Modular architecture of the microfactories for automatic micro-assembly

The construction of a new generation of MEMS which includes micro-assembly steps in the current microfabrication process is a big challenge. It is necessary to develop new production means named micromanufacturing systems in order to perform these new assembly steps. The classical approach called “top-down” which consists in a functional analysis and a definition of the tasks sequences is insufficient for micromanufacturing systems. Indeed, the technical and physical constraints of the microworld (e.g. the adhesion phenomenon) must be taken into account in order to design reliable micromanufacturing systems. A new method of designing micromanufacturing systems is presented in this paper. Our approach combines the general “top-down” approach with a “bottom-up” approach which takes into account technical constraints. The method enables to build a modular architecture for micromanufacturing systems. In order to obtain this modular architecture, we have devised an original identification technique of modules and an association technique of modules. This work has been used to design the controller of an experimental robotic micro-assembly station.     

Principles of adjustable autonomy: a framework for resilient human–machine cooperation

Unmanned ground vehicles tend to be more and more autonomous, but both complete teleoperation and full autonomy are not efficient enough to deal with all possible situations. To be efficient, the human–robot system must be able to anticipate, react and recover from errors of different kinds, i.e., to be resilient. From this observation, this paper proposes a survey on the resilience of a human–machine system and the means to control the resilience. The resilience of a system can be defined as the ability to maintain or recover a stable state when subject to disturbance. Adjustable autonomy and human–machine cooperation are considered as means of resilience for the system. This paper then proposes three indicators to assess different meanings of resilience of the system: foresight and avoidance of events, reaction to events and recovery from occurrence of events. The third of these metrics takes into consideration the concept of affordances that allows a common representation for the opportunities of action between the automated system and its environment.     

Robust anti-sliding control of autonomous vehicles in presence of lateral disturbances

Path following control problem of autonomous vehicles is investigated, concerning both unmeasurable sliding effects and lateral disturbances which lead to some difficulties in designing autonomous control under complex environment. To deal with the sliding effects, sideslip angles are modeled and reconstructed by estimating the tire cornering stiffness, which plays important role in analyzing the sliding effects. To this end, a Luenberger-type observer is designed, which is able to identify the tire cornering stiffness adaptively even in presence of time-varying lateral disturbances. Furthermore, to guarantee high-precision guidance, a sliding mode controller is designed based on chained system theory, and this controller is shown to be robust to both the lateral disturbances and the inaccuracy of the sliding reconstruction. Simulations illustrate that the proposed methods can reconstruct the sliding angles and provide high-accuracy anti-sliding control even in presence of the time-varying lateral disturbances.     

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.     

Visual quality recognition of nonwovens using generalized Gaussian density model and robust Bayesian neural network

This work is dedicated to develop an algorithm for the visual quality recognition of nonwoven materials, in which image analysis and neural network are involved in feature extraction and pattern recognition stage, respectively. During the feature extraction stage, each image is decomposed into four levels using the 9-7 bi-orthogonal wavelet base. Then the wavelet coefficients in each subband are independently modeled by the generalized Gaussian density (GGD) model to calculate the scale and shape parameters with maximum likelihood (ML) estimator as texture features. While for the recognition stage, the robust Bayesian neural network is employed to classify the 625 nonwoven samples into five visual quality grades, i.e., 125 samples for each grade. Finally, we carry out the outlier detection of the training set using the outlier probability and select the most suitable model structure and parameters from 40 Bayesian neural networks using the Occam's razor. When 18 relevant textural features are extracted for each sample based on the GGD model, the average recognition accuracy of the test set arranges from 88% to 98.4% according to the different number of the hidden neurons in the Bayesian neural network.     

Estimating the altitude of aerosol plumes over the ocean from reflectance ratio measurements in the O2 A-band

A methodology is proposed to infer the altitude of aerosol plumes over the ocean from reflectance ratio measurements in the O₂ absorption A-band (759 to 770 nm). The reflectance ratio is defined as the ratio of the reflectance in a first spectral band, strongly attenuated by O₂ absorption, and the reflectance in a second spectral band, minimally attenuated. For a given surface reflectance, simple relations are established between the reflectance ratio and the altitude of an aerosol layer, as a function of atmospheric conditions and the geometry of observation. The expected accuracy for various aerosol loadings and models is first quantified using an accurate, high spectral resolution, radiative transfer model that fully accounts for interactions between scattering and absorption. The method is developed for POLDER and MERIS, satellite sensors with adequate spectral characteristics. The simulations show that the method is only accurate over dark surfaces when aerosol optical thickness at 765 nm is relatively large (> 0.3). In this case, the expected accuracy is on the order of ± 0.5 km or ± 0.2 km for POLDER or MERIS respectively. More accurate estimates are obtained with MERIS, since in this case the spectral reflectance ratio is more sensitive to aerosol altitude. However, a precise spectral calibration is needed for MERIS. The methodology is applied to MERIS and POLDER imagery acquired over marine surfaces. The estimated aerosol altitude is compared with in situ lidar profiles of backscattering coefficient measured during the AOPEX-2004 experiment for MERIS, or obtained with the space-borne lidar CALIOP for POLDER. The retrieved altitudes agree with lidar measurements in a manner consistent with theory. These comparisons demonstrate the potential of the differential absorption methodology for obtaining information on aerosol altitude over dark surfaces.     

Platform‐independent profiling in a virtual execution environment

Virtual execution environments, such as the Java virtual machine, promote platform‐independent software development. However, when it comes to analyzing algorithm complexity and performance bottlenecks, available tools focus on platform‐specific metrics, such as the CPU time consumption on a particular system. Other drawbacks of many prevailing profiling tools are high overhead, significant measurement perturbation, as well as reduced portability of profiling tools, which are often implemented in platform‐dependent native code. This article presents a novel profiling approach, which is entirely based on program transformation techniques, in order to build a profiling data structure that provides calling‐context‐sensitive program execution statistics. We explore the use of platform‐independent profiling metrics in order to make the instrumentation entirely portable and to generate reproducible profiles. We implemented these ideas within a Java‐based profiling tool called JP. A significant novelty is that this tool achieves complete bytecode coverage by statically instrumenting the core runtime libraries and dynamically instrumenting the rest of the code. JP provides a small and flexible API to write customized profiling agents in pure Java, which are periodically activated to process the collected profiling information. Performance measurements point out that, despite the presence of dynamic instrumentation, JP causes significantly less overhead than a prevailing tool for the profiling of Java code. Copyright © 2008 John Wiley & Sons, Ltd.     

Proofs of randomized algorithms in Coq

Randomized algorithms are widely used for finding efficiently approximated solutions to complex problems, for instance primality testing and for obtaining good average behavior. Proving properties of such algorithms requires subtle reasoning both on algorithmic and probabilistic aspects of programs. Thus, providing tools for the mechanization of reasoning is an important issue. This paper presents a new method for proving properties of randomized algorithms in a proof assistant based on higher-order logic. It is based on the monadic interpretation of randomized programs as probabilistic distributions (Giry, Ramsey and Pfeffer). It does not require the definition of an operational semantics for the language nor the development of a complex formalization of measure theory. Instead it uses functional and algebraic properties of unit interval. Using this model, we show the validity of general rules for estimating the probability for a randomized algorithm to satisfy specified properties. This approach addresses only discrete distributions and gives rules for analyzing general recursive functions.We apply this theory to the formal proof of a program implementing a Bernoulli distribution from a coin flip and to the (partial) termination of several programs. All the theories and results presented in this paper have been fully formalized and proved in the Coq proof assistant.     

Deblurring by Matching

Restoration of the photographs damaged by the camera shake is a challenging task that manifested increasing attention in the recent period. Despite of the important progress of the blind deconvolution techniques, due to the ill-posed nature of the problem, the finest details of the kernel blur cannot be recovered entirely. Moreover, the additional constraints and prior assumptions make these approaches to be relative limited.
In this paper we introduce a novel technique that removes the undesired blur artifacts from photographs taken by hand-held digital cameras. Our approach is based on the observation that in general several consecutive photographs taken by the users share image regions that project the same scene content. Therefore, we took advantage of additional sharp photographs of the same scene. Based on several invariant local feature points, filtered from the given blurred/non-blurred images, our approach matches the keypoints and estimates the blur kernel using additional statistical constraints.
We also present a simple deconvolution technique that preserves edges while minimizing the ringing artifacts in the restored latent image. The experimental results prove that our technique is able to infer accurately the blur kernel while reducing significantly the artifacts of the spoilt images.     

Parallel Lattice Boltzmann Method with Blocked Partitioning

This paper presents and discusses a blocked parallel implementation of bi- and three-dimensional versions of the Lattice Boltzmann Method. This method is used to represent and simulate fluid flows following a mesoscopic approach. Most traditional parallel implementations use simple data distribution strategies to parallelize the operations on the regular fluid data set. However, it is well known that block partitioning is usually better. Such a parallel implementation is discussed and its communication cost is established. Fluid flows simulations crossing a cavity have also been used as a real-world case study to evaluate our implementation. The presented results with our blocked implementation achieve a performance up to 31% better than non-blocked versions, for some data distributions. Thus, this work shows that blocked, parallel implementations can be efficiently used to reduce the parallel execution time of the method.