One of the most natural and primary ways of data collection in wireless sensor networks is to periodically report sensed data values from sensor node to aggregator. However, this kind of data collection mechanism comes at the cost of power consumption and packet collision. In this paper, we developed an automatic ARIMA (Auto Regressive Integrated Moving Average) modeling based data aggregation ...

OBJECTIVE To evaluate the effectiveness of an adherence promotion intervention provided to patients and families referred to a clinical service. METHODS 6 patients and their caregivers representing 5 different chronic conditions were seen for comprehensive psychological intervention that was evaluated based on electronic monitoring of adherence to prescribed oral medication. RESULTS Time se...

Nonlinear structural dynamic system identification is often more a subjective art than it is a direct application of some particular method in systems theory. The nonlinear problem is subjective because although there are many analytical methods from which to choose, there is no general approach to detect, characterise, or model input-output relationships in nonlinear systems. This paper is a s...

This paper presents the adaptation of CORBFN, an evolutionary cooperative-competitive hybrid algorithm for the design of Radial Basis Function Networks, for short-term forecasting of the price of extra virgin olive oil. In the proposed cooperative-competitive environment, each individual represents a Radial Basis Function, and the entire population is responsible for the final solution. In orde...

successful modeling of hydro-environmental processes widely relies on quantity and quality of accessible data and noisy data might effect on the functioning of the modeling. on the other hand in training phase of any artificial intelligence (ai) based model, each training data set is usually a limited sample of possible patterns of the process and hence, might not show the behavior of whole pop...

It reviews the theory of Hidden Filter Hidden Markov Models and presents an extension, Mixed State Hidden Markov Models, developed jointly by Andrew Fraser and myself under his supervision. This manuscript version has only trivial differences from the original.

The time series prediction of avian influenza epidemics is a complex issue, because avian influenza has latent seasonality which is difficult to identify. Although researchers have applied a neural network (NN) model and the Box-Jenkins model for the seasonal epidemic series research area, the results are limited. In this study, we develop a new prediction seasonal auto-regressive-based support...

This paper advocates Riemannian multi-manifold modeling in the context of network-wide non-stationary timeseries analysis. Time-series data, collected sequentially over time and across a network, yield features which are viewed as points in or close to a union of multiple submanifolds of a Riemannian manifold, and distinguishing disparate time series amounts to clustering multiple Riemannian su...

Abstract: According to the chaotic features and typical fractional order characteristics of the bearing vibration intensity time series, a forecasting approach based on long range dependence (LRD) is proposed. In order to reveal the internal chaotic properties, vibration intensity time series are reconstructed based on chaos theory in phase-space, the delay time is computed with C-C method and ...