Surface modification of starch nanocrystals through ring‐opening polymerization of ε‐caprolactone and investigation of their microstructures

Bionanoparticles of starch obtained by submitting native potato starch granules to acid hydrolysis conditions. The resulted starch nanoparticles were used as core or macro initiator for polymerization of ε‐caprolactone (CL). Starch nanoparticle‐g‐polycaprolactone was synthesized through ring‐opening polymerization (ROP) of CL in the presence of Sn(Oct)₂ as initiator. The detailed microstructure of the resulted copolymer was characterized with NMR spectroscopy. Thermal characteristic of the copolymer was investigated using DSC and TGA. By introducing PCL, the range of melting temperature for starch was increased and degradation of copolymer occurred in a broader region. X‐ray diffraction and TEM micrographs confirmed that there was no alteration of starch crystalline structure and morphology of nanoparticles, respectively. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

Dielectric loaded HTS resonators as frequency standards andlow-phase noise oscillators

High-quality, high-temperature superconducting (HTS) thin-films exhibit very low losses at microwave frequencies and, as a result, allow very high Q resonators to be produced. The size of such resonators may be significantly reduced by loading with low-loss single crystal dielectric. The potential for HTS dielectric loaded resonators as practical frequency standards and reference elements for low-phase-noise oscillators is assessed, with emphasis on operation at 60 K, a temperature readily attainable with compact Stirling cycle coolers     

Statistically correlated multi-task learning for autonomous driving

Neural Computing and Applications - Autonomous driving research is an emerging area in the machine learning domain. Most existing methods perform single-task learning, while multi-task learning...     

Speech enhancement with an adaptive Wiener filter

This paper proposes an adaptive Wiener filtering method for speech enhancement. This method depends on the adaptation of the filter transfer function from sample to sample based on the speech signal statistics; the local mean and the local variance. It is implemented in the time domain rather than in the frequency domain to accommodate for the time-varying nature of the speech signals. The proposed method is compared to the traditional frequency-domain Wiener filtering, spectral subtraction and wavelet denoising methods using different speech quality metrics. The simulation results reveal the superiority of the proposed Wiener filtering method in the case of Additive White Gaussian Noise (AWGN) as well as colored noise.     

Rheological and extrusion characteristics of glass fiber-reinforced polycarbonate

An experimental investigation of the rheological properties of glass fiber-reinforced polycarbonate melts and the extrusion of such compounds through capillary and slit dies is presented. The viscosity–shear rate function seems independent of instrument for cone-plate and capillary investigations. The presence of fibers increases the level of the viscosity. Normal stresses at fixed shear stress are also increased by the presence of fibers. The extrudate swell is decreased by the presence of fibers and surface roughness is increased. Fiber orientation increases and surface roughness decreases with increasing extrusion rate.     

NN-based Prediction Interval for Nonlinear Processes Controller

Neural networks (NNs) are extensively used in modelling, optimization, and control of nonlinear plants. NN-based inverse type point prediction models are commonly used for nonlinear process control. However, prediction errors (root mean square error (RMSE), mean absolute percentage error (MAPE) etc.) significantly increase in the presence of disturbances and uncertainties. In contrast to point forecast, prediction interval (PI)-based forecast bears extra information such as the prediction accuracy. The PI provides tighter upper and lower bounds with considering uncertainties due to the model mismatch and time dependent or time independent noises for a given confidence level. The use of PIs in the NN controller (NNC) as additional inputs can improve the controller performance. In the present work, the PIs are utilized in control applications, in particular PIs are integrated in the NN internal model-based control framework. A PI-based model that developed using lower upper bound estimation method (LUBE) is used as an online estimator of PIs for the proposed PI-based controller (PIC). PIs along with other inputs for a traditional NN are used to train the PIC to predict the control signal. The proposed controller is tested for two case studies. These include, a chemical reactor, which is a continuous stirred tank reactor (case 1) and a numerical nonlinear plant model (case 2). Simulation results reveal that the tracking performance of the proposed controller is superior to the traditional NNC in terms of setpoint tracking and disturbance rejections. More precisely, 36% and 15% improvements can be achieved using the proposed PIC over the NNC in terms of IAE for case 1 and case 2, respectively for setpoint tracking with step changes.

     

An efficient and noise resistive selective image encryption scheme for gray images based on chaotic maps and DNA complementary rules

A novel image encryption algorithm in streaming mode is proposed which exhaustively employs an entire set of DNA complementary rules alongwith one dimensional chaotic maps. The proposed algorithm is highly efficient due to encrypting the subset of digital image which contains 92.125 % of information. DNA addition operation is carried out on this MSB part. The core idea of the proposed scheme is to scramble the whole image by means of piecewise linear chaotic map (PWLCM) followed by decomposition of image into most significant bits (MSB) and least significant bits (LSB). The logistic sequence is XORed with the decoded MSB and LSB parts separately and finally these two parts are combined to get the ciphered image. The parameters for PWLCM, logistic map and selection of different DNA rules for encoding and decoding of both parts of an image are derived from 128-bit MD5 hash of the plain image. Simulated experimental results in terms of quantitative and qualitative ways prove the encryption quality. Efficiency and robustness against different noises make the proposed cipher a good candidate for real time applications.     

A Saturating Extension of an Output Feedback Controller for Internally Damped Euler‐Lagrange Systems

In this research, a novel extension of the passivity‐based output feedback trajectory tracking controller is developed for internally damped Euler‐Lagrange systems with input saturation. Compared with the previous output feedback controllers, this new design of a combined adaptive controller‐observer system will reduce the risk of actuator saturation effectively via generalized saturation functions. Semi‐global uniform ultimate boundedness stability of the tracking errors and state estimation errors is guaranteed by Lyapunov stability analysis. An application of the proposed saturated output feedback controller is the stabilization of a nonholonomic wheeled mobile robot with saturated actuators towards desired trajectories. Simulation results are provided to illustrate the efficiency of the proposed controller in dealing with the actuator saturation.     

Controller Synthesis with Highly Simplified Linear Constraints

This paper deals with the problem of forbidden states in safe Petri nets to obtain a maximally permissive controller. To prevent the system from entering the forbidden states, assigning some constraints to them is possible. The constraints can be enforced on the system using control places. When the number of forbidden states is large, a large number of constraints should be assigned to them. This results in a large number of control places being added to the model of the system, which causes a complicated model. Some methods have been proposed to reduce the number of constraints. Nevertheless, they do not always give the best results. In this paper, two ideas are offered to reduce the number of constraints, giving a more simplified controller.     

Detection and classification of road signs in natural environments

An automatic road sign recognition system first locates road signs within images captured by an imaging sensor on-board of a vehicle, and then identifies the detected road signs. This paper presents an automatic neural-network-based road sign recognition system. First, a study of the existing road sign recognition research is presented. In this study, the issues associated with automatic road sign recognition are described, the existing methods developed to tackle the road sign recognition problem are reviewed, and a comparison of the features of these methods is given. Second, the developed road sign recognition system is described. The system is capable of analysing live colour road scene images, detecting multiple road signs within each image, and classifying the type of road signs detected. The system consists of two modules: detection and classification. The detection module segments the input image in the hue-saturation-intensity colour space, and then detects road signs using a Multi-layer Perceptron neural-network. The classification module determines the type of detected road signs using a series of one to one architectural Multi-layer Perceptron neural networks. Two sets of classifiers are trained using the Resillient-Backpropagation and Scaled-Conjugate-Gradient algorithms. The two modules of the system are evaluated individually first. Then the system is tested as a whole. The experimental results demonstrate that the system is capable of achieving an average recognition hit-rate of 95.96% using the scaled-conjugate-gradient trained classifiers.