Features modeling with an α-stable distribution: Application to pattern recognition based on continuous belief functions

The aim of this paper is to show the interest in fitting features with an α-stable distribution to classify imperfect data. The supervised pattern recognition is thus based on the theory of continuous belief functions, which is a way to consider imprecision and uncertainty of data. The distributions of features are supposed to be unimodal and estimated by a single Gaussian and α-stable model. Experimental results are first obtained from synthetic data by combining two features of one dimension and by considering a vector of two features. Mass functions are calculated from plausibility functions by using the generalized Bayes theorem. The same study is applied to the automatic classification of three types of sea floor (rock, silt and sand) with features acquired by a mono-beam echo-sounder. We evaluate the quality of the α-stable model and the Gaussian model by analyzing qualitative results, using a Kolmogorov–Smirnov test (K–S test), and quantitative results with classification rates. The performances of the belief classifier are compared with a Bayesian approach.     

Learning hierarchical probabilistic logic programs

Machine Learning - Probabilistic logic programming (PLP) combines logic programs and probabilities. Due to its expressiveness and simplicity, it has been considered as a powerful tool for learning...     

Simulated annealing-based fitting of CAD models to point clouds of mechanical parts’ assemblies

Engineering with Computers - This paper introduces a new fitting approach to allow an efficient part-by-part reconstruction or update of editable CAD models fitting the point cloud of a digitized...     

Multi-heuristic scheduling: three approaches to tune compromises

Most of the available industrial schedulers are based on a simulation approach using dispatching rules. These rules are often dedicated to the satisfaction of a single performance criterion, and are used whatever the characteristics of the workshop or of the set of jobs. An approach which allows one to bring in compromises between rules is set out in this paper. These compromises can be parametered in accordance with the objectives of the workshop and the characteristics of the jobs in order to introduce some reactivity in the decision system. Three ways to set up the parameters are compared: experimental design, fuzzy expert system and neural network. The method allowing one to define compromises can be implemented on each scheduler that uses a simulation approach. Tests have been made with an industrial scheduler called SIPAPLUS, the results of which are developed in this paper.     

A CP framework for testing CP

The success of several constraint-based modeling languages such as OPL, ZINC or COMET, appeals for better software engineering practices, particularly in the testing phase. This paper introduces a testing framework enabling automated test case generation for constraint programming. We propose a general framework of constraint program development which supposes that a first declarative and simple constraint model is available from the problem specifications analysis. Then, this model is refined using classical techniques such as constraint reformulation, surrogate, redundant, implied, global constraint and symmetry-breaking to form an improved constraint model that must be thoroughly tested before being used to address real-sized problems. We think that most of the faults are introduced in this refinement step and propose a process which takes the first declarative model as an oracle for detecting non-conformities and derive practical test purposes from this process. We implemented this approach in a new tool called CPTEST that was used to automatically detect non-conformities on classical benchmark programs, such as the Golomb rulers, n-queens, social golfer and the car-sequencing problems.     

Adaptive IPTV services based on a novel IP Multimedia Subsystem

Heterogeneous communication devices are emerging and changing the way of communication. Innovative multimedia applications are now accessible through these embedded systems. The 3GPP IP Multimedia Subsystem (IMS) provides a basic architecture framework for the Next Generation Network (NGN) supporting the convergence platform for service provisioning in heterogeneous networks. ETSI TISPAN standardization effort focuses on delivering IPTV services on such platform. Nevertheless, IPTV on IMS standardization suffers from a lack of efficient user-centric network management mechanisms as the end-user may consume IPTV service from different access networks, on different mobile devices, at anytime. User’s Perceived Quality of Service (PQoS) or Quality of Experience (QoE) of IPTV service may also suffer from wireless access network impairments. This paper introduces new functionalities in IPTV over IMS architecture which optimize satisfaction of the end-user and resource utilization of the operator’s networks. A context-sensitive User Profile (UP) model is used to deliver IPTV streams adapted to the user’s environment. In order to optimize the operator network usage, the impact of spatiotemporal dynamics of the video content on the deduced perceptual quality is considered. A Multimedia Content Management System (MCMS) is proposed to perform dynamic cross-layer adaptation of the IPTV stream based on PQoS measurements at the end-user side.     

A code tagging approach to software product line development

Software product line engineering seeks to systematise reuse when developing families of similar software systems so as to minimise development time, cost and defects. To realise variability at the code level, product line methods classically advocate usage of inheritance, components, frameworks, aspects or generative techniques. However, these might require unaffordable paradigm shifts for developers if the software was not thought at the outset as a product line. Furthermore, these techniques can be conflicting with a company’s coding practices or external regulations. These concerns were the motivation for the industry–university collaboration described in this paper in which we developed a minimally intrusive coding technique based on tags. The approach was complemented with traceability from code to feature diagrams which were exploited for automated configuration. It is supported by a toolchain and is now in use in the partner company for the development of flight-grade satellite communication software libraries.     

The MORPHEUS Heterogeneous Dynamically Reconfigurable Platform

Reconfigurable computing offers a wide range of low cost and efficient solutions for embedded systems. The proper choice of the reconfigurable device, the granularity of its processing elements and its memory architecture highly depend on the type of application and their data flow. Existing solutions either offer fine grain FPGAs, which rely on a hardware synthesis flow and offer the maximum degree of flexibility, or coarser grain solutions, which are usually more suitable for a particular type of data flow and applications. In this paper, we present the MORPHEUS architecture, a versatile reconfigurable heterogeneous System-on-Chip targeting streaming applications. The presented architecture exploits different reconfigurable technologies at several computation granularities that efficiently address the different applications needs. In order to efficiently exploit the presented architecture, we implemented a complete software solution to map C applications to the reconfigurable architecture. In this paper, we describe the complete toolset and provide concrete use cases of the architecture.     

Optimisation of extraction of phenolics and antioxidants from black currant leaves and buds and of stability during storage

Health benefits of a diet rich in fruits and vegetables are attributed in part to their contents of phenolics and other antioxidant compounds. In this research, the extraction of phenolics and antioxidant compounds from black currant was optimised for different plant organs. The extraction solvent affected yield: aqueous acetone was better than methanol and acetate or glycine buffer. In aqueous buffer, maximum yields of total phenolics and antioxidant activities were obtained at pH 3. Extraction from lyophilised materials yielded extracts with higher phenolic contents and antioxidant activities.     

Desorption of large molecules with light-element clusters: Effects of cluster size and substrate nature

This contribution focuses on the conditions required to desorb a large hydrocarbon molecule using light-element clusters. The test molecule is a 7.5 kDa coil of polystyrene (PS61). Several projectiles are compared, from C₆₀ to 110 kDa organic droplets and two substrates are used, amorphous polyethylene and mono-crystalline gold. Different aiming points and incidence angles are examined. Under specific conditions, 10 keV nanodrops can desorb PS61 intact from a gold substrate and from a soft polyethylene substrate. The prevalent mechanism for the desorption of intact and ‘cold’ molecules is one in which the molecules are washed away by the projectile constituents and entrained in their flux, with an emission angle close to ∼70°. The effects of the different parameters on the dynamics and the underlying physics are discussed in detail and the predictions of the model are compared with other published studies.