# Construction and Simulation of Online English Reading Model in Wireless Surface Acoustic Wave Sensor Environment Optimized by Particle Swarm Optimization

Authors: Bolan Zhou
Journal: Discrete Dynamics in Nature and Society
 (2022)

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## Abstract

English reading is an important way to consolidate and expand English language knowledge, and it is also an important way to obtain information and understand British and American culture. Therefore, reading teaching has always been an important part of English education and teaching at all levels and types of schools, and college English teaching is no exception. College English teachers have been carrying out teaching reflections in their reading teaching practice and constantly exploring teaching modes and teaching directions that improve students’ reading engagement and reading comprehension ability. However, the current daily teaching of English reading still generally maintains the traditional teaching mode. The entire reading learning process is monotonous, boring, and stylized, and the ability to acquire and process information cannot be combined with language knowledge and language skills. This kind of teaching mode severely inhibited the college students’ involvement in English learning model. Based on the electromagnetic-polarization response expression in a uniformly polarized half-space, this paper transforms the problem of polarization parameter extraction into a minimum optimization problem and constructs a fitness function. A set of polarization parameters is selected to calculate the electromagnetic-polarization response under trapezoidal waves in a uniform half-space, and the basic particle swarm algorithm is used to extract single and multiple parameters, respectively. In this paper, by adding a window to the test data in the time domain, the multiplicative and additive interference in the test signal is suppressed, and the signal-to-noise ratio of the test result is improved. We use the platform built in this article to wirelessly test the temperature characteristics of the surface acoustic wave sensor. The research results identified eight cognitive attributes of English reading and successfully generated diagnostic information at the group and individual levels and finally formed graphics and textual diagnostic feedback. There is a certain correlation between students’ vocabulary mastery and English reading performance, which shows that the vocabulary teaching method can help students better understand the reading materials and improve their reading performance. Combining two student interviews and learning logs, it can be seen that students’ understanding and frequency of use of vocabulary knowledge have increased significantly after the action research. It is generally recognized that vocabulary has a positive effect on improving reading level and can be based on the recognition and understanding of vocabulary. The mastery of vocabulary can promote the improvement of college students’ English reading level to a certain extent. Learners should strengthen vocabulary learning and face up to the importance of vocabulary knowledge in English reading.


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## Body

## 1. Introduction

With the advent of the information technology era, humans are using sensors to acquire information from the outside world. The collection and processing of information are indispensable for the information revolution [1], in which sensors, an important pillar of information collection, play a pivotal role and are ubiquitous in today’s society. Because of the importance of information acquisition, sensors have been able to develop rapidly. However, there are many occasions when the sensor and the use of cable connections between measurement units, i.e., wired real-time data transfer, are still not possible. In moving and rotating parts, for example, slip rings and brushes can be used to measure them, but this causes mechanical and electrical problems such as interruptions and noise. Traditional temperature sensors for temperature measurement, thermocouples, require physical cables for real-time data communication. If a noncontact infrared sensor is used for temperature detection, it needs to be aligned with the object to be detected and there must be no obstructions in between. With wireless sensors, there are no physical wires and no infrared sensor limitations [2].The perception of the environment and object state is an important feature of human intelligence. Intelligent sensing that imitates human sensory functions is one of the frontiers and important research contents of modern sensing technology. With the development of intelligent and automated technology and the in-depth research of intelligent structural material systems, people hope that modern intelligent structural units and systems can be self-diagnostic, self-adaptive, and self-controlled. In the research of intelligent material structure system technology, experts have proposed “particle tagging” technology, which means adding material particles sensitive to specific physical and chemical parameters to engineering materials [3]. In this way, when these materials form engineering structures, the structures themselves will be sensitive to changes in these parameters with the spatial distribution of the structure. The search for suitable marker particles is the key to the implementation of this technology, and more importantly, these marker signals (sensitive outputs) should be readable in a relatively easy way to form practical sensitive materials and structures. Therefore, it is a practical way to realize smart sensitive materials and structures by embedding some sensitive elements that can work wirelessly, especially with self-powered and self-diagnostic functions, into the engineering materials, which can obtain sensitive output signals more easily than embedding sensitive material particles.Wireless sensors can be classified according to the method of energy supply: active wireless sensors as well as passive wireless sensors. However, in many applications, it is not possible to provide power or it is difficult to replenish power. For example, in the safety surveillance of aircraft fuselages, the sensors are usually placed inside the wings and the batteries cannot be replaced. There are also existing battery technologies that cannot meet the requirements of the sensors due to the large temperature range spanning when they are operating. Passive wireless sensors rely on the received electromagnetic waves to provide energy to work; thus passive wireless sensors are more suitable for applications in harsh environments such as high temperature, vibration, humidity, and ionizing radiation. Currently developed wireless passive sensors are based on resonant loop wireless sensors, microwave resonant cavities, and wireless surface wave sensors. Among them, passive wireless SAW sensors have high sensitivity and good stability and have gained wide attention and research. SAW sensors are a relatively new branch of application of SAW devices [4], using SAW resonators as sensing units. When a passive SAW wireless sensor performs detection, a fork-finger transducer converts the electromagnetic signal received by the sensor antenna into a sound surface wave. Wireless sensors based on acoustic surface devices are now used in a wide variety of applications beyond the traditional field of signal processing: ID tagging, sensors for chemical, biological, and physical quantities in harsh environments.


## 2. Relevant Studies

In recent years, a large number of point, line, and surface array sensors have been developed, which can be easily used to measure the spatial distribution of various parameters. The paper “Study of integrated array tactile sensor technology with arbitrary distribution” proposes a piezoelectric integrated array tactile sensor technology with arbitrary distribution and self-excited oscillation and establishes an integrated piezoelectric array sensor system sensitive to stress and strain [5], which can not only measure dynamic vibration but also sense static pressure and achieve fast response distribution measurement. This distribution sensing method is considered by international authoritative experts to be of great importance for monitoring large structures. The research of miniaturized piezoelectric array sensors is then widely used in the electronic nose [6], electronic tongue, and other biological and chemical sensors, such as the highly precise determination of the odor of gases and the composition of liquids. In recent years the British literature has launched research on electronic array sensors for measuring the flow of blood in Doppler systems constructed from active surface wave devices [7]. However, these integrated array sensors require power supply or connectivity, limiting their application. The piezoelectric material itself has sensing and actuating properties in one, making it one of the ideal smart materials. Piezoelectric surface wave devices are excellent electronic devices that emerged only in the mid-1960s and are developing rapidly. Since the speed of sound surface wave propagation in piezoelectric materials is 100,000 times slower than that of electromagnetic waves, the size of the device is greatly reduced compared to that of electromagnetic wave devices of the same frequency [8–10].Since then some developed countries have been actively involved in the research of SAW passive wireless sensors and have made great achievements in the late 1990s of the last world. SAW sensors are widely used to detect physical quantities such as torque, strain, acceleration [11, 12], and vibration. The use of SAW passive wireless sensors in the Berlin Land Railway system has demonstrated their excellent immunity to interference in extreme environments. The temperature of the train brake disc is a matter of safe train operation because when its temperature is too high, it can lead to thermal degradation of the brake system performance [13]. In the paper, the SAW passive wireless temperature sensor is placed on the brake pads of the train and the query antenna is placed on the bottom of the train, and the query can adapt to various harsh environments. For the first time in the literature, a metal fork-finger transducer is fabricated on a piezoelectric substrate and an alternating electric field is applied to the fork-finger transducer to excite a sound surface wave on the substrate. The sound surface wave propagates on the substrate and the fork-finger transducer in its propagation path converts the sound surface wave into an electromagnetic signal again [14]. Since the propagation speed of the sound surface wave is only one hundred thousandth of the speed of the electromagnetic wave, this function of the fork-finger transducer on the piezoelectric substrate is suitable for electronic analog signal processing technology, which can realize a variety of analog signal processing functions [15].Relevant scholars introduced the complex resistivity into the forward calculation formula and then combined the damped least-squares method and the Occam smoothing constraint equation to perform inversion to achieve the extraction of polarization parameters [16]. Relevant scholars proposed an inversion algorithm based on the Cole-Cole model for the central loop method and the separated loop transient electromagnetic device and performed a one-dimensional forward calculation on the uniform half-space [17]. For synthetic data, to obtain the optimal inversion result, the data points directly adjacent to the sign inversion must be error-weighted, and the result obtained is finally applied to the inversion of the measured data containing a large number of IP signals. Relevant scholars have used the damped least-squares method to obtain four polarization parameters, thereby improving the resolution of deep prospecting [18]. Relevant scholars use the convolution algorithm, based on the Cole-Cole model, to derive the calculation formula of the time-domain transient electromagnetic-polarization effect excited by arbitrary waveforms in a uniform half-space and use artificial intelligence algorithms to determine the polarization in the superconducting transient electromagnetic data [19].The use of word block theory in college English reading teaching is based on “understandable input,” a necessary condition as the entry point. According to the learner’s existing knowledge mastery level, the usage of word blocks and common collocations is effectively input. In this way, the learner can not only master the knowledge of the word block while memorizing the word block but also prevent the negative transfer of the mother tongue and the influence of creative thinking. Related scholars have proposed that language knowledge includes rules for generating and regulating sentences, pragmatic rules, and restrictive and systematic rules, which are all essential elements of language communication [20]. The two scholars believe that the fluency of language output does not depend on the generative grammar rules stored in the learner’s mind but depends on the vocabulary reserves in the learner’s mind. It is these vocabularies that enable people to have a smooth expression ability.


## 3. Construction of English Reading Model Based on Particle Swarm Optimization of Wireless Sensors

### 3.1. Particle Swarm Algorithm

The particle swarm algorithm is an intelligent optimization algorithm that simulates the foraging of birds. It uses a speed-position model to search and adjust according to the fitness of the individual. Suppose a scenario like this: there is only one piece of food in an area, and a group of birds is searching for food at random. They do not know the specific location of the food, but they know the bird closest to the food, so they search for this bird. The surrounding area is the best way. We think of each bird as a particle without weight and volume, but containing position and velocity information. The particle moves at a certain speed in the search space. In the particle swarm algorithm, each particle corresponds to a fitness value, and the fitness value is determined by the objective function; each particle has a speed limit to constrain their search direction and position; all particles follow the individual optimal value and global optimal value to continuously update their speed and position. Among them, the individual optimal value is the optimal solution found by the particles, and the global optimal value is the optimal solution found by the entire population. Each search needs to compare the current fitness and optimal solution with the historical record value. If it exceeds the historical optimal value, we update the historical optimal position and optimal solution of the individual and the population.Assuming that the search space of the particle swarm is D-dimensional, there areM particles in the particle swarm (usually the number of particles is set to 5–10 times the dimension of the problem), and the position vector of the i-th particle is(1)Xi=xi0xi1xi2xi3i=0,1,2,…,M−1.This formula can better explain the construction of the article reader, and as the core formula of the article, it plays a role of connecting the previous and the next.The velocity vector of thei-th particle is(2)Vi=vi0vi1vi2vi3i=0,1,2,…,M−1The individual optimal value of the particle swarm is(3)Pbest=pi0pi1pi2pi3.The global optimal value of the particle swarm is(4)Gbest=pg1pg2pg3pg4g=0,1,2,…,M−1.All particles update their speed and position according to the following formula:(5)vijk+1=wvijk−1+c1r1xijk−Pbestik+c2r2xijk−Gbestik,xijk=xijk−1+vijk−2.Among them, the subscripti represents the i-th particle, the subscript j represents the j-th dimension of the particle, k represents the current iteration number, w represents the inertia weight, c1 and c2 are acceleration constants, generally, c1 = c2 = 2, and c1 is used to adjust the step length of particles flying to the individual optimal value, and c2 adjusts the step length of particles flying to the global optimal value. r1 and r2 are random numbers between (0, 1).


### 3.2. The Method of Polarization Parameter Extraction

The electromagnetic-polarization response calculation formula in a uniformly polarized half-space is a quaternary function of polarization parameters, and the functional relationship between the two is(6)Bt=ftX•ftX−1.In the formula,Bt is the theoretical electromagnetic-polarization magnetic field response, and X = (x1, x2, x3, x4) represents the four polarization parameters. We use △B to represent the difference between the measured electromagnetic-polarization response and the theoretical electromagnetic-polarization response, which is expressed as(7)ΔB=Bm−Bt.In the formula,Bm represents the measured electromagnetic-polarization response.When △B is very small or even tends to zero, it can be considered that the polarization parameter in the theoretical electromagnetic-polarization response is the parameter in the measured electromagnetic-polarization response. However, because the electromagnetic-polarization response calculation formula is nonlinear, and the polarization response curve does not conform to the law of the basic function, it cannot be solved directly by curve fitting. Therefore, this paper adopts a particle swarm algorithm. The fitness of the particle swarm algorithm is a sign of evaluating the quality of each particle. The minimum optimization problem established by extracting the polarization parameters corresponds to the fitness function of the particle swarm algorithm, so the fitness function is constructed as follows:(8)Yx=1n−1∏i=0n−1Bti−Bmi.


### 3.3. Rules of Operation of the Acoustic Surface Wave Wireless Sensor

Matched filters are widely used in radar, frequency selection filters, delay lines, and resonators in mobile phones and televisions. As a very competitive electronic device it has had a significant impact on the electronics industry, becoming an irreplaceable product. Piezoelectric SAWDs have many advantages: small size, lightweight, and high integration. Because SAWs are 100,000 times slower than electromagnetic waves, their size is greatly reduced compared to electromagnetic wave devices of the same frequency and they have design flexibility and ease of signal processing, repeatability, consistency, easy mass production, and extremely low cost. This is due to its fabrication using single crystal materials and with a planar process; the advantages of signal processing in real-time, high reliability, low power consumption, low cost, high-frequency large bandwidth, and being hour wide are difficult to match with traditional charge-coupled devices (CCD) and digital technology.Therefore, it is now widely used in the field of communication and microwave technology. From the equation of translational motion of a mass in an elastomer, the Verner (Verner) equation, we have(9)T=∑i=1nXi2+∑i=1nXiYi.When the body force is zero, its tensor form is(10)A=∑i=1nXi2+∑i=1nXiYi+1nx−μσ.In the literature, “waves propagating along the smooth surface of an elastomer” were discovered, and the surface waves containing the longitudinal (P-wave) component and the transverse (SV-wave) component with the displacement direction of the mass perpendicular to the surface were later called Rayleigh waves. Since the 1970s, piezoelectric surface acoustic wave devices (SAWDs) have developed rapidly as a new type of electronic device, and various surface acoustic wave (SAW) filters, delay lines, resonators, sensors, amplifiers, convolvers, memories, encoders, acoustic-optical devices, and various conversion devices have been studied so far. Its various devices have been widely used in the military, defense, and daily national economy [21].Considering the boundary conditions: (1) with the nature of surface waves, the energy of the wave is concentrated near the surface of the medium. Inside the medium at infinite depth from the surface of the medium, the displacement of the mass of the surface wave must be zero. Usually, there is no stress at the surface; i.e., the stressT = 0 [22].(11)T=ΔyΔx.∂2Ω∂u2.∂2Ω∂v2.Different piezoelectric substrate materials and their tangential directions can excite waves with different transmission modes. The common ones are horizontal shear waves (SH-SAW), lamb waves, Rayleigh waves, thickness-shear vibration mode (TSM), etc. Most of the surface wave passive wireless sensor temperature sensors in the current literature on sound surface waves use Rayleigh waves. The Rayleigh wave, which is a superposition of transverse and longitudinal waves, propagates in the piezoelectric material, and its amplitude decays exponentially with the increase of distance from the surface, so its energy is mostly (90%) concentrated in an acoustic wavelength range from the surface of the piezoelectric material. At the same time, the Rayleigh wave propagates on the surface of the piezoelectric medium, and its phase velocity is frequency independent of the nondispersive wave. Common piezoelectric substrate material for SAW temperature sensors is quartz, where quartz is only suitable for the temperature range below 300°C. The forked-finger transducer converts the electromagnetic wave received by the antenna into a SAW in the SAW sensor and converts the SAW in the resonant cavity into an electromagnetic wave. The typical structure of a fork-finger transducer is composed of several pairs of metal film electrodes arranged in parallel at periodic intervals. When a guided electromagnetic wave is loaded on the fork-finger electrodes, a potential difference is generated due to the opposite electrical properties between adjacent electrodes. This potential difference creates a high-frequency electric field on the surface of the piezoelectric substrate as well as inside. This high-frequency electric field excites an elastic surface wave at the surface of the piezoelectric substrate through an inverse piezoelectric effect. Because the fork-finger transducers are periodically arranged, the acoustic surfaces excited by adjacent electrodes can superimpose enhancement at a particular frequency according to the wave interference principle [23]. The forked-finger transducer efficiently converts electromagnetic waves into sound surface waves based on the above principle, which is based on the principle of the formula:(12)M=1n∑i=1nXiYi.x−μσ.The wireless transceiver experimental system for passive, wireless temperature sensors is shown in Figure1.Figure 1
Wireless transceiver experimental system for passive, wireless temperature sensors.In the receive cycle, the transceiver switch takes the signal reflected from the passive wireless surface wave, amplifies it, and downconverts it by 1 MHz. The following modulated signal, after data acquisition by the computer, is sent to the computer for real-time analysis and processing. The previous analysis shows that when the echo frequency is close to the natural frequency, the amplitude of the modulated wave increases greatly; when the echo frequency is far from the natural frequency, the amplitude of the modulated wave attenuates rapidly. Since the energy reflected by the passive wireless sensor is very small, the transmitting frequency is very close to the intrinsic frequency. In the system, the inclusion of an electronic switch after the signal source has a special significance. And according to the polarization matched antenna, the radar ranging equation when the maximum radiation direction is aligned with the maximum reception direction is given by the following equation [24]:(13)M=1n∑i=1nXiYi+∑i=1nXi−X¯2.The energy is mainly concentrated in the discrete spectrum on both sides of the excitation signal frequency; the longer the period of the excitation signal (T), the denser the discrete spectrum; the smaller the duty cycle of the excitation signal (T/T1), the wider the expansion of the spectrum. Therefore, when the excitation signal frequency is close to the intrinsic frequency of the device, a wider excitation signal period and a small duty cycle can be selected to obtain an output signal close enough to the intrinsic frequency of the device and larger after the sensor [25]. The composition of the array passive wireless sensing system is shown in Figure 2.Figure 2
Array passive wireless sensing system composition.


### 3.4. English Reading Model Construction

English reading is an important skill in English learning for college students. The level of English reading ability is well reflected in reading performance. English reading ability is affected by many factors, among which anxiety is one of the most important emotional factors for learners. Metacognition in foreign language reading can help learners to effectively self-regulate and self-regulate in reading learning, which has the potential to improve reading comprehension. The strategies adopted in reading under metacognitive guidance are reading metacognitive strategies, which are used to guide and control cognitive processes and strategies, and are considered to be higher-order processes that have cognitive management functions for language application. The purpose of this study was to explore the relationship between English reading metacognitive strategies, reading anxiety, and English reading performance using a structural equation modeling approach. The study predicts that reading metacognitive strategies not only directly and positively affect reading performance, but also indirectly affect reading performance by reducing the level of reading anxiety; i.e., reading metacognitive strategies negatively affect reading anxiety and anxiety negatively affects reading performance, and reading anxiety plays a mediating role between reading metacognitive strategies and reading performance, either partially or fully. The flowchart is shown in Figure3.Figure 3
English reading model construction.Since structural equation modeling has the statistical prerequisites of normal data distribution and linear and correlated variables, the conclusions are not credible if the data are severely skewed, nonlinear, or pseudocorrelated. If the data are severely skewed, nonlinear, or pseudocorrelated, the findings are not credible, no matter how well the statistical indicators meet the requirements. Therefore, before constructing the model, the normality of reading metacognitive strategies, reading anxiety, and reading achievement of non-English majors was tested. If the absolute values of the two indicators of skewness and kurtosis are less than one, the data are essentially normally distributed. The skewness and kurtosis coefficients of all variables in this study were close to 0 and their absolute values were less than 1. The data were normally or nearly normally distributed and suitable for the next step of validation factor analysis. There are two main types of concept map evaluation: objective evaluation and subjective evaluation. Objective assessment mainly consists of objective questions such as fill-in-the-blank, multiple-choice, and connect-the-dots, which focus on concepts, relationships between concepts, and expansion of concepts and are suitable for assessing the organization of the learner’s knowledge structure. The advantage of this approach is that it is more objective, direct, and easy to use. The use of this type of assessment in English reading can help learners to understand the meaning of words and phrases and to grasp the main concepts of the text. Subjective assessment is a form of a diagrammatic representation of the structure of knowledge about a topic by creating a concept map based on the content and connecting two nodes with appropriate connectives to form a proposition. This form of schematic representation has an impact on the receiver’s thinking because of its unique conceptual linking constructs that cognitively organize knowledge. The advantage of this method is that it gives learners the space to develop their knowledge, shows the structure and organization of knowledge, helps learners to rationalize their knowledge, and uncovers new knowledge and is suitable for assessing learners’ creative thinking. The use of this evaluation method has implications for expanding the evaluation system of concept maps in English reading and provides a new technique for enriching the evaluation of English reading ability.


## 3.1. Particle Swarm Algorithm

The particle swarm algorithm is an intelligent optimization algorithm that simulates the foraging of birds. It uses a speed-position model to search and adjust according to the fitness of the individual. Suppose a scenario like this: there is only one piece of food in an area, and a group of birds is searching for food at random. They do not know the specific location of the food, but they know the bird closest to the food, so they search for this bird. The surrounding area is the best way. We think of each bird as a particle without weight and volume, but containing position and velocity information. The particle moves at a certain speed in the search space. In the particle swarm algorithm, each particle corresponds to a fitness value, and the fitness value is determined by the objective function; each particle has a speed limit to constrain their search direction and position; all particles follow the individual optimal value and global optimal value to continuously update their speed and position. Among them, the individual optimal value is the optimal solution found by the particles, and the global optimal value is the optimal solution found by the entire population. Each search needs to compare the current fitness and optimal solution with the historical record value. If it exceeds the historical optimal value, we update the historical optimal position and optimal solution of the individual and the population.Assuming that the search space of the particle swarm is D-dimensional, there areM particles in the particle swarm (usually the number of particles is set to 5–10 times the dimension of the problem), and the position vector of the i-th particle is(1)Xi=xi0xi1xi2xi3i=0,1,2,…,M−1.This formula can better explain the construction of the article reader, and as the core formula of the article, it plays a role of connecting the previous and the next.The velocity vector of thei-th particle is(2)Vi=vi0vi1vi2vi3i=0,1,2,…,M−1The individual optimal value of the particle swarm is(3)Pbest=pi0pi1pi2pi3.The global optimal value of the particle swarm is(4)Gbest=pg1pg2pg3pg4g=0,1,2,…,M−1.All particles update their speed and position according to the following formula:(5)vijk+1=wvijk−1+c1r1xijk−Pbestik+c2r2xijk−Gbestik,xijk=xijk−1+vijk−2.Among them, the subscripti represents the i-th particle, the subscript j represents the j-th dimension of the particle, k represents the current iteration number, w represents the inertia weight, c1 and c2 are acceleration constants, generally, c1 = c2 = 2, and c1 is used to adjust the step length of particles flying to the individual optimal value, and c2 adjusts the step length of particles flying to the global optimal value. r1 and r2 are random numbers between (0, 1).


## 3.2. The Method of Polarization Parameter Extraction

The electromagnetic-polarization response calculation formula in a uniformly polarized half-space is a quaternary function of polarization parameters, and the functional relationship between the two is(6)Bt=ftX•ftX−1.In the formula,Bt is the theoretical electromagnetic-polarization magnetic field response, and X = (x1, x2, x3, x4) represents the four polarization parameters. We use △B to represent the difference between the measured electromagnetic-polarization response and the theoretical electromagnetic-polarization response, which is expressed as(7)ΔB=Bm−Bt.In the formula,Bm represents the measured electromagnetic-polarization response.When △B is very small or even tends to zero, it can be considered that the polarization parameter in the theoretical electromagnetic-polarization response is the parameter in the measured electromagnetic-polarization response. However, because the electromagnetic-polarization response calculation formula is nonlinear, and the polarization response curve does not conform to the law of the basic function, it cannot be solved directly by curve fitting. Therefore, this paper adopts a particle swarm algorithm. The fitness of the particle swarm algorithm is a sign of evaluating the quality of each particle. The minimum optimization problem established by extracting the polarization parameters corresponds to the fitness function of the particle swarm algorithm, so the fitness function is constructed as follows:(8)Yx=1n−1∏i=0n−1Bti−Bmi.


## 3.3. Rules of Operation of the Acoustic Surface Wave Wireless Sensor

Matched filters are widely used in radar, frequency selection filters, delay lines, and resonators in mobile phones and televisions. As a very competitive electronic device it has had a significant impact on the electronics industry, becoming an irreplaceable product. Piezoelectric SAWDs have many advantages: small size, lightweight, and high integration. Because SAWs are 100,000 times slower than electromagnetic waves, their size is greatly reduced compared to electromagnetic wave devices of the same frequency and they have design flexibility and ease of signal processing, repeatability, consistency, easy mass production, and extremely low cost. This is due to its fabrication using single crystal materials and with a planar process; the advantages of signal processing in real-time, high reliability, low power consumption, low cost, high-frequency large bandwidth, and being hour wide are difficult to match with traditional charge-coupled devices (CCD) and digital technology.Therefore, it is now widely used in the field of communication and microwave technology. From the equation of translational motion of a mass in an elastomer, the Verner (Verner) equation, we have(9)T=∑i=1nXi2+∑i=1nXiYi.When the body force is zero, its tensor form is(10)A=∑i=1nXi2+∑i=1nXiYi+1nx−μσ.In the literature, “waves propagating along the smooth surface of an elastomer” were discovered, and the surface waves containing the longitudinal (P-wave) component and the transverse (SV-wave) component with the displacement direction of the mass perpendicular to the surface were later called Rayleigh waves. Since the 1970s, piezoelectric surface acoustic wave devices (SAWDs) have developed rapidly as a new type of electronic device, and various surface acoustic wave (SAW) filters, delay lines, resonators, sensors, amplifiers, convolvers, memories, encoders, acoustic-optical devices, and various conversion devices have been studied so far. Its various devices have been widely used in the military, defense, and daily national economy [21].Considering the boundary conditions: (1) with the nature of surface waves, the energy of the wave is concentrated near the surface of the medium. Inside the medium at infinite depth from the surface of the medium, the displacement of the mass of the surface wave must be zero. Usually, there is no stress at the surface; i.e., the stressT = 0 [22].(11)T=ΔyΔx.∂2Ω∂u2.∂2Ω∂v2.Different piezoelectric substrate materials and their tangential directions can excite waves with different transmission modes. The common ones are horizontal shear waves (SH-SAW), lamb waves, Rayleigh waves, thickness-shear vibration mode (TSM), etc. Most of the surface wave passive wireless sensor temperature sensors in the current literature on sound surface waves use Rayleigh waves. The Rayleigh wave, which is a superposition of transverse and longitudinal waves, propagates in the piezoelectric material, and its amplitude decays exponentially with the increase of distance from the surface, so its energy is mostly (90%) concentrated in an acoustic wavelength range from the surface of the piezoelectric material. At the same time, the Rayleigh wave propagates on the surface of the piezoelectric medium, and its phase velocity is frequency independent of the nondispersive wave. Common piezoelectric substrate material for SAW temperature sensors is quartz, where quartz is only suitable for the temperature range below 300°C. The forked-finger transducer converts the electromagnetic wave received by the antenna into a SAW in the SAW sensor and converts the SAW in the resonant cavity into an electromagnetic wave. The typical structure of a fork-finger transducer is composed of several pairs of metal film electrodes arranged in parallel at periodic intervals. When a guided electromagnetic wave is loaded on the fork-finger electrodes, a potential difference is generated due to the opposite electrical properties between adjacent electrodes. This potential difference creates a high-frequency electric field on the surface of the piezoelectric substrate as well as inside. This high-frequency electric field excites an elastic surface wave at the surface of the piezoelectric substrate through an inverse piezoelectric effect. Because the fork-finger transducers are periodically arranged, the acoustic surfaces excited by adjacent electrodes can superimpose enhancement at a particular frequency according to the wave interference principle [23]. The forked-finger transducer efficiently converts electromagnetic waves into sound surface waves based on the above principle, which is based on the principle of the formula:(12)M=1n∑i=1nXiYi.x−μσ.The wireless transceiver experimental system for passive, wireless temperature sensors is shown in Figure1.Figure 1
Wireless transceiver experimental system for passive, wireless temperature sensors.In the receive cycle, the transceiver switch takes the signal reflected from the passive wireless surface wave, amplifies it, and downconverts it by 1 MHz. The following modulated signal, after data acquisition by the computer, is sent to the computer for real-time analysis and processing. The previous analysis shows that when the echo frequency is close to the natural frequency, the amplitude of the modulated wave increases greatly; when the echo frequency is far from the natural frequency, the amplitude of the modulated wave attenuates rapidly. Since the energy reflected by the passive wireless sensor is very small, the transmitting frequency is very close to the intrinsic frequency. In the system, the inclusion of an electronic switch after the signal source has a special significance. And according to the polarization matched antenna, the radar ranging equation when the maximum radiation direction is aligned with the maximum reception direction is given by the following equation [24]:(13)M=1n∑i=1nXiYi+∑i=1nXi−X¯2.The energy is mainly concentrated in the discrete spectrum on both sides of the excitation signal frequency; the longer the period of the excitation signal (T), the denser the discrete spectrum; the smaller the duty cycle of the excitation signal (T/T1), the wider the expansion of the spectrum. Therefore, when the excitation signal frequency is close to the intrinsic frequency of the device, a wider excitation signal period and a small duty cycle can be selected to obtain an output signal close enough to the intrinsic frequency of the device and larger after the sensor [25]. The composition of the array passive wireless sensing system is shown in Figure 2.Figure 2
Array passive wireless sensing system composition.


## 3.4. English Reading Model Construction

English reading is an important skill in English learning for college students. The level of English reading ability is well reflected in reading performance. English reading ability is affected by many factors, among which anxiety is one of the most important emotional factors for learners. Metacognition in foreign language reading can help learners to effectively self-regulate and self-regulate in reading learning, which has the potential to improve reading comprehension. The strategies adopted in reading under metacognitive guidance are reading metacognitive strategies, which are used to guide and control cognitive processes and strategies, and are considered to be higher-order processes that have cognitive management functions for language application. The purpose of this study was to explore the relationship between English reading metacognitive strategies, reading anxiety, and English reading performance using a structural equation modeling approach. The study predicts that reading metacognitive strategies not only directly and positively affect reading performance, but also indirectly affect reading performance by reducing the level of reading anxiety; i.e., reading metacognitive strategies negatively affect reading anxiety and anxiety negatively affects reading performance, and reading anxiety plays a mediating role between reading metacognitive strategies and reading performance, either partially or fully. The flowchart is shown in Figure3.Figure 3
English reading model construction.Since structural equation modeling has the statistical prerequisites of normal data distribution and linear and correlated variables, the conclusions are not credible if the data are severely skewed, nonlinear, or pseudocorrelated. If the data are severely skewed, nonlinear, or pseudocorrelated, the findings are not credible, no matter how well the statistical indicators meet the requirements. Therefore, before constructing the model, the normality of reading metacognitive strategies, reading anxiety, and reading achievement of non-English majors was tested. If the absolute values of the two indicators of skewness and kurtosis are less than one, the data are essentially normally distributed. The skewness and kurtosis coefficients of all variables in this study were close to 0 and their absolute values were less than 1. The data were normally or nearly normally distributed and suitable for the next step of validation factor analysis. There are two main types of concept map evaluation: objective evaluation and subjective evaluation. Objective assessment mainly consists of objective questions such as fill-in-the-blank, multiple-choice, and connect-the-dots, which focus on concepts, relationships between concepts, and expansion of concepts and are suitable for assessing the organization of the learner’s knowledge structure. The advantage of this approach is that it is more objective, direct, and easy to use. The use of this type of assessment in English reading can help learners to understand the meaning of words and phrases and to grasp the main concepts of the text. Subjective assessment is a form of a diagrammatic representation of the structure of knowledge about a topic by creating a concept map based on the content and connecting two nodes with appropriate connectives to form a proposition. This form of schematic representation has an impact on the receiver’s thinking because of its unique conceptual linking constructs that cognitively organize knowledge. The advantage of this method is that it gives learners the space to develop their knowledge, shows the structure and organization of knowledge, helps learners to rationalize their knowledge, and uncovers new knowledge and is suitable for assessing learners’ creative thinking. The use of this evaluation method has implications for expanding the evaluation system of concept maps in English reading and provides a new technique for enriching the evaluation of English reading ability.


## 4. Experimental Procedure and Analysis

### 4.1. The Extraction Results of Polarization Parameters

To extract the polarization parameters, first, according to the actual situation, within the reasonable value range of the polarization parameters, a theoretical polarization model is set in a uniform half-space. Its zero-frequency resistivity is 1 Ωm and the charging rate is 0.8. The time constant is 0.005 s, and the frequency correlation coefficient is 0.8. The electromagnetic-polarization response in a uniform half-space calculated using the above parameters is called the wave and size of the emission current in the theoretical electromagnetic-polarization response Bm1 theoretical model. Using Bm1 as the measured data, the particle swarm algorithm is used to extract the polarization parameters of Bm1 to verify the correctness of the algorithm.In the process of single parameter extraction, the parameter settings of the algorithm are shown in Table1. The number of iterations currently set is determined through multiple experiments. When the number of iterations is small, the fitness value will not reach the optimal value, which will affect polarization. After the algorithm iteration is completed, the single parameter extraction results are shown in Table 2. It can be seen from Table 2 that when the particle swarm algorithm is used to extract a single polarization parameter, within the allowable error range, the optimal parameter values of the four polarization parameters are consistent with the theoretical values; the corresponding fitness curve is shown in Figure 4. It can be seen from the figure that, in the iterative process, during the extraction of the four polarization parameters, the fitness value gradually decreases until it tends to zero. When the fitness value tends to 0, the electromagnetic-polarization response obtained by the inversion is consistent with the theoretical electromagnetic-polarization response, which verifies the accuracy of the particle swarm algorithm for extracting a single parameter.Table 1
Parameter settings of particle swarm algorithm.
SizepopvlimitxlimitMaxgenC1C2Dwp012(0, 0.002)(0.8, 0.9)3501210.87m12(0, 0.001)(0.83, 1)5003320.87τ11(0, 0.004)(0.9, 1)5003410.89c12(0, 0.002)(0.8, 1)5004210.92Table 2
Single parameter solution results.
Polarization parameterτcp0mTheoretical value0.00490.721.010.76Optimal parameter value0.00510.7210.79Relative error5%01%3.5%Figure 4
Fitness curve of single parameter extraction. (a) The fitness of zero-frequency resistivity extraction. (b) The fitness of charging rate extraction. (c) The fitness of time constant extraction. (d) The fitness of frequency correlation coefficient extraction.
(a)(b)(c)(d)


### 4.2. English Reading Experiment of Surface Acoustic Wave Wireless Sensor

In this paper, an 89.6 MHz SAW resonator is used as a passive wireless sensor with aQ value of 3000 and a device resonance loss of 4 dB. The isolation of both the electronic and transceiver switches exceeds −40 dB. Since the reflected echo steady-state build-up duration is only a few hundred microseconds, the amplitude of this reflected signal rapidly decreases and the duration sharply shortens as the sensing distance increases. Therefore, it is important to ensure that the data acquisition is fast enough to allow for very precise determination of the edge points and further determination of the intrinsic frequency of each sensor. Since the temperature change is a slow process, multiple averaging is used for the results. This effectively eliminates random disturbances and improves the measurement distance. The efficiency is shown in Figure 5.Figure 5
Efficiency of passive wireless sensors.An intermittent sinusoidal pulse train signal is used to excite a delayed single-port acoustic surface wave resonator, whose reflected wave is an oscillating signal, and the frequency of the oscillating signal is the intrinsic frequency of the acoustic surface wave device. The method of using the detection of the oscillating wave frequency to determine the intrinsic frequency with temperature improves the distance and sensitivity of the remote sensing measurement of the sensing system. The sensors with different delay times can be formed into an array of passive wireless sensors with an arbitrary distribution. SAW devices can not only sense temperature changes, but also measure a variety of parameters such as stress, strain, acceleration, and displacement when properly designed.Due to the introduction of the phase accumulator, the structure is a digitally controlled oscillator. n-bit variable modulus counters and phase registers form the address counter. The N-bit variable modulus counter and the phase registers form the address counter. The advance function can be used as a phase wheel in the structure. Each selected point on the phase wheel corresponds to the equivalent point of one cycle of the sinusoidal waveform. As the vector rotates along with the wheel, a corresponding sinusoidal output is generated. One revolution of the vector along with the wheel at a constant speed and one full cycle of the sinusoidal output is obtained. The phase accumulator is used to provide the equivalent of a linear rotation along the phase wheel vector. The content of the phase accumulator corresponds to the points on the output sine wave period. The number of discrete phase points contained in the wheel is determined by the resolution of the phase accumulator. The output of the phase accumulator is a linear ramp wave, not a sine wave, so a phase-to-amplitude lookup table is used to convert the instantaneous output value of the phase accumulator to sinusoidal amplitude information, and the efficiency of the conversion is plotted in Figure6.Figure 6
Efficiency graph after conversion.Compared to the score reports of traditional reading tests, the diagnostic feedback tailored to this study has the following advantages. On the one hand, it provides students with more microlevel diagnostic information in addition to the total score. Each student receives diagnostic feedback that provides a clear understanding of his or her position about the group and his or her mastery of reading skills, as well as test analysis instructions and guidance for later learning. This information helps students to understand their strengths and weaknesses at different levels and facilitates their future English learning. On the other hand, the diagnostic feedback is presented in a graphic format, which reflects the students’ diagnostic results in a more attractive way than a single boring textual description and helps students to understand the relevant diagnostic information. The final result of the study is graphic feedback. The results of the study have certain implications for English reading teaching: teachers can integrate the eight reading attributes into their reading teaching and design reading exercises for each attribute. The cluster-level diagnostic information can be used by teachers and administrators to adjust the English reading syllabus and instructional focus and to develop different interventions for students in different level groups. Unlike traditional tests that report a single score, the results of the reading cognitive diagnostic assessment provide teachers and students with more information about their reading problems. A comparison of the efficiency of English reading model construction after applying the SAW wireless sensor is shown in Figure7.Figure 7
Comparison of efficiency after applying SAW wireless sensors.Firstly, the wireless temperature sensor based on the designed miniaturized microstrip antenna was tested using the built test system, and the test results showed that the SAW temperature sensor using the miniaturized microstrip antenna can detect the temperature change wirelessly and meet the demand of wireless detection on the surface of metal parts. Then the LGS high-temperature SAW wireless sensor based on dipole antenna was tested, and it was demonstrated that the LGS high-temperature SAW wireless sensor is capable of wireless temperature detection at about 600°C. The use of the designed sensor antenna I and quartz SAW resonator structure passive wireless SAW temperature sensor can be more conducive to the construction of the English reading model, because the SAW resonator and antenna are placed at the same time to detect the surface of the metal parts, so the antenna is also affected by the ambient temperature changes. The antenna impedance bandwidth of the two designed sensors is narrow, so before conducting the sensor temperature test, it is necessary to first test the effect of the antenna impedance bandwidth with temperature change to ensure that the resonant frequency of the SAW resonator changes with temperature within the −10 dB impedance bandwidth of the antenna.


### 4.3. Data Analysis and Correlation Analysis of Pretest and Posttest Reading Comprehension

To compare whether there are significant differences between the pre- and posttests of the students’ reading scores during the entire action research stage, SPSS19.0 will conduct a paired-sampleT-test on the student’s two test scores. The results are shown in Table 3.Table 3
Paired-sample statistics.
NStandard deviationStandard error of the meanMeanPretest584.010.5429Posttest583.940.5338Table3 is a descriptive statistics table for the pretest and posttest of students’ reading comprehension. As shown in the table, the average score of the pretest is 29, and the average of the posttest is 38. From the comparison of the average value, the posttest is significantly higher than the pretest, and the average is higher. Throughout the action research stage, students’ reading scores have significant differences, with a Sig value of 0.000 (t = −15.357, df = 55, p<0.05), and the posttest scores are significantly higher than the pretest scores. It can be seen from the descriptive statistics table of the three sets of data that the p values are all less than 0.05, indicating that, after 18 weeks of action research on English reading teaching based on lexical theory, students’ sensitivity and recognition of lexical chunks have increased, and they are learning to distinguish and classify. After the vocabulary block is accurately used in reading problem solving, the reading performance is significantly improved, and the reading comprehension ability is improved.As shown in Table4, there is a significant and moderate positive correlation between the mastery of vocabulary chunks and reading performance after training (r = 0.373, p<0.05), indicating that mastery of vocabulary chunks will affect the reading performance. The results of the correlation analysis between the mastery of lexical chunks and the reading scores of college students are all moderately positive, and the posttest is higher than the pretest, which proves that the conduct of this research is logical. Figure 8 shows the comparison of the pretest and posttest scores of 58 subjects.Table 4
Correlation analysis of posttest.
Block posttestPostreading testBlock posttestN5858Pearson correlation0.980.42Significance (bilateral)0.0050.005Postreading testN5858Pearson correlation0.420.98Significance (bilateral)0.0050.005Figure 8
Comparison of pretest and posttest scores of 58 subjects.


## 4.1. The Extraction Results of Polarization Parameters

To extract the polarization parameters, first, according to the actual situation, within the reasonable value range of the polarization parameters, a theoretical polarization model is set in a uniform half-space. Its zero-frequency resistivity is 1 Ωm and the charging rate is 0.8. The time constant is 0.005 s, and the frequency correlation coefficient is 0.8. The electromagnetic-polarization response in a uniform half-space calculated using the above parameters is called the wave and size of the emission current in the theoretical electromagnetic-polarization response Bm1 theoretical model. Using Bm1 as the measured data, the particle swarm algorithm is used to extract the polarization parameters of Bm1 to verify the correctness of the algorithm.In the process of single parameter extraction, the parameter settings of the algorithm are shown in Table1. The number of iterations currently set is determined through multiple experiments. When the number of iterations is small, the fitness value will not reach the optimal value, which will affect polarization. After the algorithm iteration is completed, the single parameter extraction results are shown in Table 2. It can be seen from Table 2 that when the particle swarm algorithm is used to extract a single polarization parameter, within the allowable error range, the optimal parameter values of the four polarization parameters are consistent with the theoretical values; the corresponding fitness curve is shown in Figure 4. It can be seen from the figure that, in the iterative process, during the extraction of the four polarization parameters, the fitness value gradually decreases until it tends to zero. When the fitness value tends to 0, the electromagnetic-polarization response obtained by the inversion is consistent with the theoretical electromagnetic-polarization response, which verifies the accuracy of the particle swarm algorithm for extracting a single parameter.Table 1
Parameter settings of particle swarm algorithm.
SizepopvlimitxlimitMaxgenC1C2Dwp012(0, 0.002)(0.8, 0.9)3501210.87m12(0, 0.001)(0.83, 1)5003320.87τ11(0, 0.004)(0.9, 1)5003410.89c12(0, 0.002)(0.8, 1)5004210.92Table 2
Single parameter solution results.
Polarization parameterτcp0mTheoretical value0.00490.721.010.76Optimal parameter value0.00510.7210.79Relative error5%01%3.5%Figure 4
Fitness curve of single parameter extraction. (a) The fitness of zero-frequency resistivity extraction. (b) The fitness of charging rate extraction. (c) The fitness of time constant extraction. (d) The fitness of frequency correlation coefficient extraction.
(a)(b)(c)(d)


## 4.2. English Reading Experiment of Surface Acoustic Wave Wireless Sensor

In this paper, an 89.6 MHz SAW resonator is used as a passive wireless sensor with aQ value of 3000 and a device resonance loss of 4 dB. The isolation of both the electronic and transceiver switches exceeds −40 dB. Since the reflected echo steady-state build-up duration is only a few hundred microseconds, the amplitude of this reflected signal rapidly decreases and the duration sharply shortens as the sensing distance increases. Therefore, it is important to ensure that the data acquisition is fast enough to allow for very precise determination of the edge points and further determination of the intrinsic frequency of each sensor. Since the temperature change is a slow process, multiple averaging is used for the results. This effectively eliminates random disturbances and improves the measurement distance. The efficiency is shown in Figure 5.Figure 5
Efficiency of passive wireless sensors.An intermittent sinusoidal pulse train signal is used to excite a delayed single-port acoustic surface wave resonator, whose reflected wave is an oscillating signal, and the frequency of the oscillating signal is the intrinsic frequency of the acoustic surface wave device. The method of using the detection of the oscillating wave frequency to determine the intrinsic frequency with temperature improves the distance and sensitivity of the remote sensing measurement of the sensing system. The sensors with different delay times can be formed into an array of passive wireless sensors with an arbitrary distribution. SAW devices can not only sense temperature changes, but also measure a variety of parameters such as stress, strain, acceleration, and displacement when properly designed.Due to the introduction of the phase accumulator, the structure is a digitally controlled oscillator. n-bit variable modulus counters and phase registers form the address counter. The N-bit variable modulus counter and the phase registers form the address counter. The advance function can be used as a phase wheel in the structure. Each selected point on the phase wheel corresponds to the equivalent point of one cycle of the sinusoidal waveform. As the vector rotates along with the wheel, a corresponding sinusoidal output is generated. One revolution of the vector along with the wheel at a constant speed and one full cycle of the sinusoidal output is obtained. The phase accumulator is used to provide the equivalent of a linear rotation along the phase wheel vector. The content of the phase accumulator corresponds to the points on the output sine wave period. The number of discrete phase points contained in the wheel is determined by the resolution of the phase accumulator. The output of the phase accumulator is a linear ramp wave, not a sine wave, so a phase-to-amplitude lookup table is used to convert the instantaneous output value of the phase accumulator to sinusoidal amplitude information, and the efficiency of the conversion is plotted in Figure6.Figure 6
Efficiency graph after conversion.Compared to the score reports of traditional reading tests, the diagnostic feedback tailored to this study has the following advantages. On the one hand, it provides students with more microlevel diagnostic information in addition to the total score. Each student receives diagnostic feedback that provides a clear understanding of his or her position about the group and his or her mastery of reading skills, as well as test analysis instructions and guidance for later learning. This information helps students to understand their strengths and weaknesses at different levels and facilitates their future English learning. On the other hand, the diagnostic feedback is presented in a graphic format, which reflects the students’ diagnostic results in a more attractive way than a single boring textual description and helps students to understand the relevant diagnostic information. The final result of the study is graphic feedback. The results of the study have certain implications for English reading teaching: teachers can integrate the eight reading attributes into their reading teaching and design reading exercises for each attribute. The cluster-level diagnostic information can be used by teachers and administrators to adjust the English reading syllabus and instructional focus and to develop different interventions for students in different level groups. Unlike traditional tests that report a single score, the results of the reading cognitive diagnostic assessment provide teachers and students with more information about their reading problems. A comparison of the efficiency of English reading model construction after applying the SAW wireless sensor is shown in Figure7.Figure 7
Comparison of efficiency after applying SAW wireless sensors.Firstly, the wireless temperature sensor based on the designed miniaturized microstrip antenna was tested using the built test system, and the test results showed that the SAW temperature sensor using the miniaturized microstrip antenna can detect the temperature change wirelessly and meet the demand of wireless detection on the surface of metal parts. Then the LGS high-temperature SAW wireless sensor based on dipole antenna was tested, and it was demonstrated that the LGS high-temperature SAW wireless sensor is capable of wireless temperature detection at about 600°C. The use of the designed sensor antenna I and quartz SAW resonator structure passive wireless SAW temperature sensor can be more conducive to the construction of the English reading model, because the SAW resonator and antenna are placed at the same time to detect the surface of the metal parts, so the antenna is also affected by the ambient temperature changes. The antenna impedance bandwidth of the two designed sensors is narrow, so before conducting the sensor temperature test, it is necessary to first test the effect of the antenna impedance bandwidth with temperature change to ensure that the resonant frequency of the SAW resonator changes with temperature within the −10 dB impedance bandwidth of the antenna.


## 4.3. Data Analysis and Correlation Analysis of Pretest and Posttest Reading Comprehension

To compare whether there are significant differences between the pre- and posttests of the students’ reading scores during the entire action research stage, SPSS19.0 will conduct a paired-sampleT-test on the student’s two test scores. The results are shown in Table 3.Table 3
Paired-sample statistics.
NStandard deviationStandard error of the meanMeanPretest584.010.5429Posttest583.940.5338Table3 is a descriptive statistics table for the pretest and posttest of students’ reading comprehension. As shown in the table, the average score of the pretest is 29, and the average of the posttest is 38. From the comparison of the average value, the posttest is significantly higher than the pretest, and the average is higher. Throughout the action research stage, students’ reading scores have significant differences, with a Sig value of 0.000 (t = −15.357, df = 55, p<0.05), and the posttest scores are significantly higher than the pretest scores. It can be seen from the descriptive statistics table of the three sets of data that the p values are all less than 0.05, indicating that, after 18 weeks of action research on English reading teaching based on lexical theory, students’ sensitivity and recognition of lexical chunks have increased, and they are learning to distinguish and classify. After the vocabulary block is accurately used in reading problem solving, the reading performance is significantly improved, and the reading comprehension ability is improved.As shown in Table4, there is a significant and moderate positive correlation between the mastery of vocabulary chunks and reading performance after training (r = 0.373, p<0.05), indicating that mastery of vocabulary chunks will affect the reading performance. The results of the correlation analysis between the mastery of lexical chunks and the reading scores of college students are all moderately positive, and the posttest is higher than the pretest, which proves that the conduct of this research is logical. Figure 8 shows the comparison of the pretest and posttest scores of 58 subjects.Table 4
Correlation analysis of posttest.
Block posttestPostreading testBlock posttestN5858Pearson correlation0.980.42Significance (bilateral)0.0050.005Postreading testN5858Pearson correlation0.420.98Significance (bilateral)0.0050.005Figure 8
Comparison of pretest and posttest scores of 58 subjects.


## 5. Conclusion

In this paper, a miniaturized microstrip antenna is designed for an epi-acoustic wave wireless sensor, a test platform is built, and the signal-to-noise ratio of the test signal is improved by using the time-domain windowing method, and finally, the performance of the epi-acoustic wave wireless sensor is tested wirelessly at room temperature and high temperature. The improvement of the test signal by the time-domain window technique is investigated, and the results show that the multiplicative, as well as additive interference in the wireless test, can be significantly suppressed by using the time domain plus window, thus improving the signal-to-noise ratio of the test signal. The wireless response signal of the epi-acoustic wave sensor at different distances is investigated by using the prepared microstrip antenna and the built test platform. The results show that the sensor antenna I can achieve wireless detection at a distance of 120 cm and the sensor antenna II can achieve wireless detection at a distance of 80 cm for the Yagi-Udaiten detection antenna at a transmitting power of −5 dBm. Using the designed microstrip antenna, the response of the quartz SAW sensor at different temperatures is investigated. The results show that the resonant frequency of the SAW sensor based on the antenna I decreases monotonically from 915.349 MHz to 914.737 MHz as the temperature rises from 35°C to 158°C. Its resonant frequency variation with temperature can be used in a 2nd order fitting equation; after a series of operations, the efficiency of the construction of the English reading model tends to be more optimized. Online English reading models can help college students better understand reading materials. The online English reading model has a positive effect on the reading comprehension level of college students. Students’ knowledge of word blocks is expanded, and the frequency of using word blocks for reading learning has increased. At the same time, students’ reading scores have been improved. Through comparative analysis of data, the posttest scores of reading comprehension are significantly higher than the pretest scores, and there is a significant difference (p<0.05). Therefore, the online English reading model can improve the English reading performance of college students. The improvement plan of the reader is to optimize the construction algorithm of the reader, to make the reader more communicative, to make the reader more readable, and to make the reader’s experience better.



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*Source: 1633781-2022-01-11.xml*