A Fast Nonlinear Model Identification Method

The identification of nonlinear dynamic systems using linear-in-the-parameters models is studied. A fast recursive algorithm (FRA) is proposed to select both the model structure and to estimate the model parameters. Unlike orthogonal least squares (OLS) method, FRA solves the least-squares problem recursively over the model order without requiring matrix decomposition. The computational complexity of both algorithms is analyzed, along with their numerical stability. The new method is shown to require much less computational effort and is also numerically more stable than OLS.

Cobalt-doped ZnO nanowires on quartz: Synthesis by simple chemical method and characterization

The synthesis of cobalt-doped ZnO nanowires is achieved using a simple, metal salt decomposition growth technique. A sequence of drop casting on a quartz substrate held at 100 degrees C and annealing results in the growth of nanowires of average (modal) length similar to 200 nm and diameter of 15 +/- 4 nm and consequently an aspect ratio of similar to 13. A variation in the synthesis process, where the solution of mixed salts is deposited on the substrate at 25 degrees C, yields a grainy film structure which constitutes a useful comparator case. X-ray diffraction shows a preferred [0001] growth direction for the nanowires while a small unit cell volume contraction for Co-doped samples and data from Raman spectroscopy indicate incorporation of the Co dopant into the lattice; neither technique shows explicit evidence of cobalt oxides. Also the nanowire samples display excellent optical transmission across the entire visible range, as well as strong photoluminescence (exciton emission) in the near UV, centered at 3.25 eV. (C) 2012 Elsevier B.V. All rights reserved.

An Efficient LS-SVM-Based Method for Fuzzy System Construction

This paper proposes an efficient learning mechanism to build fuzzy rule-based systems through the construction of sparse least-squares support vector machines (LS-SVMs). In addition to the significantly reduced computational complexity in model training, the resultant LS-SVM-based fuzzy system is sparser while offers satisfactory generalization capability over unseen data. It is well known that the LS-SVMs have their computational advantage over conventional SVMs in the model training process; however, the model sparseness is lost, which is the main drawback of LS-SVMs. This is an open problem for the LS-SVMs. To tackle the nonsparseness issue, a new regression alternative to the Lagrangian solution for the LS-SVM is first presented. A novel efficient learning mechanism is then proposed in this paper to extract a sparse set of support vectors for generating fuzzy IF-THEN rules. This novel mechanism works in a stepwise subset selection manner, including a forward expansion phase and a backward exclusion phase in each selection step. The implementation of the algorithm is computationally very efficient due to the introduction of a few key techniques to avoid the matrix inverse operations to accelerate the training process. The computational efficiency is also confirmed by detailed computational complexity analysis. As a result, the proposed approach is not only able to achieve the sparseness of the resultant LS-SVM-based fuzzy systems but significantly reduces the amount of computational effort in model training as well. Three experimental examples are presented to demonstrate the effectiveness and efficiency of the proposed learning mechanism and the sparseness of the obtained LS-SVM-based fuzzy systems, in comparison with other SVM-based learning techniques.

Calibration Method for Underwater Visual Ground-Truth System

This work presents an automatic calibration method for a vision based external underwater ground-truth positioning system. These systems are a relevant tool in benchmarking and assessing the quality of research in underwater robotics applications. A stereo vision system can in suitable environments such as test tanks or in clear water conditions provide accurate position with low cost and flexible operation. In this work we present a two step extrinsic camera parameter calibration procedure in order to reduce the setup time and provide accurate results. The proposed method uses a planar homography decomposition in order to determine the relative camera poses and the determination of vanishing points of detected lines in the image to obtain the global pose of the stereo rig in the reference frame. This method was applied to our external vision based ground-truth at the INESC TEC/Robotics test tank. Results are presented in comparison with an precise calibration performed using points obtained from an accurate 3D LIDAR modelling of the environment.

The effect of moisture on decomposition processes in the disturbed peatlands of the Wainfleet bog /

The purpose of this study was to determine the effect of increased soil moisture levels on the decomposition processes in a peat-extracted bog. Field experiments, in which soil moisture levels were manipulated, were conducted using 320 microcosms in the Wainfleet Bog from May 2002 to November 2004. Decomposition was measured using litter bags and monitoring the abundance of macro invertebrate decomposers known as Collembola. Litter bags containing wooden toothpicks (n=2240), filter paper (n=480) and Betula pendula leaves (n=40) were buried in the soil and removed at regular time intervals up to one year. The results of the litter bag studies demonstrated a significant reduction of the decomposition of toothpicks (p<0.001), filter paper (p<0.001), and Betula pendula leaves (pmethod in the microcosms. By contrast the effect of increased moisture levels on the abundance of Collembola was more variable and difficult to interpret. The conclusions of this study indicate that the Wainfleet Bog is a highly disturbed peatland and that the greatest reductions in decomposition can be obtained by restoring the soil moisture levels near those of undisturbed conditions.

A study of the thermal decomposition of allyl t-butyl peroxide and 3-hydroperoxy-1-propene (allyl hydroperoxide) in toluene /|nby Krishnankutty Nair V. G. -- 260 St. Catharines [Ont. : s. n.],

Kinetics and product studies of the decompositions of allyl-t-butyl peroxide and 3-hydroperoxy- l-propene (allyl hydroperoxide ) in tolune were investigated. Decompositions of allyl-t-butyl peroxide in toluene at 130-1600 followed first order kinetics with an activation energy of 32.8 K.cals/mol and a log A factor of 13.65. The rates of decomposition were lowered in presence of the radical trap~methyl styrene. By the radical trap method, the induced decomposition at 1300 is shown to be 12.5%. From the yield of 4-phenyl-l,2- epoxy butane the major path of induced decomposition is shown to be via an addition mechanism. On the other hand, di-t-butYl peroxyoxalate induced decomposition of this peroxide at 600 proceeded by an abstraction mechanism. Induced decomposition of peroxides and hydroperoxides containing the allyl system is proposed to occur mainly through an addition mechanism at these higher temperatures. Allyl hydroperoxide in toluene at 165-1850 decomposes following 3/2 order kinetics with an Ea of 30.2 K.cals per mole and log A of 10.6. Enormous production of radicals through chain branching may explain these relatively low values of E and log A. The complexity of the reaction is indicated a by the formation of various products of the decomposition. A study of the radical attack of the hydro peroxide at lower temperatures is suggested as a further work to throw more light on the nature of decomposition of this hydroperoxide.

Studies on oxamyl : analytical method development and investigation of fate in peach seedlings and corn seeds

A high performance liquid chromatographic method employing two columns connected in series and separated~y·a.switching valve has been developed for the analysis of the insecticide/ nematicide oxamyl (methyl-N' ,N'-dimethyl-N-[(methylcarbamoyl) oxy]-l-thiooxarnimidate) and two of its metabolites. A variation of this method involving two reverse phase columns was employed to monitor the persistence and translocation of oxamyl in treated peach seedlings. It was possible to simultaneously analyse for oxamyl and its corresponding oxime (methyl-N',N'-dimethyl-N-hydroxy-l-thiooxamimidate}, a major metabolite of oxamyl in plants, without prior cleanup of the samples. The method allowed detection of 0.058 pg oxamyl and 0.035 p.g oxime. On treated peach leaves oxamyl was found to dissipate rapidly during the first two-week period, followed by a period of slow decomposition. Movement of oxamyl or its oxime did not occur in detectable quantities to untreated leaves or to the root or soil. A second variation of the method which employed a size exclusion column as·the first column and a reverse phase column as the second was used to monitor the degradation of oxamyl in treated, planted corn seeds and was suitable for simultaneous analysis of oxamyl, its oxime and dimethylcyanoformamide (DMCF), a metabolite of oxamyl. The method allowed detection of 0.02 pg oxamyl, 0.02 p.g oxime and 0.005 pg DMCF. Oxamyl was found to persist for a period of 5 - 6 weeks, which is long enough to permit oxamyl seedtreatment to be considered as a potential means of protecting young corn plants from nematode attack. Decomposition was found to be more rapid in unsterilized soil than in sterililized soil. DMCF was found to have a nematostatic effect at high concentrations ( 2,OOOpprn), but at lower concentrations no effect on nematode mobility was observed. Oxamyl, on the other hand, was found to reduce the mobility of nematodes at concentrations down to 4 ppm.

Study of cyclohexanol decomposition reaction over the ferrospinels, A1−xCuxFe2O4 (A=Ni or Co and x=0, 0.3, 0.5, 0.7 and 1), prepared by ‘soft’ chemical methods

Preparation of simple and mixed ferrospinels of nickel, cobalt and copper and their sulphated analogues by the room temperature coprecipitation method yielded fine particles with high surface areas. Study of the vapour phase decomposition of cyclohexanol at 300 °C over all the ferrospinel systems showed very good conversions yielding cyclohexene by dehydration and/or cyclohexanone by dehydrogenation, as the major products. Sulphation very much enhanced the dehydration activity over all the samples. A good correlation was obtained between the dehydration activities of the simple ferrites and their weak plus medium strength acidities (usually of the Brφnsted type) determined independently by the n-butylamine adsorption and ammonia-TPD methods. Mixed ferrites containing copper showed a general decrease in acidities and a drastic decrease in dehydration activities. There was no general correlation between the basicity parameters obtained by electron donor studies and the ratio of dehydrogenation to dehydration activities. There was a leap in the dehydrogenation activities in the case of all the ferrospinel samples containing copper. Along with the basic properties, the redox properties of copper ion have been invoked to account for this added activity.

Feature Extraction Methods Based on Linear Predictive Coding and Wavelet Packet Decomposition for Recognizing Spoken Words in Malayalam

Speech signals are one of the most important means of communication among the human beings. In this paper, a comparative study of two feature extraction techniques are carried out for recognizing speaker independent spoken isolated words. First one is a hybrid approach with Linear Predictive Coding (LPC) and Artificial Neural Networks (ANN) and the second method uses a combination of Wavelet Packet Decomposition (WPD) and Artificial Neural Networks. Voice signals are sampled directly from the microphone and then they are processed using these two techniques for extracting the features. Words from Malayalam, one of the four major Dravidian languages of southern India are chosen for recognition. Training, testing and pattern recognition are performed using Artificial Neural Networks. Back propagation method is used to train the ANN. The proposed method is implemented for 50 speakers uttering 20 isolated words each. Both the methods produce good recognition accuracy. But Wavelet Packet Decomposition is found to be more suitable for recognizing speech because of its multi-resolution characteristics and efficient time frequency localizations

A novel method for production of lipid hydroperoxide- or oxysterol-rich low-density lipoprotein

LDL oxidation may be important in atherosclerosis. Extensive oxidation of LDL by copper induces increased uptake by macrophages, but results in decomposition of hydroperoxides, making it more difficult to investigate the effects of hydroperoxides in oxidised LDL on cell function. We describe here a simple method of oxidising LDL by dialysis against copper ions at 4 degrees C, which inhibits the decomposition of hydroperoxides, and allows the production of LDL rich in hydroperoxides (626 +/- 98 nmol/mg LDL protein) but low in oxysterols (3 +/- 1 nmol 7-ketocholesterol/mg LDL protein), whilst allowing sufficient modification (2.6 +/- 0.5 relative electrophoretic mobility) for rapid uptake by macrophages (5.49 +/- 0.75 mu g I-125-labelled hydroperoxide-rich LDL vs. 0.46 +/- 0.04 mu g protein/mg cell protein in 18 h for native LDL). By dialysing under the same conditions, but at 37 degrees C, the hydroperoxides are decomposed extensively and the LDL becomes rich in oxysterols. This novel method of oxidising LDL with high yield to either a hydroperoxide- or oxysterol-rich form by simply altering the temperature of dialysis may provide a useful tool for determining the effects of these different oxidation products on cell function. (C) 2007 Elsevier Ireland Ltd. All rights reserved.

Finding the smallest eigenvalue by the inverse Monte Carlo method with refinement

Finding the smallest eigenvalue of a given square matrix A of order n is computationally very intensive problem. The most popular method for this problem is the Inverse Power Method which uses LU-decomposition and forward and backward solving of the factored system at every iteration step. An alternative to this method is the Resolvent Monte Carlo method which uses representation of the resolvent matrix [I -qA](-m) as a series and then performs Monte Carlo iterations (random walks) on the elements of the matrix. This leads to great savings in computations, but the method has many restrictions and a very slow convergence. In this paper we propose a method that includes fast Monte Carlo procedure for finding the inverse matrix, refinement procedure to improve approximation of the inverse if necessary, and Monte Carlo power iterations to compute the smallest eigenvalue. We provide not only theoretical estimations about accuracy and convergence but also results from numerical tests performed on a number of test matrices.

EMG signal filtering based on Empirical Mode Decomposition

This paper introduces a procedure for filtering electromyographic (EMG) signals. Its key element is the Empirical Mode Decomposition, a novel digital signal processing technique that can decompose my time-series into a set of functions designated as intrinsic mode functions. The procedure for EMG signal filtering is compared to a related approach based on the wavelet transform. Results obtained from the analysis of synthetic and experimental EMG signals show that Our method can be Successfully and easily applied in practice to attenuation of background activity in EMG signals. (c) 2006 Elsevier Ltd. All rights reserved.

Robust neurofuzzy rule base knowledge extraction and estimation using subspace decomposition combined with regularization and D-optimality

A new robust neurofuzzy model construction algorithm has been introduced for the modeling of a priori unknown dynamical systems from observed finite data sets in the form of a set of fuzzy rules. Based on a Takagi-Sugeno (T-S) inference mechanism a one to one mapping between a fuzzy rule base and a model matrix feature subspace is established. This link enables rule based knowledge to be extracted from matrix subspace to enhance model transparency. In order to achieve maximized model robustness and sparsity, a new robust extended Gram-Schmidt (G-S) method has been introduced via two effective and complementary approaches of regularization and D-optimality experimental design. Model rule bases are decomposed into orthogonal subspaces, so as to enhance model transparency with the capability of interpreting the derived rule base energy level. A locally regularized orthogonal least squares algorithm, combined with a D-optimality used for subspace based rule selection, has been extended for fuzzy rule regularization and subspace based information extraction. By using a weighting for the D-optimality cost function, the entire model construction procedure becomes automatic. Numerical examples are included to demonstrate the effectiveness of the proposed new algorithm.

Detection of neural correlates of self-paced motor activity using empirical mode decomposition phase locking analysis

Transient episodes of synchronisation of neuronal activity in particular frequency ranges are thought to underlie cognition. Empirical mode decomposition phase locking (EMDPL) analysis is a method for determining the frequency and timing of phase synchrony that is adaptive to intrinsic oscillations within data, alleviating the need for arbitrary bandpass filter cut-off selection. It is extended here to address the choice of reference electrode and removal of spurious synchrony resulting from volume conduction. Spline Laplacian transformation and independent component analysis (ICA) are performed as pre-processing steps, and preservation of phase synchrony between synthetic signals. combined using a simple forward model, is demonstrated. The method is contrasted with use of bandpass filtering following the same preprocessing steps, and filter cut-offs are shown to influence synchrony detection markedly. Furthermore, an approach to the assessment of multiple EEG trials using the method is introduced, and the assessment of statistical significance of phase locking episodes is extended to render it adaptive to local phase synchrony levels. EMDPL is validated in the analysis of real EEG data, during finger tapping. The time course of event-related (de)synchronisation (ERD/ERS) is shown to differ from that of longer range phase locking episodes, implying different roles for these different types of synchronisation. It is suggested that the increase in phase locking which occurs just prior to movement, coinciding with a reduction in power (or ERD) may result from selection of the neural assembly relevant to the particular movement. (C) 2009 Elsevier B.V. All rights reserved.

The decomposition of some RDX and HMX based materials in the one dimensional time to explosion apparatus. Part 2. Estimating the violence of the cook-off event

Various methods of assessment have been applied to the One Dimensional Time to Explosion (ODTX) apparatus and experiments with the aim of allowing an estimate of the comparative violence of the explosion event to be made. Non-mechanical methods used were a simple visual inspection, measuring the increase in the void volume of the anvils following an explosion and measuring the velocity of the sound produced by the explosion over 1 metre. Mechanical methods used included monitoring piezo-electric devices inserted in the frame of the machine and measuring the rotational velocity of a rotating bar placed on the top of the anvils after it had been displaced by the shock wave. This last method, which resembles original Hopkinson Bar experiments, seemed the easiest to apply and analyse, giving relative rankings of violence and the possibility of the calculation of a “detonation” pressure.