Stochastic Cellular Automata Solutions to the Density Classification Problem

In the density classification problem, a binary cellular automaton (CA) should decide whether an initial configuration contains more 0s or more 1s. The answer is given when all cells of the CA agree on a given state. This problem is known for having no exact solution in the case of binary deterministic one-dimensional CA. We investigate how randomness in CA may help us solve the problem. We analyse the behaviour of stochastic CA rules that perform the density classification task. We show that describing stochastic rules as a “blend” of deterministic rules allows us to derive quantitative results on the classification time and the classification time of previously studied rules. We introduce a new rule whose effect is to spread defects and to wash them out. This stochastic rule solves the problem with an arbitrary precision, that is, its quality of classification can be made arbitrarily high, though at the price of an increase of the convergence time. We experimentally demonstrate that this rule exhibits good scaling properties and that it attains qualities of classification never reached so far.     

Samplinghelper a web-based tool to assess the reliability of sampling strategies in sewers and receiving waters

Sampling is a key step in the analysis of chemical compounds. It is particularly important in the environmental field, for example for wastewater effluents, wet-weather discharges or streams in which the flows and concentrations vary greatly over time. In contrast to the improvements that have occurred in analytical measurement, developments in the field of sampling are less active. However, sampling errors may exceed by an order of magnitude those related to analytical processes. We proposed an Internet-based application based on a sampling theory to identify and quantify the errors in the process of taking samples. This general theory of sampling, already applied to different areas, helps to answer questions related to the number of samples, their volume, their representativeness, etc. The use of the internet to host this application facilitates use of theoretical tools and raise awareness of the uncertainties related to sampling. An example is presented, which highlights the importance of the sampling step in the quality of analytical results.     

Flow profile measurement in microchannel using the optical feedback interferometry sensing technique

The need to accurately measure flow profiles in microfluidic channels is well recognised. In this work, we present a new optical feedback interferometry (OFI) flow sensor that accurately measures local velocity in fluids and enables reconstruction of a velocity profile inside a microchannel. OFI is a self-aligned interferometric technique that uses the laser as both the transmitter and the receiver thus offering high sensitivity, fast response, and a simple and compact optical design. The system described here is based on a commercial semiconductor laser and has been designed to achieve a micrometer-range spatial resolution. The sensor performance was validated by reconstructing the velocity profile inside a circular cross-section flow-channel with 320  $\upmu $ m internal diameter, with a relative error smaller than 1.8 %. The local flow velocity is directly measured, thus avoiding the need for model based profile calculation and uncertainties inherent to this approach. The system was validated by successfully extracting the flow profiles in both Newtonian and shear-thinning liquids.     

Optical flow and advection on 2-Riemannian manifolds: a common framework

Dynamic pattern analysis and motion extraction can be efficiently addressed using optical flow techniques. This article presents a generalization of these questions to non-flat surfaces, where optical flow is tackled through the problem of evolution processes on non-Euclidian domains. The classical equations of optical flow in the Euclidian case are transposed to the theoretical framework of differential geometry. We adopt this formulation for the regularized optical flow problem, prove its mathematical well-posedness and combine it with the advection equation. The optical flow and advection problems are dual: a motion field may be retrieved from some scalar evolution using optical flow; conversely, a scalar field may be deduced from a velocity field using advection. These principles are illustrated with qualitative and quantitative evaluations from numerical simulations bridging both approaches. The proof-of-concept is further demonstrated with preliminary results from time-resolved functional brain imaging data, where organized propagations of cortical activation patterns are evidenced using our approach.     

A review of smart homes- present state and future challenges

In the era of information technology, the elderly and disabled can be monitored with numerous intelligent devices. Sensors can be implanted into their home for continuous mobility assistance and non-obtrusive disease prevention. Modern sensor-embedded houses, or smart houses, cannot only assist people with reduced physical functions but help resolve the social isolation they face. They are capable of providing assistance without limiting or disturbing the resident's daily routine, giving him or her greater comfort, pleasure, and well-being. This article presents an international selection of leading smart home projects, as well as the associated technologies of wearable/implantable monitoring systems and assistive robotics. The latter are often designed as components of the larger smart home environment. The paper will conclude by discussing future challenges of the domain.     

Fiber-optic array algebraic processing architectures

A high-accuracy fiber-optic array processor (FOAP) based on the algorithm of digital multiplication by analog convolution is proposed. The FOAP architecture is a local regularly interconnected processor that utilizes an array of identical all-optical elemental-processing lattice units, namely, an optical splitter, an optical combiner, and a binary programmable fiber-optic transversal filter. Various FOAP matrix multipliers are proposed for nonnegative and twos-complement binary arithmetic matrix-vector, matrix-matrix, triple-matrix, and high-order matrix operations. The overall performances of the FOAP matrix multipliers are compared with the time-integrating and space-integrating architectures and with the digital multipliers. Extension of the digital-multiplication-by-analog-convolution algorithm is also considered.     

Yttria-stabilized hafnia-zirconia thermal barrier coatings: The influence of hafnia addition on TBC structure and high-temperature behaviour

The search for more reliable and durable thermal barrier systems is a key factor for future aircraft turbine engines success. Hafnia is therefore an attractive ceramic component due to its similarity to zirconia and its elevated structural transformation temperatures. We report here structural and thermomechanical investigations of various plasma-sprayed coatings composed of ZrO₂+x mol% HfO₂ (x=0, 25, 50 and 100), partially stabilized by 4.53 mol% yttria. X-ray diffraction studies show that, a metastable, non-transformable, high yttrium content, tetragonal solid solution is the only phase observed on the as-sprayed samples. This phase is crystallographically equivalent to the t phase described for classical yttrium-partially stabilized zirconia (Y-PSZ) thermal barrier coatings (TBCs). Upon high-temperature annealing in air (T=1200C), however, the return of this t phase to equilibrium differs from the classical tt+c reaction. According to literature data, reactions of the type tt+c+m should prevail at the highest hafnia contents (x50). Indeed, important quantities of monoclinic phase are accordingly being observed upon cooling. Thermal cycling of TBC samples in air has been performed at 1100C. The Young's modulus of the ceramic coating, which progressively increases when hafnia is substituted for zirconia, has a strong influence on TBC thermomechanical resistance.