted power signals which are collected using a spectrum analyzer/power meter,…
In this paper, we propose an uncertainty-aware learning from demonstration method by presenting a novel uncertainty estimation method utilizing a mixture density network appropriate for modeling complex and noisy human behaviors. The…
A comparison of nonlinear vector network analyser (NVNA) measurements has been carried out involving four organisations (National Physical Laboratory, UK, University of Surrey, UK, Chalmers University of Technology, Sweden and Keysight…
Measuring the complex permittivity of material is essential in many scenarios such as quality check and component analysis. Generally, measurement methods for characterizing the material are based on the usage of vector network analyzer,…
There is a huge demand to accurately determine the magneto-electrical properties of particles in the nano sized regime due to the modern IC technology revolution and biomedical application science. In this paper, we present a microwave…
Quantum metrology uses small changes in the output probabilities of a quantum measurement to estimate the magnitude of a weak interaction with the system. The sensitivity of this procedure depends on the relation between the input state,…
Uncertainty quantification is a critical aspect of machine learning models, providing important insights into the reliability of predictions and aiding the decision-making process in real-world applications. This paper proposes a novel way…
This paper studies the utility of techniques within uncertainty quantification, namely spectral projection and polynomial chaos expansion, in reducing sampling needs for characterizing acoustic metamaterial dispersion band responses given…
Introducing accelerated reconstruction algorithms into clinical settings requires measures of uncertainty quantification that accurately assess the relevant uncertainty introduced by the reconstruction algorithm. Many currently deployed…
A key factor for ensuring safety in Autonomous Vehicles (AVs) is to avoid any abnormal behaviors under undesirable and unpredicted circumstances. As AVs increasingly rely on Deep Neural Networks (DNNs) to perform safety-critical tasks,…
We describe the use of a near-field scanning microwave microscope to image the permittivity and tunability of bulk and thin film dielectric samples on a length scale of about 1 micron. The microscope is sensitive to the linear permittivity,…
This study investigates the application of an artificial neural network to predict the complex dielectric properties of granular catalysts commonly used in microwave reaction chemistry. The study utilizes finite element electromagnetic…
A method for determining the permittivity and permeability for specimens with high refractive index and variable shape is investigated. The method extracts the permeability and permittivity tensor elements from reflection measurements made…
Evaluation of per-sample uncertainty quantification from neural networks is essential for decision-making involving high-risk applications. A common approach is to use the predictive distribution from Bayesian or approximation models and…
Roughness parameters that characterize contacting surfaces with regard to friction and wear are commonly stated without uncertainties, or with an uncertainty only taking into account a very limited amount of aspects such as repeatability of…
In this study, four different techniques are presented. 1 Rectangular waveguide measurement technique for normal microwave materials microwave properties such as permeability and permittivity. This technique removed guess parameter and…
We present a general framework for uncertainty quantification that is a mosaic of interconnected models. We define global first and second order structural and correlative sensitivity analyses for random counting measures acting on risk…