NDT.net July 2003, Vol. 8 No.07 |

Digital Signal Processing Laboratory, Computer Department, Kaunas University of Technology

Studentu 50-214c, 3031 Kaunas, LITHUANIA, E-mail: ekaza @dsplab.ktu.lt

Fig 1: Ultrasonic NDT system. |

Modern ultrasonic and radar measurement systems are widely used in the field of non-destructive testing for a long time. The limitation of a currently available ultrasonic instruments hardly lies on the property of hardware but it may lie on the lack of sufficient signal processing techniques [1]. At present, the ultrasonic A-scan type instruments are most commonly used. It is believed that the received A-scan signal may carry a lot of information on material properties and defects, but information appears in various guises of noise, which is to be deciphered completely. A lot of research has been done on ultrasonic signal processing and still now is going on in search for more reliable and versatile signal processing techniques [2-6]. Generally, the flaw signals measured in ultrasonic NDT include the effects of the measurement system and are corrupted by different kind of noise. The highly complex interaction between the defect geometry and the back-scattered ultrasonic wave inside the test piece may not be assumed as a linear process. So, the signal processing techniques which require apriory knowledge of noise statistics, are subject to fail in many situations. Therefore the approach of signal processing should be involving the noisy signal itself in constructing the signal processing method.

Let us analyze a real time system in which a transmitter and a receiver are located at predetermined points. Several M-sequences (usually the Barker code), modulated by ultrasonic wave are used as the transmitted signal and receiver receives signals reflected from the target. Such a system is depicted in Fig. 1.

This system measures the thickness of moving object **A**. The reference signal *x _{ref}* consisting of a certain coded sequence is emitted by the ultrasonic transmitter at the time moment

(1) |

The other part of the emitted reference signal *x _{ref}* reflects from the rear side of object

(2) |

where: *k*_{1} and *k*_{2} are the coefficients depending on a distance to the object, environment and object properties, D*t*_{1} and D*t*_{2} are the delay times directly proportional to the distance *d* and the thickness of object **A**:

(3) |

(4) |

Finally in the receiver we get the signal *y*:

(5) |

The task of signal processing is to determine the time instances D*t*_{1} and D*t*_{2}. Then values of control signals are calculated and transmitted to actuators. The signal processing time is restricted by properties of a technological process and the velocity of the object **A**. For determination of the time instances D*t*_{1} and D*t*_{2} usually it is used principle of obtaining the impulse response by a correlation process. Lets consider *x _{ref}* to be the transmitted sequence,

(6) |

If peaks corresponding to reflections from the targets were clearly identified in the cross correlation function (CCF), it would be easy to determine the time instances D*t*_{1} and D*t*_{2}. In practice, however, it quite difficult to identify them because of suspicious peaks in the CCF due to a noise from the surrounding medium and it is essential to cancel out effects of noise. In order to reduce the effects of a noise during transmission and reception some measures have to be taken.

Data processing is performed on both of the sampled received signals and the original M-sequence and the sampling frequency is such that there are *j* samples per unit pulse of the M-sequence. So, the expected peak on the CCF is supposed to consist of 2*j* samples. In order to minimize spurious peaks, widths of which are less than 2*j* samples, the sampled data are smoothed by performing the moving average of the data sequence:

(7) |

(8) |

This is equivalent to application of the Hanning spectral window. This method considerably minimizes the noise peaks of comparatively small width while keeping the expected peaks intact.

The system emits the reference signal *x _{ref}* periodically

(9) |

where *N* is the period of the reference signal. If the position of the object **A** during *l *periods changes a little, it is possible to average input signal *l* times:

(10) |

The noise level is reduced times. This is effective, but time wasting method and is not used in the case of signal processing time restrictions in real time systems. This method does not allow eliminating peaks caused by surrounding environment.

Some coherent peaks, additive to the expected peaks, appear on the CCF, which are confusing in regard to the clear distinction of a target. This is due to the surrounding structure or due to the effect of limitations of the measuring system. These clutter peaks appear irrespective of presence of any target. To perform the subtraction, first of all the data are collected from the test object without presence of any the target. Another data are taken with the presence of target. Coherent peaks are cancelled by taking the difference between the CCF of second data and that of the first data. This helps distinguishing the peaks corresponding to the reflections from the newly developed targets by removing the coherent noise of the system.

Passing the signal through an inverse filter can significantly reduce a random and clutter type noise. A major part of the long CCF is to be assumed as a noise except the portion corresponding to the direct signal and the signal reflected from the target. The inverse filtering operation [7-9] of a signal is described as:

(11) |

where is the output of the filter and *P *is prediction or the model order. The inverse filter is designed calculating the coefficients {*a*_{k}} based on noisy data. Coefficients {*a*_{k}} are obtained by solving the equation [10]:

(12) |

where *R*[i] is the autocorrelation function defined by:

(13) |

This autocorrelation function is constructed with *N* samples of data from a suitable portion of the received signal, which presumably contains no expected peak. Such a filter is depicted in Fig. 2

Fig 2: Inverse filter. |

This filter attempts to remove the predictable part of the signal and produce an output , which is completely unpredictable to the filter.

During the last time the wavelets have become a popular de-noising (or noise reduction) tool [11]. Donoho and Johnston [12] showed that this method has statistical optimality properties. Many algorithms define a criterion to divide wavelet transform coefficients into two groups. The first group contains the coefficients dominated by a noise, while other coefficients are rather clean. These algorithms eliminate all wavelet coefficients below a certain threshold, because these coefficients are dominated by a noise.

Lets consider the following model of the received discrete noisy signal

(14) |

or in a vector notation:

(15) |

To reconstruct the original data, a wavelet representation is used. We use simple non-redundant orthogonal, discrete wavelet transforms. An orthogonal matrix *W* can be used to represent this operation. We consider the following transform:

(16) |

These transforms localize the most important spatial and frequencies characteristics of a regular signal in a limited number of wavelet coefficients. On the other hand, it is easy to prove that an orthogonal transform of a stationary, white noise results in a stationary white noise. This means that the expected noise energy is the same in all coefficients. If this energy is not to large, the noise has a relatively small influence on the important large regular signal coefficients. These observations suggest replacing the small coefficients by zero, because they are dominated by noise and carry only a small amount of information.

The thresholding operations can be represented as

(17) |

where

(18) |

There are known two threshold methods hard threshold and soft threshold (or shrinking function) [13-15].

In the case of the hard threshold the entries of the matrix *D*_{d} are

(19) |

In the case of soft threshold the entries of the matrix
*D*_{d} are

(20) |

These threshold functions are shown in Fig. 3. A wavelet coefficient w between -d and d is set to zero, while others have the same value in the case of the hard threshold, or are shrunk in an absolute value in the case of the soft threshold.

Fig 3: Hard thresholding (a) and soft thresholding (b) functions. |

A natural question arising from this procedure is how to chose the threshold. If **y**_{d} is the result of applying threshold procedure to the wavelet coefficients of signal y, and e_{d} = **y**_{d}-*f* is the noise of this result, then an often used criterion to measure the quality of this result is its signal to noise ratio (*SNR*(d)):

(21) |

An optimal choice of d should maximize *SNR*(d). This is equivalent to minimizing the mean squared error *R*:

(22) |

Because the wavelet transform is orthogonal, we can also compute *R* from the wavelet coefficients as:

(23) |

*w*_{d} = *W*e_{d} is the noise after operation in the wavelet domain.

However, because * f* is unknown, the function

We have a definition of general cross validation:

(24) |

where .

Note that if *i*š*j*, then *d*_{ij}=0. For *i*=*j* we have

(25) |

Thus, if *Tr*(D^{'}) is the trace of D^{'},

(26) |

The results of applying the threshold procedure on the reflected signal are depicted in Fig. 4. In this case only a fragment of the Barker code is used for formatting M-sequence.

a |

b |

c |

Fig 4: The reflected signal: a - without applying de-noising procedure, b - after applying hard thresold procedure, c - after applying soft threshold procedure. |

- apply the interval adapted pyramidal algorithm of Cohen, Daubechies, Jawerth and Vial [11] to the measured data, obtaining empirical wavelet coefficients w
_{i} - apply the soft threshold nonlinearity coordinatwise to the empirical wavelet coefficients with the specially chosen threshold d;
- invert the pyramid filtering recovering ;
- apply the transform (xx) to the reference signal
*x*and de-noised data_{ref}*y*_{d}; - detect the argument of correlation function maximum value.

In order to reduce computations, the reference signal *x _{ref}* and the reflected signal

(27) |

After applying this transform we get digital signals and with logical values "0" and "1". This transform is possible because the most important information is the pulse widths of M-sequence. Results of applying procedure (27) are shown in Fig. 5

Fig 5: The transformed reflected signal: a - the reflected signal without noise, b - the reflected noisy signal after applying hard threshold procedure, c - the reflected noisy signal after applying sof threshold procedure. |

The soft threshold procedure allows achieving a better visual quality, than the hard threshold procedure as shown in Fig.4. But, when the noise level is high, applying the soft thresholding procedure more distorts pulse widths of the transformed signal as shown in Fig.5.

The results of these transforms depend on the threshold d_{1} value, which optimal value varies in accordance to the noise level e. When the threshold is low, an additional pulses emerge in the transformed signal , as shown in Fig. 5b. When the threshold value is high, the pulse widths of the transformed signal are shrinking and pulses may be distorted as shown in Fig. 5c. The optimal threshold d_{1} value was defined by computing maximum value of the correlation function:

(28) |

at different threshold d_{1} values.

The best results were achieved when d_{1}=1.4d, where d is the threshold value defined by computing GCV function. Note that GCV function computation in this case does not require any floating-point operation and may be computed by a hardware. These computations may be simplified in the case when a fixed M-sequence is used.

Such a procedure may be used for recovering a distance to the object from noisy data. It possesses three steps:

For a fast wavelet transform we need 2*N*2*F* flops, where *F* is the number of filter coefficients. For *F*=4 , we have 16*N* flops. To reconstruct the signal after operation with the optimal threshold d we need 16*N* flops.

Computation of *GCV*(d) can be performed completely in the wavelet domain. Because *GCV*(d) is an approximation itself it is not useful to compute its minimum very precisely. Moreover, in most cases this is not necessary to the curve of *R*(d) in the neighborhood of its minimum. A relative accuracy of 10^{-3} is enough. Using a classic minimization procedure (such as Fibonacci) this requires approximately 15 function evaluations. The denominator *N-Tr*(D^{'})counts the number of coefficients that are set to zero. This does not require any floating-point operation. Computation of the nominator can be done with 2*N* floating point operations. So 15 function evaluations lead to some 30*N* floating-point operations.

Computation of the signal can be done with *N* floating point operations.

Computation of the correlation function does not require any floating-point operation.

So execution of the suggested signal processing algorithm leads to 63*N* operations. Execution of a classical signal processing algorithm leads to (*L*+2*P*)*N* operations, where *N *is the* *number of samples, *L* is the length of M-sequence, *P* is the model order of the inverse filter. The suggested algorithm requires less floating-point operations, when

(*L*+2*P*)>63.

Generally the flaw signals measured in ultrasonic NDT systems are spoiled by different kind of a noise. Therefore, the approach of signal processing should be involving the noisy signal itself in constructing the signal processing method. The noise in such systems is cancelled by band modification using moving average, signal averaging, inverse filtering and noise cancellation by subtraction. These methods are time consuming and due to signal processing time restrictions not always may be used in real time systems. During the last time the wavelets have become a popular de-noising (or noise reduction) tool and this method has statistical optimality properties. New data processing method based on the wavelet transform for real time systems is suggested. It is shown that the hard threshold algorithm is preferred to the soft threshold in such systems. Execution of this method leads to less amount of floating point operations than classical signal processing methods.

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**Ikraipytu signalu apdorojimas realaus laiko DSP sistemose**

**Reziume**

Ultragarsinese sistemose daug demesio skiriama triukmo sukeltu efektu paalinimui. Daugelis triukmo sumainimo metodu nera pakankamai efektyvus, todel danai vienoje sistemoje naudojami keli metodai i karto. Del to iauga skaiciavimu apimtis, o kartu ir signalu apdorojimo trukme. iuo metu placiai pradeti taikyti vilneliu transformacija pagristi triukmu sumainimo metodai, kurie yra statistikai optimalus. Pasiulytas vilneliu transformacija pagristas signalo apdorojimo ultragarsinese nedestruktyvaus testavimo sistemose metodas. Parodyta, kad iam signalo apdorojimo metodui igyvendinti reikia maiau skaiciavimu ir pagereja labai svarbus realaus laiko sistemu parametras signalo apdorojimo trukme

Pateikta spaudai 2003 02 27

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