Search results for "Mach"
showing 10 items of 3360 documents
Improving light propagation Monte Carlo simulations with accurate 3D modeling of skin tissue
2008
In this paper, we present a 3D light propagation model to simulate multispectral reflectance images of large skin surface areas. In particular, we aim to simulate more accurately the effects of various physiological properties of the skin in the case of subcutaneous vein imaging compared to existing models. Our method combines a Monte Carlo light propagation model, a realistic three-dimensional model of the skin using parametric surfaces and a vision system for data acquisition. We describe our model in detail, present results from the Monte Carlo modeling and compare our results with those obtained with a well established Monte Carlo model and with real skin reflectance images.
Vision system for defect imaging, detection, and characterization on a specular surface of a 3D object
2002
Abstract A vision system capable of imaging, detecting, and characterizing defects onto highly reflective, non-plane surfaces, is presented in this paper. Defects are typically dust, and hair located under the metallic layer of packaging products used in cosmetic industries. The vision system comprises an innovative lighting solution to reveal defects onto highly reflective non-plane surfaces. Several image acquisitions are performed to build a synthetic image, where defects clearly appear white on a mid-gray background. Our lighting system allows imaging defects on various-shaped objects. The vision system measures the defect size to make a decision on the product rejection. The authors as…
Quality Control by Artificial Vision
2004
This PDF file contains the editorial “Quality Control by Artificial Vision” for JEI Vol. 13 Issue 03
Adaptive Population Importance Samplers: A General Perspective
2016
Importance sampling (IS) is a well-known Monte Carlo method, widely used to approximate a distribution of interest using a random measure composed of a set of weighted samples generated from another proposal density. Since the performance of the algorithm depends on the mismatch between the target and the proposal densities, a set of proposals is often iteratively adapted in order to reduce the variance of the resulting estimator. In this paper, we review several well-known adaptive population importance samplers, providing a unified common framework and classifying them according to the nature of their estimation and adaptive procedures. Furthermore, we interpret the underlying motivation …
Optimised assembly mode reconfiguration of the 5-DOF Gantry-Tau using mixed-integer programming
2010
Pulished version of an article in the journal: Meccanica. Also available from the publisher at: http://dx.doi.org/10.1007/s11012-010-9404-y This paper presents a systematic approach based on Mixed Integer Linear Programming for finding an optimal singularity-free reconfiguration path of the 5-DOF Gantry-Tau parallel kinematic machine. The results in the paper demonstrate that singularity-free reconfiguration (change of assembly mode) of the machine is possible, which significantly increases the usable workspace. The method has been applied to a full-scale prototype and the singularity-free path has been verified both in simulations and with physical experiments using real-time control of th…
FMI4j: A Software Package for working with Functional Mock-up Units on the Java Virtual Machine
2018
This paper introduces FMI4j, a software package for working with Functional Mock-up Units (FMUs) on the Java Virtual Machine (JVM). FMI4j is written in Kotlin, which is 100% interoperable with Java, and consists of programming APIs for parsing the meta-data associated with an FMU, as well as running them. FMI4j is compatible with FMI version 2.0 for Model Exchange (ME) and Co-Simulation (CS). Currently, FMI4j is the only software library targeting the JVM supporting ME 2.0. In addition to provide bare-bones access to such FMUs, it provides the means for solving them using a range of bundled fixedand variable-step solvers. A command line tool named FMU2Jar is also provided, which is capable …
Group Metropolis Sampling
2017
Monte Carlo (MC) methods are widely used for Bayesian inference and optimization in statistics, signal processing and machine learning. Two well-known class of MC methods are the Importance Sampling (IS) techniques and the Markov Chain Monte Carlo (MCMC) algorithms. In this work, we introduce the Group Importance Sampling (GIS) framework where different sets of weighted samples are properly summarized with one summary particle and one summary weight. GIS facilitates the design of novel efficient MC techniques. For instance, we present the Group Metropolis Sampling (GMS) algorithm which produces a Markov chain of sets of weighted samples. GMS in general outperforms other multiple try schemes…
Recycling Gibbs sampling
2017
Gibbs sampling is a well-known Markov chain Monte Carlo (MCMC) algorithm, extensively used in signal processing, machine learning and statistics. The key point for the successful application of the Gibbs sampler is the ability to draw samples from the full-conditional probability density functions efficiently. In the general case this is not possible, so in order to speed up the convergence of the chain, it is required to generate auxiliary samples. However, such intermediate information is finally disregarded. In this work, we show that these auxiliary samples can be recycled within the Gibbs estimators, improving their efficiency with no extra cost. Theoretical and exhaustive numerical co…
Machine learning techniques demonstrating individual movement patterns of the vertebral column: the fingerprint of spinal motion
2022
Surface topography systems enable the capture of spinal dynamic movement; however, it is unclear whether vertebral dynamics are unique enough to identify individuals. Therefore, in this study, we investigated whether the identification of individuals is possible based on dynamic spinal data. Three different data representations were compared (automated extracted features using contrastive loss and triplet loss functions, as well as simple descriptive statistics). High accuracies indicated the possible existence of a personal spinal 'fingerprint', therefore enabling subject recognition. The present work forms the basis for an objective comparison of subjects and the transfer of the method to…
Structured Output SVM for Remote Sensing Image Classification
2011
Traditional kernel classifiers assume independence among the classification outputs. As a consequence, each misclassification receives the same weight in the loss function. Moreover, the kernel function only takes into account the similarity between input values and ignores possible relationships between the classes to be predicted. These assumptions are not consistent for most of real-life problems. In the particular case of remote sensing data, this is not a good assumption either. Segmentation of images acquired by airborne or satellite sensors is a very active field of research in which one tries to classify a pixel into a predefined set of classes of interest (e.g. water, grass, trees,…