Measurement of material properties with metasurfaces

Abstract

According to the implementation of the invention, a scheme for measuring the property of a substance by using a metasurface is provided. According to the scheme, the metasurface is designed to be placed between the transmitter and the measured object or between the receiver and the measured object. A wireless signal is transmitted to the measured object, and a wireless signal reflected or transmitted by the metasurface from the measured object is received. And estimating the material attribute value of the measured object by analyzing the received wireless signal. The metasurface is optimized for material property measurements. The metasurface structure is determined, for example, by maximizing the sensitivity of received wireless signals to material property changes.

Description

Background Technology

[0001] In some cases, it may be necessary to measure the properties of certain objects, such as the concentration of specific substances within them, in a non-destructive and non-invasive manner. For example, in the medical field, continuous glucose monitoring is crucial for diabetic patients because it helps them understand their condition and prevent hypoglycemic or hyperglycemic events. Similarly, in environmental pollution and remediation, it may be necessary to monitor the concentration of pollutants in liquids (such as water) without damaging the liquid. Therefore, a non-invasive, user-friendly, and low-cost technology for measuring the properties of substances is needed. Summary of the Invention

[0002] According to the present disclosure, a method for measuring material properties using metasurfaces is proposed. In this method, the metasurface is directly or indirectly attached to the object under test, for example, attached to the surface of a container including the object under test or attached to human skin. A wireless probe signal is transmitted toward the object under test to which the metasurface is attached via a transmitter, and a response signal transmitted or transmitted from the object under test via the metasurface is received via a receiver. By analyzing the received response signal, the material property value in the object under test, such as the concentration of a target substance in the object under test, is estimated. The structure of the metasurface is optimized for the measurement of material properties. The structure of the metasurface can be determined by maximizing the sensitivity of the signal characteristics of the response signal to changes in the material property value. The material property measurement method according to the present disclosure is a non-invasive and user-friendly measurement technique. Furthermore, by optimizing the design of the metasurface for the measurement of material properties, highly accurate material property measurements can be achieved.

[0003] This section is provided to simplify the presentation of the selection of objects, which will be further described in the detailed embodiments below. This section is not intended to identify key or principal features of the claimed subject matter, nor is it intended to limit the scope of the claimed subject matter. Attached Figure Description

[0004] Figure 1 A schematic block diagram of an example measurement architecture according to some implementations of this disclosure is shown;

[0005] Figure 2A A flowchart illustrating an example optimization process for a metasurface according to some implementations of this disclosure is shown;

[0006] Figure 2B A flowchart is shown illustrating an example measurement process utilizing a metasurface according to some implementations of this disclosure;

[0007] Figure 3A A schematic diagram of an example measurement environment for liquid concentration assisted by a metasurface, according to some implementations of this disclosure, is shown;

[0008] Figure 3B Some implementations of this disclosure are shown. Figure 3A A schematic diagram of the equivalent circuit corresponding to the measurement environment shown;

[0009] Figure 4A A schematic diagram of an example structure of a metasurface unit constituting a metasurface according to some implementations of the present disclosure is shown;

[0010] Figure 4B Examples of equivalent LC circuits for some implementations of metasurfaces according to this disclosure are shown;

[0011] Figure 5A Schematic diagrams of equivalent circuits corresponding to measurement environments according to some implementations of this disclosure are shown;

[0012] Figure 5B A schematic diagram of an example process for optimizing a passive metasurface according to some implementations of this disclosure is shown;

[0013] Figure 6 The impedance of patterns with different geometric parameters according to some implementations of this disclosure is shown in a specific frequency range;

[0014] Figure 7A The amplitude versus frequency curves of the frequency response characteristics at different glucose concentrations according to some implementations of this disclosure are shown.

[0015] Figure 7B The curves showing the phase versus frequency of the frequency response characteristics at different glucose concentrations according to some implementations of this disclosure are illustrated.

[0016] Figure 8 A schematic diagram of an example finite element model for a measurement environment is shown, based on some implementations of this disclosure;

[0017] Figure 9 A flowchart of a concentration measurement process according to some implementations of this disclosure is shown;

[0018] Figure 10 A flowchart illustrating the process of designing metasurfaces according to some implementations of this disclosure is shown; and

[0019] Figure 11 A schematic block diagram of an electronic device capable of implementing various implementations of the present disclosure is shown. Detailed Implementation

[0020] This disclosure will now be discussed with reference to several example implementations. It should be understood that these implementations are discussed only to enable those skilled in the art to better understand and thus implement this disclosure, and not to imply any limitation on the scope of this disclosure.

[0021] As used herein, the term "comprising" and its variations are to be interpreted as open-ended terms meaning "including but not limited to". The term "based on" is to be interpreted as "at least partially based on". The terms "an implementation" and "an implementation" are to be interpreted as "at least one implementation". The term "another implementation" is to be interpreted as "at least one other implementation". The terms "first", "second", etc., may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0022] It should be noted that the headings of any section / subsection provided herein are not restrictive. Various implementations are described throughout this document, and any type of implementation may be included under any section / subsection. Furthermore, an implementation described in any section / subsection may be combined in any way with any other implementation described in the same section / subsection and / or different sections / subsections.

[0023] In this document, unless explicitly stated otherwise, performing a step in response to A does not mean that the step is performed immediately after A, but may include one or more intermediate steps.

[0024] As used in this paper, the term "model" refers to a model that learns the relationship between inputs and outputs from training data, enabling it to generate corresponding outputs for a given input after training. Model generation can be based on machine learning techniques. Deep learning (DL) is a machine learning algorithm that processes inputs and provides corresponding outputs using multiple layers of processing units. A neural network model is an example of a deep learning-based model. In this paper, "model" may also be referred to as a "machine learning model," "learning model," "machine learning network," or "learning network," and these terms are used interchangeably.

[0025] As mentioned above, in some cases it is necessary to measure the material properties of certain objects, such as their concentration. Taking blood glucose measurement as an example, common glucose monitoring technologies use electrochemical methods to convert glucose concentration into an electrical signal for measurement. However, this electrochemical method is invasive, such as finger pricking, thus limiting the monitoring frequency and range. Currently, several non-invasive blood glucose monitoring solutions have been proposed, including infrared spectroscopy correlation technology and dielectric spectroscopy correlation technology.

[0026] Infrared correlation technology works by utilizing the absorption of specific frequencies of light by glucose molecules due to vibration and rotation. In this approach, blood glucose is measured by emitting a laser into subcutaneous tissue and analyzing the capillary spectrum. Some approaches using ultrasound and photoacoustics calculate glucose concentration based on the pressure waves generated by the thermal expansion of glucose molecules when they absorb infrared light, and the accompanying ultrasonic signals. A drawback of this infrared correlation approach is the difficulty in reducing costs and improving the integration of the laser transceiver.

[0027] Dielectric correlation spectroscopy utilizes the change in dielectric constant caused by glucose concentration. In this approach, blood glucose concentration is estimated by analyzing the propagation characteristics of electromagnetic signals (e.g., attenuation, phase changes). However, dielectric correlation spectroscopy has limitations, particularly in medical settings. For example, it requires a relatively thick liquid sample to achieve significant attenuation or phase changes, which is difficult to achieve in applications such as blood glucose monitoring. Furthermore, this approach typically uses containers with a large surface area to ensure that most electromagnetic waves pass through the liquid. However, blood vessels are thin and fragile, making it difficult to ensure that most signals pass through them, resulting in severe multipath interference.

[0028] Therefore, a method for measuring material properties using a metasurface is proposed according to the present disclosure. In this method, a metasurface is directly or indirectly attached to the object under test. A wireless probe signal is transmitted toward the object under test to which the metasurface is attached via a transmitter, and a response signal transmitted or transmitted from the object under test via the metasurface is received via a receiver. By analyzing the received response signal, the material property value in the object under test, such as the concentration of a target substance in the object under test, is estimated. The structure of the metasurface is optimized for the measurement of material properties. The structure of the metasurface can be determined by maximizing the sensitivity of the signal characteristics of the response signal to changes in the material property value.

[0029] According to the implementation of this disclosure, the metasurface is attached directly or indirectly to the object under test without immersion in the object. This provides a non-invasive and user-friendly measurement method. Furthermore, the metasurface used is optimized for measuring material properties, thereby enabling highly accurate measurements.

[0030] The exemplary implementation of this disclosure will now be described in detail with reference to the accompanying drawings.

[0031] Example Architecture

[0032] Figure 1 A schematic block diagram of an example measurement architecture 100 according to some implementations of this disclosure is shown. In the measurement architecture 100, a measurement device 101 is used to measure target material properties of a test object 130, such as dielectric constant, composition, concentration, etc. Generally, the measurement device 101 includes a metasurface 110, a transmitter 121, and a receiver 122.

[0033] Metasurface 110 is suitable for direct or indirect attachment to the object under test 130, also known as the target object. In some implementations, metasurface 110 can be a passive metasurface. In this way, the manufacturing cost of the metasurface and the power consumption of the measuring device 101 can be reduced. The structure of metasurface 110 (e.g., geometry, thickness, material, etc.) can be optimized for the measurement of material properties. Example implementations of designing metasurface structures will be described in detail below.

[0034] Transmitter 121 is configured to transmit a wireless signal, also known as a probe signal, toward the object under test 130. The transmission orientation of this wireless signal allows it to be incident on the metasurface 110. Receiver 122 is configured to receive a wireless signal, also known as a response signal, from the object under test 130 that has propagated (e.g., reflected or transmitted) through the metasurface 110. Note that although... Figure 1 The illustration shows receiver 122 receiving a wireless signal reflected via metasurface 110, but this is merely exemplary and not intended to be limiting. In implementations of this disclosure, the wireless signal utilized by measuring device 101 can be any suitable frequency band, including but not limited to ultra-wideband (UWB) signals and wireless fidelity (WiFi) signals. In some implementations, the wireless signal can be in the UWB band (such as 1-8 GHz). In such bands, the electromagnetic loss caused by the object under test is minimal. In some implementations, the wireless signal can be a WiFi signal. Using WiFi signals, the material property measurement scheme of this disclosure can be implemented in everyday user devices, such as smartphones, smartwatches, etc.

[0035] In some implementations, the measuring device 101 may further include a controller 125. The controller 125 is at least coupled to the receiver 122 to receive a response signal received by the receiver 122, such as a reflected signal or a transmitted signal. The controller 125 may be configured to estimate the material property value of the measured object 130, such as the concentration of a target substance in the measured object 130, by analyzing the received response signal. In some implementations, the controller 125 may estimate the material property value of the measured object 130 by analyzing the frequency response characteristics of the received response signal. The frequency response characteristics may include S-parameters. For example, in the case where a reflected signal is received, the S-parameters may be analyzed. 11 Parameters are used to estimate the material properties of the object 130 being measured. For example, when a transmitted signal is received, S can be analyzed. 21 The parameters are used to estimate the material property values ​​of the tested object 130.

[0036] In some implementations, the controller 125 may also be coupled to the transmitter 121 to control the detection signal emitted by the transmitter 121, such as frequency band, amplitude, phase, etc. The controller 125 may be dedicated to the measurement device 101 for material property estimation, or it may be a general-purpose controller. The controller 125 can be implemented with any unit with processing capabilities, such as a microcontroller, a general-purpose processing unit, etc. Implementations of this disclosure are not limited in this respect.

[0037] In some implementations, the measuring device 101 may be implemented as a stand-alone device. In some implementations, the measuring device 101 may be at least partially included in any suitable electronic device, such as a wearable device.

[0038] The metasurface 110 can be directly or indirectly attached to the test object 130 containing the target substance. The test object 130 can be of any form, such as a liquid. In some implementations, the metasurface 110 may not be directly attached to the test object 130, but may be coupled to the test object 130 through a container containing the test object 130. For example, the metasurface 110 can be attached to or near the surface of a container. The container can be considered as a collection of environmental objects associated with the test object 130.

[0039] Depending on the specific measurement scenario, the measured object 130 and the target substance property can be of any suitable type. In some implementations, the estimated substance property may include properties of blood components. In some implementations, the blood component may be glucose in the blood, and correspondingly, the estimated substance property value may be blood glucose concentration. As an example, in a blood glucose measurement scenario, the metasurface 110 may be attached to the body surface of the test subject, for example, it may be placed on the wrist. The measured object 130 may be the test subject's blood, and the target substance may be glucose. In this scenario, environmental objects associated with the measured object 130 may include skin, fat, muscle, and bone, etc. Alternatively or additionally, in some implementations, the blood component may be salts or triglycerides in the blood, etc. It should be understood that glucose, salts, and triglycerides listed herein are merely examples of blood components. Implementations of this disclosure can be applied to any other blood composition, and are not limited to the examples listed herein.

[0040] In some implementations, the estimated material properties may include properties of the salt in a salt solution, such as salt concentration. As an example, in a wound healing measurement scenario, the wound healing rate can be monitored by measuring the salt concentration at the wound site to remind the injured person to change the gauze. In this scenario, the metasurface 110 can be attached to the vicinity of the injured person's wound, such as onto gauze. The measured object 130 can be the liquid at the wound site, the target substance can be salt ions, and environmental objects associated with the measured object 130 can include gauze, skin, etc.

[0041] In some implementations, the estimated material properties may include properties of water pollutants, such as composition and concentration. As an example, in a pollution monitoring scenario, the liquid to be measured (e.g., water) may be placed in a container or pipe. The measured object 130 is the liquid in the container or pipe, and the target substance may be a pollutant that may be present in the liquid.

[0042] It should be understood that the measurement scenarios described above are merely exemplary and are not intended to be limiting. The measurement scheme implemented according to this disclosure can be applied to any suitable measurement scenario. Furthermore, in the following description, the exemplary implementation of this disclosure will primarily be described using a blood glucose measurement scenario as an example. However, it should be understood that this is merely exemplary, and the principles of this disclosure described with reference to the blood glucose measurement scenario can be applied to any suitable measurement scenario, and are not limited to blood glucose measurement. Additionally, in the following description, the exemplary implementation of this disclosure will primarily be described using concentration as the estimated substance property. However, this is merely exemplary and is not intended to be limiting.

[0043] Furthermore, it should be understood that the structure and function of the various elements in the measurement architecture 100 are described for illustrative purposes only and do not imply any limitation on the scope of this disclosure.

[0044] Optimized design of metasurfaces

[0045] In some implementations, metasurface 110 can be optimized to accurately measure material properties. See below for reference. Figure 2A This section describes an example implementation of the optimized design of metasurface 110. Figure 2A A flowchart of an example optimization process 200A for a metasurface according to some implementations of this disclosure is shown. The optimization process 200A can be performed by any suitable electronic device.

[0046] In general, the optimization process 200A may include a signal characteristic determination stage 210, a target electrical characteristic determination stage 220, and a metasurface structure determination stage 230. In the signal characteristic determination stage 210, the signal characteristics, such as amplitude or phase, of the response signal reflected or transmitted from the target object 130 via the metasurface 110 are derived by modeling the measurement environment, including the metasurface 110 and the object under test 130. In the target electrical characteristic determination stage 220, the target electrical characteristics, such as target impedance, of the metasurface 110 are determined by maximizing the sensitivity of the signal characteristics of the response signal to changes in the material properties of the object under test 130 (e.g., changes in the concentration of the target substance). In the metasurface structure determination stage 230, the structure of the metasurface 110, such as geometry, thickness, and material, is determined based on the target electrical characteristics. The target electrical characteristics can be considered as the design target of the metasurface structure in the metasurface structure determination stage 230.

[0047] The following describes an example implementation of these stages. The description below will primarily use blood glucose measurement as an example, but this is merely illustrative and not intended to be limiting.

[0048] Signal characteristics are derived

[0049] like Figure 2A As shown, in some implementations, in block 211, the electrical coefficients of the test object 130 are modeled. The modeled electrical coefficients vary with frequency and are therefore also called frequency-dependent electrical coefficients. By modeling the electrical coefficients, the electrical coefficients of the test object 130 varying with frequency under different material property values ​​(e.g., different concentrations, different compositions) can be determined. That is, the relationship between these frequency-dependent electrical coefficients and material properties can be obtained.

[0050] The electrical coefficients considered could be, for example, the dielectric constant of the object being measured 130. The dielectric constant of a material, also known as the dielectric constant, is an inherent property influenced by its main components. In wireless sensing, the dielectric constant affects the transmission and reflection characteristics of wireless signals.

[0051] When electromagnetic waves encounter a analyte (e.g., a liquid), they undergo reflection and transmission. The dielectric constant of the analyte alters the characteristics of the reflected and transmitted signals (e.g., amplitude and phase). Therefore, characterizing the dielectric constant for different material property values ​​(e.g., different concentrations of the target material) can be used for the measurement of material properties.

[0052] The dielectric constant is usually expressed in terms of relative permittivity ε. r Relative permittivity is the ratio of absolute permittivity ε to vacuum permittivity ε₀, and can be expressed as a complex function of frequency: ε r (ω)=ε′ r (ω)-jε″ r (ω), where ω is the angular frequency, ε′ r ε″ represents the dielectric constant stored in the electric field (which is the real part of the relative permittivity). r It is the loss factor (imaginary part) representing the energy loss due to absorption, conduction, and relaxation.

[0053] The dielectric constant of the object under test 130 can be modeled using any suitable model. As an example, the Cole-Cole model effectively captures the frequency-dependent dielectric constant, allowing finite measurements at a specific frequency to fit the relative dielectric constant. For example, the relative dielectric constant can be expressed as follows:

[0054]

[0055] Where ∈ s and ∈ ∞denoted by ε, the dielectric constant limits at low and high frequencies are given; ∈0 represents the dielectric constant in free space; τ is the relaxation time in seconds; α is a distribution parameter describing the symmetrical broadening of the relaxation loss peak; σ i This represents ionic conductivity. The relaxation function is used to describe the loss peak and drop at ∈′.

[0056] Taking blood glucose monitoring as an example, to obtain the frequency-dependent dielectric constants of different glucose concentrations, any suitable method can be used to measure the dielectric constant values ​​(including real and imaginary parts) at some glucose concentrations. These dielectric constant values ​​can be used to fit the parameters of a Cole-Cole model, such as that shown in equation (1). For example, a unipolar Cole-Cole model (where n = 1, α = 0, simplified to a Debye model) can be used to fit the dielectric constant model for different glucose concentrations (e.g., 0 mg / dL, 100 mg / dL, 200 mg / dL, 300 mg / dL, 400 mg / dL, and 500 mg / dL). By fitting, the parameters of the dielectric constant model for different glucose concentrations can be obtained, thereby obtaining the frequency-dependent complex dielectric constants for different glucose concentrations. For example, the frequency-dependent complex dielectric constant for each glucose concentration can be represented by the fitted function.

[0057] The above describes the modeling of the dielectric constant of the object under test. Next, we analyze how changes in the dielectric constant affect the reflectivity and transmittance of electromagnetic waves at different concentrations. Depending on the dielectric constant and the thickness of the object under test, the probe signal from the transmitter undergoes reflection and transmission at the interface of different materials. In some implementations, the object under test 130 can be modeled as a circuit element with electrical characteristics in an equivalent circuit, such as a circuit element with a certain impedance. As an example, the object under test 130 can be simulated as a transmission line, involving three parameters related to the object under test: l, Z, and γ. l is the thickness of the object under test, Z is the characteristic complex impedance, and γ is the complex propagation constant. Z and γ can both be expressed using relative dielectric constants as follows:

[0058]

[0059]

[0060] Where ω is the angular frequency of the incident wireless signal, Z0 is the characteristic impedance of free space, and γ0 is the propagation constant of free space. And c is the speed of light. In the case of non-magnetic materials, such as in a blood glucose testing scenario, the μ of the object being tested... r It can be set to 1.

[0061] By calculating the transfer matrix, parameters such as reflectivity or transmittance can be derived from the equivalent circuit to represent the frequency response characteristics of a signal. An example of a transfer matrix is ​​the ABCD matrix, a 2×2 matrix used to describe the characteristics of a linear two-port network in a circuit. The elements A, B, C, and D in the ABCD matrix can be calculated based on specific components and connections to describe the transmission characteristics.

[0062] For example, a two-port network with transmission line circuitry can be modeled as the following ABCD matrix:

[0063]

[0064] The elements of this matrix describe the relationship between the input voltage and current and the output voltage and current along the transmission line.

[0065] To represent the response signal (reflected or transmitted signal) received at the receiver, the ABCD parameters can then be converted into S parameters to derive S. 11 Parameters (i.e., reflectivity) and S 21 Parameter (i.e., transmittance):

[0066]

[0067]

[0068] Equation (4) can be used to obtain the S of the response signal under different thicknesses and material properties of the measured object. 11 Curve and / or S 21 Curves, such as amplitude or phase changes with frequency. Taking a blood glucose measurement scenario as an example, when the liquid thickness is 30cm, for every 100mg / dL change in glucose concentration, the amplitude change of S11 is approximately 0.001 dB, and the phase change is approximately 0.01 degrees. This change is easily masked by ambient noise. When the liquid thickness is 10cm, for every 100mg / dL change in glucose concentration, S11... 11 The amplitude change is approximately 0.001 dB, and the phase change is approximately 0.02 degrees. Similarly, S 21 The parameters show the relationship with S 11 The parameters change considerably. This shows that when the solution thickness is very small, such as 3 mm in a blood vessel, only from sources like S... 11 or S 21 It is almost impossible to deduce the glucose concentration from the parameters.

[0069] Continue to refer to Figure 2AIn block 212, an equivalent circuit can be used to model the measurement environment. That is, an equivalent circuit is generated to represent the various components of the measurement environment. The components of the measurement environment may include the various layers in the metasurface 110, the object under test 130, and environmental objects associated with the object under test, such as the surrounding material of the object under test. In the equivalent circuit, each component can be represented as a circuit element with corresponding electrical characteristics (e.g., impedance).

[0070] For example, in block 212, a corresponding equivalent circuit can be constructed according to the specific settings of the measurement environment. Depending on the specific settings of the measurement environment, circuit elements representing each component are connected together to form an equivalent circuit. Each component is represented as a corresponding impedance. Thus, the reflected or transmitted signal can be represented by the impedance of the object under test 130, which in turn is related to the material property values.

[0071] The following describes an example construction of the equivalent circuit. In the equivalent circuit, the transmission line representing the object under test is analogous to a single capacitive element. To amplify the capacitance change caused by a change in concentration, an LC resonant circuit can be considered. In an LC resonant circuit, a small change in capacitance will alter the resonant characteristics, such as the resonant frequency and the overall frequency response curve.

[0072] The following is for reference. Figure 3A and Figure 3B A simplified example of equivalent circuit construction is described using a liquid as the object of test. Figure 3A An example measurement environment 300A for liquid concentration assisted by a metasurface, according to some implementations of this disclosure, is shown. Measurement environment 300A can be considered as... Figure 1 A simplified example of the measurement architecture is shown. In the measurement environment 300A, a metasurface 310 is placed on the surface of the liquid 330 being measured. A transceiver 320 is used to transmit wireless signals to the metasurface 310 and the liquid 330, and to receive wireless signals reflected from the metasurface 310.

[0073] Figure 3B An equivalent circuit 300B corresponding to the measurement environment 300A is shown. In the equivalent circuit 300B, an LC network 360 is used to represent the metasurface 310, and has a capacitance value C. liquid A single capacitive element 370 is used to represent the liquid 330. Therefore, the optimization objective is to optimize this LC network 360 so that it corresponds to the capacitance C of the liquid 330. liquid This optimal matching ensures that the entire circuit exhibits high-quality resonant characteristics. In this way, the capacitance value of liquid 330 can be altered to the maximum extent when it undergoes minute changes caused by concentration variations.

[0074] Next, the LC circuit introduced by the metasurface can be modeled. (Reference) Figure 4A and Figure 4B Describe an example. Figure 4A An example structure of a metasurface unit 400A constituting a metasurface according to some implementations of this disclosure is shown. The entire metasurface can be constructed using, for example... Figure 4A The metasurface unit 400A shown is implemented by repetition. As shown in Figure 4, the metasurface can be a passive metasurface, which may include a metal layer 401 and a substrate layer 402. The substrate layer 402 can be made of paper, for example, which can effectively reduce manufacturing costs. The metal layer 401 is disposed on the substrate layer 402 and formed into a pattern with a specific geometry.

[0075] exist Figure 4A The image shows a metasurface unit cell with a finger-like pattern as an example. This is a metasurface design with planar capacitors and multi-finger periodic units. Figure 4A As shown, in this example, the structural parameters used to define the geometry of the metasurface may include the size L of the metasurface unit, the length b of the metal layer 401 along the y-axis, the length a of the fingers along the x-axis, the spacing w between two adjacent fingers, and the number N of the fingers. It should be understood that... Figure 4A The pattern types shown are merely exemplary and are not intended to limit the scope of this disclosure. The metasurface optimizations described in this disclosure can be applied to metasurfaces with any pattern.

[0076] Figure 4B Examples of equivalent LC circuits 400B for metasurfaces according to some implementations of this disclosure are shown. References Figure 4A Let's describe the equivalent LC circuit 400B. When a wireless signal (e.g., an electromagnetic wave) propagates along the z-axis and interacts with the metasurface, it first encounters the metal layer 401. The metal layer 401 is equivalent to a parallel LC circuit in the circuit model. The model of the LC circuit is directly related to the pattern of the metal layer 401, which generates an equivalent capacitance (with an impedance of...). Its impedance is determined by ω (angular frequency) and inductance (jωL).

[0077] for Figure 4A In the example pattern, each pair of fingers forms a capacitor, i.e., C. i1 and C i2 And the virtual parasitic inductance caused by the connection of the capacitor, i.e., L i For simplicity, we can use the equivalent impedance Z. LCi Denotes the i-th metal layer in the metasurface, such as Figure 4B As shown. The corresponding ABCD matrix can be represented by the following formula:

[0078]

[0079] Substrate 402 can also be modeled as a transmission line, similar to the liquid modeling described above. Substrate 402 has its own unique properties, such as dielectric constant and thickness. Similarly, the ABCD matrix representing the transmission line of substrate 402 can be characterized by equation (3) using the propagation constant and characteristic impedance.

[0080] When metasurface 130 is a passive metasurface, the entire ABCD matrix of the passive metasurface is as follows:

[0081]

[0082] Where m represents the number of metal layers, Z LCi Let l represent the equivalent impedance of the i-th metal layer. pi Z p and γ p Let A represent the thickness, characteristic impedance, and propagation constant of the i-th substrate layer, respectively. p-mts 、B p-mts C p-mts and D p-mts Let A, B, C, and D represent the transfer matrix elements of the passive metasurface, respectively.

[0083] Based on the above, a complete equivalent circuit corresponding to the measurement environment can be established. (Reference) Figure 5A Describe an example. Figure 5A The present disclosure shows some implementations of an equivalent circuit 500 corresponding to a measurement environment, which can be considered as a... Figure 1 Modeling of at least a portion of the measurement architecture 100 shown. Specifically, in this example, the metasurface 110 may consist of four metal layers and a substrate layer. In the equivalent circuit 500, an LC network 510 is used to represent the metasurface 110. Four parallel impedance elements Z1, Z2, Z3, and Z4 represent the four metal layers, and four transmission line elements represent the four substrate layers. Transmission line 530 is used to represent the object under test 130. Environmental objects associated with the object under test 130 may be represented by transmission lines 541 and 542. For example, if measuring glucose concentration in blood vessels within the arm, transmission line 530 may represent blood, transmission line 541 may represent skin, fat, and muscle, and transmission line 542 may represent muscle, bone, fat, and skin.

[0084] Next, in box 213, the signal characteristics of the response signal can be determined based on the corresponding electrical coefficients (such as dielectric constants) of the various components in the equivalent circuit. For example, in the case of a reflected signal, S can be determined. 11 The amplitude and / or phase. In the case of a transmitted signal, S can be determined. 21 The amplitude and / or phase.

[0085] The following describes an example process. The equivalent circuit 500 can be represented using a transfer matrix such as an ABCD matrix. A desirable property of the ABCD matrix is ​​that the ABCD matrix of the entire circuit can be derived from the product of the ABCD matrices corresponding to the individual components. This can be achieved using... and Let ABCD represent the matrix of the four metal layers in metasurface 110. and To represent the ABCD matrix of the substrate layer in metasurface 110, using and express Figure 5A The environment object in, and The ABCD matrix is ​​used to represent the measured object 130. Correspondingly, the entire ABCD matrix of the measurement environment can be expressed as follows:

[0086]

[0087] Using equation (2) above, the characteristic impedance Z of the measured object 130 can be obtained from the relative permittivity parameter. target and γ target Then, the values ​​of the entire ABCD matrix can be calculated and combined with equation (4) to obtain S. 11 Parameter. S 11 The parameter represents the reflected frequency response characteristics when a wireless signal (e.g., electromagnetic wave) interacts with the metasurface and the object under test. Alternatively or additionally, the entire ABCD matrix value can be calculated and combined with equation (4) to obtain S. 21 Parameter. S 21 The parameters represent the transmission frequency response characteristics when a wireless signal (e.g., electromagnetic wave) interacts with the metasurface and the object under test.

[0088] The above describes an example implementation of constructing an equivalent circuit and deriving a representation of the wireless signal based on the equivalent circuit. Specifically, the S-parameters of the wireless signal can be obtained. If the response signal is a reflected signal, the S-parameters of the response signal can be obtained. 11 Parameters. If the response signal is a transmitted signal, the S-value of the response signal can be obtained. 21 parameter.

[0089] Furthermore, the use of four metal layers and a finger pattern in the foregoing description is for illustrative purposes only and is not intended to limit the scope of this disclosure. In implementations of this disclosure, the metasurface can have any suitable number of metal layers and substrate layers, and the metasurface unit can have any suitable pattern.

[0090] As an example, the construction of the equivalent circuit of the measurement environment in a blood glucose measurement scenario is described below. Figure 5B A model 501 of the measurement environment in a blood glucose measurement scenario is shown. The measured object is blood, and the environmental objects include tissues such as skin, fat, muscle, and bone. Each tissue can be modeled as a transmission line and represented by thickness l, equivalent characteristic impedance Z, and propagation constant γ. The ABCD matrix of the measurement environment can be expressed as follows:

[0091]

[0092] Where M tl Let represent the transmission line matrix for each tissue layer, with subscripts s, f, m, b, and g representing skin, fat, muscle, bone, and blood, respectively. After calculating the values ​​of the entire ABCD matrix, the reflected signal S can be obtained using equation (2). 11 .

[0093] Derivation of target electrical properties

[0094] In the target electrical property determination stage 220, the target electrical properties of the metasurface 110, such as the target impedance, can be determined by solving an optimization problem. The objective of this optimization problem may be to maximize the sensitivity of the received response signal to changes in the material properties of the measured object 130, such as the sensitivity to changes in the concentration of the target substance.

[0095] Some implementations are described using the estimated material properties as the concentration of the target material and the target electrical property as the target impedance as examples. In some implementations, sensitivity can be expressed as the difference in the received response signal at different concentrations of the target material. For example, in the case of receiving reflected signals, the objective of the optimization problem might be to maximize the S of the wireless signal. 11 The parameters differ at different concentrations of the target substance. For example, in the case of receiving transmitted signals, the objective of the optimization problem could be to maximize the S of the wireless signal. 21 The parameters differ at different concentrations of the target substance. In this way, the sensitivity and accuracy of substance property measurements can be maximized, thereby achieving effective substance monitoring. To this end, in some implementations, the optimized impedance of the passive metasurface can be obtained by maximizing the difference in response signal at different concentrations of the target substance.

[0096] To address the optimization issue, the measurement environment and various environmental parameters can be configured. Next, the liquid thickness (e.g., 3 mm), the number of metal and substrate layers in the metasurface, and the thickness of each substrate layer (e.g., 0.3 mm) can be determined. Then, the frequency range of the wireless signal can be set, for example, 5.5-6.5 GHz. Specifically, the frequency range can be set according to the specific application scenario and the available signal frequency bands.

[0097] In some implementations, the optimization goal may be to optimize the design of each metal layer so that S 11 The parameters are most sensitive to changes in the concentration of the target substance. To simplify the optimization objective, each metal layer can be represented as an equivalent impedance, where the impedance can be, for example, a purely complex number, such as jX. This is because the metal layer can be considered an ideal electrical conductor. The impedance of the metal layer varies with frequency, so X is a function of frequency, i.e., X(f). Therefore, the optimization objective can be expressed as follows:

[0098]

[0099] Where X1(f)……X k (f) represents the impedance function of k metal layers, m represents the number of concentration levels of the target substance, and c i This is the i-th concentration value. For example, [c0, c1, c2, ..., c5] = [0, 100, 200, ..., 500] mg / dL. 11 (c i ) refers to the concentration of the target substance being c. i S of the tested object under the condition 11 Parameters. The optimization objective could be to maximize S. 11 The difference in parameters between different concentrations of the target substance.

[0100] For example, S at different concentrations can be used 11 The distance between parameters is used as the optimization objective. In this way, the following advantages can be achieved: (1) Maximizing the distance in the frequency response effectively maximizes the sharpness of the resonant frequency (i.e., minimizing min(S)). 11 The difference between the resonant frequency and the maximum value of |argmin(S) 11 (c i ))-argmin(S 11 (c j (2) Compared with the min() and argmin() functions, the distance-based loss function has a gradient during backpropagation, thus supporting gradient descent optimization.

[0101] The optimization process described above requires setting parameters for the measurement environment and various environmental objects related to the object being measured. This method is suitable for some scenarios, such as measuring liquids within a container, because the container's parameters are known. However, for some scenarios, the parameters of the environmental objects are unknown. For example, in blood glucose measurement, the parameters of tissues such as human skin, fat, blood vessels, muscles, and bones are unknown. One approach is to make assumptions about the parameters of these environmental objects, as described above.

[0102] In some implementations, to further improve measurement accuracy, in block 221, an active metasurface can be used to perform calibration. Using the calibration, an optimized capacitance value of the active metasurface can be obtained as a result. In some implementations, an active metasurface can be deployed.

[0103] In other implementations, in block 222, the target electrical properties of the passive metasurface can be determined based on the results of the correction (e.g., based on the optimized capacitance value of the active metasurface), thereby enabling the deployment of the passive metasurface. See below for reference. Figure 5B Describe an example.

[0104] The active metasurface used for calibration may include a variable capacitor to adapt to the measurement environment. The variable capacitor may be implemented, for example, by a voltage-controlled varactor diode. Figure 5B An example cell 502 of an active metasurface is shown. For example... Figure 5B As shown, unit 502 includes a substrate layer 512 and a metal layer 511 (e.g., a copper layer) disposed on the substrate layer 512. The metal layer 511 has one or more capacitors 513 with variable capacitance values. The metal layer 511 may be, for example, a metal patch to serve as an antenna. The metal layer 511 may have gaps to expose the substrate layer underneath. The variable capacitors 513 may be attached to the metal layer and disposed in the gaps of the metal layer 511. The variable capacitors 513 may be implemented using varactor diodes.

[0105] In some implementations, the capacitance variation range of the active metasurface can be determined based on the signal characteristics of the response signal and the parameter range of the environmental objects associated with the measured object. The parameter range can be chosen to be as wide as possible to cover various situations that may occur in actual measurements.

[0106] In some implementations, the impedance variation range of the active metasurface can be determined based on the signal characteristics of the response signal and the parameter range of the environmental object. Then, based on the impedance variation range, the capacitance variation range of the active metasurface can be obtained using finite element modeling.

[0107] As an example, in a blood glucose monitoring scenario, a wide range of parameter values ​​can be used for each tissue layer. For instance, the following tissue thickness ranges can be used: skin (0.5-2 mm), fat (1-10 mm), muscle (10-30 mm), and bone (0-20 mm). Furthermore, publicly available dielectric constant data can be used, and given individual differences, a certain range of variation in the dielectric constant can be allowed. Thus, structures such as… Figure 5BThe equivalent circuit 503 is shown. Equivalent circuit 503 can be considered as replacing the equivalent circuit corresponding to the passive metasurface in model 501 with an active metasurface. Using equivalent circuit 503, the equivalent impedance range for the active metasurface can be determined. Then, in a finite element modeling tool, an equivalent circuit model of the active metasurface can be built to determine the range of variable capacitance based on the variation range of the equivalent impedance.

[0108] During the calibration process, the active metasurface is attached to the target environmental object, such as a matching element attached to an arm. Next, the capacitance of a variable capacitor can be adjusted within the aforementioned capacitance range. During capacitance adjustment, the signal characteristics of the response signal are tracked. The capacitance value that induces optimal resonant coupling can be determined as the optimized capacitance value for the active metasurface. Using this optimized capacitance value, a passive metasurface matching the ABCD matrix with the active metasurface can be obtained, thereby achieving a design sensitive to the concentration of the target substance.

[0109] For example, the variable capacitance of an active metasurface can be adjusted to achieve optimal resonance between the active metasurface and the measurement environment. After identifying the optimal capacitance value for the variable capacitor, the equivalent impedance of the active metasurface can be determined using finite element modeling. The next goal is to design a passive metasurface that realizes this equivalent impedance. That is, the equivalent impedance of the active metasurface thus obtained can be used as the target impedance of the passive metasurface.

[0110] Metasurface structure derived

[0111] Continue to refer to Figure 2A In stage 230, the metasurface structure is determined based on the target electrical properties of the metasurface, including the geometry, thickness, and material of the metal layers. In box 231, a structure that matches the target electrical properties of the metasurface can be determined. The following describes some implementations of metasurface structure derivation using impedance as an example of an electrical property.

[0112] In some implementations, a frequency-dependent impedance function can be derived. The impedance of the metal layer is related to its pattern. As an example, refer to... Figure 4A Example patterns are shown. For instance, with a period length of 6 mm (approximately 1 / 8 of the wavelength of a 4 GHz electromagnetic wave), the impedance can be varied from -500 J to -10 J at 4 GHz by adjusting geometric parameters such as a, b, w, L, and N. The goal of optimizing the impedance function of the metal layers can be to optimize the geometric parameters of the pattern for each metal layer, namely L, a, b, w, and N. In some implementations, for ease of fabrication, the metasurface units of each metal layer can be set to share the geometric parameter L. In this case, the optimization goal can be to find the optimal geometric parameters of the metasurface units to ensure that X(f; L, a, b, w, N) maximizes the sensitivity to changes in the target material concentration.

[0113] In some implementations, the impedance functions of metal patterns with different geometric parameters can be obtained, and the relationship between these geometric parameters and the impedance function coefficients can be established. Figure 6 The impedance (imaginary part) of patterns with different geometric parameters is shown in the frequency range of 1-6 GHz. Figure 6 Each curve in the diagram corresponds to a combination of geometric parameters L, a, b, w, and N, which in turn corresponds to a pattern. Figure 6 The curve shown can be obtained through finite element analysis.

[0114] These curves can be approximated by an nth-order polynomial function, namely X(f) = X1fn + X2fn-1 + ... + X n+1 For each X i The coefficients can fit an equation that includes geometric parameters, i.e., X. i =F i (L, a, b, N). In this way, impedance information about the frequency can be obtained directly based on the geometric parameters, thereby determining the optimal geometric parameters.

[0115] By modeling the impedance function relative to the geometric parameters of the metal pattern, the optimal metasurface design can be determined for each metal layer. For example, the objective equation can be optimized and a gradient descent optimizer can be used to maximize S. 11 The sensitivity of the parameters to the concentration of the target substance is achieved. For example, the optimization objective function can be expressed in the following form:

[0116]

[0117] Where S1(f), ..., S k (f) represents the geometric parameters of the k metal layers, namely a, w, b, and N. Furthermore, in some implementations, hyperparameter tuning techniques can be used to adjust the hyperparameters to obtain the most efficient metasurface design within a specific frequency range. Hyperparameters may include, for example, the shared period length L of each metasurface unit, the number of metal layers, and the number of substrate layers.

[0118] This is for illustrative purposes only and is not intended to impose any limitations. Figure 7A and Figure 7B The corresponding S values ​​for different glucose solution concentrations are shown. 11 Parameters. Specifically, the metasurface in this example resonates at around 6 GHz, where L = 6 mm, and has 4 metal layers and 4 substrate layers. Figure 7A The following diagram shows S at different glucose concentrations. 11The curves showing the amplitude of the parameter as a function of frequency, where curves 711, 712, 713, 714, 715, and 716 correspond to glucose concentrations of 0 mg / dL, 100 mg / dL, 200 mg / dL, 300 mg / dL, 400 mg / dL, and 500 mg / dL, respectively. From... Figure 7A It can be seen that changes in glucose concentration can cause a shift in the resonant point, with a frequency shift of approximately 0.8 MHz per 100 mg / dL. Although this frequency shift is small, the S0 at each concentration... 11 The amplitude parameters are well distinguishable. At the resonant frequency of 0 mg / dL, increasing the concentration will affect the So of other concentrations at that frequency. 11 The amplitude changed by an average of 5 dB per 100 mg / dL. Figure 7B The following diagram shows S at different glucose concentrations. 11 The curves showing the phase of the parameter as a function of frequency are shown, where curves 721, 722, 723, 724, 725, and 726 correspond to glucose concentrations of 0 mg / dL, 100 mg / dL, 200 mg / dL, 300 mg / dL, 400 mg / dL, and 500 mg / dL, respectively. The phase parameter changes by approximately 50°. This difference is greater than that without the metasurface. 11 Changes in parameters.

[0119] The above describes an example implementation of optimizing the structure of a metasurface by modeling it using equivalent circuits.

[0120] In some implementations, the structure of the passive metasurface can be determined based on the equivalent impedance of the active metasurface obtained in block 222. For example, the target transfer matrix of the passive metasurface can be determined based on the equivalent impedance of the active metasurface. The structure of the passive metasurface is determined by minimizing the difference between the transfer matrix of the passive metasurface under different structures and the target transfer matrix.

[0121] As an example, by constructing an impedance function related to the geometric parameters of the metal layer, we can obtain... Combining equation (5b) and the substrate thickness, the global ABCD matrix of the passive metasurface can be obtained, represented by the parameter set {m, L, a, w, b, N}, where m is the number of layers in the passive metasurface. The optimization objective is to match the equivalent impedance of the passive metasurface with the corrected equivalent impedance of the active metasurface, thus ensuring that the ABCD matrices of the passive and active metasurfaces are equal. The ABCD matrix of the active metasurface can be expressed as... In this case, the target loss function is as follows:

[0122]

[0123] Therefore, the goal of metasurface structure optimization is to find the set of hyperparameters that minimizes the aforementioned loss function. Hyperparameter tuning techniques can be used to adjust the hyperparameters to achieve the optimal passive metasurface design.

[0124] To compensate for the coupling effects between different metal layers, the target electrical properties (e.g., impedance) can be fine-tuned in some implementations. The target impedance of the metasurface can be obtained by fine-tuning the optimized impedance through finite element modeling of the measurement environment. In this implementation, the signal characteristics of the response signal are determined based on the finite element model of the measurement environment. Based on the finite element model, the target electrical properties (e.g., impedance) of the metasurface are optimized by maximizing the differences in the signal characteristics of the response signal across different material properties of the measured object (e.g., different concentrations, different compositions), thereby optimizing the structure of the metasurface. An example implementation is described below using impedance as an example.

[0125] Equivalent circuit models can effectively represent the impedance of each component and use ABCD matrices to simulate the frequency response of electromagnetic waves reflected by the metasurface and the object under test. Finite element modeling can accurately simulate the coupling between multiple metal layers in a metasurface, especially when the substrate thickness is small and the coupling is not negligible.

[0126] Figure 8 A schematic diagram of an example finite element model 800 for a measurement environment, according to some implementations of this disclosure, is shown. In this example, from the input port 871 to the output port 872 of the wireless signal, a metasurface 810, a container layer 821 adjacent to the metasurface 810, a test object 830 that may contain the target material, and the container layer 822 are simulated sequentially. The metasurface 810 includes four metal layers, namely metal layers 811, 812, 813, and 814.

[0127] To compensate for the coupling effects between different metal layers, any suitable type of finite element simulator can be used. Such a simulator can solve optimization problems to simulate electromagnetic behavior (e.g., near-field coupling) based on an initially optimized metasurface design. In a finite element-based simulator, the same optimization objectives described above (such as Equation (9)) can be applied.

[0128] While finite element modeling can be used directly to optimize metasurface designs, the two-step approach proposed in this disclosure offers several advantages. For example, by using the preliminary optimization results of the equivalent circuit as the starting point for finite element modeling optimization, the computational cost and time consumption of finite element solutions can be significantly reduced. Furthermore, finite element modeling is not conducive to the adjustment of hyperparameters. Adjusting hyperparameters such as the number of metal layers requires rebuilding the finite element model, which reduces optimization efficiency.

[0129] Therefore, in the implementation described above, an equivalent circuit model is established using the ABCD transfer matrix theory to obtain a rough metasurface design. Then, a finite element simulator can be used to compensate for near-field coupling between the metal layers, ultimately achieving an optimized, reliable metasurface sensitive to the target material concentration.

[0130] In some implementations, machine learning models can be used to generate metal patterns that match target electrical properties. Such machine learning models are trained using multiple training samples. Each training sample can include a reference pattern for the metal layer and the corresponding electrical properties. The reference pattern can be defined by multiple geometric parameters, such as those described above. The machine learning model trained in this way can learn the correlation between electrical properties and patterns. Accordingly, for each metal layer, the machine learning model can generate an optimized pattern for that metal layer based on its corresponding electrical properties (e.g., impedance), for example, by determining the aforementioned geometric parameters L, a, b, and N.

[0131] The machine learning model used can be implemented based on any suitable machine learning technique. For example, the machine learning model may include a convolutional neural network. Alternatively, the machine learning module may include a generative adversarial network (GAN) or a diffusion model, etc.

[0132] Example measurement process of material properties

[0133] Figure 2B A flowchart of an example measurement process 200B utilizing a metasurface according to some implementations of this disclosure is shown. Exemplary, see reference. Figure 1 To describe process 200B.

[0134] In frame 240, metasurface 110 is attached to the test object 130. For example, in a blood glucose monitoring scenario, the user wears at least a portion of the measuring device 101 on their wrist. In frame 250, a detection signal is transmitted to the test object 130 to which metasurface 110 is attached. For example, transmitter 121, under the control of controller 125, transmits a wireless signal in a predetermined frequency band toward the test object 130.

[0135] In box 260, a response signal from the object under test 130, reflected or transmitted via the metasurface 110, is received. For example, receiver 122 receives a reflected or transmitted signal from the object under test 130 that propagates via the metasurface 110.

[0136] In box 270, by analyzing the response signal, the material properties of the measured object 130 are estimated, such as the composition or concentration of the target substance. For example, in the case of a reflected signal, S can be analyzed. 11 Parameters. For example, in the case of transmitted signals, S can be analyzed.21 parameter.

[0137] In some implementations, the substance property value can be estimated by analyzing the frequency response characteristics of the response signal, as shown in box 271. In some implementations, the estimated substance property can be the concentration of the target substance. For example, the controller 125 can determine the resonant frequency of the response signal and compare it with the corresponding resonant frequencies at different concentrations of the target substance in the analyte. Thus, based on the comparison, the concentration of the target substance can be determined. The concentration whose resonant frequency is closest to the resonant frequency of the response signal can be determined as the concentration of the target substance.

[0138] In some implementations, the estimated material property can be the composition of the target material. For example, controller 125 can determine the resonant frequency of the response signal and compare it with the corresponding resonant frequencies of different compositions of the target material in the test object. Thus, based on the comparison, the composition of the target material can be determined. The composition whose resonant frequency is closest to the resonant frequency of the response signal can be identified as the composition of the target material.

[0139] In some implementations, material properties, such as dielectric constant, composition, or concentration, are estimated using machine learning models based on the response signal, as shown in box 272. Such machine learning models may include, for example, decision trees or neural networks.

[0140] Example Process

[0141] Figure 9 A flowchart of a process 900 for measuring material properties according to some implementations of this disclosure is shown. For example, process 900 can be performed in... Figure 1 The measurement equipment was implemented at 110 locations.

[0142] In block 910, the measuring device 110 transmits a probe signal to the object to be measured with the metasurface attached via a transmitter. In block 910, the measuring device 110 receives a response signal from the object to be measured via reflection or transmission through the metasurface via a receiver. In block 910, the measuring device 110 estimates the material properties of the object to be measured by analyzing the response signal.

[0143] In some implementations, the metasurface to which the object under test is attached is either an active metasurface or a passive metasurface.

[0144] In some implementations, estimating material property values ​​includes estimating material property values ​​by analyzing the frequency response characteristics of the response signal.

[0145] In some implementations, estimating material property values ​​by analyzing the frequency response characteristics of the response signal includes: determining the resonant frequency of the response signal; comparing the resonant frequency of the response signal with the corresponding resonant frequencies of the target substance at different concentrations in the analyte; and determining the concentration of the target substance based on the comparison.

[0146] In some implementations, estimating material property values ​​by analyzing the frequency response characteristics of the response signal includes: determining the resonant frequency of the response signal; comparing the resonant frequency of the response signal with the corresponding resonant frequencies of different components of the target material in the test object; and determining the composition of the target material based on the comparison.

[0147] In some implementations, estimating material property values ​​by analyzing response signals includes: estimating material property values ​​using machine learning models based on the response signals.

[0148] In some implementations, substance property values ​​include the following: components of blood, salts in a salt solution, or water contaminants.

[0149] In some implementations, blood components include at least one of the following: blood glucose, blood salts, or blood triglycerides.

[0150] In some implementations, the metasurface to which the object under test is attached is an active metasurface, and the active metasurface includes: a substrate layer; a metal layer disposed on the substrate layer, the metal layer having one or more capacitors with variable capacitance values.

[0151] In some implementations, the metasurface to which the object under test is attached is a passive metasurface, and the passive metasurface includes: a substrate (e.g., paper); and a metal pattern disposed on the substrate.

[0152] Figure 10 A flowchart of a process 1000 for designing a metasurface according to some implementations of this disclosure is shown. Process 1000 can be implemented in any suitable electronic device.

[0153] In block 1010, the electronic device determines the signal characteristics of the response signal from the object under test, reflected or transmitted via the metasurface, based on the measurement environment including the metasurface and the object under test. In block 1020, the electronic device determines the electrical properties of the metasurface by maximizing the sensitivity of the signal characteristics of the response signal to changes in the material properties of the object under test. In some implementations, in block 1030, the electronic device determines the structure of the metasurface based on its electrical properties.

[0154] In some implementations, determining the signal characteristics of a response signal from a test object via reflection or transmission through a metasurface includes: determining the electrical coefficients of the test object varying with frequency at different material property values; generating an equivalent circuit representing multiple components of the measurement environment, including various layers in the metasurface, the test object, and environmental objects associated with the test object, wherein each component is represented as a circuit element with corresponding electrical characteristics in the equivalent circuit; and determining the signal characteristics of the response signal based on the corresponding electrical coefficients of the multiple components in the equivalent circuit.

[0155] In some implementations, the metasurface is a passive metasurface, and determining the electrical characteristics of the metasurface includes: determining an optimized capacitance value for the active metasurface based on the resonant characteristics of the active metasurface over a capacitance variation range; and determining the electrical characteristics of the passive metasurface based on the optimized capacitance value. In some implementations, process 1000 further includes: determining the capacitance variation range of the active metasurface based on the signal characteristics and the parameter value range of the environmental object associated with the object under test.

[0156] In some implementations, the metasurface is an active metasurface. Determining the capacitance variation range of an active metasurface includes: determining the impedance variation range of the active metasurface based on signal characteristics, the parameter value range of the measured object and the environment; and obtaining the capacitance variation range of the active metasurface using finite element modeling based on the impedance variation range.

[0157] In some implementations, determining the structure of a metasurface includes: determining the target transfer matrix for a passive metasurface based on its electrical properties; and determining the structure of a passive metasurface by minimizing the difference between the transfer matrix of the passive metasurface under different structures and the target transfer matrix.

[0158] In some implementations, process 1000 also includes: determining the signal characteristics of the response signal based on a finite element model of the measurement environment; and optimizing the electrical properties of the metasurface by maximizing the differences in the signal characteristics of the response signal across different material property values ​​of the measured object, based on the finite element model.

[0159] In some implementations, the metasurface includes one or more metal layers, and determining the structure of the metasurface includes: using a machine learning model to determine the corresponding structure of the one or more metal layers based on the corresponding electrical properties of the metal layers.

[0160] In some implementations, the machine learning model includes at least one of the following: convolutional neural network, generative adversarial network, or diffusion model.

[0161] In some implementations, the material property value includes at least one of the following: the composition of the target substance in the measured object, or the concentration of the target substance.

[0162] Example device

[0163] Figure 11 A schematic block diagram of an electronic device capable of implementing various implementations of this disclosure is shown. It should be understood that... Figure 11 The electronic device 1100 shown is merely exemplary and should not be construed as limiting the functionality and scope of the implementation described in this disclosure.

[0164] like Figure 11 As shown, electronic device 1100 includes electronic device 1100 in the form of general computing device. Components of electronic device 1100 may include, but are not limited to, one or more processors or processing devices 1110, memory 1120, storage device 1130, one or more communication units 1140, one or more input devices 1150, and one or more output devices 1160.

[0165] In some implementations, electronic device 1100 can be implemented as a computing device, computing system, server, mainframe, or other device with computing capabilities.

[0166] Processing device 1110 may be a physical or virtual processor and is capable of performing various processes according to a program stored in memory 1120. In a multiprocessor system, multiple processing units execute computer-executable instructions in parallel to improve the parallel processing capability of electronic device 1100. Processing device 1110 may include a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor, a controller, and / or a microcontroller, etc.

[0167] Electronic device 1100 typically includes multiple computer storage media. Such media can be any available media accessible to electronic device 1100, including but not limited to volatile and non-volatile media, removable and non-removable media. Memory 1120 may include volatile memory (e.g., registers, cache, random access memory (RAM)), non-volatile memory (e.g., read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory), or some combination thereof. Storage device 1130 may include removable or non-removable media and may include computer-readable media, such as memory, flash drives, disks, or any other media capable of storing information and / or data and accessible within electronic device 1100.

[0168] Electronic device 1100 may further include additional removable / non-removable, volatile / non-volatile storage media. Although not explicitly stated... Figure 11As shown, disk drives for reading from or writing to removable, non-volatile disks and optical disc drives for reading from or writing to removable, non-volatile optical discs can be provided. In these cases, each drive can be connected to a bus (not shown) via one or more data media interfaces.

[0169] Communication unit 1140 enables communication with other computing devices via a communication medium. Additionally, the functionality of components of electronic device 1100 can be implemented as a single computing cluster or multiple computing machines capable of communicating via communication connections. Therefore, electronic device 1100 can operate in a networked environment using logical connections to one or more other servers, personal computers (PCs), or other general network nodes.

[0170] Input device 1150 can be one or more various input devices, such as a mouse, keyboard, data import device, etc. Output device 1160 can be one or more output devices, such as a monitor, data export device, etc. Electronic device 1100 can also communicate with one or more external devices (not shown) via communication unit 1140 as needed. External devices include storage devices, display devices, etc., and can communicate with one or more devices that enable user interaction with electronic device 1100, or with any device that enables electronic device 1100 to communicate with one or more other computing devices (e.g., network card, modem, etc.). Such communication can be performed via input / output (I / O) interface (not shown).

[0171] In some implementations, in addition to being integrated into a single device, some or all of the components of electronic device 1100 may be configured in the form of a cloud computing architecture. In a cloud computing architecture, these components can be remotely deployed and can work together to achieve the functions described herein. In some implementations, cloud computing provides computing, software, data access, and storage services without requiring end users to know the physical location or configuration of the systems or hardware providing these services. In various implementations, cloud computing provides services over a wide area network (such as the Internet) using appropriate protocols. For example, cloud computing providers offer applications over a wide area network, and these applications can be accessed through a web browser or any other computing component. The software or components of the cloud computing architecture, along with the corresponding data, may be stored on servers at remote locations. Computing resources in a cloud computing environment may be consolidated at remote data center locations or they may be distributed. Cloud computing infrastructure can provide services through shared data centers, even if they appear as a single access point for users. Therefore, the components and functions described herein can be provided from service providers at remote locations using a cloud computing architecture. Alternatively, they may be provided from conventional servers, or they may be installed directly or otherwise on client devices.

[0172] Electronic device 1100 can be used to implement metasurface optimization in various implementations of this disclosure. Memory 1120 may include one or more modules having one or more program instructions that can be accessed and executed by processing unit 1110 to implement the functionality of the various implementations described herein. For example, memory 1120 may include metasurface design module 1122 for performing the design of metasurfaces. Figure 11 As shown, electronic device 1100 can acquire the input required for metasurface design through input device 1150 and provide the output of metasurface design through output device 1160. In some implementations, electronic device 1100 can also receive input from other devices (not shown) via communication unit 1140.

[0173] Example implementation

[0174] The following are some example implementations of this disclosure.

[0175] In one aspect, this disclosure provides a wireless measurement method. The method includes: transmitting a probe signal to a test object with an attached metasurface via a transmitter; receiving a response signal from the test object via reflection or transmission through the metasurface via a receiver; and estimating material property values ​​in the test object by analyzing the response signal.

[0176] In some implementations, the metasurface to which the object under test is attached is either an active metasurface or a passive metasurface.

[0177] In some implementations, estimating material property values ​​includes estimating material property values ​​by analyzing the frequency response characteristics of the response signal.

[0178] In some implementations, estimating material property values ​​by analyzing the frequency response characteristics of the response signal includes: determining the resonant frequency of the response signal; comparing the resonant frequency of the response signal with the corresponding resonant frequencies of the target substance at different concentrations in the analyte; and determining the concentration of the target substance based on the comparison.

[0179] In some implementations, estimating material property values ​​by analyzing the frequency response characteristics of the response signal includes: determining the resonant frequency of the response signal; comparing the resonant frequency of the response signal with the corresponding resonant frequencies of different components of the target material in the test object; and determining the composition of the target material based on the comparison.

[0180] In some implementations, estimating material property values ​​by analyzing response signals includes: estimating material property values ​​using machine learning models based on the response signals.

[0181] In some implementations, substance property values ​​include the following: components of blood, salts in a salt solution, or water contaminants.

[0182] In some implementations, blood components include at least one of the following: blood glucose, blood salts, or blood triglycerides.

[0183] In some implementations, the metasurface to which the object under test is attached is an active metasurface, and the active metasurface includes: a substrate layer; a metal layer disposed on the substrate layer, the metal layer having one or more capacitors with variable capacitance values.

[0184] In some implementations, the metasurface to which the object under test is attached is a passive metasurface, and the passive metasurface includes: a substrate layer; and a metal pattern disposed on the substrate layer.

[0185] In one aspect, this disclosure provides a computer-implemented method. The method includes: determining signal characteristics of a response signal from the object under test, reflected or transmitted via the metasurface, based on a measurement environment including a metasurface and an object under test; and determining the electrical properties of the metasurface by maximizing the sensitivity of the signal characteristics of the response signal to changes in the material properties of the object under test.

[0186] In some implementations, the method further includes determining the structure of the metasurface based on its electrical properties.

[0187] In some implementations, determining the signal characteristics of a response signal from a test object via reflection or transmission through a metasurface includes: determining the electrical coefficients of the test object varying with frequency at different material property values; generating an equivalent circuit representing multiple components of the measurement environment, including various layers in the metasurface, the test object, and environmental objects associated with the test object, wherein each component is represented as a circuit element with corresponding electrical characteristics in the equivalent circuit; and determining the signal characteristics of the response signal based on the corresponding electrical coefficients of the multiple components in the equivalent circuit.

[0188] In some implementations, the metasurface is a passive metasurface, and determining the electrical characteristics of the metasurface includes: determining an optimized capacitance value for the active metasurface based on the resonant characteristics of the active metasurface over a capacitance variation range; and determining the electrical characteristics of the passive metasurface based on the optimized capacitance value. In some implementations, the method further includes: determining the capacitance variation range of the active metasurface based on the signal characteristics and the parameter value range of the environmental object associated with the object under test.

[0189] In some implementations, determining the capacitance variation range of an active metasurface includes: determining the impedance variation range of the active metasurface based on signal characteristics, the parameter value range of the object under test and the environment; and obtaining the capacitance variation range of the active metasurface using finite element modeling based on the impedance variation range.

[0190] In some implementations, determining the structure of a metasurface includes: determining the target transfer matrix for a passive metasurface based on its electrical properties; and determining the structure of a passive metasurface by minimizing the difference between the transfer matrix of the passive metasurface under different structures and the target transfer matrix.

[0191] In some implementations, the method also includes: determining the signal characteristics of the response signal based on a finite element model of the measurement environment; and optimizing the electrical properties of the metasurface by maximizing the differences in the signal characteristics of the response signal across different material property values ​​of the measured object, based on a finite element model.

[0192] In some implementations, the metasurface includes one or more metal layers, and determining the structure of the metasurface includes: using a machine learning model to determine the corresponding structure of the one or more metal layers based on the corresponding electrical properties of the metal layers.

[0193] In some implementations, the machine learning model includes at least one of the following: convolutional neural network, generative adversarial network, or diffusion model.

[0194] In some implementations, the material property value includes at least one of the following: the composition of the target substance in the measured object, or the concentration of the target substance.

[0195] In one aspect, this disclosure provides a wireless measurement device. The wireless measurement device includes: a metasurface attached to a test object; a transmitter configured to transmit a probe signal to the test object; a receiver configured to receive a response signal from the test object reflected or transmitted via the metasurface; and a processor configured to estimate material property values ​​of the test object by analyzing the response signal.

[0196] In some implementations, the metasurface to which the object under test is attached is either an active metasurface or a passive metasurface.

[0197] In some implementations, the processor is further configured to estimate material property values ​​by analyzing the frequency response characteristics of the response signal.

[0198] In some implementations, the processor is further configured to: determine the resonant frequency of the response signal; compare the resonant frequency of the response signal with the corresponding resonant frequencies of the target substance at different concentrations in the test object; and determine the concentration of the target substance based on the comparison.

[0199] In some implementations, the processor is further configured to: determine the resonant frequency of the response signal; compare the resonant frequency of the response signal with the corresponding resonant frequencies of different components of the target substance in the test object; and determine the composition of the target substance based on the comparison.

[0200] In some implementations, the processor is further configured to estimate material property values ​​based on the response signal using a machine learning model.

[0201] In some implementations, substance property values ​​include the following: components of blood, salts in a salt solution, or water contaminants.

[0202] In some implementations, blood components include at least one of the following: blood glucose, blood salts, or blood triglycerides.

[0203] In some implementations, the metasurface to which the object under test is attached is an active metasurface, and the active metasurface includes: a substrate layer; a metal layer disposed on the substrate layer, the metal layer having one or more capacitors with variable capacitance values.

[0204] In some implementations, the metasurface to which the object under test is attached is a passive metasurface, and the passive metasurface includes: a substrate layer; and a metal pattern disposed on the substrate layer.

[0205] In another aspect, this disclosure provides an electronic device. The electronic device includes: a processor; and a memory coupled to the processor and containing instructions stored thereon, the instructions, when executed by the processor, causing the device to perform the following actions: determining, based on a measurement environment including a metasurface and a test object, signal characteristics of a response signal from the test object reflected or transmitted via the metasurface; determining electrical characteristics of the metasurface by maximizing the sensitivity of the signal characteristics of the response signal to changes in the material properties of the test object; and determining the structure of the metasurface based on the electrical characteristics of the metasurface.

[0206] In some implementations, determining the signal characteristics of a response signal from a test object via reflection or transmission through a metasurface includes: determining the electrical coefficients of the test object varying with frequency at different material property values; generating an equivalent circuit representing multiple components of the measurement environment, including various layers in the metasurface, the test object, and environmental objects associated with the test object, wherein each component is represented as a circuit element with corresponding electrical characteristics in the equivalent circuit; and determining the signal characteristics of the response signal based on the corresponding electrical coefficients of the multiple components in the equivalent circuit.

[0207] In some implementations, the metasurface is a passive metasurface, and determining the electrical properties of the metasurface includes: determining the capacitance variation range of the active metasurface based on signal characteristics and the parameter value range of the environmental object associated with the test object; determining the optimal capacitance value of the active metasurface based on the resonant characteristics of the active metasurface within the capacitance variation range; and determining the electrical properties of the passive metasurface based on the optimized capacitance value.

[0208] In some implementations, determining the capacitance variation range of an active metasurface includes: determining the impedance variation range of the active metasurface based on signal characteristics, the parameter value range of the measured object and the environment; and obtaining the capacitance variation range of the active metasurface using finite element modeling based on the impedance variation range.

[0209] In some implementations, determining the structure of a metasurface includes: determining the target transfer matrix for a passive metasurface based on its electrical properties; and determining the structure of a passive metasurface by minimizing the difference between the transfer matrix of the passive metasurface under different structures and the target transfer matrix.

[0210] In some implementations, the actions also include: determining the signal characteristics of the response signal based on a finite element model of the measurement environment; and optimizing the electrical properties of the metasurface by maximizing the differences in the signal characteristics of the response signal across different material property values ​​of the measured object, based on the finite element model.

[0211] In some implementations, the metasurface includes one or more metal layers, and determining the structure of the metasurface includes: using a machine learning model to determine the corresponding structure of the one or more metal layers based on the corresponding electrical properties of the metal layers.

[0212] In some implementations, the machine learning model includes at least one of the following: convolutional neural network, generative adversarial network, or diffusion model.

[0213] In some implementations, the material property value includes at least one of the following: the composition of the target substance in the measured object, or the concentration of the target substance.

[0214] In another aspect, this disclosure provides a computer program product tangibly stored in a computer storage medium and including computer-executable instructions that, when executed by a device, cause the device to perform the following actions: transmitting a probe signal to a test object with a metasurface attached via a transmitter; receiving a response signal from the test object reflected or transmitted via the metasurface via a receiver; and estimating material property values ​​in the test object by analyzing the response signal.

[0215] In some implementations, the metasurface to which the object under test is attached is either an active metasurface or a passive metasurface.

[0216] In some implementations, estimating material property values ​​includes estimating material property values ​​by analyzing the frequency response characteristics of the response signal.

[0217] In some implementations, estimating material property values ​​by analyzing the frequency response characteristics of the response signal includes: determining the resonant frequency of the response signal; comparing the resonant frequency of the response signal with the corresponding resonant frequencies of the target substance at different concentrations in the analyte; and determining the concentration of the target substance based on the comparison.

[0218] In some implementations, estimating material property values ​​by analyzing the frequency response characteristics of the response signal includes: determining the resonant frequency of the response signal; comparing the resonant frequency of the response signal with the corresponding resonant frequencies of different components of the target material in the test object; and determining the composition of the target material based on the comparison.

[0219] In some implementations, estimating material property values ​​by analyzing response signals includes: estimating material property values ​​using machine learning models based on the response signals.

[0220] In some implementations, substance property values ​​include the following: components of blood, salts in a salt solution, or water contaminants.

[0221] In some implementations, blood is composed of glucose.

[0222] In some implementations, the metasurface to which the object under test is attached is an active metasurface, and the active metasurface includes: a substrate layer; a metal layer disposed on the substrate layer, the metal layer having one or more capacitors with variable capacitance values.

[0223] In some implementations, the metasurface to which the object under test is attached is a passive metasurface, and the passive metasurface includes: a substrate layer; and a metal pattern disposed on the substrate layer.

[0224] In another aspect, this disclosure provides a computer program product tangibly stored in a computer storage medium and including computer-executable instructions that, when executed by a device, cause the device to perform the following actions: determining, based on a measurement environment including a metasurface and a test object, the signal characteristics of a response signal from the test object reflected or transmitted via the metasurface; and determining the electrical characteristics of the metasurface by maximizing the sensitivity of the signal characteristics of the response signal to changes in the material properties of the test object.

[0225] In some implementations, the action also includes: determining the structure of the metasurface based on the electrical properties of the metasurface.

[0226] In some implementations, determining the signal characteristics of a response signal from a test object via reflection or transmission through a metasurface includes: determining the electrical coefficients of the test object varying with frequency at different material property values; generating an equivalent circuit representing multiple components of the measurement environment, including various layers in the metasurface, the test object, and environmental objects associated with the test object, wherein each component is represented as a circuit element with corresponding electrical characteristics in the equivalent circuit; and determining the signal characteristics of the response signal based on the corresponding electrical coefficients of the multiple components in the equivalent circuit.

[0227] In some implementations, the metasurface is a passive metasurface, and determining the electrical properties of the metasurface includes: determining the capacitance variation range of the active metasurface based on signal characteristics and the parameter value range of the environmental object associated with the test object; determining the optimal capacitance value of the active metasurface based on the resonant characteristics of the active metasurface within the capacitance variation range; and determining the electrical properties of the passive metasurface based on the optimized capacitance value.

[0228] In some implementations, determining the capacitance variation range of an active metasurface includes: determining the impedance variation range of the active metasurface based on signal characteristics, the parameter value range of the measured object and the environment; and obtaining the capacitance variation range of the active metasurface using finite element modeling based on the impedance variation range.

[0229] In some implementations, determining the structure of a metasurface includes: determining the target transfer matrix for a passive metasurface based on its electrical properties; and determining the structure of a passive metasurface by minimizing the difference between the transfer matrix of the passive metasurface under different structures and the target transfer matrix.

[0230] In some implementations, the method also includes: determining the signal characteristics of the response signal based on a finite element model of the measurement environment; and optimizing the electrical properties of the metasurface by maximizing the differences in the signal characteristics of the response signal across different material property values ​​of the measured object, based on a finite element model.

[0231] In some implementations, the metasurface includes one or more metal layers, and determining the structure of the metasurface includes: using a machine learning model to determine the corresponding structure of the one or more metal layers based on the corresponding electrical properties of the metal layers.

[0232] In some implementations, the machine learning model includes at least one of the following: convolutional neural network, generative adversarial network, or diffusion model.

[0233] In some implementations, the material property value includes at least one of the following: the composition of the target substance in the measured object, or the concentration of the target substance.

[0234] In another aspect, this disclosure provides a computer-readable medium having stored thereon computer-executable instructions that, when executed by a device, cause the device to perform one or more example implementations of the methods described above.

[0235] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, without limitation, example types of hardware logic components that can be used include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Load Programmable Logic Devices (CPLDs), and so on.

[0236] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0237] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0238] Furthermore, although the operations are described in a specific order, this should be understood as requiring that such operations be performed in the specific order shown or in sequential order, or requiring that all illustrated operations be performed to achieve the desired result. In certain environments, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of this disclosure. Certain features described in the context of a single implementation may also be implemented in combination in a single implementation. Conversely, various features described in the context of a single implementation may also be implemented individually or in any suitable sub-combination in multiple implementations.

[0239] Although the subject matter has been described using language specific to structural features and / or methodological logic, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or actions described above. Rather, the specific features and actions described above are merely illustrative examples of implementing the claims.

Claims

1. A wireless measurement method, comprising: The transmitter sends a probe signal to the object to be tested with a metasurface attached. The receiver receives the response signal from the object under test via reflection or transmission through the metasurface; as well as By analyzing the response signal, the material property values ​​of the tested object are estimated.

2. The method of claim 1, wherein estimating the material property value comprises: The material property values ​​are estimated by analyzing the frequency response characteristics of the response signal.

3. The method according to claim 2, wherein estimating the material property value by analyzing the frequency response characteristics of the response signal comprises: Determine the resonant frequency of the response signal; The resonant frequency of the response signal is compared with the corresponding resonant frequencies of different components of the target substance in the tested object; and Based on the comparison, the composition of the target substance is determined.

4. The method of claim 2, wherein estimating the material property value by analyzing the response signal comprises: Based on the response signal, the material property values ​​are estimated using a machine learning model.

5. The method of claim 1, wherein the metasurface to which the object under test is attached is an active metasurface, and the active metasurface comprises: Substrate layer; A metal layer disposed on the substrate layer, the metal layer having one or more capacitors with variable capacitance values.

6. The method of claim 1, wherein the metasurface to which the object under test is attached is a passive metasurface, and the passive metasurface comprises: Substrate layer; as well as Metal pattern arranged on the substrate layer.

7. A metasurface optimization method, comprising: Based on the measurement environment including the metasurface and the object under test, the signal characteristics of the response signal from the object under test reflected or transmitted through the metasurface are determined; as well as The electrical properties of the metasurface are determined by maximizing the sensitivity of the signal characteristics of the response signal to changes in the material properties of the object under test.

8. The method according to claim 7, further comprising: The structure of the metasurface is determined based on its electrical properties.

9. The method of claim 7, wherein determining the signal characteristics of the response signal from the object under test reflected or transmitted via the metasurface comprises: Determine the electrical coefficients of the tested object as a function of frequency under different material property values; An equivalent circuit is generated to represent multiple components of the measurement environment, including various layers in the metasurface, the object under test, and environmental objects associated with the object under test. In the equivalent circuit, each of the multiple components is represented as a circuit element with corresponding electrical characteristics. as well as The signal characteristics of the response signal are determined based on the corresponding electrical coefficients of the plurality of components in the equivalent circuit.

10. The method of claim 7, wherein the metasurface is a passive metasurface, and determining the electrical properties of the metasurface comprises: Based on the resonant characteristics of the active metasurface within the capacitance variation range, the optimal capacitance value of the active metasurface is determined. as well as Based on the optimized capacitance value, the electrical properties of the passive metasurface are determined.

11. The method of claim 10, further comprising: Based on the signal characteristics and the parameter value range of the environmental object associated with the measured object, the capacitance variation range of the active metasurface is determined.

12. The method of claim 11, wherein determining the capacitance variation range of the active metasurface comprises: Based on the signal characteristics, the parameter value range of the object under test and the environment, the impedance variation range of the active metasurface is determined. as well as Based on the impedance variation range, the capacitance variation range of the active metasurface is obtained using finite element modeling.

13. The method of claim 8, wherein the metasurface is a passive metasurface, and determining the structure of the metasurface comprises: Based on the electrical characteristics, the target transfer matrix for the passive metasurface is determined; as well as The structure of the passive metasurface is determined by minimizing the difference between the transfer matrix of the passive metasurface under different structures and the target transfer matrix.

14. The method of claim 7, further comprising: Based on the finite element model of the measurement environment, the signal characteristics of the response signal are determined; as well as Based on the finite element model, the electrical properties of the metasurface are optimized by maximizing the difference in the signal characteristics of the response signal among different material property values ​​of the tested object.

15. The method of claim 8, wherein the metasurface comprises one or more metal layers, and determining the structure of the metasurface comprises: Based on the corresponding electrical properties of the one or more metal layers, the corresponding structure of the one or more metal layers is determined using a machine learning model.

16. A wireless measuring device, comprising: Metasurface, attached to the object being measured; A transmitter is configured to send a probe signal to the object under test; A receiver is configured to receive a response signal from the object under test via reflection or transmission through the metasurface; as well as The processor is configured to estimate the material property values ​​of the object under test by analyzing the response signal.

17. The device of claim 16, wherein the metasurface to which the object under test is attached is an active metasurface, and the active metasurface comprises: Substrate layer; A metal layer disposed on the substrate layer, the metal layer having one or more capacitors with variable capacitance values.

18. The device of claim 16, wherein the metasurface to which the object under test is attached is a passive metasurface, and the passive metasurface comprises: Substrate layer; as well as Metal pattern arranged on the substrate layer.

19. An electronic device comprising: Processing unit; as well as A memory, coupled to the processing unit and containing instructions stored thereon, which, when executed by the processing unit, cause the device to perform the following actions: Based on the measurement environment including the metasurface and the object under test, the signal characteristics of the response signal from the object under test reflected or transmitted through the metasurface are determined; The electrical properties of the metasurface are determined by maximizing the sensitivity of the signal characteristics of the response signal to changes in the material properties of the object under test.

20. The device of claim 19, wherein the action further comprises: The structure of the metasurface is determined based on its electrical properties.

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