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Advanced Digital Signal Processing And Noise Reduction

This is a review of recent efforts to develop and implement forest and wildland fire management decision support systems (FMDSS) that fire managers can use to enhance their management of their fire suppression activities. Fire managers need to resolve complex decisions associated with fuel management, fire prevention, detection, the suppression of potentially destructive wildfires and the use of prescribed fire to achieve an appropriate balance between the beneficial and detrimental social, economic and ecological impacts of fire on flammable landscapes, often under considerable uncertainty. This review focuses on the use of operational research and management science (OR/MS) methods to address their suppression resource management decision support needs.

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A Review of Recent Forest and Wildland Fire Management Decision Support Systems Research

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Nonparametric segmentation of nonstationary time series

An effective and efficient suppression of a bushfire is predicated on synchronised collaborative planning, information sharing, scheduling and tasks coordination between multiple agencies. This paper aims to develop a bushfire suppression optimisation model, which minimises the completion times and total costs within resource constraints. An algorithm is developed to tackle the highly complex operational response to bushfire suppression via aerial firefighting. A real case study of 2009 Black Saturday bushfires in Victoria is used to create the situational context and to derive model parameters. Three scenarios are designed to incorporate time and resource constraints to reflect a highly uncertain and dynamic situated bushfire environment. This study has generated bushfire suppression plans in emergency situations by improving resource utilisation efficiency and time scheduling. In the baseline scenario, the model indicates the ability of fire agencies to complete all operational activities, but in the scenarios where resource shortage and time limitations exist, the cost of unmet demands fell slightly. The outcomes of the proposed approach will help to enhance multi-agencies coordination in fire suppression evacuation plans to improve operational response and maximise geographic coverage of service delivery.

A cooperative (or coordinated) multi-agency response to enhance the effectiveness of aerial bushfire suppression operations

Rekindles have a significant presence in the Portuguese forest fire management system and an important impact on fire suppression resources in particular (Pacheco, Claro, and Oliveira 2014). In most of the cases, mop-up is made exclusively with water, without suitable tools to carry out the work effectively, allowing the fire to rekindle. Thus, it's pretended to develop a discrete event simulation (DES) of rekindles as a rework problem and, after this, assay the impact of different suppression policies on rekindles.

A simulation analysis of the impact of forest fire suppression policies on rekindles

We present a method for early forest fire detection from a satellite image using the belonging probability matrix image. We have considered each satellite image matrix line as a realization of a nonstationary random process in the thermal infra-red (TIR) spectral band and then divided each line into very small stationary and ergodic intervals to obtain an adequate mathematical model. Furthermore, the pixels of the satellite image are considered to be statistically independent; thus, any small interval of each line behaves, naturally, as a Gaussian stationary noise. In this work, we have, therefore, selected the latter as a mathematical model for modelling these intervals of a satellite image without fire, and then, we have determined the parameters of this Gaussian realization. So, when a fire occurs in this forest zone, we can use these parameters to calculate its belonging probability to the original image without fire. This probability should be very small because the fire, in any forest, can be considered as a rare event. As a consequence, we have presented a matrix image of the probability inverse of each interval for a better fire detection observation.

Resume

Nous presentons une methode de detection precoce des feux de forets par image satellite en utilisant l'image matricielle de la probabilite d'appartenance. Nous avons considere chaque ligne matricielle de l'image satellite comme une realisation d'un processus aleatoire non stationnaire dans la bande spectrale TIR (Thermal Infra-Rouge), puis divise chaque ligne en tres petits intervalles stationnaires et ergodiques, afin d'obtenir un modele mathematique adequat. Ensuite, les pixels de l'image satellite sont consideres comme statistiquement independants, et donc chaque petit intervalle de chaque ligne se comporte, naturellement, comme un bruit gaussien stationnaire. Dans ce travail, nous avons donc selectionne ce dernier comme modele mathematique pour modeliser ces intervalles d'une image satellite sans feu et nous avons determine les parametres de cette realisation gaussienne. Ainsi, lorsqu'un feu survient dans cette zone de foret, nous pouvons utiliser ces parametres pour calculer sa probabilite d'appartenance a l'image originale sans feu. Cette probabilite doit etre tres faible puisque le feu, dans toute foret, peut etre considere comme un evenement rare. Par consequent, nous presentons une image matricielle de l'inverse de la probabilite de chaque intervalle pour une meilleure observation de detection des feux.

Belonging probability inverse image approach for forest fire detection

This paper presents a novel method for modeling the joint behavior of a number of synchronized Independent Component Analysis Mixture Models (ICAMM), which we have named Multi-chain ICAMM (MCICAMM). This allows flexible estimation of complex densities of data, subspace classification, blind source separation, accurate local dynamic learning, and global dynamic interaction. Furthermore, the proposed method can also be used for classification following the maximum a posteriori, forward-backward, or Viterbi procedures. MCICAMM outperformed competitive methods such as ICAMM, SICAMM, and Dynamic Bayesian Networks for the classification of simulated data and the automatic staging of electroencephalographic (EEG) data from epileptic patients performing a neuropsychological test for short-term memory. Therefore, the potential of the method to suit different kind of data densities and to deal with the changing non-stationarity and non-linearity of brain dynamics was demonstrated. MCICAMM parameters provide a structured result that might be interpreted in several applications.

Synchronized Multi-chain Mixture of Independent Component Analyzers

Heart Rate Variability (HRV) signals, derived from an Electrocardiogram signal (ECG), are strongly related to the activity of the Autonomous Nervous System (ANS). This paper suggests the new estimation technique to calculate the duration of the two activities of ANS (Sympathetic and Parasympathetic). The signal HRV is, naturally, non stationary because of the non steady state of the sympathetic and parasympathetic behaviour. The proposed method is, therefore, based on dividing this signal into stationary segments to obtain steady states. Each temporal segment is represented by a Gaussian white noise whose variance is also its Power Spectral Density (PSD). The results obtained, in this work, allows to localize as well as, more importantly, to estimate the dominance duration of either the sympathetic or the parasympathetic activities during the test, as result of this, we can calculate the duration of a person's stress in sensitive situations.

A hybrid method for Heart Rate Variability analysis

The Autonomous Nervous System (ANS) sympathovagal balance was studied using several features derived from Heart Rate Variability signals (HRV). The HRV signals are, however naturally, non-stationary since their statistical properties vary under time transition. A useful approach to quantifying them is, therefore, to consider them as consisting of some intervals that are themselves stationary. To obtain the latter, we have applied the so called the KS-segmentation algorithm which is an approach deduced from the KolmogorovSmirnov (KS) statistics. To determine, accurately, these features, we have used the ReliefF algorithm which is one of the most successful strategies in feature selection; this step allows us to select the most relevant features from thirty three features at the beginning. As result the ratio between LF and HF band powers of HRV signal, the Standard Deviation of RR intervals (SDNN), and Detrended Fluctuation Analysis with Short term slope (DFA α1), are more accurate for each stationary segment, and present the best results comparing with other features for the classification of the three stages of stress in real world driving tasks (Low, Medium and High stress). Key-Words: HRV, KS-segmentation algorithm, Wavelet packet, ANS sympatho-vagal balance evolution

Ahmed Bouziane

Papers

Belonging probability inverse image approach for forest fire detection

Synchronized Multi-chain Mixture of Independent Component Analyzers

A hybrid method for Heart Rate Variability analysis

Study of the ANS Sympathovagal Behavior Using the ReliefF and the KS-Segmentation Algorithms

Acetone discriminator and concentration estimator for diabetes monitoring in human breath