Simultaneous electrolytic production of xylitol and xylonic acid from xylose

A new electrocatalytic process for simultaneous electrolytic production of xylitol and xylonic acid or the corresponding xylonate salts directly from -d-xylose in alkaline solutions of neutral salt as supporting electrolyte has been developed and presented. An electrocatalytically active cathode coating with essentially hydridic features has been employed to enhance Faradaic yields in xylitol production at low current densities by heterogeneous reaction of hydrogenation with H-adatoms, and the entire mechanism has been proved by experimental evidence. An anionic selective anodic coating for titanium substrate was also used to suppress oxygen evolution and optimize the xylonic acid production by direct oxidation with anodically generated bromine, and to carry out the electrode process at almost equilibrium potential. The kinetic effect of specifically adsorbable anions for decreasing cathodic current yields in xylitol production and the resulting necessity for membrane separation of catholyte from anolyte, with proper optimization of the supporting neutral salt composition, has also been emphasized.Paper partly presented at 39th and 40th ISE-Meetings at Glasgow UK, 1988 and Kyoto, Japan, 1989.     

Triple-product optical processor for analysis of pulse-repetition and carrier frequencies of radar signals

A triple-product acousto-optic processor output was studied analytically and numerically. The processor is capable of detecting the carrier frequency and the modulation envelope of a signal.     

Synchronization of diffusively-coupled limit cycle oscillators

We develop analytical and numerical conditions to determine whether limit cycle oscillations synchronize in diffusively coupled systems. We examine two classes of systems: reaction–diffusion PDEs with Neumann boundary conditions, and compartmental ODEs, where compartments are interconnected through diffusion terms with adjacent compartments. In both cases the uncoupled dynamics are governed by a nonlinear system that admits an asymptotically stable limit cycle. We provide two-time scale averaging methods for certifying stability of spatially homogeneous time-periodic trajectories in the presence of sufficiently small or large diffusion and develop methods using the structured singular value for the case of intermediate diffusion. We highlight cases where diffusion stabilizes or destabilizes such trajectories.     

Comments on “The Cramer-Rao lower bounds for signals withconstant amplitude and polynomial phase”

For original paper see IEEE Trans. Signal Processing, vol.39, p.749-52 (March 1991). Different expressions for the Cramer-Rao lower bounds (CRLBs) of constant amplitude polynomial phase signals embedded in white Gaussian noise appear in the literature. The present paper revisits the derivation of the bounds reported by Peleg and Porat (1991) and indicates that the resulting expressions depend on the interval over which the signal is defined. The proper choice of the interval is the one that centers the signal around zero and results in the minimum lower bounds     

Mobility Tracking in Cellular Networks Using Particle Filtering

Mobility tracking based on data from wireless cellular networks is a key challenge that has been recently investigated both from a theoretical and practical point of view. This paper proposes Monte Carlo techniques for mobility tracking in wireless communication networks by means of received signal strength indications. These techniques allow for accurate estimation of mobile station's (MS) position and speed. The command process of the MS is represented by a first-order Markov model which can take values from a finite set of acceleration levels. The wide range of acceleration changes is covered by a set of preliminary determined acceleration values. A particle filter and a Rao-Blackwellised particle filter are proposed and their performance is evaluated both over synthetic and real data. A comparison with an extended Kalman filter (EKF) is performed with respect to accuracy and computational complexity. With a small number of particles the RBPF gives more accurate results than the PF and the EKF. A posterior Cramer Rao lower bound (PCRLB) is calculated and it is compared with the filters' root- mean-square error performance.     

Improvement of visual perceptual capabilities by feedback structures for robotic system FRIEND

The main aim behind the design of rehabilitation robotic systems is to support disabled people in daily life situations as well as in the working environment. This requires the autonomous execution of different tasks. In order to achieve the ability of the robotic system to operate autonomously, the sensor system for the scene observation providing the necessary inputs for the manipulator control is essential. In this paper, the main emphasis is on the development and integration of the closed-loop controls at the visual sensory input level in order to increase the robustness of the vision-based system control. Moreover, the integration of additional sensors in order to support the vision system and to increase the reliability of the whole robotic system is discussed.     

Bayesian Multi-Object Filtering With Amplitude Feature Likelihood for Unknown Object SNR

In many tracking scenarios, the amplitude of target returns are stronger than those coming from false alarms. This information can be used to improve the multiple-target state estimation by obtaining more accurate target and false-alarm likelihoods. Target amplitude feature is well known to improve data association in conventional tracking filters, such as probabilistic data association and multiple hypothesis tracking, and results in better tracking performance of low signal-to-noise ratio (SNR) targets. The advantage of using the target amplitude approach is that targets can be identified earlier through the enhanced discrimination between target and false alarms. One of the limitations of this approach is that it is usually assumed that the SNR of the target is known. We show that the reliable estimation of the SNR requires a significant number of measurements, and so we propose an alternative approach for situations where the SNR is unknown. We illustrate this approach in the context of multiple targets for different SNRs in the framework of finite set statistics (FISST). Furthermore, we illustrate how this can be incorporated into approximate multiple-object filters derived from FISST, including probability hypothesis density (PHD) and cardinalized PHD (CPHD) filters. We present simulation results for Gaussian mixture implementations of the filters that demonstrate a significant improvement in performance over just using location measurements.     

Effects of Tm3+ Additions on the Crystallization of LaF3 Nanocrystals in Oxyfluoride Glasses: Optical Characterization and Up‐Conversion

The influence of the addition of 1 mol% Tm₂O₃ on the nanocrystallization of LaF₃ in a glass of composition 55SiO₂–20Al₂O₃–15Na₂O–10LaF₃ (mol%) has been studied. Tm₂O₃ affects the phase separation in the glass and delays the onset of crystallization with respect to the undoped glass. Additionally, the maximum LaF₃ crystal size is slightly greater than that in the undoped glass–ceramics. The microstructural and compositional changes in the glass matrix have been studied using several techniques, including viscosity, dilatometry, X‐ray and neutron diffraction (XRD, ND), quantitative Rietveld refinement, transmission electron microscopy (TEM), differential scanning calorimetry (DSC), and Raman spectroscopy. Photoluminescence measurements indicate that the Tm³⁺ ions are distributed between the glassy matrix and LaF₃ crystals. Eu₂O₃ has been used as structure probe and part of the Eu³⁺ ions are reduced to Eu²⁺ when incorporated in the LaF₃ nano‐crystals. Up‐conversion spectra under IR‐excitation show a higher intensity of the blue emission in the Tm‐doped glass–ceramic compared with that in the glass.     

Neural network Reinforcement Learning for visual control of robot manipulators

It is known that most of the key problems in visual servo control of robots are related to the performance analysis of the system considering measurement and modeling errors. In this paper, the development and performance evaluation of a novel intelligent visual servo controller for a robot manipulator using neural network Reinforcement Learning is presented. By implementing machine learning techniques into the vision based control scheme, the robot is enabled to improve its performance online and to adapt to the changing conditions in the environment. Two different temporal difference algorithms (Q-learning and SARSA) coupled with neural networks are developed and tested through different visual control scenarios. A database of representative learning samples is employed so as to speed up the convergence of the neural network and real-time learning of robot behavior. Moreover, the visual servoing task is divided into two steps in order to ensure the visibility of the features: in the first step centering behavior of the robot is conducted using neural network Reinforcement Learning controller, while the second step involves switching control between the traditional Image Based Visual Servoing and the neural network Reinforcement Learning for enabling approaching behavior of the manipulator. The correction in robot motion is achieved with the definition of the areas of interest for the image features independently in both control steps. Various simulations are developed in order to present the robustness of the developed system regarding calibration error, modeling error, and image noise. In addition, a comparison with the traditional Image Based Visual Servoing is presented. Real world experiments on a robot manipulator with the low cost vision system demonstrate the effectiveness of the proposed approach.     

CAD based shape optimization for gas turbine component design

In order to improve product characteristics, engineering design makes increasing use of Robust Design and Multidisciplinary Design Optimisation. Common to both methodologies is the need to vary the object’s shape and to assess the resulting change in performance, both executed within an automatic loop. This shape change can be realised by modifying the parameter values of a suitably parameterised Computer Aided Design (CAD) model. This paper presents the adopted methodology and the achieved results when performing optimisation of a gas turbine disk. Our approach to hierarchical modelling employing design tables is presented, with methods to ensure satisfactory geometry variation by commercial CAD systems. The conducted studies included stochastic and probabilistic design optimisation. To solve the multi-objective optimisation problem, a Pareto optimum criterion was used. The results demonstrate that CAD centric approach enables significant progress towards automating the entire process while achieving a higher quality product with the reduced susceptibility to manufacturing imperfections.