RU2365980C1 - Device for picking up useful signal against background of noise with minimisation of end effects through piecewise multiplication of estimations - Google Patents

Abstract

FIELD: information technology.

SUBSTANCE: proposed invention relates to information measuring devices and can be used in computer engineering, in signal control and processing systems. The devices has a register for storing measurement results (1), delay unit (2), approximation unit (3), register for storing estimations (4), averaging unit (5), control unit (6), clock generator (7), and a comparator unit (8).

EFFECT: pickup of useful signal against a background of noise with minimisation of end effects, in conditions of insufficient prior information on statistical characteristics of adaptive noise and useful signal function given a single realisation of the measuring process.

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Description

The present invention relates to the field of computer technology and can be used in control systems and signal processing.

.The input implementation of the measurement results is the only discrete sequence y ₁ , y ₂ , ..., y _n , where y _k = y (t _k ),

.

The mathematical model of the measurement results can be represented as:

where S _k is a useful component; u _k is the additive noise component.

With respect to the random noise component, it is assumed that Mu _k = 0,

Du _k = σ ² , and its values at different instants of time are uncorrelated (i.e., cov (u _k , u _s ) = 0, k ≠ s).

принадлежит известному классу функций и определяется конечным числом параметров, используются параметрические методы оценивания (сюда входят методы регрессионного анализа, основу которых составляет классическая теория наименьших квадратов). В случаях, когда отсутствует априорная информация о функции полезной составляющей, для ее оценивания используются непараметрические методы, такие как сглаживание. Известно, что наилучший способ сглаживания - усреднение по ансамблю реализации y_i,k,

исходного процесса.The main problem to be solved is the selection of a useful signal against a background of noise with minimization of end effects, in conditions of insufficient a priori information about the statistical characteristics of additive noise and the function of a useful signal in the presence of a single implementation of the measured process. It is a priori assumed that the initial useful component is Anderson-smooth, i.e. at some intervals, it can be fairly accurately approximated by a polynomial not higher than the second degree. A similar problem may arise: 1) in the operation of transceiver devices for long-distance or space communications; 2) in radio engineering when evaluating the noise immunity of signal processing circuits (algorithms); 3) in meteorology when changing various characteristics of the state of the atmosphere, etc. In cases where the useful component S _k ,

belongs to a well-known class of functions and is determined by a finite number of parameters, parametric estimation methods are used (this includes methods of regression analysis, which are based on the classical theory of least squares). In cases where there is no a priori information about the function of the useful component, non-parametric methods such as smoothing are used to evaluate it. It is known that the best smoothing method is averaging over the ensemble of the implementation of y _{i, k} ,

source process.

However, in practice, as a rule, it is assumed that there is a single implementation of the measured process. In this case, it is advisable to use smoothing methods.

результатов измерений процесса y(t), полученных в n равноотстоящих моментов времени t₁, t₂, …, t_n. Эти результаты наблюдений можно представить в виде матрицы реализации:There is such a method of isolating a useful component, such as averaging over the ensemble of implementation (Bendat J., Pirsol A. Applied analysis of random data. - M .: Mir, 1989. - 540 p.). For its implementation, it is necessary to have N implementations of the original process. Each implementation is a time series.

the measurement results of the process y (t) obtained at n equally spaced points in time t ₁ , t ₂ , ..., t _n . These observation results can be represented in the form of a realization matrix:

where y _j1 , y _j2 , ..., y _jn is the jth implementation of the original process, which is the sum of the useful signal function S _k and the noise component u _k .

The method of averaging over the implementation ensemble involves storing N input implementations y _j1 , y _j2 , ..., y _jn (j = 1, 2, ..., N), calculating the arithmetic mean of these implementations at each moment in time, replacing the values of the original implementations of the random process with average rating.

When applying this method, the arithmetic mean columns of the implementation matrix (2) are calculated, as a result we obtain a smoothed time series

The features of the analog device, which coincide with the features of the claimed technical solution, are as follows: storing a digital signal, finding the arithmetic mean, replacing the original time series with a smoothed one.

The disadvantages of the known device are:

- to use an analog device, you must have several implementations.

The reasons that impede the achievement of the required technical result are as follows:

- the features of the analog device do not allow to process a single implementation of the original process, and also do not allow it to be applied to already smoothed values (in contrast to methods that "work" with a single implementation);

- the result of processing several implementations substantially depends on the number of sales, the statistical characteristics of the noise component, and the signal-to-noise ratio.

The block diagram of a device that implements the considered method contains, for N implementations, N buffer blocks, the inputs of which are information inputs of the device, and the outputs are connected through switches to the inputs of the storage blocks of measurement results, the outputs of which are connected to the inputs of the arithmetic device also through switches, whose output is connected to the input of the trend storage register, and the output of the register is the information output of the device.

In patent No. 2207622, IPC 7 G06F 17/18, a method for multiplying estimates with a limited amount of a priori data and a single implementation of the original process was proposed.

The considered analog device assumes: 1) storing the input implementation y ₁ , y ₂ , ..., y _n ; 2) dividing the input implementation into intervals by random numbers having a uniform distribution law; 3) checking the condition that the intervals include at least L-values of the original implementation; if the condition is not met, random partition numbers are regenerated; 4) finding on each interval of the input implementation estimates of the coefficients of the approximating polynomial a + bt + ct ² using the least squares method; 5) repetition of the procedures described in paragraphs 2-4 K, times; 6) finding the smoothing function as the arithmetic mean of “piecewise quadratic” approximating functions at each moment in time.

The features of the analog device, which coincide with the features of the claimed technical solution, are as follows: storing a digital signal, finding the arithmetic mean, replacing the original time series with a smoothed one.

The disadvantages of the known device are:

- to process the implementation, it is necessary to remember the entire sample;

- it is impossible to implement the processing of the original implementation in real time;

- the growth of the error of the selection of the useful signal with a limited increase in the reproduction of the original implementation.

The reasons that impede the achievement of the required technical result are as follows:

- for processing the initial implementation, it is necessary to have the whole sample available, the possibilities of using the method of multiplying estimates in real time are extremely limited;

- the assumption that on each partition interval of the initial implementation the useful signal can be described by a polynomial of the second degree leads to an increase in the error of the selection of the useful signal with a decrease in the length of the partition interval and an increase in the dispersion of the additive noise component.

The block diagram of a device that implements the considered method contains a buffer block, the input of which is the information input of the device, and the output is connected to the information inputs of the storage blocks of the measurement results, to the control inputs of which the outputs of the splitting block of the original implementation are connected via switches, which contains a random number generator distributed according to uniform law, the output of which is connected to the input of the unit for eliminating related values, the output of which is connected to the input of the unit is ranked oi, the output of which is connected to a register for storing a sample of random numbers, whose output is the information output of the partition block; the outputs of the storage blocks are connected to the inputs of the approximation blocks, the outputs of which are connected to the inputs of the storage registers of estimates of the initial function, the outputs of which are connected to the inputs of the arithmetic summing device, the output of which is connected to the input of the trend storage register, whose output is the information output of the device. The synchronization of the device is provided by the clock generator.

A known method of exponential smoothing [Bendat J., Piersol A. Applied analysis of random data. - M .: Mir, 1989. - 540 s]. Its peculiarity lies in the fact that in the procedure for finding the estimate of the useful component, only the previous values of the input implementation of the measurement results taken with a certain "weight" are used, and the value of the "weights" decreases to the beginning of the implementation. To apply this method, one implementation y ₁ , y ₂ , ..., y _{n of the} original process is sufficient.

The method of exponential smoothing involves storing the initial discrete implementation of the measurement results y ₁ , y ₂ , ..., y _{n of a} random process, choosing the smoothing parameter α (0 <α <1), the value of Q ₀ , calculating the estimate of the useful component using the recurrence formula:

k=1, 2, …, n

k = 1, 2, ..., n

replacing the initial values of the measurement results y ₁ , y ₂ , ..., y _{n with the} smoothed values of Q ₁ , Q ₂ , ..., Q _n .

To use exponential smoothing of the measurement results, the initial value Q _{0 of the} estimate of the useful component and the smoothing parameter α are determined. Wrong choice of initial conditions can have a significant impact on the result of processing the initial discrete implementation of the measurement results. In practical recommendations on the use of exponential smoothing [Bendat J., Pirsol A. Applied analysis of random data. - M .: Mir, 1989. - 540 p.] It is proposed to choose as the initial value of Q ₀ either the first value of the measurement results or the arithmetic average of several first members of the measurement results, for example Q ₀ = (y ₁ + y ₂ + y ₃ ) / 3. On the other hand, the influence of the initial conditions decreases with an increase in the number of measurement results and becomes insignificant with a large number of measurements.

The features of the analog device that coincide with the features of the claimed technical solution are as follows: storing a digital signal, presenting the evaluation values of the useful component in the form of a polynomial from the values of the initial discrete implementation of the measurement results, replacing the values of the initial implementation of the measurement results with smoothed values.

The disadvantages of the known device are:

- the uncertainty of the choice of the smoothing parameter α, in some cases it is proposed (unreasonably) to determine the value of α based on the volume of the smoothed implementation: α = 2 / (n + 1);

- the uncertainty of the choice of the parameter Q ₀ , which leads to the groundlessness of repeated re-application of the method of exponential smoothing for other values of α and Q ₀ .

The reasons that impede the achievement of the required technical result are as follows: the method of exponential smoothing is not a "self-adjusting" method, since the choice of parameters α and Q _{0 is} carried out subjectively and depends on the experience and practical skills of the researcher, the values of α and Q ₀ are functions of the waveform, noise, sample size.

The structural diagram of a device that implements the considered method comprises a pulse generator, a switch, a control unit, a storage register, an adder, a multiplication unit, an output register for storing the useful component estimate.

A known method of median smoothing [Kolemaev V.A., Kalinina V.N. Theory of Probability and Mathematical Statistics. - M .: INFRA-M, 1997. - 302]. To apply this method, one discrete implementation of the measurement results y ₁ , y ₂ , ..., y _{n is} sufficient. The main advantage of median smoothing is emission resistance. The basis of the method is the calculation of the moving median.

, замена центрального значения интервала y₁, y₂,…, y_m медианой

, сдвиг «скользящего окна» на одно значение вправо (т.е. выбор вместо интервала y_k, y_k+1, …, y_k+m-1 реализации другого интервала y_k+1, y_k+2,…, y_k+m), вычисление медианы на новом интервале входной реализации результатов измерений, и так до тех пор, пока не будет достигнуто правого конца исходной реализации результатов измерений.The method of median smoothing involves storing the initial discrete implementation of the measurement results y ₁ , y ₂ , ..., y _n , determining the length m of the interval interval of the series y ₁ , y ₂ , ..., Y _n (or the width of the "sliding window") for which the calculation will be performed medians, i.e. ranking the selected interval of the input implementation of the measurement results, determining the median

, replacing the central value of the interval y ₁ , y ₂ , ..., y _{m by the} median

, shift of the "sliding window" by one value to the right (i.e., instead of choosing the interval y _k , y _{k + 1} , ..., y _{k + m-1, the} implementation of another interval y _{k + 1} , y _{k + 2} , ..., y _{k + m} ), the calculation of the median on the new interval of the input implementation of the measurement results, and so on, until the right end of the original implementation of the measurement results is reached.

The features of the analog device, which coincide with the features of the claimed technical solution, are as follows: storing the input implementation of the measurement results, highlighting time periods, saving the implementation of smoothed values.

The disadvantages of the known device are:

- the first p and last p values of the measurement results are not smoothed;

- due to the nonlinearity of the processing method, it is impossible to strictly distinguish between the influence of median filtering on the signal and noise;

- median smoothing can only be considered as an effective method of pre-processing the input implementation of the measurement results in the case of pulsed interference.

The reasons that impede the achievement of the required technical result are as follows:

- if the width of the smoothing window is 2p ₊₁ , then the first p and last p values of the initial implementation of the measurement results are not processed;

- median filtering is a non-linear processing method;

- the dependence of the smoothing efficiency of the measurement results on the shape of the useful and noise component.

The block diagram of a device that implements the considered method contains a pulse generator, a switch, a control unit, a storage register, a ranking unit, an average value selection unit, an output register, where an estimate of the initial discrete implementation of the measurement results is stored.

Of the known methods for isolating a useful component, the closest in technical essence to the claimed device is a method for isolating a trend by the method of sliding multiplication of trend estimates of its only initial implementation (“MOLE”) and a device for its implementation (patent 2257610, IPC7 G06F 17/18). Consider this device as a prototype. To apply this method, one implementation y ₁ , y ₂ , ..., y _{n of the} original process is sufficient.

, где m<n/2 (ширина «скользящего окна»), на котором будет производиться аппроксимация полиномом второй степени по методу наименьших квадратов, замена ряда

значениями аппроксимирующей функции

, в результате чего получается ряд

, сдвиг «скользящего окна» на одно значение вправо (т.е. выбор вместо отрезка

ряда следующего отрезка

из

, повторение процедур, описанных выше, пока не будет достигнут правый конец ряда, т.е. j=n-m.The method of rolling reproduction of trend estimates involves: remembering the implementation of the measurement results

, where m <n / 2 (the width of the "sliding window"), on which the second-degree polynomial approximation by the least squares method will be performed, replacing the series

values of the approximating function

, resulting in a series

, shift of the "sliding window" by one value to the right (i.e., selection instead of a segment

next row

of

, repeating the procedures described above until the right end of the row is reached, i.e. j = nm.

. Из условия минимума суммы квадратов отклонения:The width of the “sliding window” m <n / 2 is determined a priori, at which the second-degree polynomial will be approximated by the least squares method. Having determined the number m, the first segment of the original series is remembered

. From the condition of minimum the sum of the squares of the deviation:

,

,

,

,

аппроксимирующего полинома:coefficients are determined

,

,

approximating polynomial:

на отрезке ряда

и выполняется ее запоминание. Сдвигается на один отсчет «скользящее окно» и получается новый отрезок исходного ряда

. Заново производится аппроксимация полиномом второй степени по методу наименьших квадратов, находится оценка

и запоминается. Таким образом, мы получим r=n-m+1 отрезков исходного ряда и r их оценок. Исходный ряд

разбитый на отрезки длины m, можно представить в виде матрицы размера

:An estimate of the approximating function is determined

in a row

and its memorization is performed. The “sliding window” is shifted by one count and a new segment of the original series is obtained

. The second degree polynomial is approximated again using the least squares method, and the estimate

and remembered. Thus, we get r = n-m + 1 segments of the original series and r of their estimates. Source row

divided into segments of length m can be represented as a matrix of size

:

определяется усреднением по побочным диагоналям:Approximation function

determined by averaging over the side diagonals:

The features of the prototype device, which coincide with the features of the claimed technical solution, are as follows: shifting the “sliding window” by one value to the right, replacing the original time series with a smoothed one, approximating a polynomial of the second degree by the least squares method.

The disadvantages of this method are:

- on the intervals [1, m] and [n-m, n], the selection of the useful component is not effective enough.

The reasons that impede the achievement of the required technical result are as follows:

- for the first and last m values of the initial implementation, the sets of estimates contain a different number of elements.

The structural diagram of a device that implements the considered method comprises a register for storing measurement results, a delay unit, an approximation unit, an estimation storage register, an averaging unit, a control unit, and a clock generator.

, где y_k=y(t_k), k=1, 2, …, n, представляющего собой сумму полезного сигнала и шума, т.е. y_k=S_k+u_k. Априорная информация об исследуемом процессе заключается в том, что на выбранном интервале m<ⁿ/₂ и m_min<m полезный сигнал достаточно точно описывается полиномом второй степени S_k=a+bk+ck².The proposed device for extracting a useful signal against a background of noise with minimizing end effects by the method of piecewise multiplication of estimates proceeds from the presence of a single discrete implementation of the process under study

, where y _k = y (t _k ), k = 1, 2, ..., n, which is the sum of the useful signal and noise, i.e. y _k = S _k + u _k . A priori information about the process under study is that on the selected interval m < ⁿ / ₂ and m _min <m, the useful signal is rather accurately described by a polynomial of the second degree S _k = a + bk + ck ² .

(j=1, 2, …, m-m_min+1) случайного процесса, где m<n/2 (ширина «скользящего окна»), на котором будет производиться аппроксимация полиномом второй степени по методу наименьших квадратов, m_min<m минимальная ширина «скользящего окна»; 2) запоминание значений аппроксимирующей функции

, в результате чего получается ряд

; 3) увеличение ширины начального окна m_min на единицу, в результате чего получается ряд

, запоминание значений аппроксимирующей функции

; 4) повторение процедур 1-3 пока ширина начального окна не достигнет значения m; 5) сдвиг «скользящего окна» на одно значение вправо (т.е. выбор вместо отрезка

ряда следующего отрезка

из

; 6) замена ряда

(j=1, 2, …, n-m) значениями аппроксимирующей функции

, в результате чего получается ряд

; 7) повторение процедур 5-6, пока не будет достигнут правый конец ряда, т.е. j=n-m; 8) уменьшение ширины m «скользящего окна» на единицу, т.е. выбор ряда

; 9) запоминание значений аппроксимирующей функции

, в результате чего получается ряд

; 10) повторение процедуры уменьшения ширины окна, пока оно не достигнет значения m_min. Процедура разбиения ряда

поясняет фиг.1.The considered method involves the following sequence of steps: 1) remembering the implementation

(j = 1, 2, ..., mm _min +1) of the random process, where m <n / 2 (width of the “sliding window”), on which the second-degree polynomial approximation by the least squares method will be performed, m _min <m minimum width "Sliding window"; 2) storing the values of the approximating function

, resulting in a series

; 3) an increase in the width of the initial window m _min by one, resulting in a series

, storing the values of the approximating function

; 4) repeating procedures 1-3 until the width of the initial window reaches the value m; 5) shift of the "sliding window" by one value to the right (i.e., selection instead of a segment

next row

of

; 6) replacement row

(j = 1, 2, ..., nm) by the values of the approximating function

, resulting in a series

; 7) repeat the procedures 5-6 until the right end of the row is reached, i.e. j = nm; 8) decreasing the width m of the "sliding window" by one, i.e. row selection

; 9) storing the values of the approximating function

, resulting in a series

; 10) repeating the procedure for reducing the width of the window until it reaches the value of m _min . Series splitting procedure

explains figure 1.

The initial m _min <m and the main m <n / 2 window widths are determined a priori, at which the second-degree polynomial will be approximated by the least squares method. At the initial interval [1, m], the initial implementation is divided into overlapping intervals with a fixed left boundary and an increasing length of the partition interval from m _min to m. On the interval [2, n], the initial implementation is partitioned by shifting the “sliding window” by one value to the right. On the interval [nm, n], the initial implementation is partitioned with a decrease in the length of the intervals to the minimum value m _min with a fixed right boundary. Each of the obtained intervals is approximated by a polynomial of the second degree by the least squares method.

Thus, we obtain r = n-m + 2 · m _min segments of the original series and r their estimates. The initial series can be represented as a matrix:

The approximating function S (t) is determined by averaging over the side diagonals:

A device for extracting a useful signal against a background of noise with minimizing end effects by way of piecewise multiplication of estimates contains (Fig. 2) a measurement storage register 1, the input of which is the information input of the device, and the output is connected to the input of the delay unit 2, the output of which is connected to the input of the approximation unit 3, the output of which is the information input of the register of estimates storage 4, the output of which is connected to the input of the averaging unit 5, the output of which is the information output of the device, to additional the information inputs of blocks 2, 3, 4, 5, 8 are connected to the output of the control unit 6. An additional input of the measurement results storage register is connected to the input of the comparison unit 8, the output of which is connected to the input of the delay unit 2 and the input of the measurement results storage register 1. Device synchronization provided by a clock 7.

A device for extracting a useful signal against a background of noise with minimizing end effects by the method of piecewise multiplication of estimates works as follows. On the initial interval [1, m], the first m _min values of the initial implementation of the measurement results are recorded in the buffer block (m _min is the minimum width of the "sliding window"), then m _min +1 values are written in the buffer block. As the data become available, the last received values are approximated by the least squares method by a polynomial of the second degree. The resulting estimates are recorded in the block storage estimates for further averaging. When the window width reaches m, that is, m _min + i = m, i = 1 ... mm _min , the data are supplied to the approximation block with a delay of one count. The resulting grades are recorded in the grades storage unit. Upon reaching the end of the implementation by the “sliding window”, its right boundary decreases by one count until the value [nm, n] is reached. According to the arithmetic mean method, at each instant of time, the final assessment of the useful component is determined, which arrives at the output of the device.

A device for extracting a useful signal against a background of noise with minimizing end effects by the method of piecewise multiplication of estimates is implemented as follows. In the control unit, the width of the sliding window m and the value of the minimum window width m _{min are set} , information about which is supplied to the additional information inputs of the comparison unit 8, delay unit 2, approximation unit 3, storage unit for estimates 4 and averaging unit 5. In the comparison unit 8, the signal from the register of storage of measurement results checks the condition m _min = m, if the condition is not met, then the size of the register of storage of measurement results increases by one. If the condition m _min = m in the comparison unit 8 is satisfied, a permission signal is supplied to the delay unit 2. Delay unit 2 delays the data stored in the measurement results storage register by one clock cycle if a delay enable signal from block 8 is supplied to its additional input. In the event input data is stopped in block 1 (ie, the “sliding window” has reached the end of implementation ), in the block of comparison 8, the condition m _min + i = m is stopped, and the input sample is divided into intervals with decreasing unit length to the minimum value of m _min with a fixed right boundary. Data from the storage register of the measurement results 1 is sent to the approximation block 3, where they are approximated by the least squares polynomial of the second degree. The estimates obtained in the approximation block 3 are recorded in the register of estimates storage 4 and enter the averaging block 5, in which the estimates obtained in the approximation block 3 are averaged. The output of the averaging block 5 is the information output of the device.

The technical result is the selection of a useful signal against a background of noise with minimizing end effects in conditions of insufficient a priori information about the statistical characteristics of additive noise and the function of the useful signal in the presence of a single implementation of the measured process.

Through simulation it was found that the device for extracting a useful signal against a background of noise with minimizing end effects by the method of piecewise multiplication of estimates has the following advantages:

The mean square error of the estimation of the useful signal function is much smaller than the estimation errors when using the other methods considered with a limited amount of a priori information about the process under study.

The proposed method allows the processing of source data in real time.

Evaluation of the useful signal function, regardless of the type of the initial function of the useful signal and the statistical characteristics of additive noise, adequately reflects the basic patterns of change in the useful signal.

Figure 3 presents an example of the multiplication of estimates of the useful component (graph - 2), the original signal (graph - 1). A comparative analysis of the results presented in Fig. 2 shows that in the case of using the method for extracting a useful signal by piecewise multiplying estimates with minimizing end effects, its values are more closely located to the values of the original signal than for the case of piecewise multiplying estimates without minimizing end effects.

Figure 4 shows the dependences σ _ost = f (m) when processing a harmonic-shaped signal model by the method of piecewise multiplication of estimates and the method of piecewise multiplication of estimates with minimizing end effects in the absence of a noise component in the initial implementation (σ _w = 0). In Fig. 4, the following notation is adopted: graph 1 corresponds to the case of processing at p = 1, with an error estimate in the interval [0, n]; graph 2 correspond to processing by the method of piecewise multiplication of estimates with minimization of end effects at p = 1 (p is the degree of the approximating polynomial), with an error estimate on the interval [0, n], graph 3 is obtained at p = 1, with an error estimate on the interval [m , nm] (excluding end effects). Graphs 4-6 were obtained under the same conditions as graphs 1-3, but for the case p = 2. An analysis of the graphs shows that the use of the method of piecewise multiplication of estimates with minimization of end effects when processing a model of harmonic shape allows, on average, to reduce the estimate error by 2 times in comparison with piecewise reproduction of estimates without minimizing end effects.

Figure 5 shows the dependences σ _ost = f (m), when processing the implementation with the model, the harmonic signal + noise (σ _w = 0.1).

The previously adopted designations for figure 3 remain unchanged for figure 4. Using the method of piecewise multiplication of estimates with minimizing end effects allows you to reduce the overall error of the estimate of the signal (Fig. 5, graphs 2, 5), on average, by 5 - 30%, and for large m - up to 50% compared with the method of piecewise reproduction of estimates without minimizing end effects. The nonlinear nature of the dependence σ _ost = f (m) when evaluating the processing error (graph 3) is explained by the fact that, for some values of m, signal intervals where the estimation error reaches its maximum fall into the intervals [0, m] and [nm, n] values.

Claims (1)

A device for extracting a useful signal against a background of noise with minimizing end effects by the method of piecewise multiplication of estimates, containing a register for storing measurement results, the input of which is the information input of the device, the output of which is connected to the input of the delay unit, the output of the delay unit is connected to the input of the approximation unit, the output of which is connected to the input of the register for storing estimates, the output of the register for storing estimates is connected to the input of the averaging unit, the output of which is the information output of the device, to the control the output of the control unit is connected to the control input of the measurement results storage register, the input of which is connected to the output of the control unit, is connected to the enable input of the delay unit additional output of the comparison unit, and the synchronization of the device is provided by the clock generator.

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