CN113671009A - Matrix type broad-spectrum substance detection sensor built based on artificial intelligence algorithm - Google Patents
Matrix type broad-spectrum substance detection sensor built based on artificial intelligence algorithm Download PDFInfo
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- CN113671009A CN113671009A CN202110880462.7A CN202110880462A CN113671009A CN 113671009 A CN113671009 A CN 113671009A CN 202110880462 A CN202110880462 A CN 202110880462A CN 113671009 A CN113671009 A CN 113671009A
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- 238000001514 detection method Methods 0.000 title claims abstract description 87
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 58
- 239000011159 matrix material Substances 0.000 title claims abstract description 55
- 239000000126 substance Substances 0.000 title claims abstract description 27
- 238000006243 chemical reaction Methods 0.000 claims abstract description 19
- 239000000463 material Substances 0.000 claims abstract description 15
- 230000005611 electricity Effects 0.000 claims abstract description 5
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N Silicium dioxide Chemical compound O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 claims description 16
- XLOMVQKBTHCTTD-UHFFFAOYSA-N Zinc monoxide Chemical compound [Zn]=O XLOMVQKBTHCTTD-UHFFFAOYSA-N 0.000 claims description 12
- 239000003054 catalyst Substances 0.000 claims description 11
- 239000000758 substrate Substances 0.000 claims description 11
- 239000004065 semiconductor Substances 0.000 claims description 10
- 239000000377 silicon dioxide Substances 0.000 claims description 9
- 229910052681 coesite Inorganic materials 0.000 claims description 8
- 229910052906 cristobalite Inorganic materials 0.000 claims description 8
- 235000012239 silicon dioxide Nutrition 0.000 claims description 8
- 229910052682 stishovite Inorganic materials 0.000 claims description 8
- 229910052905 tridymite Inorganic materials 0.000 claims description 8
- 239000011787 zinc oxide Substances 0.000 claims description 6
- UQSXHKLRYXJYBZ-UHFFFAOYSA-N Iron oxide Chemical compound [Fe]=O UQSXHKLRYXJYBZ-UHFFFAOYSA-N 0.000 claims description 4
- 239000010409 thin film Substances 0.000 claims description 3
- XOLBLPGZBRYERU-UHFFFAOYSA-N tin dioxide Chemical compound O=[Sn]=O XOLBLPGZBRYERU-UHFFFAOYSA-N 0.000 claims description 2
- 229910001887 tin oxide Inorganic materials 0.000 claims description 2
- 239000011859 microparticle Substances 0.000 claims 1
- 238000007781 pre-processing Methods 0.000 abstract 1
- 239000007789 gas Substances 0.000 description 34
- 238000007792 addition Methods 0.000 description 12
- 238000013528 artificial neural network Methods 0.000 description 6
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 230000035945 sensitivity Effects 0.000 description 4
- 239000010408 film Substances 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 230000006872 improvement Effects 0.000 description 2
- 231100000614 poison Toxicity 0.000 description 2
- 230000007096 poisonous effect Effects 0.000 description 2
- 239000011882 ultra-fine particle Substances 0.000 description 2
- 108010001267 Protein Subunits Proteins 0.000 description 1
- 238000009825 accumulation Methods 0.000 description 1
- 230000004888 barrier function Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 239000013078 crystal Substances 0.000 description 1
- 238000005034 decoration Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- VBMVTYDPPZVILR-UHFFFAOYSA-N iron(2+);oxygen(2-) Chemical class [O-2].[Fe+2] VBMVTYDPPZVILR-UHFFFAOYSA-N 0.000 description 1
- 239000012528 membrane Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005036 potential barrier Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- QHGNHLZPVBIIPX-UHFFFAOYSA-N tin(ii) oxide Chemical class [Sn]=O QHGNHLZPVBIIPX-UHFFFAOYSA-N 0.000 description 1
- 231100000331 toxic Toxicity 0.000 description 1
- 230000002588 toxic effect Effects 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
- RNWHGQJWIACOKP-UHFFFAOYSA-N zinc;oxygen(2-) Chemical class [O-2].[Zn+2] RNWHGQJWIACOKP-UHFFFAOYSA-N 0.000 description 1
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
- G01N27/26—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating electrochemical variables; by using electrolysis or electrophoresis
- G01N27/416—Systems
- G01N27/4162—Systems investigating the composition of gases, by the influence exerted on ionic conductivity in a liquid
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
Abstract
The invention provides a matrix type broad-spectrum substance detection sensor built based on an artificial intelligence algorithm, and belongs to the technical field of sensors. This matrix broad-spectrum material detection sensor based on artificial intelligence algorithm is built includes the signal conversion board, be provided with the communication interface who is used for with the host computer connection on the signal conversion board, the signal conversion board openly is provided with the detecting element that a plurality of is the matrix and arranges, the signal conversion board back is equipped with artificial intelligence algorithm module, every detecting element all with the input electricity of artificial intelligence algorithm module is connected, artificial intelligence algorithm module output and communication interface connect with output analysis result. The sensor is convenient to use, accurate in detection, capable of preprocessing data and good in practicability, and requirements for a subsequent processor are reduced.
Description
Technical Field
The invention belongs to the technical field of sensors, and particularly relates to a matrix type broad-spectrum substance detection sensor built based on an artificial intelligence algorithm.
Background
The sensor is a detection device which can sense the information to be measured and convert the detected information into electric signals or other information in required forms according to a certain rule for output, and is widely applied to the aspects of water conservancy, geology, meteorology, chemical industry, medical treatment and health and the like.
With the increasing of living standard, building decoration is more and more entered into people's life, can improve people's living quality of living through decorating, but what accompanies is that a large amount of poisonous harmful substance that contains in the ornamental material, along with people's increasing of this degree of attention to this day to the requirement of building house ornamentation field to environmental protection material is increasing day by day, and the market is to the demand of all kinds of high sensitivity poisonous harmful gas sensor rapid growth. At present, the gas sensor mainly comprises a semiconductor gas sensor, an electrochemical gas sensor and an infrared gas sensor. The sensors have the problems of poor detection stability, low detection sensitivity, single detection, poor universality and the like, so that the toxic and harmful gas is not accurately and comprehensively detected, and the detection effect is limited. For this reason, someone has designed the matrix sensor, arranges a plurality of the same or different kinds of sensor or incomplete the same again adopt the matrix to realize pluralism, the detection of high accuracy, but this kind of sensor has the big problem of the detection data volume of follow-up treater output, and it is higher to the treater requirement of continuous host computer for the application degree of difficulty of follow-up processing chip increases by a wide margin, and has influenced the processing speed.
Disclosure of Invention
In view of this, the invention aims to provide a matrix type broad-spectrum substance detection sensor built based on an artificial intelligence algorithm, so as to solve the problem that in the prior art, the matrix type sensor has too large detection data volume, which causes difficulty in processing of a subsequent processor.
The purpose of the invention can be realized by the following technical scheme: the utility model provides a matrix broad-spectrum material detection sensor based on artificial intelligence algorithm is built, includes the signal conversion board, be provided with the communication interface who is used for being connected with the host computer on the signal conversion board, a serial communication port, the signal conversion board openly is provided with the detecting element that a plurality of is the matrix and arranges, the signal conversion board back is equipped with artificial intelligence algorithm module, every detecting element all with the input electricity of artificial intelligence algorithm module is connected, artificial intelligence algorithm module output and communication interface connect with output analysis result.
In the matrix type broad-spectrum substance detection sensor built based on the artificial intelligence algorithm, the artificial intelligence algorithm module comprises a logic algorithm unit, the electrode is connected with the input end of the logic algorithm unit, the output end of the logic algorithm unit is connected with a communication interface, detection signals generated in the detection elements are transmitted to the logic algorithm unit, and the logic algorithm unit is used for carrying out logic operation and intelligent analysis on the detection signals and outputting analysis results.
In the matrix type broad-spectrum substance detection sensor built based on the artificial intelligence algorithm, the artificial intelligence algorithm module further comprises a multiplication and addition matrix operation unit, and an output port of the multiplication and addition matrix operation unit is connected with an input port of the logic algorithm unit; the detection signals generated in the detection elements are transmitted to a multiplication and addition matrix operation unit for AI operation, and the detection signals subjected to AI operation are transmitted to the logic algorithm unit, wherein the logic algorithm unit is used for performing logic operation and intelligent analysis on the detection signals and outputting analysis results.
In foretell matrix broad spectrum nature material detection sensor based on artificial intelligence algorithm is built, detect and use the component including insulating substrate, electrode and gas sensitive layer, the gas sensitive layer is formed on the insulating substrate, the electrode sets up in gas sensitive layer both sides, the electrode with artificial intelligence algorithm module connects.
In the matrix broad-spectrum substance detection sensor built based on the artificial intelligence algorithm, the gas-sensitive layer is made of one or more of tin oxide series, zinc oxide series or iron oxide series semiconductor materials, and the gas-sensitive layer is formed on the insulating substrate in an ultra-fine particle or thin film mode during implementation.
In the matrix type broad-spectrum substance detection sensor built based on the artificial intelligence algorithm, the gas-sensitive layer is also covered with a SiO2 filter layer. The interference of macromolecular gas such as ethanol and the like on the detection of micromolecular H2 can be filtered out by the SiO2 filter layer.
In the matrix broad-spectrum substance detection sensor built based on the artificial intelligence algorithm, the gas-sensitive layer covers the Pd-Pt catalyst layer, and the Pd-Pt catalyst layer is arranged between the gas-sensitive layer and the SiO2 filter layer.
In the aforesaid matrix broad spectrum material detection sensor based on artificial intelligence algorithm is built, the component for the detection still includes signal amplifier, the electrode with signal amplifier's input electricity is connected, signal amplifier's output with artificial intelligence algorithm module electricity is connected.
In the matrix type broad-spectrum substance detection sensor built based on the artificial intelligence algorithm, the artificial intelligence algorithm module further comprises a storage unit, and the storage unit is connected with the logic algorithm unit and the multiplication and addition matrix operation unit at the same time.
Compared with the prior art, the matrix broad-spectrum substance detection sensor built based on the artificial intelligence algorithm has the following advantages: the invention adopts matrix arrangement, realizes multi-element detection and eliminates the problem of single detection in the prior art; in addition, an artificial intelligence algorithm module is adopted, so that the detection signals acquired by all detection elements can be intelligently preprocessed, the output data is greatly reduced, the data processing efficiency of a subsequent upper computer is improved, and the application difficulty of a subsequent processing chip is reduced; meanwhile, the weak signal is amplified, and the detection accuracy of the weak signal is improved.
Drawings
Fig. 1 is a schematic structural diagram of a front side of a matrix type broad-spectrum substance detection sensor constructed based on an artificial intelligence algorithm according to an embodiment.
Fig. 2 is a schematic structural diagram of the back side of the matrix type broad-spectrum substance detection sensor constructed based on an artificial intelligence algorithm according to the embodiment.
FIG. 3 is a circuit block diagram of a matrix type broad-spectrum substance detection sensor built based on an artificial intelligence algorithm according to an embodiment.
FIG. 4 is a cross-sectional view of a detection element according to an embodiment.
Fig. 5 is a circuit block diagram of a matrix type broad-spectrum substance detection sensor built based on an artificial intelligence algorithm according to the second embodiment.
FIG. 6 is a sectional view of a third detecting element of the embodiment.
In the figure, 1, a signal conversion plate; 2. a communication interface; 3. a detection element; 4. an artificial intelligence algorithm module; 5. a logic algorithm unit; 6. a storage unit; 7. an insulating substrate; 8. an electrode; 9. a gas-sensitive layer; 10. a signal amplifier; 11. SiO22A filter layer; 12. a multiplication and addition matrix operation unit; 13. a Pd-Pt catalyst layer; 14. a membrane layer.
Detailed Description
The following are specific embodiments of the present invention and are further described with reference to the drawings, but the present invention is not limited to these embodiments.
The first embodiment is as follows:
as shown in figures 1-4, the matrix type broad-spectrum substance detection sensor built based on the artificial intelligence algorithm comprises a signal conversion board 1, a communication interface 2 used for being connected with an upper computer is arranged on the signal conversion board 1, a plurality of detection elements 3 arranged in a matrix form are arranged on the front face of the signal conversion board 1, an artificial intelligence algorithm module 4 is arranged on the back face of the signal conversion board 1, each detection element 3 is electrically connected with the input end of the artificial intelligence algorithm module 4, and the output end of the artificial intelligence algorithm module 4 is connected with the communication interface 2 to output an analysis result. In the present embodiment, the detecting elements 3 are arranged in a 3 × 4 matrix, and each detecting element 3 is used for detecting a plurality of the same gases, different gases, or incompletely identical gases.
Referring to fig. 3, in the present embodiment, the artificial intelligence algorithm module 4 includes a logic algorithm unit 5 and a storage unit 6, and the storage unit 6 is connected to the logic algorithm unit 5. The electrodes 8 of the detection elements 3 are connected to the input end of the logic algorithm unit 5, the output end of the logic algorithm unit 5 is connected with the communication interface 2, detection signals generated in the detection elements 3 are transmitted to the logic algorithm unit 5, and the logic algorithm unit 5 is used for performing logic operation and intelligent analysis on the detection signals and outputting analysis results. The logic algorithm unit 5 may be divided into a plurality of sub-units, and may be respectively used for performing logic operation and intelligent analysis on the detection data of different gases. In addition, a self-learning subunit can be further arranged in the logic algorithm unit 5, and the self-learning subunit is used for carrying out statistical analysis and prejudgment on detection data of the detection environment detected for a long time so as to output the increasing and decreasing trend of the content of each gas component in the future detection environment.
The element for detection has an insulating substrate 7, an electrode 8, and a gas-sensitive layer 9, the electrode 8 is printed on the insulating substrate 7, and the gas-sensitive layer 9 is formed on the insulating substrate 7. The number of the electrodes 8 is two, the electrodes are respectively arranged on two sides of the gas sensitive layer 9, one end of each electrode 8 is connected with the gas sensitive layer 9, and the other end of each electrode 8 is connected with the signal conversion plate 1. In addition, in order to improve the detection sensitivity of the detection element 3, a signal amplifier 10 is connected to the detection element 3, the signal amplifier 10 is arranged in the signal conversion plate 1, and the electrode 8 is connected to the signal amplifier 10 through a lead and then electrically connected to the input end of the artificial intelligence algorithm module 4.
The gas sensing layer 9 is one or more of tin oxide semiconductor materials, zinc oxide semiconductor materials or iron oxide semiconductor materials, and the gas sensing layer 9 is formed on the insulating substrate 7 in an ultrafine particle or thin film manner. In this embodiment, the gas sensing layer 9 is described by taking a zinc oxide-based ZnO film as an example, and the SiO2 filter layer 11 is covered on the ZnO film. The interference of macromolecular gas such as ethanol and the like on the detection of micromolecular H2 can be filtered out by the SiO2 filter layer 11.
Example two:
as shown in fig. 5, the present embodiment is a further improvement based on the first embodiment, and the difference between the present embodiment and the first embodiment is: the artificial intelligence algorithm module 4 in this embodiment further includes a multiplication and addition matrix operation unit 12. The output port of the multiplication and addition matrix arithmetic unit 12 is connected with the input port of the logic arithmetic unit 5; the detection signals generated in the detection elements 3 are firstly transmitted to the multiply-add matrix operation unit 12 for AI operation, and the detection signals subjected to AI operation are transmitted to the logic algorithm unit 5, and the logic algorithm unit 5 is used for performing logic operation and intelligent analysis on the detection signals and outputting analysis results. The multiplication and addition matrix operation unit 12 is a CMOS, and includes a plurality of neural network layers, an output terminal of a preceding neural network is connected to an input terminal of a succeeding neural network, and an output terminal of a last neural network is connected to an input terminal of the logic algorithm unit 5. Each neural network corresponds to an input value, an output value and a weight value so as to realize the calculation of different levels of the neural network, and the specific weight value can be manually set according to the actual situation. The multiplication and addition matrix operation unit 12 is provided with a matrix element storage subunit, a sign operation subunit, a numerical value operation subunit and an accumulation subunit, and is used for performing multiplication and addition operation on a plurality of matrixes. By the multiplication and addition matrix operation unit 12, the operation speed of the artificial intelligence algorithm module 4 is improved, and the power consumption is reduced.
Example three:
as shown in fig. 6, the present embodiment is a further improvement based on the first embodiment, and the difference between the present embodiment and the first embodiment is: a Pd — Pt catalyst layer is further covered on the gas sensing layer 9, a Pd — Pt catalyst layer 13 is provided between the gas sensing layer 9 and the SiO2 filter layer 12, and a separator layer 14 is further provided between the Pd — Pt catalyst layer 13 and the gas sensing layer 9. In this structure, since the barrier layer 14 is provided between the ZnO film layer of semiconductor material and the Pd — Pt catalyst layer 13, the resistance value of the element in the air increases by about one order of magnitude, and as a result, the sensitivity of the element for detecting gas increases. Particularly, after the catalyst is added, the catalyst is filled between the semiconductor crystal grains to reduce the contact potential barrier, thereby further improving the gas-sensitive effect. The detection accuracy of the detection element 3 is improved.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.
Claims (9)
1. The utility model provides a matrix broad-spectrum material detection sensor based on artificial intelligence algorithm is built, includes signal conversion board (1), be provided with on signal conversion board (1) and be used for communication interface (2) with the host computer connection, a serial communication port, signal conversion board (1) openly is provided with detection element (3) that a plurality of is the matrix and arranges, signal conversion board (1) back is equipped with artificial intelligence algorithm module (4), every detection element (3) all with the input electricity of artificial intelligence algorithm module (4) is connected, artificial intelligence algorithm module (4) output and communication interface (2) are connected with output analysis result.
2. The matrix type broad-spectrum substance detection sensor built based on the artificial intelligence algorithm according to claim 1, wherein the artificial intelligence algorithm module (4) comprises a logic algorithm unit (5), the electrodes (8) are connected with the input end of the logic algorithm unit (5), the output end of the logic algorithm unit (5) is connected with a communication interface (2), a detection signal generated in each detection element (3) is transmitted to the logic algorithm unit (5), and the logic algorithm unit (5) is used for performing logic operation and intelligent analysis on each detection signal and outputting an analysis result.
3. The matrix type broad-spectrum substance detection sensor built based on the artificial intelligence algorithm according to claim 2, wherein the artificial intelligence algorithm module (4) further comprises a multiplication and addition matrix operation unit (12), and an output port of the multiplication and addition matrix operation unit (12) is connected with an input port of the logic algorithm unit (5); the detection signals generated in the detection elements (3) are transmitted to a multiplication and addition matrix operation unit (12) for AI operation, the detection signals subjected to AI operation are transmitted to the logic algorithm unit (5), and the logic algorithm unit (5) is used for performing logic operation and intelligent analysis on the detection signals and outputting analysis results.
4. The matrix type broad-spectrum substance detection sensor built based on artificial intelligence algorithm according to claim 1, 2 or 3, wherein the element for detection comprises an insulating substrate (7), an electrode (8) and a gas-sensitive layer (9), the gas-sensitive layer (9) is formed on the insulating substrate (7), the electrode (8) is arranged on two sides of the gas-sensitive layer (9), and the electrode (8) is connected with the artificial intelligence algorithm module (4).
5. The matrix type broad-spectrum substance detection sensor built based on the artificial intelligence algorithm according to claim 4, wherein the gas-sensitive layer (9) is made of one or more of tin oxide semiconductor materials, zinc oxide semiconductor materials or iron oxide semiconductor materials, and the gas-sensitive layer (9) is formed on the insulating substrate (7) in an ultra-micro-particle or thin-film mode.
6. The matrix type broad-spectrum substance detection sensor built based on the artificial intelligence algorithm as claimed in claim 4, wherein the gas sensitive layer (9) is further covered with a SiO2 filter layer (11).
7. The matrix type broad-spectrum substance detection sensor built based on artificial intelligence algorithm as claimed in claim 6, wherein the gas sensitive layer (9) is covered with a Pd-Pt catalyst layer (13), and the Pd-Pt catalyst layer (13) is arranged between the gas sensitive layer (9) and a SiO2 filter layer (11).
8. The matrix type broad-spectrum substance detection sensor built based on artificial intelligence algorithm according to claim 4, wherein the detection element further comprises a signal amplifier (10), the electrode (8) is electrically connected with the input end of the signal amplifier (10), and the output end of the signal amplifier (10) is electrically connected with the artificial intelligence algorithm module (4).
9. The matrix type broad-spectrum substance detection sensor built based on artificial intelligence algorithm according to claim 3, wherein the artificial intelligence algorithm module (4) further comprises a storage unit (6), and the storage unit (6) is connected with the logic algorithm unit (5) and the multiplication and addition matrix operation unit (12) at the same time.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108052934A (en) * | 2018-01-29 | 2018-05-18 | 安徽云塔电子科技有限公司 | A kind of intelligent Sensorsystem framework and its implementation, device |
| CN111010883A (en) * | 2018-08-06 | 2020-04-14 | 华为技术有限公司 | Matrix processing method and device and logic circuit |
| CN111047034A (en) * | 2019-11-26 | 2020-04-21 | 中山大学 | On-site programmable neural network array based on multiplier-adder unit |
| US11045111B1 (en) * | 2020-05-18 | 2021-06-29 | Canary Health Technologies Inc. | Real time breath analyzer for detecting volatile organic compounds and identifying diseases or disorders |
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- 2021-08-02 CN CN202110880462.7A patent/CN113671009A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108052934A (en) * | 2018-01-29 | 2018-05-18 | 安徽云塔电子科技有限公司 | A kind of intelligent Sensorsystem framework and its implementation, device |
| CN111010883A (en) * | 2018-08-06 | 2020-04-14 | 华为技术有限公司 | Matrix processing method and device and logic circuit |
| CN111047034A (en) * | 2019-11-26 | 2020-04-21 | 中山大学 | On-site programmable neural network array based on multiplier-adder unit |
| US11045111B1 (en) * | 2020-05-18 | 2021-06-29 | Canary Health Technologies Inc. | Real time breath analyzer for detecting volatile organic compounds and identifying diseases or disorders |
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