Memetic electromagnetism algorithm for surface reconstruction with rational bivariate Bernstein basis functions

Surface reconstruction is a very important issue with outstanding applications in fields such as medical imaging (computer tomography, magnetic resonance), biomedical engineering (customized prosthesis and medical implants), computer-aided design and manufacturing (reverse engineering for the automotive, aerospace and shipbuilding industries), rapid prototyping (scale models of physical parts from CAD data), computer animation and film industry (motion capture, character modeling), archaeology (digital representation and storage of archaeological sites and assets), virtual/augmented reality, and many others. In this paper we address the surface reconstruction problem by using rational Bézier surfaces. This problem is by far more complex than the case for curves we solved in a previous paper. In addition, we deal with data points subjected to measurement noise and irregular sampling, replicating the usual conditions of real-world applications. Our method is based on a memetic approach combining a powerful metaheuristic method for global optimization (the electromagnetism algorithm) with a local search method. This method is applied to a benchmark of five illustrative examples exhibiting challenging features. Our experimental results show that the method performs very well, and it can recover the underlying shape of surfaces with very good accuracy.This research is kindly supported by the Computer Science National Program of the Spanish Ministry of Economy and Competitiveness, Project #TIN2012-30768, Toho University, and the University of Cantabria. The authors are particularly grateful to the Department of Information Science of Toho University for all the facilities given to carry out this work. We also thank the Editor and the two anonymous reviewers who helped us to improve our paper with several constructive comments and suggestions

Memetic simulated annealing for data approximation with local-support curves

This paper introduces a new memetic optimization algorithm called MeSA (Memetic Simulated Annealing) to address the data fitting problem with local-support free-form curves. The proposed method hybridizes simulated annealing with the COBYLA local search optimization method. This approach is further combined with the centripetal parameterization and the Bayesian information criterion to compute all free variables of the curve reconstruction problem with B-splines. The performance of our approach is evaluated by its application to four different shapes with local deformations and different degrees of noise and density of data points. The MeSA method has also been compared to the non-memetic version of SA. Our results show that MeSA is able to reconstruct the underlying shape of data even in the presence of noise and low density point clouds. It also outperforms SA for all the examples in this paper.This work has been supported by the Spanish Ministry of Economy and Competitiveness (MINECO) under grants TEC2013-47141-C4-R (RACHEL) and #TIN2012-30768 (Computer Science National Program) and Toho University (Funabashi, Japan)

Memetic electromagnetism algorithm for surface reconstruction with rational bivariate Bernstein basis functions

Surface reconstruction is a very important issue with outstanding applications in fields such as medical imaging (computer tomography, magnetic resonance), biomedical engineering (customized prosthesis and medical implants), computer-aided design and manufacturing (reverse engineering for the automotive, aerospace and shipbuilding industries), rapid prototyping (scale models of physical parts from CAD data), computer animation and film industry (motion capture, character modeling), archaeology (digital representation and storage of archaeological sites and assets), virtual/augmented reality, and many others. In this paper we address the surface reconstruction problem by using rational Bézier surfaces. This problem is by far more complex than the case for curves we solved in a previous paper. In addition, we deal with data points subjected to measurement noise and irregular sampling, replicating the usual conditions of real-world applications. Our method is based on a memetic approach combining a powerful metaheuristic method for global optimization (the electromagnetism algorithm) with a local search method. This method is applied to a benchmark of five illustrative examples exhibiting challenging features. Our experimental results show that the method performs very well, and it can recover the underlying shape of surfaces with very good accuracy.This research is kindly supported by the Computer Science National Program of the Spanish Ministry of Economy and Competitiveness, Project #TIN2012-30768, Toho University, and the University of Cantabria. The authors are particularly grateful to the Department of Information Science of Toho University for all the facilities given to carry out this work. We also thank the Editor and the two anonymous reviewers who helped us to improve our paper with several constructive comments and suggestions

Volumetric Data Analysis for Reverse Engineering and Solid Additive Manufacturing: A Framework for Geometric Metrological Analysis

Poor geometric quality is one of the main constraints that hinders the wide adoption of reverse engineering (RE) and additive manufacturing (AM). RE models from a single scan will most likely generate inaccurate representations of the original design due to the uncertainties existing in individual parts and scanning procedures. On the other hand, metrological methodologies for AM significantly differ from those for the traditional manufacturing processes. Conventional statistical methodologies overlook these three-dimensional (3D) feature-independent processing techniques. In this dissertation, we develop a novel statistical data analysis framework---volumetric data analysis (VDA)---to deal with the uniqueness of both technologies. In general, this framework also addresses the rising analytical needs of 3D geometric data. Through VDA, we can simultaneously analyze the measured points on the outer surfaces and their relationships to acquire manufacturing knowledge. The main goal of this dissertation is to apply the proposed framework in multiple RE and AM applications related to their geometric quality characteristics. First, we demonstrate a novel estimator to increase the precision of RE-generated models. We built a Bayesian model with prior domain knowledge to model the landmarks’ uncertainty. We also proposed a bi-objective optimization model to answer the RE process-planning questions, e.g., how many scans and parts are required to achieve the precision requirements. The second major contribution is a study of tolerance estimation procedure for the re-manufacturing of legacy parts. We propose a systematic geometric inspection methodology for the RE and AM systems. Moreover, based on the domain knowledge in production-process design and planning, we developed methods to estimate empirical tolerances from a small batch of legacy parts. The third major contribution of this dissertation is to design an automated variance modeling algorithm for 3D scanners. The algorithm utilizes a physical object’s local geometric descriptors and Bayesian extreme learning machines to predict the landmarks’ variances. Lastly, we introduce the VDA framework to AM-oriented experimental analysis. Specifically, we propose a high-dimensional hypothesis testing procedure to statistically compare the geometric production accuracy under two AM process settings. We present new visualization tools for deviation diagnostics to aid in interpreting and comparing the process outputs

Performance analysis for wireless G (IEEE 802.11G) and wireless N (IEEE 802.11N) in outdoor environment

This paper described an analysis the different capabilities and limitation of both IEEE technologies that has been utilized for data transmission directed to mobile device. In this work, we have compared an IEEE 802.11/g/n outdoor environment to know what technology is better. The comparison consider on coverage area (mobility), throughput and measuring the interferences. The work presented here is to help the researchers to select the best technology depending of their deploying case, and investigate the best variant for outdoor. The tool used is Iperf software which is to measure the data transmission performance of IEEE 802.11n and IEEE 802.11g

Performance Analysis For Wireless G (IEEE 802.11 G) And Wireless N (IEEE 802.11 N) In Outdoor Environment

This paper described an analysis the different capabilities and limitation of both IEEE technologies that has been utilized for data transmission directed to mobile device. In this work, we have compared an IEEE 802.11/g/n outdoor environment to know what technology is better. the comparison consider on coverage area (mobility), through put and measuring the interferences. The work presented here is to help the researchers to select the best technology depending of their deploying case, and investigate the best variant for outdoor. The tool used is Iperf software which is to measure the data transmission performance of IEEE 802.11n and IEEE 802.11g