CN113610942B - Pulse waveform segmentation method, system, equipment and storage medium - Google Patents
Pulse waveform segmentation method, system, equipment and storage medium Download PDFInfo
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- G06F17/141—Discrete Fourier transforms
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Abstract
The invention discloses a pulse waveform segmentation method, a system, equipment and a storage medium, wherein the method comprises the following steps of 1, converting an analog pulse signal output by a sensor into a digital pulse signal; step 2, converting the digital pulse signals into two-dimensional waveform images in the airspace; step 3, extracting the skeleton of the two-dimensional waveform image in the space domain to obtain an equal-length space domain waveform skeleton; step 4, dividing the equal-length airspace waveform skeleton into N sections of curves; and step 5, mapping the segmented waveform back to the time domain, and performing waveform segmentation precision control so as to obtain a waveform time domain segmentation result meeting the precision requirement. According to the invention, after the space domain and the frequency domain of the same digital signal are analyzed according to the specific physical signal output by the sensor, the digital signal is divided in the time domain according to the shape and the frequency characteristic of the digital signal, and different filtering strategies can be adopted for different parts in the follow-up process, so that finer and more effective processing effects are achieved.
Description
Technical Field
The invention belongs to the technical field of digital signal analysis and processing, and particularly relates to a pulse waveform segmentation method, a system, equipment and a storage medium based on time domain, frequency domain and space domain joint analysis.
Background
In the times of internet and internet of things, a large number of sensors with different forms and functions are always distributed at a network terminal, pulse signals carrying different physical information are output by different sensors, the signals are often analog signals, in the network layout, the near end of the sensor is followed by a waveform digitizing device, the analog signals output by the sensors are converted into digital signals through analog/digital conversion as soon as possible, and the digital signals have the following commonalities:
1) The digital signal at this time can be represented by a series of (time, amplitude) data sequences which can be transformed into a two-dimensional spatial waveform by time/space conversion; fourier transforming the pulse data sequence can become a series of (frequency, intensity) spectral data. When these conversions are completed, the shape and frequency characteristics of a digital signal can be clearly presented, and the shape of the different parts of the waveform corresponds to its spectral characteristics.
2) Noise and interference are aliased in the signals output by the waveform digitizing apparatus, and various digital filters are subsequently employed to minimize noise while retaining the original physical information as much as possible. However, in the process that the digital signal is processed by the traditional filter, the shape of the signal and the effective information carried by the signal can be changed while the superimposed noise is eliminated, so that the processing effect is poor, and the data reliability and the data processing precision are affected.
3) Different types of physical waveforms output by the same sensor are different in shape, and the shapes of the same physical signals output by the same sensor are highly similar. The same filtering strategy is adopted for signals with different waveform shapes, and the processing precision and reliability are also affected.
Disclosure of Invention
Aiming at the problems, the invention provides a pulse waveform segmentation method, which is characterized in that after the space domain and the frequency domain of the same digital signal are analyzed according to the specific of the physical signal output by a sensor, the digital signal is segmented in the time domain according to the shape and the frequency characteristic of the digital signal, and different filtering strategies can be adopted for different parts subsequently, so that finer and more effective processing effects are achieved.
The invention is realized by the following technical scheme:
a pulse waveform segmentation method, comprising:
step 1, converting an analog pulse signal output by a sensor into a digital pulse signal;
step 2, converting the digital pulse signals into two-dimensional waveform images in the airspace;
step 3, extracting the skeleton of the two-dimensional waveform image in the space domain to obtain an equal-length space domain waveform skeleton;
step 4, dividing the equal-length airspace waveform skeleton into N sections of curves;
and step 5, mapping the segmented waveform back to the time domain, and performing waveform segmentation precision control so as to obtain a waveform time domain segmentation result meeting the precision requirement.
The invention can implement the same waveform segmentation scheme aiming at determining the determined type of physical signals output by the sensor, and the scheme can be directly used for processing the same type of signals output by the sensor once determined. The segmented signals adopt different processing strategies at different positions, so that the processing precision of the high-frequency part can be improved, and the data quantity of the low-frequency part can be reduced.
Preferably, the waveform division accuracy control performed in step 5 of the present invention specifically includes:
step 5.1, sequentially carrying out Fourier transform on the time domain data of each section of curve to obtain the frequency domain data of each section of curve;
step 5.2, analyzing the frequency spectrum of each section of waveform, comparing the frequency spectrum with the frequency spectrums of the left and right adjacent sections of waveform, and judging whether the confidence degree of the division points arranged on the two sides of the section of waveform meets the preset requirement;
and 5.3, if the preset requirement is met, considering the segmentation point set by the section of waveform to meet the preset requirement, otherwise, returning to the step 4 to re-determine the segmentation point until the preset requirement is met.
Preferably, the determining whether the confidence σ of the waveform division point in step 5.2 of the present invention meets the preset requirement specifically includes:
step 5.21, finding out each section of spectrum peak value f in the N sections of waveform frameworks i ，i＝2，…N；
Step 5.22, finding out the half-width fw of each section of frequency spectrum i I=2, … N, i.eWidth of spectrum peak;
step 5.23, calculating the peak distance delta f of two adjacent sections of waveforms i I.e. Δf i ＝f i -f i-1 The initial value of i is 2;
step 5.24, if Δf i ＞fw i If σ=1, i.e. the i-th division point meets the requirement, i=i+1, returning to step 5.23 until i > N; if Deltaf i ＜fw i σ=0, and returns to step 4 to redefine the i-th division point.
Preferably, step 4 of the present invention specifically includes:
step 4.1, obtaining an extreme point with a slope of 0 in a waveform skeleton;
step 4.2, searching points, of which the values of the second derivative are smaller than the preset precision value and closest to the extreme points, at the left and right of the extreme points as dividing points;
step 4.3, respectively performing curve fitting on the left and right of the dividing points;
and 4.4, re-acquiring other extreme points with the slope of 0 in the waveform skeleton, and returning to the step 4.2-step 4.3 to divide the waveform skeleton into N sections of curves.
Preferably, in step 2 of the present invention, a bwmorph algorithm is adopted to convert the digital signal obtained in step 1 into a two-dimensional binary waveform image in the airspace.
Preferably, in the step 3 of the invention, skeleton extraction is performed on the space two-dimensional waveform image by adopting a krnk algorithm.
In a second aspect, the present invention provides a pulse waveform segmentation system, including a segmentation module and a control module;
the segmentation module comprises a digitizing unit, a shape extraction unit, a skeleton extraction unit, an image recognition unit and a mapping unit;
the digitizing unit is used for converting the analog pulse signals output by the sensor into digital pulse signals;
the shape extraction unit changes the digital pulse signal into a two-dimensional waveform image in the space domain;
the skeleton extraction unit performs skeleton extraction on the space domain two-dimensional waveform image to obtain an equal-length space domain waveform skeleton;
the image recognition unit divides the waveform skeleton into N sections;
the mapping unit maps the divided waveforms back to the time domain, and waveform division precision control is carried out through the control module, so that the system outputs waveform time domain division results meeting the precision requirement.
Preferably, the control module of the invention comprises a frequency domain unit, a frequency domain data extraction unit and a confidence judging unit;
the frequency domain unit sequentially performs Fourier transform on each section of time domain data to obtain frequency domain data of each section of curve;
the frequency domain data extraction unit obtains frequency spectrum data of each section from the frequency domain data of each section of curve;
the confidence judging unit judges the confidence of the waveform dividing point according to the frequency spectrum data of each section, if the preset requirement is met, the waveform dividing point meets the requirement, otherwise, the image identifying unit is controlled by the output control signal to re-determine the dividing point.
In a third aspect, the invention proposes a computer device comprising a memory storing a computer program and a processor implementing the steps of the method of the invention when the processor executes the computer program.
In a fourth aspect, the invention proposes a computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, carries out the steps of the method according to the invention.
The invention has the following advantages and beneficial effects:
according to the invention, a pulse is divided into a plurality of sections according to the characteristics of the waveform shape and the frequency of the digital pulse signal, and a signal processing scheme is formulated in a sectionalized manner, so that different signal processing strategies can be adopted for different parts of the same signal in the follow-up process, the signal processing precision of a high-frequency part can be improved, the data quantity of a low-frequency part can be effectively reduced, and the fidelity compression ratio can be improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiments of the invention. In the drawings:
FIG. 1 is a schematic flow chart of the method of the present invention.
Fig. 2 is a schematic diagram illustrating confidence determination of waveform segmentation points according to the present invention.
FIG. 3 is a schematic diagram of a computer device according to the present invention.
Fig. 4 is a schematic diagram of a system structure according to the present invention.
Detailed Description
For the purpose of making apparent the objects, technical solutions and advantages of the present invention, the present invention will be further described in detail with reference to the following examples and the accompanying drawings, wherein the exemplary embodiments of the present invention and the descriptions thereof are for illustrating the present invention only and are not to be construed as limiting the present invention.
Example 1
The present embodiment proposes a pulse waveform segmentation method, as shown in fig. 1, where the method in this embodiment mainly includes two parts: and performing a waveform segmentation step and a waveform segmentation precision control step according to the shape characteristics of the digitized pulse signals.
The step of dividing the waveform according to the shape of the digitized pulse signal specifically comprises the following steps:
and step 1, converting an analog pulse signal output by the sensor into a digital pulse signal.
In this embodiment, after the sensor outputs the detected physical signal in the form of analog amplitude pulse, the detected physical signal is output by the subsequent waveform digitizer in the form of f s Is converted into a series of digitized (time, amplitude) sequences, i.e. digitized signals. Expressed as:
V(t)＝(v 1 ，v 2 ，…v i ，…v n ) (1)
in the formula, v i Represents the i-th sequence of digital pulse amplitudes, where i=1, 2, … n.
Step 2, converting the digital signal obtained in the step 1 into a two-dimensional binary waveform image in a space domain by utilizing a bwmorph algorithm, wherein the two-dimensional binary waveform image is expressed as:
in BW []Representing time domain sequence to space domainAn operator of the binary image; (x) i ，y j ) Representing a value at the position of the ith row and jth column of the spatial image, in particular 0 or 1, where i=1, 2, … n; j=1, 2, … n.
Step 3, skeleton extraction is carried out on the space domain two-dimensional binary waveform image M (x, y) by utilizing a krik algorithm, and an equilong airspace waveform skeleton sequence M (x ', y') is obtained:
in Srk []An operator for extracting a skeleton from an existing image; (x' i ，y′ j ) Representing a value at a j-th column position of an i-th row in the waveform skeleton image, specifically 0 or 1, wherein i=1, 2, … n; j=1, 2, … n.
And 4, dividing the skeleton sequence into N segments.
In the embodiment, the equal-length airspace waveform skeleton sequence M (x ', y') is fitted into a plurality of sections of primary curves or secondary curves point by utilizing a maximum likelihood algorithm. The method comprises the following specific steps:
(1) Obtaining an extreme point with a slope of 0 in a skeleton image, and searching a point, of which the value of the second derivative is smaller than a preset precision value delta 0 and closest to the extreme point, at the left and right of the extreme point as a segmentation point P i ；
(2) At the dividing point P i Respectively performing curve fitting on the left and right sides;
(3) Then adopting the steps (1) - (2), fitting the skeleton representing the signal shape into [ M ] by using N sections of curves 1 ，M 2 ，…M N ]。
Step 5, [ M ] 1 ，M 2 ，…M N ]Inversely converting back to the time domain to obtain an N-segment time domain signal sequence, wherein the N-segment time domain signal sequence is expressed as follows:
[Y 1 ，Y 2 ，…Y N ]＝BW -1 [M 1 ，M 2 ，…M N ] (4)
in BW -1 []A transform operator representing the spatial/temporal domain.
After mapping each part of the segmented waveform skeleton back to the time domain, performing waveform segmentation precision control, wherein the specific process mainly comprises the following steps:
step 6, for [ Y ] 1 ，Y 2 ，…Y N ]Performing discrete Fourier transform segment by segment to obtain a spectrum sequence of a mapping waveform of the skeleton in a time domain, which can be expressed as:
in the method, in the process of the invention,representing fourier transform operators.
Step 7, searching a peak value corresponding to the peak value of each section of signal spectrum, wherein the peak value is expressed as:
(f 1 ，f 2 ，…，f N )＝Max1[F 1 ，F 2 ，…F N ] (6)
in the formula, max1[ ] represents a coordinate position operator corresponding to the search function value.
Step 8, calculating half-width (fw) of each spectral peak 1 ，fw 2 ，…，fw N ) Expressed as:
(fw 1 ，fw 2 ，…，fw N )＝FHFW[F 1 ，F 2 ，…F N ] (7)
where FHFW [ ] represents the calculated peak half-width operator.
And 9, judging the confidence degree of each division point. The method comprises the following specific steps:
(1) Let i=2: n, step 1, enter the cycle.
(2) Calculating the peak position distance delta f of two adjacent wave forms i ：
Δf i ＝f i -f i-1
(3) Comparing the space between the spectrum peak positions of two adjacent sections of waveforms with the half-width of the spectrum peak of the current division point, if delta f i ＞fw i σ=1; i=i+1; confirming the ith division point time coordinatet i Enter the next cycle (return to (2) of executing step 9) until i > N; otherwise σ=0, jumping out of the cycle, returning to step (1) in step 4, moving the fitting division point leftwards by a preset step length to serve as a new division point, and in order to achieve efficiency and precision, the preset step length in the embodiment is preferably 2 points, and repeating steps 4 to 9. As shown in fig. 2.
Step 10, obtaining the preset precision delta 0 A waveform time domain segmentation scheme with confidence of 1.
If the time-domain time-frame sequence of a time-domain pulse signal is expressed as:
[(0，v 0 )，(f s ，v 1 )，…，(nf s ，v n )]the pulse signal is divided into N segments, denoted as [ Y ] 1 ，Y 2 ，…Y N ]Wherein Y is i-1 And Y i The abscissa of the dividing point between the two is t i The entire waveform is divided into N segments by N-1 division points, the positions of which are expressed as: t= (T 1 ，…，t N-1 )。
The embodiment also provides a computer device for executing the method of the embodiment.
As particularly shown in fig. 3, the computer device includes a processor, an internal memory, and a system bus; various device components, including internal memory and processors, are connected to the system bus. A processor is a piece of hardware used to execute computer program instructions by basic arithmetic and logical operations in a computer system. Internal memory is a physical device used to temporarily or permanently store computing programs or data (e.g., program state information). The system bus may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus. The processor and the internal memory may communicate data via a system bus. The internal memory includes a Read Only Memory (ROM) or a flash memory (not shown), and a Random Access Memory (RAM), which generally refers to a main memory loaded with an operating system and computer programs.
Computer devices typically include an external storage device. The external storage device may be selected from a variety of computer readable media, which refers to any available media that can be accessed by a computer device, including both removable and fixed media. For example, computer-readable media includes, but is not limited to, flash memory (micro-SD card), CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer device.
The computer device may be logically connected to one or more network terminals in a network environment. The network terminal may be a personal computer, server, router, smart phone, tablet computer, or other public network node. The computer device is connected to a network terminal through a network interface (local area network LAN interface). Local Area Networks (LANs) refer to computer networks of interconnected networks within a limited area, such as a home, school, computer laboratory, or office building using network media. WiFi and twisted pair wired ethernet are the two most common technologies used to construct local area networks.
It should be noted that other computer systems including more or fewer subsystems than computer devices may also be suitable for use with the invention.
As described in detail above, the computer apparatus suitable for the present embodiment can perform the specified operation of the pulse waveform dividing method. The computer device performs these operations in the form of software instructions that are executed by a processor in a computer-readable medium. The software instructions may be read into memory from a storage device or from another device via a lan interface. The software instructions stored in the memory cause the processor to perform the method of processing group member information described above. Furthermore, the invention may be implemented by means of hardware circuitry or by means of combination of hardware circuitry and software instructions. Thus, implementation of the present embodiments is not limited to any specific combination of hardware circuitry and software.
Example 2
The embodiment provides a pulse waveform segmentation system, which is particularly shown in fig. 4, and comprises a segmentation module and a control module;
the segmentation module of the embodiment comprises a digitizing unit, a shape extracting unit, a skeleton extracting unit, an image identifying unit and a mapping unit;
the digitizing unit is used for converting the analog pulse signals output by the sensor into digital pulse signals; the digitizing unit of this embodiment is implemented by using the algorithm of step 1 of embodiment 1, which is not described herein.
The shape extraction unit changes the digital pulse signal into a two-dimensional waveform image in the space domain; the shape extraction unit of this embodiment is implemented by adopting the algorithm of step 2 of embodiment 1, which is not described herein.
The skeleton extraction unit is used for extracting the skeleton of the space two-dimensional waveform image to obtain an equal-length space waveform skeleton; the skeleton extraction unit of this embodiment is implemented by using the algorithm of step 3 in embodiment 1, which is not described herein.
The image recognition unit divides the waveform skeleton into N sections; the image recognition unit of this embodiment is implemented by adopting the algorithm of step 4 of embodiment 1, which is not described herein.
The mapping unit maps the divided waveforms back to the time domain, and waveform division precision control is carried out through the control module, so that the system outputs waveform time domain division results meeting the precision requirement. The mapping unit of this embodiment is implemented by using the algorithm in step 5 of the foregoing embodiment 1, which is not described herein.
The control module of the embodiment comprises a frequency domain unit, a frequency domain data extraction unit and a confidence judging unit;
the frequency domain unit sequentially performs Fourier transform on each section of time domain data to obtain frequency domain data of each section of curve; the frequency domain unit of this embodiment is implemented by using the algorithm of step 6 of embodiment 1, which is not described herein.
The frequency domain data extraction unit obtains frequency spectrum data of each section from the frequency domain data of each section of curve; the frequency domain data extraction unit of this embodiment is implemented by using the algorithm of step 7-8 in embodiment 1, which is not described here again.
The confidence judging unit judges the confidence of the waveform dividing point according to the frequency spectrum data of each section, if the preset requirement is met, the waveform dividing point meets the requirement, otherwise, the image identifying unit is controlled by the output control signal to redetermine the dividing point. The confidence judging unit of this embodiment is implemented by adopting the algorithm of step 9 in embodiment 1, which is not described herein.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (7)
1. A pulse waveform segmentation method, comprising:
step 1, converting an analog pulse signal output by a sensor into a digital pulse signal;
step 2, converting the digital pulse signals into two-dimensional waveform images in the airspace;
step 3, extracting the skeleton of the two-dimensional waveform image in the space domain to obtain an equal-length space domain waveform skeleton;
step 4, dividing the equal-length airspace waveform skeleton into N sections of curves; the step 4 specifically includes:
step 4.1, obtaining an extreme point with a slope of 0 in a waveform skeleton;
step 4.2, searching points, of which the values of the second derivative are smaller than the preset precision value and closest to the extreme points, at the left and right of the extreme points as dividing points;
step 4.3, respectively performing curve fitting on the left and right of the dividing points;
step 4.4, re-acquiring other extreme points with the slope of 0 in the waveform skeleton, and returning to the step 4.2-step 4.3, and dividing the waveform skeleton into N sections of curves;
step 5, mapping the segmented waveform back to the time domain, and performing waveform segmentation precision control so as to obtain a waveform time domain segmentation result meeting the precision requirement;
the controlling of the waveform segmentation accuracy in the step 5 specifically includes:
step 5.1, sequentially carrying out Fourier transform on the time domain data of each section of curve to obtain the frequency domain data of each section of curve;
step 5.2, analyzing the frequency spectrum of each section of waveform, comparing the frequency spectrum with the frequency spectrums of the left and right adjacent sections of waveform, and judging whether the confidence degree of the division points arranged on the two sides of the section of waveform meets the preset requirement;
step 5.3, if yes, considering that the segmentation point set by the section of waveform meets the preset requirement, otherwise, returning to the step 4 to re-determine the segmentation point until the preset requirement is met;
the step 5.2 of judging whether the confidence level sigma of the waveform dividing point meets the preset requirement specifically includes:
step 5.21, finding out each section of spectrum peak value f in the N sections of waveform frameworks i ，i＝2，…N；
Step 5.22, finding out the half-width fw of each section of frequency spectrum i I=2, … N, i.eWidth of spectrum peak;
step 5.23, calculating the peak distance delta f of two adjacent sections of waveforms i I.e. Δf i ＝f i -f i-1 The initial value of i is 2;
step 5.24, if Δf i ＞fw i If σ=1, i.e. the i-th division point meets the requirement, i=i+1, returning to step 5.23 until i > N; if Deltaf i ＜fw i σ=0, and returns to step 4 to redefine the i-th division point.
2. The method of claim 1, wherein the step 2 converts the digital signal obtained in the step 1 into a two-dimensional binary waveform image in a space domain by using a bwmorph algorithm.
3. The pulse waveform segmentation method according to claim 1, wherein the step 3 adopts a krik algorithm to perform skeleton extraction on the space two-dimensional waveform image.
4. The pulse waveform segmentation system is characterized by comprising a segmentation module and a control module;
the segmentation module comprises a digitizing unit, a shape extraction unit, a skeleton extraction unit, an image recognition unit and a mapping unit;
the digitizing unit is used for converting the analog pulse signals output by the sensor into digital pulse signals;
the shape extraction unit changes the digital pulse signal into a two-dimensional waveform image in the space domain;
the skeleton extraction unit performs skeleton extraction on the space domain two-dimensional waveform image to obtain an equal-length space domain waveform skeleton;
the image recognition unit divides the waveform skeleton into N sections; the waveform skeleton segmentation process specifically comprises the following steps:
step 4.1, obtaining an extreme point with a slope of 0 in a waveform skeleton;
step 4.2, searching points, of which the values of the second derivative are smaller than the preset precision value and closest to the extreme points, at the left and right of the extreme points as dividing points;
step 4.3, respectively performing curve fitting on the left and right of the dividing points;
step 4.4, re-acquiring other extreme points with the slope of 0 in the waveform skeleton, and returning to the step 4.2-step 4.3, and dividing the waveform skeleton into N sections of curves;
the mapping unit maps the divided waveforms back to the time domain, and the control module is used for controlling the waveform division precision, so that the system outputs a waveform time domain division result meeting the precision requirement;
the waveform segmentation precision control process specifically comprises the following steps:
step 5.1, sequentially carrying out Fourier transform on the time domain data of each section of curve to obtain the frequency domain data of each section of curve;
step 5.2, analyzing the frequency spectrum of each section of waveform, comparing the frequency spectrum with the frequency spectrums of the left and right adjacent sections of waveform, and judging whether the confidence degree of the division points arranged on the two sides of the section of waveform meets the preset requirement;
step 5.3, if yes, considering that the segmentation point set by the section of waveform meets the preset requirement, otherwise, returning to the step 4.1-step 4.4 to re-determine the segmentation point until the preset requirement is met;
the judging whether the confidence sigma of the waveform division point meets the preset requirement specifically comprises the following steps:
step 5.21, finding out each section of spectrum peak value f in the N sections of waveform frameworks i ，i＝2，…N；
Step 5.22, finding out the half-width fw of each section of frequency spectrum i I=2, … N, i.eWidth of spectrum peak;
step 5.23, calculating the peak distance delta f of two adjacent sections of waveforms i I.e. Δf i ＝f i -f i-1 The initial value of i is 2;
step 5.24, if Δf i ＞fw i If σ=1, i.e. the i-th division point meets the requirement, i=i+1, returning to step 5.23 until i > N; if Deltaf i ＜fw i σ=0, and the step 4.1-step 4.4 is performed back to redetermine the ith division point.
5. The pulse waveform segmentation system as set forth in claim 4, wherein the control module comprises a frequency domain unit, a frequency domain data extraction unit, and a confidence level judgment unit;
the frequency domain unit sequentially performs Fourier transform on each section of time domain data to obtain frequency domain data of each section of curve;
the frequency domain data extraction unit obtains frequency spectrum data of each section from the frequency domain data of each section of curve;
the confidence judging unit judges the confidence of the waveform dividing point according to the frequency spectrum data of each section, if the preset requirement is met, the waveform dividing point meets the requirement, otherwise, the image identifying unit is controlled by the output control signal to re-determine the dividing point.
6. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1-3 when the computer program is executed.
7. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1-3.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4970593A (en) * | 1989-08-28 | 1990-11-13 | Sperry Marine Inc. | Video image enhancement utilizing a two-dimensional digital aperture correction filter |
| WO2002007362A1 (en) * | 2000-07-19 | 2002-01-24 | Digimarc Corporation | Wavelet domain watermarks |
| JP2005338004A (en) * | 2004-05-28 | 2005-12-08 | Mitsubishi Electric Corp | Radar installation |
| CN101106384A (en) * | 2006-07-10 | 2008-01-16 | 哈曼贝克自动系统股份有限公司 | Partitioned fast convolution in the time and frequency domain |
| CN101113936A (en) * | 2007-07-20 | 2008-01-30 | 广州市计量检测技术研究院 | Virtual oscillating table detection signal processing method and equipment thereof |
| CN101719387A (en) * | 2009-12-10 | 2010-06-02 | 清华大学 | External ball passage detecting method and device for ball-bed high temperature gas cooled reactor |
| CN105678822A (en) * | 2016-01-13 | 2016-06-15 | 哈尔滨理工大学 | Three-regular magnetic resonance image reconstruction method based on Split Bregman iteration |
| CN111966960A (en) * | 2020-07-21 | 2020-11-20 | 北京邮电大学 | All-optical short-time Fourier transform system and method |
-
2021
- 2021-06-11 CN CN202110655807.9A patent/CN113610942B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4970593A (en) * | 1989-08-28 | 1990-11-13 | Sperry Marine Inc. | Video image enhancement utilizing a two-dimensional digital aperture correction filter |
| WO2002007362A1 (en) * | 2000-07-19 | 2002-01-24 | Digimarc Corporation | Wavelet domain watermarks |
| JP2005338004A (en) * | 2004-05-28 | 2005-12-08 | Mitsubishi Electric Corp | Radar installation |
| CN101106384A (en) * | 2006-07-10 | 2008-01-16 | 哈曼贝克自动系统股份有限公司 | Partitioned fast convolution in the time and frequency domain |
| CN101113936A (en) * | 2007-07-20 | 2008-01-30 | 广州市计量检测技术研究院 | Virtual oscillating table detection signal processing method and equipment thereof |
| CN101719387A (en) * | 2009-12-10 | 2010-06-02 | 清华大学 | External ball passage detecting method and device for ball-bed high temperature gas cooled reactor |
| CN105678822A (en) * | 2016-01-13 | 2016-06-15 | 哈尔滨理工大学 | Three-regular magnetic resonance image reconstruction method based on Split Bregman iteration |
| CN111966960A (en) * | 2020-07-21 | 2020-11-20 | 北京邮电大学 | All-optical short-time Fourier transform system and method |
Non-Patent Citations (1)
| Title |
|---|
| 光学磁场传感器中超宽带模拟信号的重构;苏洋;李玉权;;微波学报(第06期);第67-70页 * |
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