Estimation of nonlinear systems via a Chebyshev approximation approach

This paper proposes to decompose the nonlinear dynamic of a chaotic system with Chebyshev polynomials to improve performances of its estimator. More widely than synchronization of chaotic systems, this algorithm is compared to other nonlinear stochastic estimator such as Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). Chebyshev polynomials orthogonality properties is used to fit a polynomial to a nonlinear function. This polynomial is then used in an Exact Polynomial Kalman Filter (ExPKF) to run real time state estimation. The ExPKF offers mean square error optimality because it can estimate exact statistics of transformed variables through the polynomial function. Analytical expressions of those statistics are derived so as to lower ExPKF algorithm computation complexity and allow real time applications. Simulations under the Additive White Gaussian Noise (AWGN) hypothesis, show relevant performances of this algorithm compared to classical nonlinear estimators.     

Authentication and authorization infrastructure for Grids—issues,technologies, trends and experiences

Authentication and authorization for Grids is a challenging security issue. In this paper, key issues for the establishment of Grid authentication and authorization infrastructures are discussed, and an overview of major Grid authentication and authorization technologies is presented. Related to this, recent developments in Grid authentication and authorization infrastructures suggest adoption of the Shibboleth technology which offers advantages in terms of usability, confidentiality, scalability and manageability. When combined with advanced authorization technologies, Shibboleth-based authentication and authorization infrastructures provide role-based, fine-grained authorization. We share our experience in constructing a Shibboleth-based authentication and authorization infrastructure and believe that such infrastructure provides a promising solution for the security of many application domains.     

Motion study of the hip joint in extreme postures

Many causes can be at the origin of hip osteoarthritis (e.g., cam/pincer impingements), but the exact pathogenesis for idiopathic osteoarthritis has not yet been clearly delineated. The aim of the present work is to analyze the consequences of repetitive extreme hip motion on the labrum cartilage. Our hypothesis is that extreme movements can induce excessive labral deformations and lead to early arthritis. To verify this hypothesis, an optical motion capture system is used to estimate the kinematics of patient-specific hip joint, while soft tissue artifacts are reduced with an effective correction method. Subsequently, a physical simulation system is used during motion to compute accurate labral deformations and to assess the global pressure of the labrum, as well as any local pressure excess that may be physiologically damageable. Results show that peak contact pressures occur at extreme hip flexion/abduction and that the pressure distribution corresponds with radiologically observed damage zones in the labrum.

Automated Proofs of Block Cipher Modes of Operation

We present a Hoare logic for proving semantic security and determining exact security bounds of a block cipher mode of operation. We propose a simple yet expressive programming language to specify encryption modes, semantic functions for each command (statement) in the language, an assertion language that allows to state predicates and axioms, and rules to propagate the predicates through the commands of a program. We also provide heuristics for finding loop invariants that are necessary for the application of our rule on for-loops. This enables us to prove the security of protocols that take arbitrary length messages as input. We implemented a prototype that uses this logic to automatically prove the security of block cipher modes of operation. This prototype can prove the security of many standard modes of operation, such as Cipher Block Chaining (CBC), Cipher FeedBack mode (CFB), Output FeedBack (OFB), and CounTeR mode (CTR).     

Parsing human skeletons in an operating room

Multiple human pose estimation is an important yet challenging problem. In an operating room (OR) environment, the 3D body poses of surgeons and medical staff can provide important clues for surgical workflow analysis. For that purpose, we propose an algorithm for localizing and recovering body poses of multiple human in an OR environment under a multi-camera setup. Our model builds on 3D Pictorial Structures and 2D body part localization across all camera views, using convolutional neural networks (ConvNets). To evaluate our algorithm, we introduce a dataset captured in a real OR environment. Our dataset is unique, challenging and publicly available with annotated ground truths. Our proposed algorithm yields to promising pose estimation results on this dataset.     

Portfolio Management in Double Unknown Situations: Technological Platforms and the Role of Cross‐Application Managers

This article investigates portfolio management in double unknown situations. Double unknown refers to a situation in which the level of uncertainty is high and both technology and markets are as yet unknown. This situation can be an opportunity for new discoveries, creation of new performance solutions and giving direction to portfolio structuring. The literature highlights that the double unknown situation is a prerequisite to designing generic technologies that are able to address many existing and emerging markets and create value across a broad range of applications. The purpose of this paper is to investigate the initial phases of generic technology governance and associated portfolio structuring in multi‐project firms. We studied three empirical contexts of portfolio structuring at the European semiconductor provider STMicroelectronics. The results demonstrate that (1) portfolio management for generic technologies is highly transversal and comprises creating both modules to address market complementarities and the core element of a technological system – the platform, and (2) the design of generic technologies requires ‘cross‐application’ managers who are able to supervise the interactions among innovative concepts developed in different business and research groups and who are responsible for structuring and managing technological and marketing exploration portfolios within the organizational structures of a company.     

Gambling versus Designing: Organizing for the Design of the Probability Space in the Energy Sector

The objective of this paper is to elucidate an organizational process for the design of generic technologies (GTs). While recognizing the success of GTs, the literature on innovation management generally describes their design according to evolutionary strategies featuring multiple and uncertain trials, resulting in the discovery of common features among multiple applications. This random walk depends on multiple market and technological uncertainties that are considered exogenous: as smart as he can be, the ‘gambler’ must play in a given probability space. However, what happens when the innovator is not a gambler but a designer, i.e., when the actor is able to establish new links between previously independent emerging markets and technologies? Formally speaking, the actor designs a new probability space. Building on a case study of two technological development programmes at the French Center for Atomic Energy, we present cases of GTs that correspond to this logic of designing the probability space, i.e. the logic of intentionally designing common features that bridge the gap between a priori heterogeneous applications and technologies. This study provides another example showing that the usual trial‐and‐learning strategy is not the only strategy to design GTs and that these technologies can be designed by intentionally building new interdependences between markets and technologies. Our main result is that building these interdependences requires organizational patterns that correspond to a ‘design of exploration’ phase in which multiple technology suppliers and application providers are involved in designing both the probability space itself and the instruments to explore and benefit from this new space.     

Graphical models for optimal power flow

Optimal power flow (OPF) is the central optimization problem in electric power grids. Although solved routinely in the course of power grid operations, it is known to be strongly NP-hard in general, and weakly NP-hard over tree networks. In this paper, we formulate the optimal power flow problem over tree networks as an inference problem over a tree-structured graphical model where the nodal variables are low-dimensional vectors. We adapt the standard dynamic programming algorithm for inference over a tree-structured graphical model to the OPF problem. Combining this with an interval discretization of the nodal variables, we develop an approximation algorithm for the OPF problem. Further, we use techniques from constraint programming (CP) to perform interval computations and adaptive bound propagation to obtain practically efficient algorithms. Compared to previous algorithms that solve OPF with optimality guarantees using convex relaxations, our approach is able to work for arbitrary tree-structured distribution networks and handle mixed-integer optimization problems. Further, it can be implemented in a distributed message-passing fashion that is scalable and is suitable for “smart grid” applications like control of distributed energy resources. Numerical evaluations on several benchmark networks show that practical OPF problems can be solved effectively using this approach.