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Saint-Etienne, France

Clackdoyle R.,CNRS Hubert Curien Laboratory
IEEE Transactions on Nuclear Science | Year: 2013

In medical imaging, a fanbeam projection refers to a collection of line integrals of some two-dimensional function, for lines converging on a point known as the source point for X-ray applications. In this work, necessary and sufficient consistency conditions (also known as range conditions) are given for fanbeam projections when the source trajectory follows an infinite straight line. The conditions are specified for both angular and linear parameterizations of the projection rays. In both cases, integrals of the fanbeam projections multiplied by a certain function will be a polynomial in the trajectory variable, so these consistency conditions can be considered true analogs of the well-known Helgason-Ludwig range conditions for parallel projections. © 1963-2012 IEEE.

Bossuet L.,CNRS Hubert Curien Laboratory
Sustainable Computing: Informatics and Systems | Year: 2014

The electronics industry today is not yet green and/or sustainable. Indeed, the microelectronics industry is a consumer of primary materials, chemical products, water and energy. The manufacture of electronic products and their disposal at the end of their lives results in large quantities of waste products of varying degrees of toxicity that are difficult to deal with. Due to their high replacement rate, the lifespan of electronic products is spectacularly short. To reduce the environmental impact of electronic products the usual reduce-reuse-recycle (3R) trilogy appears to be insufficient. To achieve the objective of sustainable electronics, in this paper we suggest adding a fourth R for reconfigure. We recommend the use of the reconfiguration capacities of reconfigurable circuits such as FPGAs to reduce the functional obsolescence of electronic products by updating hardware. This paper is a survey of the sustainability of microelectronic. It presents some examples of pioneer works to illustrate the architecture of sustainable reconfigurable computing systems. © 2014 Elsevier Inc. All rights reserved.

Fischer V.,CNRS Hubert Curien Laboratory
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2012

The issue of random number generation is crucial for the implementation of cryptographic systems. Random numbers are often used in key generation processes, authentication protocols, zeroknowledge protocols, padding, in many digital signature and encryption schemes, and even in some side channel attack countermeasures. For these applications, security depends to a great extent on the quality of the source of randomness and on the way this source is exploited. The quality of the generated numbers is checked by statistical tests. In addition to the good statistical properties of the obtained numbers, the output of the generator used in cryptography must be unpredictable. Besides quality and unpredictability requirements, the generator must be robust against aging effects and intentional or unintentional environmental variations, such as temperature, power supply, electromagnetic emanations, etc. In this paper, we discuss practical aspects of a true random number generator design. Special attention is given to the analysis of security requirements and on the way how this requirements can be met in practice. © 2012 Springer-Verlag.

Combes C.,CNRS Hubert Curien Laboratory | Azema J.,Jean Monnet University
Decision Support Systems | Year: 2013

The aim of this paper is to find feature-patterns related to the autonomy-disability level of elderly people living in nursing homes. These levels correspond to profiles based on the people's ability to perform activities of daily living like being able to wash, dress and move. To achieve this aim, an unsupervised approach is used. In this article, we propose a new clustering approach based on principal component analysis (PCA) to better approximate clusters. We want to automatically find categories or groups of residents based on their degree of autonomy-disability. All residents in a group have similar patterns. The main function of PCA is to explore the links between variables and the similarities between examples (individuals). The proposed algorithm uses the PCA technique to direct the determination of the clusters with self-organizing partitions by using the Euclidian distance. The study was carried out in close collaboration with the French cooperative health organization called the "Mutualité Française de la Loire". The quantitative data arises from the databases of four different nursing homes located in the city of Saint-Etienne in France. The study concerns 2271 observations of dependence evaluations corresponding to 628 residents. © 2012 Elsevier B.V.

Fernando B.,Catholic University of Leuven | Fromont E.,CNRS Hubert Curien Laboratory | Tuytelaars T.,Catholic University of Leuven
International Journal of Computer Vision | Year: 2014

Mid-level or semi-local features learnt using class-level information are potentially more distinctive than the traditional low-level local features constructed in a purely bottom-up fashion. At the same time they preserve some of the robustness properties with respect to occlusions and image clutter. In this paper we propose a new and effective scheme for extracting mid-level features for image classification, based on relevant pattern mining. In particular, we mine relevant patterns of local compositions of densely sampled low-level features. We refer to the new set of obtained patterns as Frequent Local Histograms or FLHs. During this process, we pay special attention to keeping all the local histogram information and to selecting the most relevant reduced set of FLH patterns for classification. The careful choice of the visual primitives and an extension to exploit both local and global spatial information allow us to build powerful bag-of-FLH-based image representations. We show that these bag-of-FLHs are more discriminative than traditional bag-of-words and yield state-of-the-art results on various image classification benchmarks, including Pascal VOC. © 2014 Springer Science+Business Media New York.

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