Wildfire is an unplanned natural disaster that can threaten human lives. The assessment of wildfires and smoke flows is difficult and needs systems with good precision and rapidity. This study proposes an effective workflow for detecting fire and smoke in RGB video surveillance images with two basic stages: we suggest first to enhance the image quality and denoise the noisy images with a lightweight convolutional encoder decoder architecture then we detect fire and smoke with a preprocessing steps combined with an adaptive level set algorithm. The experimental results of the proposed method prove its ability and efficiency in detecting fires and smokes with a minimum of false negatives regions and over than 90% value of Jaccard index.

Abstract In recent decades, global warming has contributed to an increase in the number and intensity of wildfires destroying millions hectares of forest areas and causing many casualties each year. Firemen must therefore have the most effective means to prevent any wildfire from breaking out and to fight the blaze before being unable to contain and extinguish it. This article will present a new network architecture based on Convolutional Neural Network to detect and locate smoke and fire. This network generates fire and smoke masks in an RGB image by segmentation. The purpose of this work is to help firemen in assessing the extent of fire or monitor an incipient fire in real time with a camera embedded in a vehicle. To train this network, a database with the corresponding images and masks has been created. Such a database will allow to compare the performances of different networks. A comparison of this network with the best segmentation networks such as U‐Net and Yuan networks has highlighted its efficiency in terms of location accuracy, reduction of false positive classifications such as clouds or haze. This architecture is also efficient in real time.

Source separation in the statistical framework is usually managed using tensor decompositions or matrix joint diagonalization. In this letter, we propose one of the first coordinate algorithms for non-orthogonal simultaneous diagonalization of any order complex tensors. It relies on the optimization of an inverse criterion and on a particular decomposition allowing to derive each parameter in an independent way. In the framework of digital telecommunication source separation, computer simulations show the interest of using sets of high-order tensors. They also illustrate the overall interesting performances of the proposed algorithm in comparison to a Jacobi-like algorithm of matrix joint diagonalization and to a canonical polyadic decomposition algorithm.

Research on video analysis for fire detection has become a hot topic in computer vision. However, the conventional algorithms use exclusively rule-based models and features vector to classify whether a frame is fire or not. These features are difficult to define and depend largely on the kind of fire observed. The outcome leads to low detection rate and high false-alarm rate. A different approach for this problem is to use a learning algorithm to extract the useful features instead of using an expert to build them. In this paper, we propose a convolutional neural network (CNN) for identifying fire in videos. Convolutional neural network are shown to perform very well in the area of object classification. This network has the ability to perform feature extraction and classification within the same architecture. Tested on real video sequences, the proposed approach achieves better classification performance as some of relevant conventional video fire detection methods and indicates that using CNN to detect fire in videos is very promising.

Stabilizing blood glucose level for type 1 diabetic patients is a continuous challenge, the patient tries always to avoid hyperglycemia and hypoglycemia. In fact, the analysis of blood glucose measurements is one of the most important tasks in order to assess the glucose metabolic control. In this paper, we propose to use a numerical method to show that the blood glucose evolution of type 1 diabetes patients is a chaotic phenomenon. Indeed, we used Mutual Information (I) and the False Nearest Neighbors (FNN) to obtain the embedding delay (τ) and the embedding dimension (m) of our system and then to reconstruct the attractor. From the long time series of the blood glucose evolution of a type 1 diabetes patient including 17000 data, we have computed the Largest Lyapunov Exponent (LLE) that we found positive and which is the signature of a chaotic behavior. Thus, we have found that the characteristic time of this phenomenon should be about 11 minutes. As a consequence, it is possible to define a range of confidence in which we may predict the blood glucose evolution level of type 1 diabetic patients. Out of this range the process is chaotic.

In this paper, we consider the problem of non-orthogonal joint diagonalization of a set of hermitian matrices. This appears in many blind signal processing problems as source separation and independent component analysis. We propose a new Jacobi like algorithm based on a LU decomposition. The main point consists of the analytical derivation of the elementary two by two matrix. In order to determine the diagonalizing matrix parameters, we propose a useful approximation. Numerical simulations illustrate the overall good performances of the proposed algorithm in comparison to two other Jacobi like algorithms existing in the literature.

We consider the problem of non-orthogonal joint diagonalization of a set of complex matrices. This appears in many signal processing problems and is instrumental in source separation. We propose a new Jacobi-like algorithm based both on a special parameterization of the diagonalizing matrix and on an adapted local criterion. The optimization scheme is based on an alternate estimation of the useful parameters. Numerical simulations illustrate the overall very good performances of the proposed algorithm in comparison to two other Jacobi-like algorithms and to a global algorithm existing in the literature.

The total harmonic distortion (THD) is an important performance criterion for almost any communication device. In most cases, the THD of a periodic signal, which has been processed in some way, is either measured directly or roughly estimated numerically, while analytic methods are employed only in a limited number of simple cases. However, the knowledge of the theoretical THD may be quite important for the conception and design of the communication equipment (e.g. transmitters, power amplifiers). The aim of this paper is to present a general theoretic approach, which permits to obtain an analytic closed-form expression for the THD. It is also shown that in some cases, an approximate analytic method, having good precision and being less sophisticated, may be developed. Finally, the mathematical technique, on which the proposed method is based, is described in the appendix.

This paper introduces a new method to maximize kurtosis-based contrast functions. Such contrast functions appear in the problem of blind source separation of convolutively mixed sources: the corresponding methods recover the sources one by one using a deflation approach. The proposed maximization algorithm is based on the particular nature of the criterion. The method is similar in spirit to a gradient ascent method, but differs in the fact that a “reference” contrast function is considered at each line search. The convergence of the method to a stationary point of the criterion can be proved. The theoretical result is illustrated by simulation.

Non-Gaussian processes may require not only the information provided by first two moments, but also that given by the higher-order statistics, in particular, by the third- and fourth-order moments or cumulants. This paper addresses a fourth-order cumulant estimation problem for real discrete-time random non-i.i.d. signal, that can be approximated as an MA stochastic process. An unbiased estimator is proposed, studied and compared to two other frequently used estimators of the fourth-order cumulant (natural estimator and fourth$k$-statistics). Statistical comparative studies are undertaken from both bias and MSE points of view, for different distribution laws and MA filters. Algorithms, aiming to reduce computational complexity of the proposed estimator, as well as that of the fourth$k$-statistics bias, are also provided.

An optimum internal model with constraints is proposed and discussed for the control of a speech robot, which is based on the human-like behavior. The main idea of the study is that the robot movements are carried out in such a way that the length of the path traveled in the internal space, under external acoustical and mechanical constraints, is minimized. This optimum strategy defines the designed internal model, which is responsible for the robot task planning. First, an exact analytical way to deal with the problem is proposed. Next, by using some empirical findings, an approximate solution for the designed internal model is developed. Finally, the implementation of this solution, which is applied to the control of a speech robot, yields interesting results in the field of task-planning strategies, task anticipation (namely, speech coarticulation), and the influence of force on the accuracy of executed tasks.

This paper deals with the problem of blind source separation of convolutive MIMO mixtures by a deflation procedure. Contrast functions showing a quadratic dependence with respect to the searched parameters have recently been proposed. Combined with a fast SVD-based optimization technique, they proved to be very efficient for the extraction of one source signal. In this contribution, we examine how these contrast functions behave in a deflation scenario. We show that the SVD-based optimization method requires a good knowledge of the filter orders due to its sensitivity on a rank estimation. To overcome the difficulty, we propose an optimal step size gradient algorithm.

The paper deals with the problem of the blind extraction of a source signal after a MIMO convolutive mixture. The extraction is performed using a MISO equalizer. A contrast function based on high order statistics is first proposed. It is more general than the existing contrast in the same context and exhibits a cubic dependence w.r.t. the unknown equalizer parameters. This allows us to propose a new extraction algorithm based on a third order tensor decomposition. Computer simulations illustrate the good behavior and the usefulness of our algorithm.

This letter addresses the problem of the nonunitary joint block diagonalization of a given set of complex matrices whose potential applications stem from the blind separation of convolutive mixtures of sources and from the array processing. The proposed algorithm is based on the algebraic optimization of a least-mean-square criterion. One of its advantage is that a pre-whitening stage is no more compulsorily required when this algorithm is applied in the blind source separation context. Computer simulations are provided in order to illustrate its behavior in three cases: when exact block-diagonal matrices are built, then when they are progressively perturbed by an additive Gaussian noise and, finally, in the context of blind separation of convolutive mixtures of temporally correlated sources with estimated correlation matrices. A comparison with a classical orthogonal joint block diagonalization algorithm is also performed, and a new performance index is introduced to measure the performance of the separation.

In this communication, we propose a new method to blindly identify the mixing matrix of a possibly under-determined mixture of cyclostationary source signals. It is based on the use of a linear operator applied on the observations correlation matrix. Exploiting the properties of the above transformed matrix, a set of cyclic frequencies is first estimated. Then it is used to construct different estimations of the mixing matrix column vectors. Finally using a classification procedure, the mixing matrix is then estimated

The paper deals with blind source separation by contrast function maximization. A general class of separation criteria valid for both i.i.d. and non i.i.d. sources is exhibited; it is based on third or higher order cross-cumulants between the separator outputs and fixed signals, called references. We show that this approach is applicable not only in a semi-blind context, but also in a completely blind scenario. The most interesting feature concerning our criteria is their quadratic form. It follows a highly simplified optimization procedure which we describe. Simulation results illustrate the validity of our approach, and the appeal of this new class of contrast functions.

This paper is concerned with blind separation of source signals using time-frequency representations. We show that the separation can be realized through the non-orthogonal joint zero diagonalization of spatial quadratic time frequency matrices. One advantage of the proposed method is that it does not require any whitening stage and thus it is intended to work even with a class of correlated signals.

The separation problem of an instantaneous mixture of deterministic or random source signals is addressed. We show that the separation can be realized through the nonorthogonal joint diagonalization of spatial quadratic time-frequency matrices. One advantage of the proposed method is that it does not require any whitening stage, and thus, it is intended to work even with a class of correlated signals. We also propose a general automatic time-frequency point selection procedure for the determination of the above matrices to be joint diagonalized. An analytical example and computer simulations are provided in order to illustrate the effectiveness of the proposed approach and to compare it with classical ones.

The paper is concerned with blind separation of convolutive mixtures of mutually independent signals. We consider the MISO extraction of one source signal based on the maximization of a contrast function (CF); a new, so-called "reference" CF is proposed, which is based on cross-statistics between the estimated output and a reference signal. The proposed CF is valid both for i.i.d. and non i.i.d. sources. It presents the advantage over other CFs to be a quadratic function, which makes its optimization much easier to realize. Finally, simulations demonstrate the validity of this CF and show that it leads to improved separation performances.

In this letter, we consider contrast functions, which are useful tools for the sources separation problem. We present new generalized contrasts by considering a so-called reference signal. In particular, this allows us to show that a criterion recently proposed in the literature is a contrast. Furthermore, a link with a joint-diagonalization criterion is also emphasized. Finally, we show that another classical contrast can also be extended by considering a reference signal.

In this paper, we consider the estimation problem of the frequency characteristics of a nonlinear FM and monocomponent signal. The instantaneous frequency (IF) of such a signal can be highlighted by a Time-Frequency Transform (TFT). Using the properties of the cross TFT, we develop an iterative algorithm taking into consideration the presence of wrong frequencies in the estimated IF sequence of the signal. Finally, a computer simulation is performed to illustrate the behavior of this iterative scheme with different TFT.

In this paper, we consider the blind signal separation problem in the convolutive case. More precisely, we present a generalization of classical contrast functions to more flexible asymmetric forms and give examples of these new criteria. We also realize a statistical study of the proposed source separation approach, including both the consistency and the asymptotic normality aspects.

We consider the problem of estimation of a nonstationary signal. The time-frequency characteristics and, more precisely, the instantaneous frequency (IF) of such a signal can be highlighted by the Wigner-Ville transform (WVT). Using the properties of the cross WVT, we develop an iterative algorithm taking into consideration the presence of wrong frequencies in the estimated IF sequence of the signal. Finally, computer simulations are performed in order to illustrate the behavior of this iterative scheme.

We consider the problem of blind multivariate signal equalization. Assuming that the input signals are i.i.d. and statistically mutually independent, we propose both a generalization of some available equalization criteria and a generalization of some source separation criteria to the convolutive case. Hence, we obtain a new generalized class of objective function for blind equalization.

We consider the problem of convolutive blind signal separation through the optimization of contrast functions. We show that some links between contrasts and joint diagonalization criteria can be exhibited in the convolutive case. This allows us to devise a constructive algorithm performing MIMO blind equalization, with the help of a joint approximate diagonalization of a set of matrices built from the observations. This analytical algorithm can be run block-wise, which is appropriate in the context of short burst communications.

In this paper, considering the complex case, we extend some results leading to the popular JADE algorithm to cumulants of any order greater than or equal to three. We first exhibit a new contrast function which constitutes a generalization for the underlying contrast of JADE which thus appears as a particular case. Then we generalize the link between this new contrast and a joint-diagonalization criterion of a set of matrices. Moreover, in the two sources case, we show that the generalized contrast can be written as a simple quadratic form whatever the cumulant order. Finally, some computer simulations illustrate the potential advantage one can take of considering statistics of different orders for the joint-diagonalization of cumulant matrices.

This paper deals with independence measures for linear mixtures of mutually independent random variables. Such measures, also known as contrasts, constitute useful criteria in solving blind source separation problems. By making use of the Schur convexity properties, we show that it is possible to define a wide-ranging class of contrast functions based on the auto-cumulants of the components of the random vector being considered. Among the most appealing characteristics of these new contrast functions is that they can be used to combine cumulants of different orders in a flexible way. Furthermore, extensions of existing cross-cumulant-base contrasts are proposed. Finally, some particularization of our approach to measures of decorrelation is considered. A general characterization of these decorrelation measures using strictly Schur convex functions is provided.

In this paper, we consider the source separation problem through a block algorithm based on the maximization of contrast functions. We propose a new contrast with parameterized cross-cumulants. It allows us to put three classical contrasts in a common framework. Following the same spirit of the independent component analysis (ICA) algorithm, we derive the analytical solution for the case of two sources. Finally, a computer simulation is performed to illustrate the behavior of a Jacobi-like algorithm for the maximization of the new contrast.

Contrast-based separation of sources have a number of advantages. Among others, they are optimal (in a precise sense) in the presence of noise of unknown statistics. Here a new contrast is proposed that allows not only to obtain the optimal solution analytically, but also yields better performances in terms of variance of the estimated mixing matrix. This contrast needs the source kurtosis to have the same sign, and is thus appropriate to multichannel blind equalization in communications.

Extended forms of contrast functions are introduced to provide statistical measures of independence for orthogonal mixtures. We also define semicontrasts based on second-order statistics which, in some cases, may be sufficient to separate the mixed sources. The corresponding criteria are then used to obtain an optimized representation of a stochastic process in an orthonormal basis of wavelet packets or local cosines.

Two contrasts for the problem of multichannel blind deconvolution have been given and theoretically studied by Comon [1996]. The maximization of these criteria allows us to solve the problem of multi-input/multi-output (MIMO) blind deconvolution. In this paper, we show that many other contrast functions may be considered. The two aforementioned criteria are proved to be included in the wide class of contrast functions, which is here defined through simple conditions.

In order to perform separation of a mixture of sources, an interesting approach is to maximise a contrast function: e.g. the contrast of Comon. This paper brings two novel contributions (i) a novel algorithm as proposed in order to adaptively maximise Comon's contrast. However it requires a preprocessing whitening operation which is awkward when the mixture is ill-conditioned. (ii) A new criterion is defined that is free of the prewhitening step. In the case of two sources it can be proved that this criterion is a contrast. This contrast can also be adaptively maximized and has the additional advantage not to require identical signs for the fourth-order cumulants of the sources. Achievement of these two adaptive algorithms is demonstrated using a new performance index.>