EP0834845A1 - Method for frequency analysis of a signal - Google Patents

Method for frequency analysis of a signal Download PDF

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Publication number
EP0834845A1
EP0834845A1 EP96115952A EP96115952A EP0834845A1 EP 0834845 A1 EP0834845 A1 EP 0834845A1 EP 96115952 A EP96115952 A EP 96115952A EP 96115952 A EP96115952 A EP 96115952A EP 0834845 A1 EP0834845 A1 EP 0834845A1
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Prior art keywords
wavelet
signal
fuzzy logic
pass filter
frequency
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EP96115952A
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German (de)
French (fr)
Inventor
Marc Pierre Dr. Thuillard
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Cerberus AG
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Cerberus AG
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Priority to EP96115952A priority Critical patent/EP0834845A1/en
Priority to DE59706608T priority patent/DE59706608D1/en
Priority to US09/077,106 priority patent/US6011464A/en
Priority to PL97327070A priority patent/PL327070A1/en
Priority to JP10517041A priority patent/JP2000503438A/en
Priority to EP97939930A priority patent/EP0865646B1/en
Priority to AT97939930T priority patent/ATE214504T1/en
Priority to KR1019980704157A priority patent/KR19990071873A/en
Priority to PCT/CH1997/000354 priority patent/WO1998015931A1/en
Priority to CN97191373A priority patent/CN1129879C/en
Publication of EP0834845A1 publication Critical patent/EP0834845A1/en
Withdrawn legal-status Critical Current

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    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B29/00—Checking or monitoring of signalling or alarm systems; Prevention or correction of operating errors, e.g. preventing unauthorised operation
    • G08B29/18—Prevention or correction of operating errors
    • G08B29/185—Signal analysis techniques for reducing or preventing false alarms or for enhancing the reliability of the system
    • G08B29/186—Fuzzy logic; neural networks
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B17/00—Fire alarms; Alarms responsive to explosion
    • G08B17/02—Mechanical actuation of the alarm, e.g. by the breaking of a wire

Definitions

  • the invention relates to a method for frequency analysis of a signal using wavelets and Fuzzy logic, in particular an output signal from a safety detector such as one Flame detectors, noise detectors, fire detectors, passive infrared detectors or the like to avoid false alarms.
  • a safety detector such as one Flame detectors, noise detectors, fire detectors, passive infrared detectors or the like to avoid false alarms.
  • the output signals from safety detectors are often typical for them Frequency spectra marked. By analyzing these frequency spectra, the origin can be determined of the signals can be determined, and especially real alarm signals from interference signals differentiate and avoid false alarms. Especially with flame detectors the typical low frequency flickering of a flame is analyzed to determine the radiation from real flames from a source of interference such as reflected sunlight or to distinguish flickering light source.
  • the wavelet transform is as described, for example, in "The Fast Wavelet Transform” (Mac A. Cody, Dr. Dobb's Journal, April 1992), a transformation or illustration of a signal from the time domain to the frequency domain and is therefore basically the Fourier transform and Fast Fourier transform similar. It differs from these but through the basic function of the transformation, after which the signal is developed.
  • a Fourier transform uses a sine and cosine function, which in Frequency range are localized sharply and are undetermined in the time range.
  • a wavelet transformation a so-called wavelet or wave packet is used.
  • Gaussian, Spline or Haar wavelet each through two parameters arbitrarily shifted in the time domain and stretched in the frequency domain or can be compressed.
  • a wavelet transformation can therefore be used in both Signals localized in time as well as in the frequency domain are transformed.
  • a fast Wavelet transformation is carried out by the pyramid algorithm according to Mallat, which is based on repeated application of a low-pass and high-pass filter, by which the low-frequency from the high-frequency signal components are separated. Doing so in each case the output signal of the low-pass filter in turn a pair of low / high-pass filters fed.
  • a series of approximations of the original signal results, of which each has a coarser resolution than the previous one.
  • the number of operations for the Transformation is required is proportional to the length of the original signal, while in the Fourier transform this number is disproportionate to the signal length.
  • the fast wavelet transformation can also be performed inversely by using the original signal from the approximated values and coefficients for the reconstruction is restored.
  • the algorithm for the decomposition and reconstruction of the signal as well as a table of the coefficients of the decomposition and reconstruction are based on the example for a spline wavelet in "An Introduction to Wavelets” by Charles K. Chui (Academic Press, San Diego, 1992).
  • Fuzzy logic is well known.
  • Signal values known as fuzzy sets, or unsharp amounts according to a Membership function, where the value of the membership function, or the degree of belonging to a fuzzy set is between zero and one. It is important that the membership function can be normalized, i.e. the sum of all Values of the membership function equal to one, where by the fuzzy logic evaluation one clear interpretation of the signal allowed.
  • Known applied analyzes for the output signals of security detectors are for For example the Fourier analysis, the Fast Fourier analysis, the zero crossing method or Turning point method. The latter is used in GB 2 277 989 for flame detectors described. Here the time spans between radiation maxima (turning points) measured and checked for their regularities and irregularities. In doing so Irregular radiation maxima as a flame and regular as a disturbance interpreted.
  • the object of the invention is to provide a method for frequency analysis of a signal create that is combined with a fuzzy logic evaluation and compared to State of the art analysis method with a smaller number of calculation steps is carried out so that in a shorter time a result of the same or higher accuracy is achieved. Furthermore, the method is intended to be a simpler processor and thereby be more cost-effective.
  • the object is achieved according to the invention by a method for frequency analysis of a Signal solved that a fast wavelet transformation of the signal with a fuzzy logic evaluation united, with the original signal in the wavelet transform a multi-stage filter cascade of high / low pass filter pairs is carried out and by each Level of the filter cascade from the initial values of the high pass filter one Membership function is generated directly in this form for further analysis of the Frequency signal is used according to fuzzy logic rules.
  • a Safety detectors allow the results of the fuzzy evaluation to decide whether an alarm is issued or an interference signal is present.
  • the number of required Computational steps for the wavelet analysis is significant in comparison to Fourier analyzes reduced. This is the necessary computing time to identify the signal, and it this reduces the cost of the processor.
  • the original digitized signal is first replaced by a fast one Wavelet transformation analyzed.
  • the signal is processed according to the Mallat algorithm through several stages of a cascade of high and low pass filter pairs. From the Results of the high-pass filter then become a membership function at each filter level ⁇ generated, which contains the sum of the calculated values from the high pass filter and by the sum of the squares of the original signal values is divided. The sum of the Membership functions ⁇ that are generated here for each filter level are the same or almost equal to one.
  • Frequency analysis of this type gives the following advantages.
  • the high-pass filters of the wavelet transform first provide information about the high-frequency signals. This is Particularly advantageous in the flame report, since it contains information about the higher ones Frequencies speeds up the identification of the type of signal and increases its accuracy can be. For example, if a high-frequency signal of over 15 Hz is detected, this interpreted as an interference signal. The subsequent message, fault signal or alarm signal, takes place earlier and is more certain to be correct.
  • Wavelets are often very in their form simple, such as a hair wavelet, and allow analysis with few Arithmetic steps, which additionally shortens the computing time and decision time. Are less Lines of code required, an inexpensive processor can also be used. The Shortening the decision time is not, however, a loss of accuracy Signal identification connected.
  • a is used for the wavelet transformation orthonormal or semi-orthonormal wavelet or a wavelet packet basis used.
  • the membership functions are derived from the results of the high pass filter and the wavelet coefficient for the reconstruction of the original signal. More specifically, the membership function contains a weighted by the wavelet coefficients Sum of the squared values of the high pass filter and in the denominator the sum of the squared Value of the original signal. The sum of these membership functions is approximate here equal to one, especially if the original signal contains enough values.
  • the membership functions are then used for a fuzzy logic evaluation of the Frequency information used.
  • the wavelet transformation is carried out using an orthonormal or semi-orthonormal wavelet or a wavelet packet basis, where at a membership function is created for each filter level, which is the sum of the squared Output values of the high pass filter and in the denominator the sum of the squared values of the original signal contains.
  • membership functions are in turn normalized and are used in this form directly for a fuzzy logic evaluation of the frequency information used.
  • FIGS. 1 and 2 show a block diagram the method with the fast wavelet analysis through several filter stages and Further analysis using fuzzy logic.
  • Figure 2 shows membership functions using the example of a Frequency analysis using a fast hair wavelet transformation.
  • a fast wavelet transformation is first carried out using any wavelet as is known in the prior art.
  • An orthonormal or semi-orthonormal wavelet or a wavelet packet base is preferably used.
  • the signal values are denoted by x i, k and y i, k , where x means the original signal values and the values from the low-pass filters (LP) and y the values from the high-pass filters (HP).
  • the index i denotes the level of the filter cascade in increasing numbers, the original signal being at level zero.
  • the index k denotes an individual value of a signal.
  • An original signal x 0, k at zero level is assumed, which is transformed by several filterings.
  • the output signal of the first high-pass filter gives the values y 1, k and the output signal of the low-pass filter gives the values x 1, k , which also forms the input signal for the second filter stage.
  • the output signal of the second high-pass filter gives the values y 2, y , that of the low-pass filter x 2, k is in turn fed to a third pair of filters , etc. It should be noted here that the number of values resulting from the filter stages is different for each stage . More precisely, at each level the number of values decreases by a factor of two.
  • the coefficients p and q for the wavelet reconstruction can again be found in the above-mentioned book.
  • the membership functions ⁇ i are now generated from the output values of the high-pass filter of the respective filter stage and the associated coefficients q for the wavelet reconstruction.
  • the digitized raw values x 0 , k are subjected to a quick hair analysis.
  • membership functions ⁇ are shown as a function of the frequency ⁇ , which have been generated from the results of a fast Haar wavelet transformation.
  • ⁇ N + 1 illustrate the degree of affiliation of very low frequencies
  • ⁇ N that of low frequencies
  • ⁇ 1 and ⁇ 2 the degree of affiliation of high and medium frequencies ⁇ . It is clearly evident here that the sum of the curve values is one for each selected frequency ⁇ .
  • This method is suitable for differentiation when used on flame detectors between interference signals, such as periodic signals above 15 Hz, and real ones Flame signals, such as narrow-band signals of low frequency or broadband signals in the low frequency range.
  • interference signals such as periodic signals above 15 Hz
  • Flame signals such as narrow-band signals of low frequency or broadband signals in the low frequency range.
  • the procedure for evaluating signals is also passive for noise detectors Infrared detector, for the spectral analysis of the signals of individual pixels in image processing as well as for various sensors such as gas and vibration sensors.

Abstract

The method involves a rapid wavelet transformation and fuzzy logic. The original signal is passed through a cascade of high/low pass filter pairs during wavelet transformation. The rapid wavelet transformation is combined with a fuzzy logic evaluation, whereby a corresp. function is generated from the resultant of the high pass filter for each filter stage. The corresp. function is used for the further analysis of the frequency signal using fuzzy logic rules.

Description

Die Erfindung betrifft ein Verfahren zur Frequenzanalyse eines Signals mittels Wavelets und Fuzzy-Logik, insbesondere eines Ausgangssignals eines Sicherheitsmelders wie einem Flammenmelder, Geräuschmelder, Brandmelder, passiven Infrarotmelder oder dergleichen zwecks Vermeidung von Fehlalarmen.The invention relates to a method for frequency analysis of a signal using wavelets and Fuzzy logic, in particular an output signal from a safety detector such as one Flame detectors, noise detectors, fire detectors, passive infrared detectors or the like to avoid false alarms.

Die Ausgangssignale von Sicherheitsmeldern sind häufig durch für sie typische Frequerzspektren gekennzeichnet. Durch Analyse dieser Frequenzspektren kann die Herkunft der Signale bestimmt werden, und es können vor allem echte Alarmsignale von Störsignalen unterschieden und dadurch Fehlalarme vermieden werden. Insbesondere bei Flammenmeldem wird das typische niederfrequente Flackern einer Flamme analysiert, um die Strahlung von echten Flammen von der einer Störquelle wie zum Beispiel reflektiertem Sonnenlicht oder einer flackernden Lichtquelle zu unterscheiden.The output signals from safety detectors are often typical for them Frequency spectra marked. By analyzing these frequency spectra, the origin can be determined of the signals can be determined, and especially real alarm signals from interference signals differentiate and avoid false alarms. Especially with flame detectors the typical low frequency flickering of a flame is analyzed to determine the radiation from real flames from a source of interference such as reflected sunlight or to distinguish flickering light source.

Die Wavelet-Transformation ist, wie sie zum Beispiel in "The Fast Wavelet-Transform" (Mac A. Cody, Dr. Dobb's Journal, April 1992) beschrieben ist, eine Transformation oder Abbildung eines Signals vom Zeitbereich in den Frequenzbereich und ist also grundsätzlich der Fourier-Transformation und Fast-Fourier-Transformation ähnlich. Sie unterscheidet sich von diesen aber durch die Basisfunktion der Transformation, wonach das Signal entwickelt wird. Bei einer Fourier-Transformation wird eine Sinus- und Cosinus-Funktion verwendet, die im Frequenzbereich scharf lokalisiert und im Zeitbereich unbestimmt sind. Bei einer Wavelet-Transformation wird ein sogenanntes Wavelet oder Wellenpaket verwendet. Hiervon gibt es verschiedene Typen wie zum Beispiel ein Gauss-, Spline- oder Haar-Wavelet, die jeweils durch zwei Parameter beliebig im Zeitbereich verschoben und im Frequenzbereich gedehnt oder komprimiert werden können. Es können also durch eine Wavelet-Transformation sowohl im Zeit- als auch im Frequenzbereich lokalisierte Signale transformiert werden. Eine schnelle Wavelet-Transformation erfolgt durch den Pyramiden-Algorithmus nach Mallat, der auf wiederholter Anwendung eines Tiefpass- und Hochpassfilters beruht, durch welche die niederfrequenten von den hochfrequenten Signalkomponenten getrennt werden. Dabei wird jeweils das Ausgangssignal des Tiefpassfilters wiederum einem Tief-/Hochpassfilterpaar zugeführt. Es resultiert eine Reihe von Approximationen des ursprünglichen Signals, wovon jede eine gröbere Auflösung besitzt als die vorhergehende. Die Anzahl Operationen, die für die Transformation erforderlich sind, ist jeweils proportional zur Länge des ursprünglichen Signals, während bei der Fourier-Transformation diese Anzahl überproportional zur Signallänge ist. Die schnelle Wavelet-Transformation kann auch invers durchgeführt werden, indem das ursprüngliche Signal aus den approximierten Werten und Koeffizitenten für die Rekonstruktion wiederhergestellt wird. Der Algorithmus für die Zerlegung und die Rekonstruktion des Signals sowie auch eine Tabelle der Koeffizienten der Zerlegung und Rekonstruktion sind am Beispiel für ein Spline Wavelet in "An Introduction to Wavelets" von Charles K. Chui (Academic Press, San Diego, 1992) gegeben.The wavelet transform is as described, for example, in "The Fast Wavelet Transform" (Mac A. Cody, Dr. Dobb's Journal, April 1992), a transformation or illustration of a signal from the time domain to the frequency domain and is therefore basically the Fourier transform and Fast Fourier transform similar. It differs from these but through the basic function of the transformation, after which the signal is developed. At a Fourier transform uses a sine and cosine function, which in Frequency range are localized sharply and are undetermined in the time range. With a wavelet transformation a so-called wavelet or wave packet is used. There are of these different types such as a Gaussian, Spline or Haar wavelet, each through two parameters arbitrarily shifted in the time domain and stretched in the frequency domain or can be compressed. A wavelet transformation can therefore be used in both Signals localized in time as well as in the frequency domain are transformed. A fast Wavelet transformation is carried out by the pyramid algorithm according to Mallat, which is based on repeated application of a low-pass and high-pass filter, by which the low-frequency from the high-frequency signal components are separated. Doing so in each case the output signal of the low-pass filter in turn a pair of low / high-pass filters fed. A series of approximations of the original signal results, of which each has a coarser resolution than the previous one. The number of operations for the Transformation is required is proportional to the length of the original signal, while in the Fourier transform this number is disproportionate to the signal length. The fast wavelet transformation can also be performed inversely by using the original signal from the approximated values and coefficients for the reconstruction is restored. The algorithm for the decomposition and reconstruction of the signal as well as a table of the coefficients of the decomposition and reconstruction are based on the example for a spline wavelet in "An Introduction to Wavelets" by Charles K. Chui (Academic Press, San Diego, 1992).

Die Fuzzy-Logik ist allgemein bekannt. In Bezug auf diese Erfindung ist hervorzuheben, dass Signalwerte sogenannten Fuzzy sets, oder unscharfen Mengen, gemäss einer Zugehörigkeitsfunktion zugeteilt werden, wobei der Wert der Zugehörigkeitsfunktion, oder der Grad der Zugehörigkeit zu einer unscharfen Menge, zwischen Null und Eins beträgt. Wichtig dabei ist, dass die Zugehörigkeitsfunktion normalisierbar sind, d.h. die Summe aller Werte der Zugehörigkeitsfunktion gleich Eins ist, wo durch die Fuzzy-Logik-Auswertung eine eindeutige Interpretation des Signals erlaubt.Fuzzy logic is well known. With regard to this invention, it should be emphasized that Signal values known as fuzzy sets, or unsharp amounts, according to a Membership function, where the value of the membership function, or the degree of belonging to a fuzzy set is between zero and one. It is important that the membership function can be normalized, i.e. the sum of all Values of the membership function equal to one, where by the fuzzy logic evaluation one clear interpretation of the signal allowed.

Bekannte angewandte Analysen für die Ausgangssignale von Sicherheitsmeldem sind zum Beispiel die Fourier-Analyse, die Fast-Fourier-Analyse, die Zero-Crossing-Methode oder Turning-Point-Methode. Letztere ist in GB 2 277 989 in Anwendung an Flammenmelder beschrieben. Hier werden die Zeitspannen zwischen Strahlungsmaxima (turning points) gemessen und auf ihre Regelmässigkeiten und Unregelmässigkeiten geprüft. Dabei werden unregelmässig auftretende Strahlungsmaxima als Flamme und regelmässige als Störung interpretiert.Known applied analyzes for the output signals of security detectors are for For example the Fourier analysis, the Fast Fourier analysis, the zero crossing method or Turning point method. The latter is used in GB 2 277 989 for flame detectors described. Here the time spans between radiation maxima (turning points) measured and checked for their regularities and irregularities. In doing so Irregular radiation maxima as a flame and regular as a disturbance interpreted.

In EP 0 718 814 wird die Frequenz der detektierten Strahlung analysiert und dabei zwischen regelmässigen und unregelmässigen Signalen in bestimmten Frequensbereichen unterschieden. Die Auswertung der verschiedenen Signale in den gegebenen Frequenzbereichen erfolgt nach mehreren Fuzzy-Logik-Regeln. Durch dieses Verfahren ist eine genauere Unterscheidung zwischen echten Flammensignalen und anderen Störsignalen und somit die Fehlalarmsicherheit ermöglicht. Die Erzeugung des Frequenzspektrums erfolgt hier zum Beispiel durch schnelle Fourier-Transformation, was bezüglich der für die Transformation erforderlichen Zeit, des notwendigen Prozessors und der Prozessorkosten aufwendig ist. Für die Bestimmung eines detektierten Signals sind zum Teil bis zu drei Sekunden erforderlich. Eine kürzere Auswertezeit und Reaktionszeit bis zur Alarmgebung ist jedoch in bestimmten Anwendungen erwünscht. Verfahren wie die Zero-Crossing- oder Turning-Point-Methode beschleunigen den Entscheidungsprozess, sind aber weniger genau.In EP 0 718 814 the frequency of the detected radiation is analyzed and between Distinguish regular and irregular signals in certain frequency ranges. The various signals in the given frequency ranges are evaluated several fuzzy logic rules. This procedure makes a more precise distinction between real flame signals and other interference signals and thus false alarm security enables. The frequency spectrum is generated here, for example, by fast ones Fourier transform, which in terms of the time required for the transformation, the necessary processor and the processor costs is expensive. For determining a Detected signals are sometimes required up to three seconds. A shorter one Evaluation time and response time until the alarm is given in certain applications he wishes. Procedures such as the zero crossing or turning point method accelerate the Decision making process, but are less accurate.

Der Erfindung ist die Aufgabe gestellt, ein Verfahren zur Frequenzanalyse eines Signals zu schaffen, das mit einer Fuzzy-Logik-Auswertung vereinigt ist und im Vergleich zu Analyseverfahren des Standes der Technik mit einer kleineren Anzahl Rechenschritten durchgeführt wird, sodass in kürzerer Zeit ein Resultat von gleicher oder höherer Genauigkeit erzielt wird. Ferner soll das Verfahren durch einen einfacheren Prozessor und dadurch kostengünstiger durchführbar sein.The object of the invention is to provide a method for frequency analysis of a signal create that is combined with a fuzzy logic evaluation and compared to State of the art analysis method with a smaller number of calculation steps is carried out so that in a shorter time a result of the same or higher accuracy is achieved. Furthermore, the method is intended to be a simpler processor and thereby be more cost-effective.

Die Aufgabe wird gemäss der Erfindung durch ein Verfahren zur Frequenzanalyse eines Signals gelöst, das eine schnelle Wavelet-Transformation des Signals mit einer Fuzzy-Logik-Auswertung vereinigt, wobei in der Wavelet-Transformation das ursprüngliche Signal durch eine mehrstufige Filterkaskade von Hoch/Tiefpassfilterpaaren geführt wird und indem bei jeder Stufe der Filterkaskade aus den Ausgangswerten des Hochpassfilters eine Zugehörigkeitsfunktion erzeugt wird, die direkt in dieser Form für eine weitere Analyse des Frequenzsignals nach Fuzzy-Logik-Regeln verwendet wird. In der Anwendung an einen Sicherheitsmelder erlauben die Resultate der Fuzzy-Auswertung einen Entscheid darüber, ob ein Alarm abgegeben wird oder ein Störsignal vorliegt. Die Anzahl erforderlicher Rechenschritte für die Wavelet-Analyse ist im Vergleich zu Fourier-Analysen bedeutend reduziert. Dadurch ist die notwendige Rechnerzeit zur Identifizierung des Signals, und es verringern sich dadurch die Kosten für den Prozessor.The object is achieved according to the invention by a method for frequency analysis of a Signal solved that a fast wavelet transformation of the signal with a fuzzy logic evaluation united, with the original signal in the wavelet transform a multi-stage filter cascade of high / low pass filter pairs is carried out and by each Level of the filter cascade from the initial values of the high pass filter one Membership function is generated directly in this form for further analysis of the Frequency signal is used according to fuzzy logic rules. In use to one Safety detectors allow the results of the fuzzy evaluation to decide whether an alarm is issued or an interference signal is present. The number of required Computational steps for the wavelet analysis is significant in comparison to Fourier analyzes reduced. This is the necessary computing time to identify the signal, and it this reduces the cost of the processor.

Gemäss der Erfindung wird das ursprüngliche digitalisierte Signal zunächst durch eine schnelle Wavelet-Transformation analysiert. Hierfür wird das Signal nach dem Algorithmus von Mallat durch mehrere Stufen einer Kaskade von Hoch- und Tiefpassfilterpaaren geführt. Aus den Resultaten der Hochpassfilter werden sodann bei jeder Filterstufe eine Zugehörigkeitsfurktion µ erzeugt, welche die Summe der gerechneten Werte aus dem Hochpassfilter enthält und durch die Summe der Quadrate der ursprünglichen Signalwerte dividiert ist. Die Summe der Zugehörigkeitsfunktionen µ, die hier bei jeder Filterstufe erzeugt werden, ist gleich oder nahezu gleich Eins. Diese normalisierten Zugehörigkeitsfunktionen werden sodann in dieser Form für eine Weiterführung der Frequenzanalyse mit Fuzzy-Logik verwendet.According to the invention, the original digitized signal is first replaced by a fast one Wavelet transformation analyzed. For this, the signal is processed according to the Mallat algorithm through several stages of a cascade of high and low pass filter pairs. From the Results of the high-pass filter then become a membership function at each filter level µ generated, which contains the sum of the calculated values from the high pass filter and by the sum of the squares of the original signal values is divided. The sum of the Membership functions µ that are generated here for each filter level are the same or almost equal to one. These normalized membership functions are then in this Form used for a continuation of frequency analysis with fuzzy logic.

Eine Frequenzanalyse dieser Art ergibt folgende Vorteile. Die Hochpassfilter der Wavelet-Transformation ergeben zuerst Informationen über die hochfrequenten Signale. Dies ist insbesondere in der Flammenmeldung vorteilhaft, da mit der Information über die höheren Frequenzen die Identifizierung der Art des Signals beschleunigt und ihre Genauigkeit erhöht werden kann. Wird zum Beispiel ein hochfrequentes Signal von über 15 Hz entdeckt, wird dieses als Störsignal gedeutet. Die darauffolgende Meldung, Störsignal oder Alarmsignal, erfolgt früher und ist mit grösserer Sicherheit richtig. Wavelets sind in ihrer Form oft sehr einfach, wie zum Beispiel ein Haar-Wavelet, und ermöglichen eine Analyse mit wenigen Rechenschritten, was die Rechenzeit und Entscheidungszeit zusätzlich verkürzt. Sind weniger Zeilen von Code erforderlich, kann auch ein kostengünstiger Prozessor eingesetzt werden. Die Verkürzung der Entscheidungszeit ist jedoch nicht mit einer Einbusse in der Genauigkeit der Signalidentifizierung verbunden. Frequency analysis of this type gives the following advantages. The high-pass filters of the wavelet transform first provide information about the high-frequency signals. This is Particularly advantageous in the flame report, since it contains information about the higher ones Frequencies speeds up the identification of the type of signal and increases its accuracy can be. For example, if a high-frequency signal of over 15 Hz is detected, this interpreted as an interference signal. The subsequent message, fault signal or alarm signal, takes place earlier and is more certain to be correct. Wavelets are often very in their form simple, such as a hair wavelet, and allow analysis with few Arithmetic steps, which additionally shortens the computing time and decision time. Are less Lines of code required, an inexpensive processor can also be used. The Shortening the decision time is not, however, a loss of accuracy Signal identification connected.

In einer ersten Ausführung der Erfindung wird für die Wavelet-Transformation ein orthonormales oder semi-orthonormales Wavelet oder auch eine Wavelet-Paket-Basis verwendet. Die Zugehörigkeitsfunktionen werden aus den Resultaten der Hochpassfilter und den Wavelet-Koeffizienten für die Rekonstruktion des ursprünglichen Signals gebildet. Genauer enthält die Zugehörigkeitsfunktion eine durch die Wavelet-Koeffizienten gewichtete Summe der quadrierten Werte des Hochpassfilters und im Nenner die Summe der quadrierten Wert des ursprünglichen Signals. Die Summe dieser Zugehörigkeitsfunktionen ist hier ungefähr gleich Eins, insbesondere dann, wenn das ursprüngliche Signal genügend viele Werte enthält. Die Zugehörigkeitsfunktionen werden sodann für eine Fuzzy-Logik-Auswertung der Frequenzinformation verwendet.In a first embodiment of the invention, a is used for the wavelet transformation orthonormal or semi-orthonormal wavelet or a wavelet packet basis used. The membership functions are derived from the results of the high pass filter and the wavelet coefficient for the reconstruction of the original signal. More specifically, the membership function contains a weighted by the wavelet coefficients Sum of the squared values of the high pass filter and in the denominator the sum of the squared Value of the original signal. The sum of these membership functions is approximate here equal to one, especially if the original signal contains enough values. The membership functions are then used for a fuzzy logic evaluation of the Frequency information used.

In einer zweiten Ausführung wird die Wavelet-Transformation mittels einem orthonormalen oder semi-orthonormalen Wavelet oder einer Wavelet-Paket-Basis durchgeführt, wobei bei jeder Filterstufe eine Zugehörigkeitsfunktion erstellt wird, welche die Summe der quadrierten Ausgangswerte des Hochpassfilters und im Nenner die Summe der quadrierten Werte des ursprünglichen Signals enthält. Diese Zugehörigkeitsfunktionen sind wiederum normalisiert und werden in dieser Form direkt für eine Fuzzy-Logik-Auswertung der Frequenzinformation verwendet.In a second embodiment, the wavelet transformation is carried out using an orthonormal or semi-orthonormal wavelet or a wavelet packet basis, where at a membership function is created for each filter level, which is the sum of the squared Output values of the high pass filter and in the denominator the sum of the squared values of the original signal contains. These membership functions are in turn normalized and are used in this form directly for a fuzzy logic evaluation of the frequency information used.

Die Erfindung wird anhand der Figuren 1 und 2 näher erläutert. Figur 1 zeigt ein Blockschema des Verfahrens mit der schnellen Wavelet-Analyse durch mehrere Filterstufen und Weiteranalyse durch Fuzzy-Logik. Figur 2 zeigt Zugehörigkeitsfunktionen am Beispiel einer Frequenzanalyse mittels einer schnellen Haar-Wavelet-Transformation.The invention is explained in more detail with reference to FIGS. 1 and 2. Figure 1 shows a block diagram the method with the fast wavelet analysis through several filter stages and Further analysis using fuzzy logic. Figure 2 shows membership functions using the example of a Frequency analysis using a fast hair wavelet transformation.

In der ersten Ausführung der Erfindung wird zunächst eine schnelle Wavelet-Transformation mittels einem beliebigen Wavelet durchgeführt wie sie im Stand der Technik bekannt ist. Vorzugsweise wird ein orthonormales oder semi-orthonormales Wavelet oder eine Wavelet-Paket-Basis verwendet. Im folgenden sind die Signalwerte mit xi,k und yi,k bezeichnet, wobei x die ursprünglichen Signalwerte und die Werte aus den Tiefpassfiltern (LP) und y die Werte aus den Hochpassfiltern (HP) bedeuten. Der Index i bezeichnet in steigender Zahl die Stufe der Filterkaskade, wobei das ursprüngliche Signal auf Stufe Null ist. Der Index k bezeichnet einen individuellen Wert eines Signals. Es wird von einem ursprünglichen Signal x0,k auf der Stufe Null ausgegangen, das durch mehrere Filterungen transformiert wird. Das Ausgangssignal des ersten Hochpassfilters ergibt die Werte y1, k und das Ausgangssignal des Tiefpassfilters die Werte x1,k , das zugleich das Eingangssignal für die zweite Filterstufe bildet. Das Ausgangssignal des zweiten Hochpassfilters ergibt die Werte y2,y, das des Tiefpassfilters x2,k wird wiederum einem dritten Filterpaar zugeführt usw. Es ist hier zu bemerken, dass die Anzahl Werte, die aus den Filterstufen hervorgehen jeweils bei jeder Stufe verschieden ist. Genauer, bei jeder Stufe verkleinert sich die Anzahl Werte um den Faktor zwei. Bei der Stufe i+1 werden beispielsweise die Ausgangswerte eines Hochpassfilters durch yi+ 1,k = l al- 2k xi,l und die Ausgangswerte eines Tiefpassfilters durch xi+ 1,k = l bl- 2k xi,l ausgedrückt. Die Koeffizienten a und b für die Transformation sind im allgemeinen bekannt und können mit Hilfe des obengenannten Buches von Chui berechnet werden. Zum Beispiel für ein Haar-Wavelet sind a0=a1=1/2, b0=1/2 und b1=-1/2. Der Index 1 nimmt jeweils ganzzahlige Werte an, für die die Koeffizienten ungleich Null sind. Die Rekonstruktion des ursprünglichen Signals erfolgt stufenweise, indem die Werte jeder Filterstufe aus den Werten der vorherigen Stufe erstellt werden, nämlich x i,k = i (pk- 2l xi+ 1,l + qk- 2l yi+ 1,l ) . Die Koefzzienten p und q für die Wavelet-Rekonstruktion sind wiederum in obengenanntem Buch zu finden.In the first embodiment of the invention, a fast wavelet transformation is first carried out using any wavelet as is known in the prior art. An orthonormal or semi-orthonormal wavelet or a wavelet packet base is preferably used. In the following, the signal values are denoted by x i, k and y i, k , where x means the original signal values and the values from the low-pass filters (LP) and y the values from the high-pass filters (HP). The index i denotes the level of the filter cascade in increasing numbers, the original signal being at level zero. The index k denotes an individual value of a signal. An original signal x 0, k at zero level is assumed, which is transformed by several filterings. The output signal of the first high-pass filter gives the values y 1, k and the output signal of the low-pass filter gives the values x 1, k , which also forms the input signal for the second filter stage. The output signal of the second high-pass filter gives the values y 2, y , that of the low-pass filter x 2, k is in turn fed to a third pair of filters , etc. It should be noted here that the number of values resulting from the filter stages is different for each stage . More precisely, at each level the number of values decreases by a factor of two. At stage i + 1, for example, the output values of a high-pass filter are checked y i + 1 , k = l a l- 2nd k x i, l and the output values of a low-pass filter x i + 1 , k = l b l- 2nd k x i, l expressed. The coefficients a and b for the transformation are generally known and can be calculated using the Chui book mentioned above. For example, for a Haar wavelet, a 0 = a 1 = 1/2, b 0 = 1/2 and b 1 = -1 / 2. Index 1 takes integer values for which the coefficients are not equal to zero. The original signal is reconstructed in stages by creating the values of each filter stage from the values of the previous stage, namely x i, k = i ( p k- 2nd l x i + 1 , l + q k- 2nd l y i + 1 , l ) . The coefficients p and q for the wavelet reconstruction can again be found in the above-mentioned book.

Gemäss der Erfindung werden nun die Zugehörigkeitsfunktionen µi aus den Ausgangswerten des Hochpassfilters der jeweiligen Filterstufe und den dazugehörigen Koeffizienten q für die Wavelet-Rekonstruktion erzeugt.According to the invention, the membership functions μ i are now generated from the output values of the high-pass filter of the respective filter stage and the associated coefficients q for the wavelet reconstruction.

Dabei ist µ i = l qk -2l yi,l l' x 0,l' für i=1, 2, ....., N und µ N+ 1 = l pk -2l xN,l l' x 0,l' für i=N+1 , wobei N die Anzahl der Filterstufen ist. Die letztere Funktion µN+1 wird also durch die Ausgangswerte des letzten Tiefpassfilters gebildet.It is µ i = l q k -2 l y i, l l ' x 0 , l ' for i = 1, 2, ....., N and µ N + 1 = l p k -2 l x N, l l ' x 0 , l ' for i = N + 1, where N is the number of filter stages. The latter function µ N + 1 is thus formed by the output values of the last low-pass filter.

Diese Zugehörigkeitsfunktionen sind normalisiert, indem i µ i = 1 These membership functions are normalized by i µ i = 1

Eine oft gute Annäherung dieser Zugehörigkeitsfunktionen ist durch folgende Gleichung gegeben: µ i = l yi.l l' x 0,l' für i=1,2,....., N, und µ N+ 1 = l xN,l l' x 0,l' für i=N+1. An often good approximation of these membership functions is given by the following equation: µ i = l y il l ' x 0 , l ' for i = 1,2, ....., N, and µ N + 1 = l x N, l l ' x 0 , l ' for i = N + 1.

Bei dieser Annäherung ist die Funktion nahezu normalisiert, indem

Figure 00050001
With this approach, the function is almost normalized by
Figure 00050001

Bei einer besonderen Ausführung des Verfahrens werden die digitalisierten Rohwerte x0, k einer schnellen Haar-Analyse unterworfen. Aus den Werten yi,k jeder Filterstufe i werden Zugehörigkeitsfunktionen µi gebildet, nämlich: µ i = l yi,l l' x 0,l' für i=1,2,....., N, und µ N+ 1 = l xN,l l' x 0,l' für i=N+1. In a special embodiment of the method, the digitized raw values x 0 , k are subjected to a quick hair analysis. Membership functions µ i are formed from the values y i, k of each filter stage i, namely: µ i = l y i, l l ' x 0 , l ' for i = 1,2, ....., N, and µ N + 1 = l x N, l l ' x 0 , l ' for i = N + 1.

Diese Zugehörigkeitsfunktionen sind in diesem Fall normalisiert, indem i µ i = 1 ist.In this case, these membership functions are normalized by i µ i = 1 is.

In Figur 2 sind Zugehörigkeitsfunktionen µ als Funktion der Frequenz ω gezeigt, die aus den Resultaten einer schnellen Haar-Wavelet-Transformation erzeugt worden sind. Von den verschiedenen Kurven illustrieren µN+1 den Grad der Zugehörigkeit von sehr tiefen Frequenzen, µN den von tiefen Frequenzen, und µ1 und µ2 den Grad der Zugehörigkeit von hohen beziehungsweise mittleren Frequenzen ω. Es ist hier klar ersichtlich, dass bei jeder gewählten Frequenz ω die Summe der Kurvenwerte Eins beträgt.In FIG. 2, membership functions μ are shown as a function of the frequency ω, which have been generated from the results of a fast Haar wavelet transformation. Of the various curves, µ N + 1 illustrate the degree of affiliation of very low frequencies, µ N that of low frequencies, and µ 1 and µ 2 the degree of affiliation of high and medium frequencies ω. It is clearly evident here that the sum of the curve values is one for each selected frequency ω.

Bei allen Ausführungen des Verfahrens werden diese Zugehörigkeitsfunktionen für die Auswertung nach Fuzzy-Logik-Regeln verwendet, worauf eine Entscheidung gefällt wird, ob ein Alarmsignal ausgelöst wird oder das Signal als Störung bewertet wird.In all executions of the method, these membership functions for the Evaluation according to fuzzy logic rules is used, after which a decision is made as to whether an alarm signal is triggered or the signal is assessed as a fault.

In der Anwendung an Flammenmelder eignet sich dieses Verfahren zur Unterscheidung zwischen Störsignalen, wie zum Beispiel periodischen Signalen von über 15 Hz, und echten Flammensignalen, wie zum Beispiel schmalbandigen Signalen niederer Frequenz oder breitbandigen Signalen in niederem Frequenzbereich. Durch die schnelle Identifizierung von hochfrequenten Signalen werden die Störsignale dieser Frequenz und deren Resonanzfrequenzen vom Signal eliminiert, was die Frequenzanalyse des Signals beschleunigt. Durch die Beschleunigung der Frequenzanalyse durch die Wavelet-Transformation kann die erforderliche Zeit für eine Entscheidung über die Art des Signals und die abzugebende Meldung zum Beispiel von drei Sekunden auf eine Sekunde verringert werden.This method is suitable for differentiation when used on flame detectors between interference signals, such as periodic signals above 15 Hz, and real ones Flame signals, such as narrow-band signals of low frequency or broadband signals in the low frequency range. By quickly identifying high-frequency signals are the interference signals of this frequency and their Resonance frequencies eliminated from the signal, which speeds up the frequency analysis of the signal. By accelerating the frequency analysis through the wavelet transformation, the time required to decide on the type of signal and the type of signal to be delivered Message, for example, can be reduced from three seconds to one second.

Das Verfahren zur Auswertung von Signalen ist weiter auch für Geräuschmelder, passive Infrarotmelder, für die Spektralanalyse der Signale einzelner Pixel in der Bildverarbeitung sowie für verschiedene Sensoren wie Gas- und Vibrationssensoren geeignet.The procedure for evaluating signals is also passive for noise detectors Infrared detector, for the spectral analysis of the signals of individual pixels in image processing as well as for various sensors such as gas and vibration sensors.

Claims (6)

Verfahren zur Frequenzanalyse eines Signals mittels einer schnellen Wavelet-Transformation und Fuzzy-Logik, bei dem in der schnellen Wavelet-Transformation das ursprüngliche Signal durch eine mehrstufige Filterkaskade von Hoch-/Tiefpassfilterpaaren geführt wird, dadurch gekennzeichnet, dass die schnelle Wavelet-Transformation mit einer Fuzzy-Logik-Auswertung vereinigt wird, indem bei jeder Filterstufe der Wavelet-Transformation aus den Resultaten des Hochpassfilters jeweils eine Zugehörigkeitsfunktion erzeugt wird, die zur Weiteranalyse des Frequenzsignals nach Fuzzy-Logik-Regeln verwendet wird.Method for frequency analysis of a signal using a fast wavelet transformation and fuzzy logic, in which the fast wavelet transform original signal through a multi-stage filter cascade of high / low pass filter pairs is performed, characterized in that the fast wavelet transformation with a Fuzzy logic evaluation is combined by using the wavelet transform at each filter stage a membership function from the results of the high pass filter is generated, which is used for further analysis of the frequency signal according to fuzzy logic rules becomes. Verfahren nach Patentanspruch 1, dadurch gekennzeichnet, dass das für die schnelle Wavelet-Transformation verwendete Wavelet ein orthonormales oder semi-orthonormales Wavelet oder eine Wavelet-Paket-Basis ist und die erzeugten Zugehörigkeitsfunktionen jeweils die durch die Wavelet-Koeffizienten gewichtete Summe der quadrierten Werte des Hochpassfilters (HP) und die Summe der quadrierten Werte des ursprünglichen Signals enthalten und in normalisierter Form für die Weiteranalyse des Frequenzsignals nach Fuzzy-Logik-Regeln verwendet werden.Method according to claim 1, characterized in that for the fast Wavelet transform used Wavelet an orthonormal or semi-orthonormal Wavelet or a wavelet packet base and the membership functions generated in each case the sum of the squared values of the weighted by the wavelet coefficients High pass filter (HP) and the sum of the squared values of the original signal included and in normalized form for the further analysis of the frequency signal according to fuzzy logic rules be used. Verfahren nach Patentanspruch 1, dadurch gekennzeichnet, dass das für die Wavelet-Transformation verwendete Wavelet ein orthonormales oder semi-orthonormales Wavelet oder eine Wavelet-Paket-Basis ist und die erzeugten Zugehörigkeitsfunktionen jeweils die Summe der quadrierten Ausgangswerte des Hochpassfilters und die Summe der quadrierten Werte des ursprünglichen Signals enthalten und in normalisierter Form für die Auswertung des Frequenzsignals nach Fuzzy-Logik-Regeln verwendet werden.Method according to claim 1, characterized in that for the wavelet transformation Wavelet used an orthonormal or semi-orthonormal wavelet or is a wavelet packet basis and the membership functions generated are each the sum the squared output values of the high pass filter and the sum of the squared values of the Original signal included and in normalized form for the evaluation of the Frequency signal according to fuzzy logic rules are used. Verfahren nach den Patentansprüchen 1, 2 oder 3, dadurch gekennzeichnet, dass die Ausgangssignale die eines Sicherheitsmelders sind.Method according to claims 1, 2 or 3, characterized in that the Output signals that are a safety detector. Verfahren nach Patentanspruch 4, dadurch gekennzeichnet, dass die Ausgangssignale die eines Flammenmelders sind.Method according to claim 4, characterized in that the output signals that are a flame detector. Verfahren nach Patentanspruch 5, dadurch gekennzeichnet, dass die Frequenzanalyse und Auswertung der Ausgangssignale des Flammenmelders 100 ms bis 10 s dauert.Method according to claim 5, characterized in that the frequency analysis and evaluation of the output signals of the flame detector takes 100 ms to 10 s.
EP96115952A 1996-10-04 1996-10-04 Method for frequency analysis of a signal Withdrawn EP0834845A1 (en)

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DE59706608T DE59706608D1 (en) 1996-10-04 1997-09-19 METHOD FOR ANALYZING THE SIGNAL OF A RISK DETECTOR AND RISK DETECTOR FOR IMPLEMENTING THE METHOD
US09/077,106 US6011464A (en) 1996-10-04 1997-09-19 Method for analyzing the signals of a danger alarm system and danger alarm system for implementing said method
PL97327070A PL327070A1 (en) 1996-10-04 1997-09-19 Method of analysing signals from a hazard signalling device and hazard signalling device as such
JP10517041A JP2000503438A (en) 1996-10-04 1997-09-19 Method for analyzing a signal of a danger detector and a danger detector for implementing the method
EP97939930A EP0865646B1 (en) 1996-10-04 1997-09-19 Method for analyzing the signals of a danger alarm system and danger alarm system for implementing said method
AT97939930T ATE214504T1 (en) 1996-10-04 1997-09-19 METHOD FOR ANALYZING THE SIGNAL OF A HAZARD DETECTOR AND HAZARD DETECTOR FOR IMPLEMENTING THE METHOD
KR1019980704157A KR19990071873A (en) 1996-10-04 1997-09-19 A method for analyzing a hazard detection signal and a risk detector for performing the method
PCT/CH1997/000354 WO1998015931A1 (en) 1996-10-04 1997-09-19 Method for analyzing the signals of a danger alarm system and danger alarm system for implementing said method
CN97191373A CN1129879C (en) 1996-10-04 1997-09-19 Method for analyzing signal of danger alarm system and denger alarm system for implementing said method

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
AU776482B2 (en) * 2000-03-15 2004-09-09 Siemens Building Technologies Ag Method for the processing of a signal from an alarm and alarms with means for carrying out said method
WO2007012331A2 (en) * 2005-07-29 2007-02-01 V & M Deutschland Gmbh Method for error-free checking of tubes for surface faults

Families Citing this family (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6742183B1 (en) 1998-05-15 2004-05-25 United Video Properties, Inc. Systems and methods for advertising television networks, channels, and programs
US6219373B1 (en) * 1998-06-15 2001-04-17 The United States Of America As Represented By The Secretary Of The Navy Wavelet-based interference filtering for spread-spectrum signal
US6184792B1 (en) * 2000-04-19 2001-02-06 George Privalov Early fire detection method and apparatus
BR0209543A (en) * 2001-05-11 2005-04-26 Detector Electronics Flame detection and fire detection method and apparatus
FR2841424A1 (en) * 2002-06-25 2003-12-26 Koninkl Philips Electronics Nv METHOD FOR DETECTING BLOCK ARTEFACTS
US7202794B2 (en) * 2004-07-20 2007-04-10 General Monitors, Inc. Flame detection system
CN101711393A (en) * 2007-01-16 2010-05-19 Utc消防及保安公司 System and method based on the fire detection of video
US8094015B2 (en) * 2009-01-22 2012-01-10 International Business Machines Corporation Wavelet based hard disk analysis
US8941734B2 (en) * 2009-07-23 2015-01-27 International Electronic Machines Corp. Area monitoring for detection of leaks and/or flames
US8359616B2 (en) 2009-09-30 2013-01-22 United Video Properties, Inc. Systems and methods for automatically generating advertisements using a media guidance application
US8949901B2 (en) 2011-06-29 2015-02-03 Rovi Guides, Inc. Methods and systems for customizing viewing environment preferences in a viewing environment control application
CN103501205B (en) * 2013-10-11 2016-05-11 北京理工大学 Target Frequency Hopping Signal recognition methods based on fuzzy comprehensive evoluation

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4866420A (en) * 1988-04-26 1989-09-12 Systron Donner Corp. Method of detecting a fire of open uncontrolled flames
EP0706142A2 (en) * 1994-09-30 1996-04-10 Sensormatic Electronics Corporation Method and apparatus for detecting an EAS marker using wavelet transform signal processing
EP0718814A1 (en) * 1994-12-19 1996-06-26 Cerberus Ag Method and device for flame detection

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5453733A (en) * 1992-07-20 1995-09-26 Digital Security Controls Ltd. Intrusion alarm with independent trouble evaluation
US5815198A (en) * 1996-05-31 1998-09-29 Vachtsevanos; George J. Method and apparatus for analyzing an image to detect and identify defects

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4866420A (en) * 1988-04-26 1989-09-12 Systron Donner Corp. Method of detecting a fire of open uncontrolled flames
EP0706142A2 (en) * 1994-09-30 1996-04-10 Sensormatic Electronics Corporation Method and apparatus for detecting an EAS marker using wavelet transform signal processing
EP0718814A1 (en) * 1994-12-19 1996-06-26 Cerberus Ag Method and device for flame detection

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
AKAY Y M ET AL: "NONINVASIVE DETECTION OF CORONARY ARTERY DISEASE", IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE, vol. 13, no. 5, November 1994 (1994-11-01), pages 761 - 764, XP000598968 *
CHAKRABARTI C ET AL: "EFFICIENT REALIZATIONS OF THE DISCRETE AND CONTINUOUS WAVELET TRANSFORMS: FROM SINGLE CHIP IMPLEMENTATIONS TO MAPPINGS ON SIMD ARRAY COMPUTERS", IEEE TRANSACTIONS ON SIGNAL PROCESSING, vol. 43, no. 3, 1 March 1995 (1995-03-01), pages 759 - 771, XP000507252 *
MUFTI M ET AL: "AUTOMATED FAULT DETECTION AND IDENTIFICATION USING A FUZZY-WAVELET ANALYSIS TECHNIQUE", CONFERENCE RECORD AUTOTESTCON '95, ATLANTA, AUG. 8 - 10, 1995, vol. 31, 8 August 1995 (1995-08-08), INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS, pages 169 - 175, XP000555102 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
AU776482B2 (en) * 2000-03-15 2004-09-09 Siemens Building Technologies Ag Method for the processing of a signal from an alarm and alarms with means for carrying out said method
WO2007012331A2 (en) * 2005-07-29 2007-02-01 V & M Deutschland Gmbh Method for error-free checking of tubes for surface faults
WO2007012331A3 (en) * 2005-07-29 2007-04-19 V&M Deutschland Gmbh Method for error-free checking of tubes for surface faults
US7783432B2 (en) 2005-07-29 2010-08-24 V & M Deutschland Gmbh Method for nondestructive testing of pipes for surface flaws

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