Flying over the reality gap: From simulated to real indoor airships

Because of their ability to naturally float in the air, indoor airships (often called blimps) constitute an appealing platform for research in aerial robotics. However, when confronted to long lasting experiments such as those involving learning or evolutionary techniques, blimps present the disadvantage that they cannot be linked to external power sources and tend to have little mechanical resistance due to their low weight budget. One solution to this problem is to use a realistic flight simulator, which can also significantly reduce experimental duration by running faster than real time. This requires an efficient physical dynamic modelling and parameter identification procedure, which are complicated to develop and usually rely on costly facilities such as wind tunnels. In this paper, we present a simple and efficient physics-based dynamic modelling of indoor airships including a pragmatic methodology for parameter identification without the need for complex or costly test facilities. Our approach is tested with an existing blimp in a vision-based navigation task. Neuronal controllers are evolved in simulation to map visual input into motor commands in order to steer the flying robot forward as fast as possible while avoiding collisions. After evolution, the best individuals are successfully transferred to the physical blimp, which experimentally demonstrates the efficiency of the proposed approach. Jean-Christophe Zufferey and Alexis Guanella contributed equally to this work.     

Doubly-striped disk mirroring: Reliable storage for video servers

Disk striping is essential in Video-On-Demand servers to allow concurrent access for a large number of users to the same copy of a video object. On the other hand, disk striping makes the system more vulnerable to component failures. We propose a replication scheme for video content and an associated I/O scheduling algorithm that enables the system to tolerate a component failure. The scheme, called doubly-striped mirrors, compares favorably to other approaches for implementing redundancy such as RAID in terms of cost, bandwidth efficiency, buffer usage and system availability.     

Timed substitutions for regular signal-event languages

In the classical framework of formal languages, a refinement operation is modeled by a substitution and an abstraction by an inverse substitution. These mechanisms have been widely studied, because they describe a change in the specification level, from an abstract view to a more concrete one, or conversely. For timed systems, there is up to now no uniform notion of substitution. In this paper, we study timed substitutions in the general framework of signal-event languages, where both signals and events are taken into account. We prove that regular signal-event languages are closed under substitution and inverse substitution. To obtain these results, we use in a crucial way a “well known” result: regular signal-event languages are closed under intersection. In fact, while this result is indeed easy for languages defined by Alur and Dill’s timed automata, it turns out that the construction is much more tricky when considering the most involved model of signal-event automata. We give here a construction working on finite and infinite signal-event words and taking into account signal stuttering, unobservability of zero-duration τ-signals and Zeno runs. Note that if several constructions have been proposed in particular cases, it is the first time that a general construction is provided.     

Approximate bisimulation relations for constrained linear systems

In this paper, we define the notion of approximate bisimulation relation between two continuous systems. While exact bisimulation requires that the observations of two systems are and remain identical, approximate bisimulation allows the observations to be different provided the distance between them remains bounded by some parameter called precision. Approximate bisimulation relations are conveniently defined as level sets of a so-called bisimulation function which can be characterized using Lyapunov-like differential inequalities. For a class of constrained linear systems, we develop computationally effective characterizations of bisimulation functions that can be interpreted in terms of linear matrix inequalities and optimal values of static games. We derive a method to evaluate the precision of the approximate bisimulation relation between a constrained linear system and its projection. This method has been implemented in a Matlab toolbox: MATISSE. An example of use of the toolbox in the context of safety verification is shown.     

Response inhibition deficit is involved in poor decision making under risk in nonamnesic individuals with alcoholism.

Individuals with alcoholism exhibit poor decision making as reflected by their continued alcohol use despite encountering problems and by low performance in laboratory tasks of decision making. Here, the authors investigated the relative contribution of several distinct processes of executive functions in performance on the Iowa Gambling Task (IGT; A. Bechara, A. R. Damasio, H. Damasio, & S. W. Anderson, 1994) in recently detoxified individuals with alcoholism. Compared to matched healthy participants, individuals with alcoholism showed below-normal scores in the last 20 trials of the IGT as well as on other tasks of executive functions, specifically those assessing the capacity to manipulate information stored in working memory, detect abstract rules, or inhibit prepotent responses. Prepotent response inhibition best predicted performance in the late trials of the IGT, that is, when participants have likely acquired knowledge about the reward/punishment contingencies of the task. These results underline the important role that response inhibition plays in decision making, especially in risky situations, when knowledge of the probability of a given outcome becomes available (i.e. decisions under risk). (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Embedded holonic fault diagnosis of complex transportation systems

The use of electronic equipment and embedded computing technologies in modern complex transportation systems continues to grow in a highly competitive market, in which product maintainability and availability is vital. These technological advances also make fault diagnosis and maintenance interventions much more challenging, since these operations require a deep understanding of the entire system. This paper proposes a holonic cooperative fault diagnosis approach, along with a generic architecture, to increase the embedded diagnosis capabilities of complex transportation systems. This concept is applied to the fault diagnosis of door systems of a railway transportation system.     

Quantised control of nonlinear systems: analysis of robustness to parameter uncertainty,measurement errors,and exogenous disturbances

We propose a variant of the recently introduced strategy for stabilisation with limited information by D. Liberzon and J.P. Hespanha and analyse its robustness properties. We show that, if the nominal plant can be made input-to-state stable with respect to measurement errors, parameter uncertainty and exogenous disturbances, then this robustness is preserved with this quantised feedback. More precisely, if a sufficient bandwidth is available on the communication network, then the resulting closed loop is shown to be semiglobally input-to-state practically stable.     

Energy Aware Signal Processing for Software Defined Radio Baseband Implementation

The fast pacing diversity and evolution of wireless communications require a wide variety of baseband implementations within a short time-to-market. Besides, the exponentially increased design complexity and design cost of deep sub-micron silicon highly desire the designs to be reused as much as possible. This yields an increasing demand for reconfigurable/ programmable baseband solutions. Implementing all baseband functionalities on programmable architectures, as foreseen in the tier-2 SDR, will become necessary in the future. However, the energy efficiency of SDR baseband platforms is a major concern. This brings a challenging gap that is continuously broadened by the exploding baseband complexity. We advocate a system level approach to bridge the gap. Specifically, we fully leverage the advantages (programmability) of SDR platforms to compensate its disadvantages (energy efficiency). Highly flexible and dynamic baseband signal processing algorithms are designed and implemented to exploit the abundant dynamics in the environment and the user requirement. Instead of always performing the best effort, the baseband can dynamically and autonomously adjust its work load to optimize the average energy consumption. In this paper, we will introduce such baseband signal processing techniques optimized for SDR implementations. The methodology and design steps will be presented together with 3 representative case studies in HSDPA, WiMAX and 3GPP LTE.     

A comparison analysis of prefiltering methods in space-time trellis-based reduced-state turbo-detection

In this paper, we investigate two different prefiltering strategies, i.e., multiple input multiple output (MIMO) channel shortening and MIMO whitened matched filtering, to decode iteratively a space time bit interleaved coded modulation transmitted over MIMO block fading intersymbol interference channels using a trellis-based post-detector. Indeed, resorting to such front-ends allows significant complexity gain when compared to the optimal maximum a posteriori-based turbo-receiver. While those two approaches have been exhaustively visited in the past, their fair comparison in terms of trade-off between complexity and performance has – to our knowledge – never been tackled before. This paper aims at giving some valuable insights about the respective benefits of these two prefiltering methods with respect to the channel characteristics and overall receiver complexity.