Classic and inverse compositional Gauss-Newton in global DIC

Today, effective implementations of digital image correlation (DIC) are based on iterative algorithms with constant linear operators. A relevant idea of the classic finite element (or, more generally, global) DIC solver consists in replacing the gradient of the deformed state image with that of the reference image, so as to obtain a constant operator. Different arguments (small strains, small deformations, equality of the two gradients close to the solution, etc) have been given in the literature to justify this approximation, but none of them are fully accurate. Indeed, the convergence of the optimization algorithm has to be investigated from its ability to produce descent directions. Through such a study, this paper attempts to explain why this approximation works and what is its domain of validity. Then, an inverse compositional Gauss-Newton implementation of finite element DIC is proposed as a cost-effective and mathematically sound alternative to this approximation.     

The Logarithmic Image Processing Model: Connections with Human Brightness Perception and Contrast Estimators

The logarithmic image processing (LIP) model is amathematical framework based on abstract linear mathematicswhich provides a set of specific algebraic and functionaloperations that can be applied to the processing of intensityimages valued in a bounded range. The LIP model has been provedto be physically justified in the setting of transmitted lightand to be consistent with several laws and characteristics ofthe human visual system. Successful application examples havealso been reported in several image processing areas, e.g.,image enhancement, image restoration, three-dimensional imagereconstruction, edge detection and image segmentation.The aim of this article is to show that the LIP model is atractable mathematical framework for image processing which isconsistent with several laws and characteristics of humanbrightness perception. This is a survey article in the sensethat it presents (almost) previously published results in arevised, refined and self-contained form. First, an introductionto the LIP model is exposed. Emphasis will be especially placedon the initial motivation and goal, and on the scope of themodel. Then, an introductory summary of mathematicalfundamentals of the LIP model is detailed. Next, the articleaims at surveying the connections of the LIP model with severallaws and characteristics of human brightness perception, namelythe brightness scale inversion, saturation characteristic, Weber'sand Fechner's laws, and the psychophysical contrast notion. Finally,it is shown that the LIP model is a powerful and tractable framework for handling the contrast notion. This is done througha survey of several LIP-model-based contrast estimators associated with special subparts (point, pair of points,boundary, region) of intensity images, that are justified bothfrom a physical and mathematical point of view.     

Non-intrusive Coupling: Recent Advances and Scalable Nonlinear Domain Decomposition

This paper provides a detailed review of the global/local non-intrusive coupling algorithm. Such method allows to alter a global finite element model, without actually modifying its corresponding numerical operator. We also look into improvements of the initial algorithm (Quasi-Newton and dynamic relaxation), and provide comparisons based on several relevant test cases. Innovative examples and advanced applications of the non-intrusive coupling algorithm are provided, granting a handy framework for both researchers and engineers willing to make use of such process. Finally, a novel nonlinear domain decomposition method is derived from the global/local non-intrusive coupling strategy, without the need to use a parallel code or software. Such method being intended to large scale analysis, we show its scalability. Jointly, an efficient high level Message Passing Interface coupling framework is also proposed, granting an universal and flexible way for easy software coupling. A sample code is also given.     

Proteomic Helicobacter pylori biomarkers discriminative of low-grade gastric MALT lymphoma and duodenal ulcer

To date no reliable diagnostic method exists to predict, among the very large and clinically heterogeneous group of Helicobacter pylori‐infected patients, the extremely small group at risk for developing low‐grade gastric MALT lymphoma (LG‐MALT). Search of proteomic biomarkers holds promise for the classification of the H. pylori strains with regard to this severe clinical outcome. In the present study 69 H. pylori strains isolated from patients with two different H. pylori‐associated diseases, duodenal ulcer (DU, n=29) and LG‐MALT (n=40) were used. Protein expression patterns of the strains were analyzed by using the high‐throughput methodology SELDI. Selected proteins were purified by means of chromatographic and electrophoretic methods in view of further sequencing by LC‐MS/MS. Univariate analysis (Mann–Whitney test) of the protein expression patterns generated nine significant biomarkers that can discriminate between H. pylori strains from patients with DU and LG‐MALT. These biomarkers are of low molecular weight, ranging from 6 to 26.6 kDa. Among them, two are overexpressed in LG‐MALT strains and seven – in DU strains. Two biomarker proteins, one overexpressed in LG‐MALT strains (13.2 kDa) and another one – overexpressed in DU strains (26.6 kDa), were purified to homogeneity and identified by using LC‐MS/MS as a 50S ribosomal protein L7/L12 and a urease subunit, respectively. These biomarkers can be included in novel protein arrays for the differential diagnosis of H. pylori‐associated clinical outcomes.     

ERP data sharing framework using the Generic Product Model (GPM)

Nowadays, all product life cycle processes are investigated deeply in order to get an advantage over competitors. To support these processes, several software applications are available. However, this wide range of heterogeneous applications leads to a large variety of hardware and operating systems, data management software, data models, schemas, and data semantics that hinder the information sharing process. To tackle this problem, Hitachi Company has developed a modeling language called Generic Product Model (GPM) in addition to several translators from native formats to GPM for storing, sharing and visualizing product data in a single data warehouse. In order to broaden the range of application data able to be stored and shared using the GPM data warehouse, this paper presents a methodology and a translator that allow management data to be included in a GPM data warehouse. Given the fact that most management data are stored in Enterprise Resource Planning (ERP) software, and that the latter allows extraction of database contents into Excel files, we propose a translator that handles Enterprise Resource Planning management data that are already extracted into Excel Format.The proposed framework enables data management contained in flat Excel Files to be translated into structured GPM data. Translation methodologies are given for the translation of mass production and customized product data through two case studies, one in the computer retail sector and the other in the extrusion machine industry.     

On the decoding of binary cyclic codes with the Newton identities

We revisit in this paper the concept of decoding binary cyclic codes with Gröbner bases. These ideas were first introduced by Cooper, then Chen, Reed, Helleseth and Truong, and eventually by Orsini and Sala. We discuss here another way of putting the decoding problem into equations: the Newton identities. Although these identities have been extensively used for decoding, the work was done manually, to provide formulas for the coefficients of the locator polynomial. This was achieved by Reed, Chen, Truong and others in a long series of papers, for decoding quadratic residue codes, on a case-by-case basis. It is tempting to automate these computations, using elimination theory and Gröbner bases.Thus, we study in this paper the properties of the system defined by the Newton identities, for decoding binary cyclic codes. This is done in two steps, first we prove some facts about the variety associated with this system, then we prove that the ideal itself contains relevant equations for decoding, which lead to formulas.Then we consider the so-called online Gröbner basis decoding, where the work of computing a Gröbner basis is done for each received word. It is much more efficient for practical purposes than preprocessing and substituting into the formulas. Finally, we conclude with some computational results, for codes of interesting length (about one hundred).     

General Adaptive Neighborhood Choquet Image Filtering

A novel framework entitled General Adaptive Neighborhood Image Processing (GANIP) has been recently introduced in order to propose an original image representation and mathematical structure for adaptive image processing and analysis. The central idea is based on the key notion of adaptivity which is simultaneously associated with the analyzing scales, the spatial structures and the intensity values of the image to be addressed. In this paper, the GANIP framework is briefly exposed and particularly studied in the context of Choquet filtering (using fuzzy measures), which generalizes a large class of image filters. The resulting spatially-adaptive operators are studied with respect to the general GANIP framework and illustrated in both the biomedical and materials application areas. In addition, the proposed GAN-based filters are practically applied and compared to several other denoising methods through experiments on image restoration, showing a high performance of the GAN-based Choquet filters.

The memetic self-organizing map approach to the vehicle routing problem

The paper presents an extension of the self- organizing map (SOM) by embedding it into an evolutionary algorithm to solve the Vehicle Routing Problem (VRP). We call it the memetic SOM. The approach is based on the standard SOM algorithm used as a main operator in a population based search. This operator is combined with other derived operators specifically dedicated for greedy insertion moves, a fitness evaluation and a selection operator. The main operators have a similar structure based on the closest point findings and local moves performed in the plane. They can be interpreted as performing parallels and massive insertions, simulating the behavior of agents which interact continuously, having localized and limited abilities. This self-organizing process is intended to allow adaptation to noisy data as well as to confer robustness according to demand fluctuation. Selection is intended to guide the population based search toward useful solution compromises. We show that the approach performs better, with respect to solution quality and/or computation time, than other neural network applications to the VRP presented in the literature. As well, it substantially reduces the gap to classical Operations Research heuristics, specifically on the large VRP instances with time duration constraint.     

Combining ergonomics, culture and scenario for the design of a cooperation platform

Analyzing the way computer technologies are used is crucial for their development. Such analyses make it possible to evaluate these technologies and enhance their evolution. The present article presents some ideas drawn from the development of a cooperation platform for elementary school children (10–11 years old). On the basis of an obvious ergonomic requirement, we worked on two other dimensions: cultural aspects and the teaching scenario. The goal was to set up observation situations and analyze the conversations produced during those situations, in order to understand what using the platform meant to both the pupils and their teachers.