On the Performance Analysis of a Class of Transform-domain NLMS Algorithms with Gaussian Inputs and Mixture Gaussian Additive Noise Environment

Chan, S. C.; Zhou, Yuanfeng

doi:10.1007/s11265-010-0494-5

Cited by 7 publications

(4 citation statements)

References 26 publications

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“…), we have segmented each matrix line into small stationary intervals. To represent each of these intervals by a Gaussian stationary noise (Rice, ; Hida & Hitsuda, ; Chan & Zhou, ; Chang & Liu, ; Nakamori, ) as described above, we have computed the Gaussian parameters the mean

{\overset{false^}{m}}_{i n}

and the variance

{\overset{false^}{italicσ}}_{i n}^{2}

for each resulting interval using the following expressions, respectively:

{\overset{m}{true}}_{i n} = \frac{1}{L} \sum_{k = l - L}^{l - 1} x_{i n} false(k false)

{\overset{σ}{true}}_{i n}^{2} = \frac{1}{L} \sum_{k = l - L}^{l - 1} {false(x_{i n} (k) - {\overset{m}{true}}_{i n} false)}^{2}

where x i ( l − L ),…, x i ( l − 1) are the values of the n th ( n = 1,2… N ) interval of the i th ( i = 1,2… I ) image matrix line, L is the interval length and l = n . L .…”

Section: Resultsmentioning

confidence: 99%

Belonging probability inverse image approach for forest fire detection

et al. 2013

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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.Key words: belonging probability matrix image, forest fire, satellite image, thermal infra-red spectral band R esum e Nous pr esentons une m ethode de d etection pr ecoce des feux de forêts par image satellite en utilisant l'image matricielle de la probabilit e d'appartenance. Nous avons consid er e chaque ligne matricielle de l'image satellite comme une r ealisation d'un processus al eatoire non stationnaire dans la bande spectrale TIR (Thermal InfraRouge), puis divis e chaque ligne en tr es petits intervalles stationnaires et ergodiques, afin d'obtenir un mod ele math ematique ad equat. Ensuite, les pixels de l'image satellite sont consid er es comme statistiquement ind ependants, et donc chaque petit intervalle de chaque ligne se comporte, naturellement, comme un bruit gaussien stationnaire. Dans ce travail, nous avons donc s electionn e ce dernier comme mod ele math ematique pour mod eliser ces intervalles d'une image satellite sans feu et nous avons d etermin e les param etres de cette r ealisation gaussienne. Ainsi, lorsqu'un feu survient dans cette zone de forêt, nous pouvons utiliser ces param etres pour calculer sa probabilit e d'appartenance a l'image originale sans feu. Cette probabilit e doitêtre tr es faible puisque le feu, dans toute forêt, peutêtre consid er e comme un ev enement rare. Par cons equent, nous pr esentons une image matricielle de l'inverse de la probabilit e de chaque intervalle pour une meilleure observation de d etection des feux.

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{\overset{false^}{m}}_{i n}

and the variance

{\overset{false^}{italicσ}}_{i n}^{2}

for each resulting interval using the following expressions, respectively:

{\overset{m}{true}}_{i n} = \frac{1}{L} \sum_{k = l - L}^{l - 1} x_{i n} false(k false)

{\overset{σ}{true}}_{i n}^{2} = \frac{1}{L} \sum_{k = l - L}^{l - 1} {false(x_{i n} (k) - {\overset{m}{true}}_{i n} false)}^{2}

where x i ( l − L ),…, x i ( l − 1) are the values of the n th ( n = 1,2… N ) interval of the i th ( i = 1,2… I ) image matrix line, L is the interval length and l = n . L .…”

Section: Resultsmentioning

confidence: 99%

Belonging probability inverse image approach for forest fire detection

et al. 2013

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“…As mentioned earlier the signal Eigenvalue can be minimized by converting the signal to Wavelet domain and the Krylov method can be used to address the sparsity [9][10].The materials and methods are illustrated in Fig. 1.through following phases.…”

Section: Methodsmentioning

confidence: 99%

“…In [8] Krylov subspace is used to reduce the rank of large scale state space models by taking fusion machine as an example. In [9] This latest research is related to our proposed research because it customs the Krylov matrix to mollify their issues [5][6][7][8], whereas it caters convergence issue [9].…”

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confidence: 99%

Investigating Multi-Array Antenna Signal Convergence using Wavelet Transform and Krylov Sequence

Sikander¹,

Hussain²,

Memon³

2018

Mehran Univ. res. j. eng. technol.

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“…1 -Segment the signal (or each matrix line of the image) into small enough stationary segments 2 -Compute the parameters (means and variances) of each of these normal segments using equations ( 3) and (4) below to represent each of them by a corresponding GWN model [20,21].…”

mentioning

confidence: 99%

Fault detection in photovoltaic systems using the inverse of the belonging individual Gaussian probability

Sendjasni¹,

Yagoubi²,

Daoud³

et al. 2023

Diagnostyka

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This article addresses the problem of fault early detection in photovoltaic systems. In the production field, solar power plants consist of many photovoltaic arrays, which may suffer from many different types of malfunctions over time. Hence, fault early detection before it affects PV systems and leads to a full system failure is essential to monitor these systems. The fields of control and monitoring of systems have been extensively approached by many researchers using various fault detection methods. Despite all this research, to early detect and locate faults in a very large photovoltaic power plant, we must, in particular, think of an effective method that allows us to do so at the lowest costs and time. Thus, we propose a new robust technique based on the inverse of the belonging individual Gaussian probability (IBIGP) to early detect and locate faults in the power curve as well as in the Infrared image of the photovoltaic systems. While most fault detection methods are well incorporated in other domains, the IBIGP technique is still in its infancy in the photovoltaic field. We will show, however, in this work that the IBIGP technique is a very promising tool for fault early detection enhancement.

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On the Performance Analysis of a Class of Transform-domain NLMS Algorithms with Gaussian Inputs and Mixture Gaussian Additive Noise Environment

Cited by 7 publications

References 26 publications

Belonging probability inverse image approach for forest fire detection

Belonging probability inverse image approach for forest fire detection

Investigating Multi-Array Antenna Signal Convergence using Wavelet Transform and Krylov Sequence

Fault detection in photovoltaic systems using the inverse of the belonging individual Gaussian probability

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