Time series multistep-ahead predictability estimation and ranking

A predictability index was defined as the ratio of the variance of the optimal prediction to the variance of the original time series by Granger and Anderson (1976) and Bhansali (1989). A new simplified algorithm for estimating the predictability index is introduced and the new estimator is shown to be a simple and effective tool in applications of predictability ranking and as an aid in the preliminary analysis of time series. The relationship between the predictability index and the position of the poles and lag p of a time series which can be modelled as an AR(p) model are also investigated. The effectiveness of the algorithm is demonstrated using numerical examples including an application to stock prices.

Dual-orthogonal radial basis function networks for nonlinear time series prediction

A new structure of Radial Basis Function (RBF) neural network called the Dual-orthogonal RBF Network (DRBF) is introduced for nonlinear time series prediction. The hidden nodes of a conventional RBF network compare the Euclidean distance between the network input vector and the centres, and the node responses are radially symmetrical. But in time series prediction where the system input vectors are lagged system outputs, which are usually highly correlated, the Euclidean distance measure may not be appropriate. The DRBF network modifies the distance metric by introducing a classification function which is based on the estimation data set. Training the DRBF networks consists of two stages. Learning the classification related basis functions and the important input nodes, followed by selecting the regressors and learning the weights of the hidden nodes. In both cases, a forward Orthogonal Least Squares (OLS) selection procedure is applied, initially to select the important input nodes and then to select the important centres. Simulation results of single-step and multi-step ahead predictions over a test data set are included to demonstrate the effectiveness of the new approach.

Robust blind deconvolution algorithm: variance approximation and series decoupling

An alternative blind deconvolution algorithm for white-noise driven minimum phase systems is presented and verified by computer simulation. This algorithm uses a cost function based on a novel idea: variance approximation and series decoupling (VASD), and suggests that not all autocorrelation function values are necessary to implement blind deconvolution.

Polyunsaturated fatty acids of the n-6 and n-3 series: effects on postprandial lipid and apolipoprotein levels in healthy men

OBJECTIVE: The present study was carried out to determine effects of test meals of different fatty acid compositions on postprandial lipoprotein and apolipoprotein metabolism. DESIGN: The study was a randomized, single blind design. SETTING: The study was carried out in the Clinical Investigation Unit of the Royal Surrey County Hospital. SUBJECTS: Twelve male normal subjects with an average age of 22.4 +/- 1.4 years (mean +/- SD) were selected from the student population of the University of Surrey; one subject dropped out of the study because he found the test meal unpalatable. INTERVENTIONS: The subjects were given three evening test meals on three separate occasions, in which the oils used were either a mixed oil (rich in saturated fatty acids and approximated the fatty acid intake of the current UK diet), corn oil (rich in n-6 fatty acids), or fish oil (rich in n-3 fatty acids) 40 g of the oil under investigation were incorporated into a rice-based test meal. Triacylglycerol-rich lipoproteins-triacylglycerol (TRL-TAG), TRL-cholesterol (TRL-cholesterol), plasma-TAG, plasma cholesterol (T-C), and serum apolipoprotein A-I and B (apo A-I and B) responses were measured. Postprandial responses were followed for 11 h. RESULTS: Postprandial plasma-TAG responses, calculated as incremental areas under the response curves (IAUC) were significantly reduced following the fish oil meal [365.5 +/- 145.4 mmol/l x min (mean +/- SD)[ compared with the mixed oil meal (552.0 +/- 141.7 mmol/l x min) (P < 0.05) and there was a strong trend towards the same direction in the TRL-TAG responses. In all instances, plasma-and TRL-TAG showed a biphasic response with increased concentrations occurring at 1h and between 3 and 7h postprandially. TRL-cholesterol, T-C, and serum apo A-I and B responses to the three meals were similar. CONCLUSIONS: The findings support the view that fish oils decrease postprandial lipaemia and this may be an important aspect of their beneficial effects in reducing risk of coronary heart disease (CHD). Further work is required to determine the mechanisms responsible for this effect.

Hardware development of the Yorick series of active vision systems

This paper presents a review of the design and development of the Yorick series of active stereo camera platforms and their integration into real-time closed loop active vision systems, whose applications span surveillance, navigation of autonomously guided vehicles (AGVs), and inspection tasks for teleoperation, including immersive visual telepresence. The mechatronic approach adopted for the design of the first system, including head/eye platform, local controller, vision engine, gaze controller and system integration, proved to be very successful. The design team comprised researchers with experience in parallel computing, robot control, mechanical design and machine vision. The success of the project has generated sufficient interest to sanction a number of revisions of the original head design, including the design of a lightweight compact head for use on a robot arm, and the further development of a robot head to look specifically at increasing visual resolution for visual telepresence. The controller and vision processing engines have also been upgraded, to include the control of robot heads on mobile platforms and control of vergence through tracking of an operator's eye movement. This paper details the hardware development of the different active vision/telepresence systems.

Modelling of surface identifying characteristics using Fourier series

Texture and small-scale surface details are widely recognised as playing an important role in the haptic identification of objects. In order to simulate realistic textures in haptic virtual environments, it has become increasingly necessary to identify a robust technique for modelling of surface profiles. This paper describes a method whereby Fourier series spectral analysis is employed in order to describe the measured surface profiles of several characteristic surfaces. The results presented suggest that a bandlimited Fourier series can be used to provide a realistic approximation to surface amplitude profiles.

Periodic time series modeling of environmental proxy records with guaranteed positive growth rate estimation

Identifying a periodic time-series model from environmental records, without imposing the positivity of the growth rate, does not necessarily respect the time order of the data observations. Consequently, subsequent observations, sampled in the environmental archive, can be inversed on the time axis, resulting in a non-physical signal model. In this paper an optimization technique with linear constraints on the signal model parameters is proposed that prevents time inversions. The activation conditions for this constrained optimization are based upon the physical constraint of the growth rate, namely, that it cannot take values smaller than zero. The actual constraints are defined for polynomials and first-order splines as basis functions for the nonlinear contribution in the distance-time relationship. The method is compared with an existing method that eliminates the time inversions, and its noise sensitivity is tested by means of Monte Carlo simulations. Finally, the usefulness of the method is demonstrated on the measurements of the vessel density, in a mangrove tree, Rhizophora mucronata, and the measurement of Mg/Ca ratios, in a bivalve, Mytilus trossulus.