Prolonged sensitivity of immobilized thylakoid membranes in cross-linked matrix to atrazine

Freshly prepared pea thylakoid membranes were immobilized in bovine serum albumin-glutaraldehyde cross-linked matrix (BSA-GA matrix) and their stability under long term storage was analyzed by Pulse-Amplitude-Modulated (PAM) chlorophyll fluorescence and photosynthetic oxygen evolution measured by oxygen rate electrode. The thylakoid membranes stored at 4 °C showed prolonged stability in BSA-GA matrix and additional adsorption on nitrocellulose membrane filters gave them more stability. The sensitivity of the parameters of the oxygen evolution of thylakoid membranes to atrazine increased with immobilization. The half-inhibition time for oxygen evolution and quantum efficiency of photosynthesis could be prolonged to more than 15 days. These results suggest that the immobilized thylakoid membranes in BSA-GA matrix can be used as biological receptor in biosensors for a long period of time (up to 25 days) applying the proposed new method for atrazine detection by using polarographic oxygen rate electrode. This method is more sensitive, faster and easier to use than other methods for detection of herbicides based on determination of the photochemical activity of photosystem II.     

Evanescent wave sensor based on permanently bent single mode optical fiber

A novel refractive index sensing scheme based on evanescent wave interaction through locally and permanently bent single mode optical fibers is proposed. Local and permanent bends in single mode optical fibers enable significant power coupling between core and cladding modes. Order and number of excited cladding modes depend on bend features and determine the field profile at the output of the bent region. This in turn constitutes a simple mechanism to tailor the field distribution in single mode optical fibers useful for spatial light modulation. Moreover, since guided cladding modes are strongly influenced by the surrounding refractive index (SRI), the power transmitted at the output of the bent region as well as its dependence on the optical wavelength are strongly sensitive to the SRI opening new scenarios in sensing applications.     

Stochastic approximation learning for mixtures of multivariate elliptical distributions

Most of the current approaches to mixture modeling consider mixture components from a few families of probability distributions, in particular from the Gaussian family. The reasons of these preferences can be traced to their training algorithms, typically versions of the Expectation-Maximization (EM) method. The re-estimation equations needed by this method become very complex as the mixture components depart from the simplest cases. Here we propose to use a stochastic approximation method for probabilistic mixture learning. Under this method it is straightforward to train mixtures composed by a wide range of mixture components from different families. Hence, it is a flexible alternative for mixture learning. Experimental results are presented to show the probability density and missing value estimation capabilities of our proposal.     

Fault tolerant machine learning for nanoscale cognitive radio

We introduce a machine learning-based classifier that identifies free radio channels for cognitive radio. The architecture is designed for nanoscale implementation, under nanoscale implementation constraints; we do not describe all physical details but believe future physical implementation to be feasible. The system uses analog computation and consists of cyclostationary feature extraction and a radial basis function network for classification. We describe a model for nanoscale faults in the system, and simulate experimental performance and fault tolerance in recognizing WLAN signals, under different levels of noise and computational errors. The system performs well under expected non-ideal manufacturing and operating conditions.     

Effect of micro-electrode geometry on NO2 gas-sensing characteristics of one-dimensional tin dioxide nanostructure microsensors

Electrodes with micro-gaps are fabricated by using dc-sputtering and FIB techniques. SnO₂ nanowires are deposited on the micro-gap (1-30 μm) by suspension dropping method to fabricate a micro-gas sensor. The sensing ability of various SnO₂ micro-gap sensors is measured. A comparison between sensors reveals that the short-gap electrode has numerous advantages in terms of reliability, high sensitivity and detection of low concentrations of NO₂, while the large-gap electrode is relatively sensitive for high concentrations. Conductance measurements are carried out at different surface temperatures and NO₂ concentrations in order to investigate the effects that the gap size has on the overall sensor conductance. The results suggest that the interface between the electrode and sensitive layer has a very important role for the sensing mechanism of tin dioxide gas sensors.     

Structural and properties of nanocrystalline WO3/TiO2-based humidity sensors elements prepared by high energy activation

Nanocrystalline WO₃/TiO₂-based powders have been prepared by the high energy activation method with WO₃ concentration ranging from 1 to 10 mol%. The samples were thermal treated in a microwave oven at 600 °C for 20 min and their structural and micro-structural characteristics were evaluated by X-ray diffraction, Raman spectroscopy, EXAFS measurements at the Ti K-edge, and transmission electron microscopy. Nitrogen adsorption isotherms and H₂ Temperature Programmed Reduction were also carried out for physical characterization. The crystallite and particle mean sizes ranged from 30 to 40 nm and from 100 to 190 nm, respectively. Good sensor response was obtained for samples with at least 5 mol% WO₃ activated for at least 80 min. Ceramics heat-treated in microwave oven for 20 min have shown similar sensor response as those prepared in conventional oven for 120 min, which is highly cost effective. These results indicate that WO₃/TiO₂ ceramics can be used as a humidity sensor element.     

Model predictive control for systems with stochastic multiplicative uncertainty and probabilistic constraints

Robust predictive control handles constrained systems that are subject to stochastic uncertainty but propagating the effects of uncertainty over a prediction horizon can be computationally expensive and conservative. This paper overcomes these issues through an augmented autonomous prediction formulation, and provides a method of handling probabilistic constraints and ensuring closed loop stability through the use of an extension of the concept of invariance, namely invariance with probability p.     

Suppressing intersample behavior in iterative learning control

Iterative Learning Control (ILC) is a control strategy to improve the performance of digital batch repetitive processes. Due to its digital implementation, discrete time ILC approaches do not guarantee good intersample behavior. In fact, common discrete time ILC approaches may deteriorate the intersample behavior, thereby reducing the performance of the sampled-data system. In this paper, a generally applicable multirate ILC approach is presented that enables to balance the at-sample performance and the intersample behavior. Furthermore, key theoretical issues regarding multirate systems are addressed, including the time-varying nature of the multirate ILC setup. The proposed multirate ILC approach is shown to outperform discrete time ILC in realistic simulation examples.     

Tracking control for boundary controlled parabolic PDEs with varying parameters: Combining backstepping and differential flatness

The combination of backstepping-based state-feedback control and flatness-based trajectory planning and feedforward control is considered for the design of an exponentially stabilizing tracking controller for a linear diffusion-convection-reaction system with spatially and temporally varying parameters and nonlinear boundary input. For this, in a first step the backstepping transformation is utilized to determine a state-feedback controller, which transforms the original distributed-parameter system into an appropriately chosen exponentially stable distributed-parameter target system of a significantly simpler structure. In a second step, the flatness property of the target system is exploited in order to determine the feedforward controller, which allows us to realize the tracking of suitably prescribed trajectories for the system output. This results in a systematic procedure for the design of an exponentially stabilizing tracking controller for the considered general linear diffusion-convection-reaction system with varying parameters, whose applicability and tracking performance is evaluated in simulation studies.     

Stabilizing switching design for switched linear systems: A state-feedback path-wise switching approach

This paper addresses the problem of switching stabilization for discrete-time switched linear systems. Based on the abstraction-aggregation methodology, we propose a state-feedback path-wise switching law, which is a state-feedback concatenation from a finite set of switching paths each defined over a finite time interval. We prove that the set of state-feedback path-wise switching laws is universal in the sense that any stabilizable switched linear system admits a stabilizing switching law in this set. We further develop a computational procedure to calculate a stabilizing switching law in the set.