Output feedback of Markov jump linear systems with no mode observation: An automotive throttle application

The note presents an output feedback control strategy for Markov jump linear systems with no mode observation. Based on minimizing a finite‐time quadratic cost, we derive an algorithm that generates output feedback gains that satisfy a necessary optimality condition. These gains can be computed off‐line relying only on the initial condition of the system. This result expands a previous one from the literature that considered state‐feedback only. To illustrate the usefulness of the approach, real‐time laboratory experiments were performed to control an automotive electronic throttle valve subject to Markov‐driven voltage fluctuations. Copyright © 2015 John Wiley & Sons, Ltd.     

Learning Temperature Dynamics on Agar-Based Phantom Tissue Surface During Single Point CO $$_2$$ 2 Laser Exposure

Neural Processing Letters - This paper presents an approach to determine a model of superficial tissue temperature dynamics during continuous wave CO $$_2$$ laser irradiation. The main contribution...     

A selective conductive polymer-based sensor for volatile halogenated organic compounds (VHOC)

A novel poly(p-xylylene), PPX, derivative bearing alkoxyphenyl side groups was electrochemically synthesized in 87% yield. The polymer, poly(4′-hexyloxy-2,5-biphenyleneethylene) (PHBPE), presented a fraction (92%) soluble in common organic solvents. It showed to be thermally resistant up to 185 °C. UV–vis analysis revealed an E_gap of 3.5 eV. Gas sensors made from thin films of 10-camphorsulfonic acid-doped PHBPE deposited on interdigitated electrodes exhibited significant changes in electrical conductance upon exposure to five VHOCs: 1,2-dichloroethane, bromochloromethane, trichloromethane, dichloromethane and tetrachloromethane. The conductance decreased after exposure to tetrachloromethane and increased after exposure to all the other VHOCs. Three-dimensional plots of relative response versus time of half response versus time of half recovery showed good discrimination between the five VHOCs tested.     

Light Controlled Biohybrid Microbots

Biohybrid microbots integrate biological actuators and sensors into synthetic chassis with the aim of providing the building blocks of next-generation micro-robotics. One of the main challenges is the development of self-assembled systems with consistent behavior and such that they can be controlled independently to perform complex tasks. Herein, it is shown that, using light-driven bacteria as propellers, 3D printed microbots can be steered by unbalancing light intensity over different microbot parts. An optimal feedback loop is designed in which a central computer projects onto each microbot a tailor-made light pattern, calculated from its position and orientation. In this way, multiple microbots can be independently guided through a series of spatially distributed checkpoints. By exploiting a natural light-driven proton pump, these bio-hybrid microbots are able to extract mechanical energy from light with such high efficiency that, in principle, hundreds of these systems can be controlled simultaneously with a total optical power of just a few milliwatts.     

On the necessity of explicit cross-layer data formats in near-data processing systems

Distributed and Parallel Databases - Massive data transfers in modern data-intensive systems resulting from low data-locality and data-to-code system design hurt their performance and scalability....     

CeO<Subscript>2</Subscript> and Co<Subscript>3</Subscript>O<Subscript>4</Subscript>–CeO<Subscript>2</Subscript> nanoparticles: effect of the synthesis method on the structure and catalytic properties in COPrOx and methanation reactions

CeO₂ and Co₃O₄–CeO₂ nanoparticles were synthesized, thoroughly characterized, and evaluated in the COPrOx reaction. The CeO₂ nanoparticles were synthesized by the diffusion-controlled precipitation method with ethylene glycol. A notably higher yield was obtained when H₂O₂ was used in the synthesis procedure. For comparison, two commercial samples of CeO₂ nanoparticles (Nyacol^®)—one calcined and the other sintered—were also studied. Catalytic results of bare CeO₂ calcined at 500 °C showed a strong influence of the method of synthesis. Despite having similar BET area values, the CeO₂ synthesized without H₂O₂ was the most active sample. Co₃O₄–CeO₂ catalysts with three different Co/(Co + Ce) atomic ratios, 0.1, 0.3, and 0.5, were prepared by the wet impregnation of the CeO₂ nanoparticles. TEM and STEM observations showed that impregnation produced mixed oxides composed of small CeO₂ nanoparticles located both over the surface and inside the Co₃O₄ crystals. The mixed oxide catalysts prepared with a cobalt atomic ratio of 0.5 showed methane formation, which started at 200 °C due to the reaction between CO₂ and H₂. However, above 250 °C, the reaction between CO and H₂ became important, thus contributing to CO elimination with a small H₂ loss. As a result, CO could be totally eliminated in a wide temperature range, from 200 to 400 °C. The methanation reaction was favored by the reduction of the cobalt oxide, as suggested by the TPR experiments. This result is probably originated in Ce–Co interactions, related to the method of synthesis and the surface area of the mixed oxides obtained.     

Nonlinear Versus Linear Learning Devices: A Procedural Perspective

We provide a discussion of bounded rationality learning behind traditional learning mechanisms, i.e., Recursive Ordinary Least Squares and Bayesian Learning . These mechanisms lack for many reasons a behavioral interpretation and, following the Simon criticism, they appear to be substantively rational. In this paper, analyzing the Cagan model, we explore two learning mechanisms which appear to be more plausible from a behavioral point of view and somehow procedurally rational: Least Mean Squares learning for linear models and Back Propagation for Artificial Neural Networks . The two algorithms look for a minimum of the variance of the error forecasting by means of a steepest descent gradient procedure. The analysis of the Cagan model shows an interesting result: non-convergence of learning to the Rational Expectations Equilibrium is not due to the restriction to linear learning devices; also Back Propagation learning for Artificial Neural Networks may fail to converge to the Rational Expectations Equilibrium of the model.