Classifications

• • G—PHYSICS
  • G01—MEASURING; TESTING
  • G01T—MEASUREMENT OF NUCLEAR OR X-RADIATION
  • G01T1/00—Measuring X-radiation, gamma radiation, corpuscular radiation, or cosmic radiation
  • G01T1/16—Measuring radiation intensity
  • G01T1/17—Circuit arrangements not adapted to a particular type of detector

Definitions

• the field of the present invention relates to the processing of a noisy temporal signal constituting an information carrier making it possible to characterize a set of events produced randomly by an event source.
• the invention relates in particular to a method for processing a noisy digital time signal yk corresponding to an initial time signal Xt after having been conditioned by a conditioning line, said initial signal Xt being representative of information on radiations. from a source of radiation, these radiations may have an energy distribution.
• Such a method, or system capable of implementing such a method is generally used in the context of detection, counting and measurement of events, such as said emitted radiation.
• radiation any radiation capable of interacting with a detection means so as to have a usable time signal.
• the radiation targeted by the invention relates in particular to photons, in particular X and gamma, nuclear particles or more generally any particle or any packet of particles.
• a system of the type of that of the invention may in particular have the object of constructing a spectrum having a number of particles detected at a given energy as a function of energy.
• a spectrum obtained from such a source may include energy lines that are characteristic of it.
• the examination makes it possible to identify the nature of the source studied.
• a system of the aforementioned type can provide a spectrum of lines, as shown in FIG. 1, which makes it possible to identify the radioelements that make up this source, and therefore of characterize the latter.
• FIG. 1 represents in particular a standard energy spectrum for cesium 137.
• This signal comprises a plurality of pulses of different amplitude and duration which represent, for example, a current developed in the detector by the passage of a photon.
• the signal supplied by the detector the current detector signal
• the signal supplied by the detector the current detector signal
• the detector current signal shown in FIG. 2 is ideal.
• a preamplifier is generally installed at the output of the detector in order to implement a first shaping of the detector current signal.
• FIGS. 3 and 4 illustrate a detector 1 followed, respectively, by a capacitive feedback pre-amplifier 2 and a resistive feedback pre-amplifier 3 which comprises a feedback loop between an output and an input.
• an amplifier 4 composed of a capacitor 5 in parallel with a resistor 6.
• These preamplifiers are generally followed by a differentiating circuit 7 in the case of a capacitive feedback and a pole-zero PZ 8 correction circuit in the case of a resistive feedback.
• the two figures 5 and 6 respectively show an example of an ideal time signal at the output of the two types of preamplifiers mentioned above when they are excited at the input by the same detector current signal, it being understood that noises of an electronic nature are not represented here.
• a known and important step consists in shaping the pulses to extract information of the energy type.
• Such a step is often referred to as a "power" shaping step.
• the information can be extracted from a measurement of the amplitude or area of each pulse.
• this energy shaping step degrades the current sensor signal-at least the preamplified signal-especially by a time elongation of the pulses.
• Another disadvantage of the aforementioned systems is that their use is not very flexible. In particular, it is not possible to adapt or modify parameters of the energy shaping step during analysis of the radiation source.
• Setting a worst case may include adjusting the system based on the highest measured pulse intensity.
• An object of the invention is therefore to overcome the aforementioned drawbacks and thus improve the performance of existing systems.
• the invention proposes a system and a method that do not implement the energy shaping step systematically used in the state of the art.
• the present invention proposes a method for processing a noisy digital time signal yk corresponding to an initial time signal Xt after having been conditioned by a conditioning chain, said initial signal Xt being representative of information on radiations.
• corpuscular cells originating from a radiation source said radiations having an energy distribution, characterized in that a state model representative of the conditioning imposed by said chain is used to switch from the initial temporal signal Xt to the time signal noisy digital yk, in order to get from the digital signal noisy yk, a non-noisy digital estimation signal of the initial temporal signal Xt.
• Non-limiting preferred aspects of this method are as follows:
• the method furthermore comprises a preliminary step in which the noisy digital time signal yk is segmented into successive sections of N samples, so as to obtain a non-noisy digital estimation signal per section;
• a variable to estimate mk, of the hidden variable type which can be worth at each sampling step k one of two values to indicate a presence or absence of one or more pulses in the initial temporal signal Xt, to moment corresponding to the step k in question,
• a state vector Xk to be estimated comprising a first component which corresponds to a digitized representation Xk of the initial temporal signal Xt at the instant corresponding to the step k in question,
• the hidden variable is a Markovian hidden variable mk which can be equal, at each sampling step k, to the value 1 if the non-noise digital estimation signal must comprise one or more pulses at the sampling step k in question, and 0 if the non-noisy digital estimate signal should have no pulse at said sampling step k;
• the method further comprises a step in which an algorithm is implemented which processes the N samples by increasing steps k and cooperates with the state model to provide a filtered estimate Xk / k of the state vector Xk, and therefore to provide a filtered numerical estimation signal Xk / k not noisy that one seeks to obtain, this estimate Xk / k being an estimate conditionally past;
• the method further comprises a step in which an algorithm is implemented which processes the N samples by increasing steps k and cooperates with the state model to provide a filtered estimate Xk / k of the state vector Xk, and therefore to provide a noiseless Xk / k filtered numerical estimation signal sought to be obtained, this filtered estimate being a conditionally past filtered estimate from the noisy digital time signal samples yk to step k question;
• the algorithm is a Kalman filter whose observation corresponds to the noisy digital time signal yk;
• the method comprises a step in which the hidden variable mk is estimated at each step k, in order to obtain a signal of occupation representative of an estimate of the presence or absence of one or more pulses at each of the steps k in question, and in that the filter is able to determine, by means of said occupation signal obtained, the filtered estimate Xk / k of the state vector Xk, and thus to determine the filtered numerical estimation signal Xk / k noiseless that one seeks to obtain;
• the estimation of the hidden variable mk is implemented by making an assumption that, at the instant corresponding to the sampling step k considered, there is no pulse in the initial time signal Xt, then in verifying if the hypothesis is true by means of a comparison between the square of an innovation I of the Kalman filter and a threshold variable determined by the latter; - Innovation I is of the form:
• Ik + l yk + 1 - yk + l / k
• yk + i is the observation at the sampling step k + 1 and yk + i / k a prediction of this observation, at step k + 1, computed from a previously determined prediction Xk + i / k of the vector of state Xk, at step k + 1;
• the threshold variable consists of a coefficient multiplied by a variance of innovation I;
• the method further comprises a step of implementing a second smoothing algorithm which processes the N samples in decreasing k-steps and which makes it possible to provide an improved smoothed Xk / N estimate of the state vector Xk, and therefore to provide an improved smoothed Xk / N estimate of the signal sought to be obtained, which estimate is conditionally estimated from the past, present and future from the samples of the observation signal yk to the pitch N and the samples of the state vector Xk / ki, previously determined by the first algorithm, from the step k to N;
• the smoothing algorithm is arranged so that the smoothed estimate Xk / N maximizes in the state vector Xk the probability of having the state vector Xk at the pitch k knowing the N samples of the noisy digital time signal yk , the probability:
• the method further comprises a step where the energy of the radiations is determined by implementing the following operations:
• the energy of each pulse is weighted according to a measurement of a quality obtained from the estimation signal, this measurement being determined by the Kalman filter or the smoothing algorithm;
• the method further comprises a step in which an energy histogram of the radiation source is constructed from the determined energies;
• the energy histogram represents a spectrum in energy
• radiation is particle radiation; radiation is radiation from nuclear particles;
• radiation is photon radiation
• the method further comprises a radiation detection step which provides the initial time signal Xt;
• the conditioning of the initial time signal Xt by the conditioning chain begins with a first step in which the initial signal Xt is pre-amplified and ends in a sampling step at a predetermined frequency fe, so as to obtain the noisy digital time signal yk.
• a computer program for performing a processing of a noisy digital time signal yk corresponding to a temporal signal Xt, said signal Xt being representative of information on radiation originating from a source of radiation, these radiations having an energy distribution, characterized in that it implements the above-mentioned treatment method according to one or more of the preferred aspects of the above-mentioned treatment method, taken alone or in combination.
• a system for processing a noisy digital time signal yk corresponding to an initial time signal Xt after having been conditioned by a conditioning chain is proposed, said initial signal Xt being representative of information on radiations.
• corpuscular particles originating from a radiation source these radiations having a power distribution, characterized in that it comprises means able to implement a state model, representative of the conditioning imposed by said chain to switch from the initial temporal signal Xt to the noisy digital time signal yk, in order to obtain from the noisy digital signal yk a non-noisy digital estimation signal of the initial time signal Xt.
• the state model comprises:
• a hidden markovian variable to be estimated which may be equal to the value 1 if the non-noise digital estimation signal must comprise one or more pulses at the sampling step k, and the value 0 if the digital estimation signal is not noisy shall not include any impulses at the sampling interval k,
• a state vector Xk to be estimated comprising a first component that corresponds to a digitized representation Xk of the initial temporal signal Xt at the instant corresponding to the step k in question;
• the system further comprises means for implementing a Kalman filter whose observation corresponds to the noisy digital time signal yk, the Kalman filter cooperating with the state model to provide a filtered estimate Xk / k of the vector of Xk state, and therefore to provide a noise-free Xk / k filtered numerical estimation signal that one seeks to obtain, this filtered estimate being a filtered estimate Xk / k conditional on the past from samples of the noisy digital time signal yk to the step k in question;
• the system comprises means for implementing a smoothing algorithm
• the smoothing algorithm is arranged so as to maximize in the state vector Xk the probability of having the state vector Xk at the pitch k knowing the N samples of the noisy digital time signal yk; the probability: the conditioning chain comprises a preamplifier and an analog-digital converter;
• the preamplifier is of the type chosen from the following list:
• the conditioning line also comprises at the output of the preamplifier: a pole-zero correction circuit if the preamplifier is of the resistive feedback type, or a differentiating circuit if the preamplifier is of the capacitive feedback type;
• the conditioning line further comprises an amplifier;
• the packaging line further comprises an anti-aliasing filter;
• the conditioning chain is successively constituted, from upstream to downstream, of the preamplifier, of the differentiating circuit if the preamplifier is of the capacitive feedback type, or of the pole-zero correction circuit if the preamplifier is of the counter-type; resistive reaction, amplifier, anti-aliasing filter and analog-to-digital converter; the system comprises storage means for memorizing, in particular, the signals, the state model, the Kalman filter and the smoothing algorithm;
• the computing unit is a processor
• the system comprises display means for displaying in particular the results of the estimation.
• the detector current signal is not processed by means of filtering of the type of the prior art; it is estimated, especially using a state model.
• This model includes a hidden variable which, once determined, allows first of all a better knowledge of the content, in terms of pulses, of the current detector signal.
• a method and a system according to the invention therefore offer many advantages.
• the operation of the method is automatically adapted as a function of the counting rate.
• the process initialized no intervention is required once the process initialized, and this both to perform a new parameter setting of an algorithm, for example, that for a radical change thereof given a maladaptation to a rate of counting become too high.
• the resolution is automatically optimized at the count rate.
• FIG. 1 presented in the text above, illustrates an energy spectrum of a cesium 137 source, the axis units being arbitrary,
• FIG. 2 shown in an example above, illustrates an ideal detector current signal generated by a gamma ray detector, the axis units being arbitrary
• FIG. 3 schematically represents a first example of a part of a known packaging line, comprising downstream of a detector: a capacitive feedback pre-amplifier followed by a differentiating circuit,
• FIG. 4 schematically represents a second example of part of a known packaging line, including downstream a detector: a resistive feedback preamplifier followed by a pole-zero correction circuit;
• FIG. 5 shows, in a nonlimiting manner, a signal coming from the conditioning chain of the first example, this chain further comprising an analog-digital converter, the units of the axes being arbitrary,
• FIG. 6 shows, in a nonlimiting manner, a signal coming from the chain of the second example, this chain further comprising an analog-digital converter, the units of the axes being arbitrary
• FIG. 7 schematically shows a complete system enabling implement the method of the invention
• FIG. 8 graphically shows, by way of nonlimiting example, a noise model included in the state model
• FIG. 9 shows, by way of indication and without limitation, a simulation result of an observed time signal yk when the detector current signal is that presented in FIG. 2,
• FIG. 10 shows a nonlimiting example of instructions of an embodiment of the Kalman filter
• FIG. 11 represents a busy signal obtained in the case where the observed time signal yk is that of FIG. 9,
• FIG. 12 is a temporal graph showing which samples are taken into account at a step k + 1 in the case of the smoothing algorithm and in the case of the Kalman filter,
• FIG. 13 shows in a nonlimiting manner steps of the smoothing algorithm
• FIG. 14 represents an estimation signal obtained after implementation of the Kalman filter, and then of the smoothing algorithm
• FIG. 15 schematically represents an experimental system capable of implementing the method of the invention and which makes it possible to illustrate the efficiency and the advantages of the invention from measured results.
• Such a system essentially comprises a detector 20 for detecting radiation from a radiation source.
• This detector is capable of providing a detector current signal 40 which will be conditioned by a packaging line CH shown in FIG. 7.
• This packaging line comprises, by way of nonlimiting example, a preamplifier of the aforementioned type, ie the type of resistive feedback or capacitive feedback.
• the invention is not limited to these types of preamplifier.
• the preamplified signal 41 thus obtained is then presented to a block 22 in which a zero-pole correction circuit PZ or a differentiation circuit is used according to the choice made on the type of the preamplifier 21.
• the conditioning line CH ends with an analog-digital converter 23 at the output of the block 22. It will be noted here that the packaging line CH as shown in the figure may include many variants that the skilled person will naturally consider.
• the invention is in no way limited to respect such an arrangement of the blocks.
• analog-digital converter is placed further upstream of the system and that the packaging line CH ends with the block 21 (digital pre-amplification) then 22.
• the block 22 may implement additional signal conditioning steps.
• the block 22 may further comprise circuits, such as an amplifier and / or an anti-aliasing filter.
• the digitized signal 42 is stored in a dedicated memory 24, then a computing unit 25, such as a microprocessor or a DSP for example
• DSP Digital Signal Processing
• this current time signal will be designated detector by Xt
• an energy histogram of the radiation source studied it is in particular the unit 25 which implements all the steps making it possible to obtain an estimation signal of the current detector time signal (in the following of the text, this current time signal will be designated detector by Xt) and subsequently an energy histogram of the radiation source studied.
• the signals corresponding to the energies of the radiation 44 calculated by the unit 25 are stored in the memory 24 (they can also be stored in a separate memory).
• this unit 25 manages peripherals 26 such as a display screen, a keyboard, a mouse, etc.
• peripherals 26 such as a display screen, a keyboard, a mouse, etc.
• k represents a sampling step.
• variable index k / k will designate an estimate obtained from the first algorithm, in particular from the Kalman filter; an index k / N denotes an estimate obtained from the second algorithm, in particular from the smoothing algorithm; an index k + l / k will designate a prediction of the indexed variable as such.
• Xk will designate a digital representation that we want to estimate of the Xt signal.
• the computing unit 25 proceeds first of all with a segmentation of the signal 42 which has been previously stored in the memory 24.
• Such a segmentation is arranged so as to obtain successive sections of N samples.
• the computing unit 25 will then deduce the noisy digital time signal yk from the signal 42.
• an estimation signal in constituted sections will be obtained.
• the calculation unit 25 implements a state model representative of the conditioning imposed by said chain CH to switch from the signal Xt to yk signal.
• model of state is model which can always be defined in the following way:
• - k is a discrete time; - Xk, Uk, Yk are respectively state, control and observation vectors of respective dimensions n, m, r;
• the initial state Xo is Gaussian of average mo, of covariance Po and is independent of the sequences (Wk) k and (Vk) k, Fk is a state transition matrix, Hk is an observation matrix, Gk and Jk are respectively state and observation control matrices, Bk and Dk are noise matrices respectively of state and of observation. 'observation.
• the state model of the invention includes a hidden variable, denoted im, that is to say a variable to be determined, but which can not be equal to two distinct values to choose from.
• One of the values corresponds to an indication that at the sampling instant corresponding to the step k considered, the signal Xt comprises one or more pulses, while the other value corresponds to an indication that at said instant of sampling , the signal Xt does not include a pulse.
• the value is 1 and in the absence of a pulse the value is 0.
• the hidden variable is a Markovian variable so that the state model becomes of the HMM (Hidden Markov Model) type.
• the conditioning chain CH can now be described by the following state system.
• Xk is a state vector that one seeks to estimate, this vector comprising as first component said numerical representation Xk that one seeks to estimate the signal Xt.
• mk is the Markovian hidden variable or in other words the said occupation signal, it will be further noted that no priori is given on the form of the signal Xt except for its positivity,
• - bk is a bias corresponding to a baseline of the form: ⁇ k being a Gaussian random variable of zero mean and standard deviation Ob,
• FIG. 8 schematically represents this noise model in terms of power depending on the frequency
• Vk is an internal state noise related to 8k by a relation of the form:
• F is a matrix of passage of the form: 0 0 0 0 0
• H is an observation matrix of the form:
• N the length of a section or the last index k possible in the section
• the estimation of a component of a vector (xk for example) will in any case be deduced from an estimate of the vector comprising this component (Xk according to the above example) .
• FIG. 9 shows as a nonlimiting indication a simulation result of the observed time signal yk when the ideal detector current signal corresponds to that presented in FIG.
• the state model is thus established, it is provided in a preferred embodiment of the invention to implement an algorithm that processes the N samples of a section by increasing steps k and that cooperates with the state model to provide a first filtered estimate Xk / k of the state vector Xk, thereby determining the busy signal.
• the filtered estimate Xk / k inevitably provides a filtered estimate Xk / k of Xk. It will be noted that such an estimate represents an estimate conditionally in the past, which means for a statistician that it has been implemented at essentially from the samples of the observation yk to the step k in question.
• the algorithm is a Kalman filter and the observation of this filter is the observation signal yk.
• the first step is to determine all the mk, thus the entire occupancy signal, and the second step uses the busy signal to update the state model parameters and thus obtain the filtered estimate Xk / k .
• the determination of the busy signal mk is implemented by making the assumption that, at the instant corresponding to the sampling step k, there is no pulse in the initial temporal signal. xt.
• the variable mk is therefore initially equal to 0 regardless of the value of the step k.
• the innovation reflects the difference between the observation yk and the prediction that is made from a prediction Xk + i / k of Xk.
• innovation I at a step k + 1 can be expressed as follows: where yk + i is the observation at the sampling step k + 1 and yk + i / k the prediction of this observation at step k + 1 given the first k samples, this one being computed from the prediction Xk + i / k previously determined from the state vector Xk, at step k + 1 given the first k samples.
• the innovation has a value greater than said threshold, it is considered that said hypothesis is not verified, that is to say that at the step in question the variable must be equal to 1 so that the signal occupation contains an impulse.
• the threshold value allowing the comparison with the innovation is a multiple of the variance of the innovation I.
• Figure 10 a non-limiting example of instructions corresponding to this first step.
• the first step is to implement an initialization of the parameters.
• Pk / k is a matrix corresponding to the variance of the filter estimation error of Xk.
• the invention also provides that it is possible to implement each of these two steps independently by using a first loop in the first step and a second loop in the second step.
• step 100 the step following the initialization step is step 100.
• the prediction Xk + i / k is determined from, in particular, the estimate Xk / k.
• Step 103 corresponds to the test to determine if the assumption of no pulse at the k + 1 step in question is true.
• the variance is multiplied by the square of the coefficient ⁇ , the result being compared to the square of the innovation I.
• the filter If the gap between the innovation and the variance multiple is greater than or equal to zero, the filter then assumes that the assumption is false.
• the busy signal must then indicate the presence of a pulse, the hidden variable at step k + 1 takes the value 1.
• FIG. 11 shows the occupancy signal obtained in the case where the observation signal yk is that of FIG. 9. As can be seen, this signal comprises vertical sticks of value 1 more or less spaced in time and of greater or lesser duration.
• the occupancy signal is used to update the parameters of the state model and thus obtain the filtered estimation signal Xk / k by the filtered estimate Xk / k of the vector of state Xk.
• Instructions 107 to 109 of this second step are given by way of non-limiting example still in FIG. 10, following steps 104 or 105 according to the result of the test 103.
• this second step makes it possible to go from the forecast Xk + i / k used in the first step, to the estimate
• Step 110 marks the end of the implementation of the Kalman filter thus defined.
• an estimation signal Xk / k of the signal Xt in this case the current detector signal, is available, this signal having less noise than the observation yk, and this without having resorted to a energy shaping step of the prior art.
• the quality of the estimation signal is further improved by implementing a second smoothing algorithm which processes the N samples in decreasing k-steps and which makes it possible to provide an Xk / N smoothed estimate. improved state vector Xk. Therefore, this algorithm provides a smoothed estimate signal Xk / N that is sought to obtain.
• this smoothing algorithm is arranged so as to maximize Xk the probability of having the state vector Xk at the step k knowing the N samples of the observation signal yk, ie the probability :
• FIG. 12 shows a temporal graph showing which samples are taken into account in the case of smoothing (axis 151) and in the case of the Kalman filter (axis 150), knowing that in this figure these two algorithms are supposed to implement k + 1 step calculations. Moreover, steps of this smoothing algorithm are given in FIG.
• FIG. 14 shows the final non-noise numerical estimation signal Xk / N resulting from the implementation of the aforementioned method.
• the non-noisy digital estimation signal Xk / N has pulses that correspond well to those of FIG. 9, and that the noise has disappeared.
• An object of the invention is to provide an energy spectrum of the radiation source studied, the method may further comprise additional steps for generating an energy signal from the estimation signal Xk / k or Xk / N, depending on whether Kalman filtering is implemented only or that it is supplemented by the smoothing algorithm.
• the steps k corresponding to these instants are stored in the memory 24.
• the estimation signal for example Xk / N, is then used to search and store its values at the stored steps k.
• the method is then able to provide a histogram of the energies thus estimated.
• the preceding estimate of the energies can also be refined by weighting them by a coefficient which represents, for example, a measurement of an estimate quality finally obtained.
• the smoothing algorithm advantageously provides the variance of the smoothing estimation error Pk / N.
• Kalman filter also provides the variance of the filter estimation error, denoted Pk / k.
• the method of the invention initially comprises steps which make it possible in particular to estimate at best the baseline level of the signal (or bias) in order to start, for example, the Kalman filtering in the best possible way. conditions. This is done using a robust method of taking the histogram mode of the values taken by the first samples of the observation signal yk.
• noise parameters ⁇ , ⁇ n , ⁇ t> they can be estimated by spectral analysis of the detector current signal, and for example in areas where few pulses are present.
• ⁇ P it is generally fixed at a value much greater than ⁇ n and
• FIG. 15 there are essentially blocks 20 to 25 shown in FIG. 7, that is to say from upstream to downstream:
• a conditioning chain comprising a preamplifier 21, a block 22 in which a pole-zero correction circuit is used
• the conditioning line CH furthermore comprises an amplifier 22 'and an anti-aliasing filter 22 "arranged between the block 22 and the A / D converter 23.
• the sampling frequency fe is limited by the performance of the converters and by the capabilities of the computing unit to perform the processing of the digitized data stream in real time.
• the bandwidth of the signals from the detector 20 may exceed 30 Mhz, which would lead to a prohibitive sampling rate for the current technology.
• An allowable sampling frequency is between 10 and 20MHz, currently.
• This unit must be sized to perform the calculations related to the presented algorithms.
• the samples are conveyed from the converter 23 to the memory 24 of the section calculation unit.
• the invention proposes, by way of nonlimiting example, a "ping-pong" operation: a sample section is stored in memory while the computing unit processes a range located in another portion of his memory.
• the pulses are identified by analyzing the busy signal as described above and the corresponding energies are stored in the memory 27.
• the signal memories 24 and 27 are not necessarily distinct from the calculation unit 25. In this case, in the system presented here these memories are integral parts of the memory of this unit 25.
• the resolution typically achieved at low count rate is 1.7 keV @ 1332 keV, a little better than the manufacturer resolution announced at 1.8 keV, due in particular to a very neat analog electronics.
• the method of the invention adapts automatically and in real time to the counting rate.
• Random signals modeling, estimation, detection; Michel Guglielmi, 2004.

Landscapes

• Physics & Mathematics (AREA)
• Health & Medical Sciences (AREA)
• Life Sciences & Earth Sciences (AREA)
• General Physics & Mathematics (AREA)
• High Energy & Nuclear Physics (AREA)
• Molecular Biology (AREA)
• Spectroscopy & Molecular Physics (AREA)
• Measurement Of Radiation (AREA)

Applications Claiming Priority (2)

Publications (1)

Family

ID=34979852

Family Applications (1)

Country Status (4)

Families Citing this family (4)

Family Cites Families (24)

• 2004
  • 2004-12-15 FR FR0413325A patent/FR2879305B1/fr not_active Expired - Fee Related
• 2005
  • 2005-02-14 US US11/057,650 patent/US7885775B2/en not_active Expired - Fee Related
  • 2005-12-14 WO PCT/EP2005/056790 patent/WO2006064024A2/fr not_active Ceased
  • 2005-12-14 EP EP05823783A patent/EP1831722A2/de not_active Withdrawn

Non-Patent Citations (1)

Also Published As

Similar Documents

Legal Events