Real-time Monitoring Applications Research Articles

Accuracy Evaluation of Multicomponent Spectrophotometry and Its Application in Real-Time Monitoring of the Degradation of Dyes in Mixed Solutions

Accuracy Evaluation of Multicomponent Spectrophotometry and Its Application in Real-Time Monitoring of the Degradation of Dyes in Mixed Solutions

From Multispectral-Stereo to Intraoperative Hyperspectral Imaging: a Feasibility Study

Abstract Spectral imaging allows to analyze optical tissue properties that are invisible to the naked eye. Different spectral setups have been introduced in the last years to allow intraoperative image-guidance. All systems suffer from different disadvantages, such as lack of real-time capability, limited spectral coverage or low resolution. We present a novel approach using two multispectral snapshot cameras covering different spectral ranges as a stereo-system. The proposed method allows for continuous hyperspectral imaging of the surgical scene, while also enabling 3D scene reconstruction. The study demonstrates the feasibility of this approach and its potential applications in real-time monitoring and tissue identification of surgical interventions. The paper also discusses the technical challenges and future directions for improving the imaging system.

Chitosan-Based Electrochemical Sensors for Pharmaceuticals and Clinical Applications.

Chitosan (CTS), a biocompatible and multifunctional material derived from chitin, has caught researchers' attention in electrochemical detection due to its unique properties. This review paper provides a comprehensive overview of the recent progress and applications of CTS-based electrochemical sensors in the analysis of pharmaceutical products and other types of samples, with a particular focus on the detection of medicinal substances. The review covers studies and developments from 2003 to 2023, highlighting the remarkable properties of CTS, such as biocompatibility, chemical versatility, and large surface area, that make it an excellent candidate for sensor modification. Combining CTS with various nanomaterials significantly enhances the detection capabilities of electrochemical sensors. Various types of CTS-based sensors are analyzed, including those utilizing carbon nanomaterials, metallic nanoparticles, conducting polymers, and molecularly imprinted CTS. These sensors exhibit excellent sensitivity, selectivity, and stability, enabling the precise and reliable detection of medications. The manufacturing strategies used for the preparation of CTS-based sensors are described, the underlying detection mechanisms are elucidated, and the integration of CTS sensors with transducer systems is highlighted. The prospects of CTS-based electrochemical sensors are promising, with opportunities for miniaturization, simultaneous detection, and real-time monitoring applications.

Fast and Non-Invasive Evaluation of Yeast Viability in Fermentation Processes Using Raman Spectroscopy and Machine Learning

Fermentation processes used for producing alcoholic beverages such as beer, wine, and cider have a long history, having been developed early on across different civilizations. In most instances, yeast strains are used for fermentation processes, e.g., at breweries and wineries. Monitoring of yeast viability, cell count, and growth behavior is essential to ensure a controlled fermentation process. However, classical microbiological techniques to monitor fermentation process parameters are time-consuming and require sampling, along with the risk of contamination. Nowadays, industries are moving toward automation and digitalization. This necessitates state-of-the-art process analytical technologies to ensure an efficient and controlled process to obtain high-quality product outputs. Hence, there is a strong need for a fast, non-invasive, and generally applicable method to evaluate the viability of yeast cells during fermentation to warrant the standardization and purity of produced products in industrial applications. The aim of our study is to discriminate between viable and non-viable yeast in various culture media using Raman spectroscopy (RS) followed by data analysis with machine learning (ML) tools. These techniques allow for rapid, non-invasive analysis addressing the limitations of traditional methods. The present work primarily focuses on the evaluation of RS combined with predictive ML models in a non-real-time setting. Our goal is to adapt these techniques for future application in real-time monitoring and determination of yeast viability in biotechnological processes. We demonstrate that RS, in combination with ML, is a promising tool for non-invasive inline monitoring of fermentation processes.

Machine learning-assisted wearable triboelectric-electromagnetic vibration sensor for monitoring human rehabilitation training

Wearable vibration sensors for human motion monitoring are beneficial for doctors to observe the process of patient rehabilitation diagnosis and treatment in real-time. However, realizing smart sensing and self-powering of wearable devices is still challenging. Herein we developed a portable and reproducible triboelectric-electromagnetic vibration sensor (TE-VS) composed of a rolling-type triboelectric nanogenerator (R-TENG) and a slider-type electromagnetic generator (S-EMG), which aims to achieve highly sensitive detection and rapid identification of human motion. The ingenious mechanical design of the TE-VS enables the R-TENG component to adopt tangential contact friction and the S-EMG component to mutually move between magnets and coils in a non-contact way, which greatly improves the capture efficiency of weak motion, reduces material wear and increases the service life of the device. Multi-channel self-powered sensing signals fusion in a miniaturized space aims to enhance the algorithms detection accuracy of TE-VS. Importantly, the spring damping system improves the device's response speed to external vibrations and reduces the impact of shocks, which contributes to improving operation stability. The output responses of different frequency responses and different accelerations were characterized, which validated the potential of TE-VS in gesture recognition and limb disorder detection. Additionally, TE-VS was also demonstrated as a powerful vibration energy harvester to fully charge a μF-level commercial capacitor in a short time. The proposed TE-VS realizes battery-free operation, solves the long-term power supply problem of wearable devices, and facilitates the real-time monitoring application of human rehabilitation exercises. This is definitely a promising application for human rehabilitation medical devices.

On-site colorimetric food spoilage monitoring with smartphone embedded machine learning

Real-time and on-site food spoilage monitoring is still a challenging issue to prevent food poisoning. At the onset of food spoilage, microbial and enzymatic activities lead to the formation of volatile amines. Monitoring of these amines with conventional methods requires sophisticated, costly, labor-intensive, and time consuming analysis. Here, anthocyanins rich red cabbage extract (ARCE) based colorimetric sensing system was developed with the incorporation of embedded machine learning in a smartphone application for real-time food spoilage monitoring. FG-UV-CD100 films were first fabricated by crosslinking ARCE-doped fish gelatin (FG) with carbon dots (CDs) under UV light. The color change of FG-UV-CD100 films with varying ammonia vapor concentrations was captured in different light sources with smartphones of various brands, and a comprehensive dataset was created to train machine learning (ML) classifiers to be robust and adaptable to ambient conditions, resulting in 98.8% classification accuracy. Meanwhile, the ML classifier was embedded into our Android application, SmartFood++, enabling analysis in about 0.1 s without internet access, unlike its counterpart using cloud operation via internet. The proposed system was also tested on a real fish sample with 99.6% accuracy, demonstrating that it has a great advantage as a potent tool for on-site real-time monitoring of food spoilage by non-specialized personnel.

Abnormal heart rate detection through real-time heart monitoring application

Health monitoring that requires doctors and patients at the healthcare center may not be practical during the coronavirus disease 2019 (COVID-19) pandemic. Alternatively, mobile health (mHealth) should be embraced to minimize contact between patients and healthcare personnel. This research aims to enhance the detection of abnormal heart rate (HR) detection by developing a real-time heart rate monitoring (RTHM) application. Sixteen healthy adults participated in a physical real-time HR monitoring testbed. Participants HR was measured for three minutes resting and three minutes performing moderate-intensity physical activity. The results were compared with the polar beat app. Additionally, the energy consumption, the time taken to receive an alarm message, and an acceptance test were analyzed. The app is acceptably accurate, the mean absolute percentage error less than 2%. The response time to receive the alarm message is 30 seconds on average, which is under an acceptable range of medical standards. Moreover, the app is power efficient, 477 mW on average. Participants show a positive attitude towards using RTHM. RTHM is expected to provide a more plausible tool for monitoring the heart towards enhancing abnormal HR detection by promoting patient-oriented healthcare and minimizing sudden deaths due to heart failure.

Abnormal heart rate detection through real-time heart monitoring application

Health monitoring that requires doctors and patients at the healthcare center may not be practical during the coronavirus disease 2019 (COVID-19) pandemic. Alternatively, mobile health (mHealth) should be embraced to minimize contact between patients and healthcare personnel. This research aims to enhance the detection of abnormal heart rate (HR) detection by developing a real-time heart rate monitoring (RTHM) application. Sixteen healthy adults participated in a physical real-time HR monitoring testbed. Participants HR was measured for three minutes resting and three minutes performing moderate-intensity physical activity. The results were compared with the polar beat app. Additionally, the energy consumption, the time taken to receive an alarm message, and an acceptance test were analyzed. The app is acceptably accurate, the mean absolute percentage error less than 2%. The response time to receive the alarm message is 30 seconds on average, which is under an acceptable range of medical standards. Moreover, the app is power efficient, 477 mW on average. Participants show a positive attitude towards using RTHM. RTHM is expected to provide a more plausible tool for monitoring the heart towards enhancing abnormal HR detection by promoting patient-oriented healthcare and minimizing sudden deaths due to heart failure.

BDS Dual-Frequency Carrier Phase Multipath Hemispherical Map Model and Its Application in Real-Time Deformation Monitoring.

The BDS multipath delay error is highly related to the surrounding monitoring environment, which cannot be eliminated or mitigated by applying the double difference observation model. In the actual monitoring environment, due to the complexity of the BDS constellation, it is difficult for existing algorithms to consider GEO, IGSO, MEO and other different orbital types of satellites for real-time and efficient multipath error reduction. Therefore, we propose a novel BDS dual-frequency multipath error reduction method for real deformation monitoring for BDS considering various satellite orbit types. This method extracts the single error residual of each satellite based on the assumption of "zero mean" and divides the appropriate grid density of GEO and IGSO/MEO, respectively, to construct a dual-frequency multipath hemispherical map model suitable for BDS satellites with different orbital types. This method can realize the multipath error elimination of the observed values of different orbits and different frequencies. The results of simulation experiments and real deformation monitoring data demonstrate that this method can effectively eliminate low-frequency multipath delay errors in the observation domain and coordinate domain. After multipath correction, the precision of the horizontal coordinates and height coordinates are 1.7 mm and 4.6 mm. The precision of the horizontal coordinate and height coordinate is increased by 50% and 60%, respectively. The fixed rate of ambiguity increased by 5-7%.

Integrated Edge Deployable Fault Diagnostic Algorithm for the Internet of Things (IoT): A Methane Sensing Application.

The Internet of Things (IoT) is seen as the most viable solution for real-time monitoring applications. But the faults occurring at the perception layer are prone to misleading the data driven system and consume higher bandwidth and power. Thus, the goal of this effort is to provide an edge deployable sensor-fault detection and identification algorithm to reduce the detection, identification, and repair time, save network bandwidth and decrease the computational stress over the Cloud. Towards this, an integrated algorithm is formulated to detect fault at source and to identify the root cause element(s), based on Random Forest (RF) and Fault Tree Analysis (FTA). The RF classifier is employed to detect the fault, while the FTA is utilized to identify the source. A Methane (CH4) sensing application is used as a case-study to test the proposed system in practice. We used data from a healthy CH4 sensing node, which was injected with different forms of faults, such as sensor module faults, processor module faults and communication module faults, to assess the proposed model's performance. The proposed integrated algorithm provides better algorithm-complexity, execution time and accuracy when compared to FTA or standalone classifiers such as RF, Support Vector Machine (SVM) or K-nearest Neighbor (KNN). Metrics such as Accuracy, True Positive Rate (TPR), Matthews Correlation Coefficient (MCC), False Negative Rate (FNR), Precision and F1-score are used to rank the proposed methodology. From the field experiment, RF produced 97.27% accuracy and outperformed both SVM and KNN. Also, the suggested integrated methodology's experimental findings demonstrated a 27.73% reduced execution time with correct fault-source and less computational resource, compared to traditional FTA-detection methodology.

Dataplane-Based Fast Failover in SDN-Enabled Wide Area Measurement System of Smart Grid

In the wide area measurement system (WAMS) of smart grid, the real-time monitoring and protection applications have stringent requirements for the end-to-end transmission delays between PMUs and PDC, and fast failover is required to ensure the communication performance after link failures. In this work, the software-defined network (SDN) technology is utilized to enable datapath failover upon a link failure with a global view of the communication network. Then, a novel dataplane based fast failover (DFF) mechanism is proposed to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">directly</i> re-route the data packet in dataplane without interacting with the SDN controller. Based on the mathematical analysis over the WAMS topology features, the proposed DFF optimizes two procedures of failover: backup path construction, and backup path installation. Using the proposed backup path construction algorithms, the 3-approximate and (1+2 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\varepsilon$</tex-math></inline-formula> )-approximate ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$0&lt; \varepsilon &lt; 1$</tex-math></inline-formula> ) backup paths can be constructed, theoretically guaranteeing the data transmission delays both <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">during</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">after</i> failover. Using the proposed LinkID-based FF group table installation method, the conflict of forwarding rules between original and backup paths can be eliminated, while the storage cost is also optimized. The simulation results on six IEEE benchmark test power systems show that the proposed DFF mechanism could achieve lower data transmission delays during and after failover compared to the existing control plane based and dataplane based failover mechanisms.

An impedance-based chemiresistor for the real-time, simultaneous detection of gut microbiota-generated short-chain fatty acids

Short-chain fatty acids (SCFAs) are key molecules, produced by gut bacteria in the intestine, that are absorbed into the bloodstream and strongly influence human health. SCFA disruption and imbalances have been linked to many diseases; however, SCFAs are seldom used diagnostically as their detection requires extensive sample preparation and expensive equipment. In this work, an electrochemical sensor was developed to enable real-time (requiring less than 2 min per measurement), quantitative measurement of SCFAs from complex samples in liquid phase without the need for extraction, evaporation, or destruction. An impedance-based sensor for in vitro detection of acetic acid, propionic acid, and butyric acid (accounting for more than 85% of SCFAs in the intestine) was fabricated by the deposition of ZnO and polyvinyl alcohol (PVA) on the surface of a microfabricated interdigitated gold electrode. The sensor was first exposed to a broad, physiologically relevant range of concentrations of SCFAs in isolation (0.5–20 mg/ml in electroanalytical cell), and, unlike previously published SCFA sensors that could detect only in gas form with the aid of evaporation, it was able to detect them directly in the liquid phase at room temperature. Subsequently, Electrochemical Impedance Spectroscopy (EIS) analysis was utilized in a complex medium, accompanied by Fe(CN)63−/4−. This was applied to a mixture of SCFAs, prepared in varying ratios and concentrations spanning from 0.5 to 10 mg/ml. The analysis successfully demonstrated the sensor's capacity to accurately measure SCFAs in a mixture of experimental relevance. The recorded faradaic responses were then used to train a fit-to-data model to screen human bacterial isolates and detect which species secrete SCFAs in vitro. This research paves the way for swift, non-destructive assessment of SCFA levels in complex biological samples. The end product is a miniaturized sensor that is highly stable and sensitive, making it ideal for real-time monitoring applications.

Hydrogenated amorphous silicon high flux x-ray detectors for synchrotron microbeam radiation therapy

Objective. Microbeam radiation therapy (MRT) is an alternative emerging radiotherapy treatment modality which has demonstrated effective radioresistant tumour control while sparing surrounding healthy tissue in preclinical trials. This apparent selectivity is achieved through MRT combining ultra-high dose rates with micron-scale spatial fractionation of the delivered x-ray treatment field. Quality assurance dosimetry for MRT must therefore overcome a significant challenge, as detectors require both a high dynamic range and a high spatial resolution to perform accurately. Approach. In this work, a series of radiation hard a-Si:H diodes, with different thicknesses and carrier selective contact configurations, have been characterised for x-ray dosimetry and real-time beam monitoring applications in extremely high flux beamlines utilised for MRT at the Australian Synchrotron. Results. These devices displayed superior radiation hardness under constant high dose-rate irradiations on the order of 6000 Gy s−1, with a variation in response of 10% over a delivered dose range of approximately 600 kGy. Dose linearity of each detector to x-rays with a peak energy of 117 keV is reported, with sensitivities ranging from (2.74 ± 0.02) nC/Gy to (4.96 ± 0.02) nC/Gy. For detectors with 0.8 μm thick active a-Si:H layer, their operation in an edge-on orientation allows for the reconstruction of micron-size beam profiles (microbeams). The microbeams, with a nominal full-width-half-max of 50 μm and a peak-to-peak separation of 400 μm, were reconstructed with extreme accuracy. The full-width-half-max was observed as 55 ± 1 μm. Evaluation of the peak-to-valley dose ratio and dose-rate dependence of the devices, as well as an x-ray induced charge (XBIC) map of a single pixel is also reported. Significance. These devices based on novel a-Si:H technology possess a unique combination of accurate dosimetric performance and radiation resistance, making them an ideal candidate for x-ray dosimetry in high dose-rate environments such as FLASH and MRT.

A temperature sensor based on Er3+/Yb3+ co-doped tellurite fiber for real-time thermal monitoring of transformers

In this paper, a temperature sensor based on fluorescence intensity ratio (FIR) technique is prepared by the dual curing method. The pumping threshold is only 0.1 mW for the application of real-time thermal monitoring on small transformers. This not only greatly reduces the thermal effects accumulated by laser irradiation but also contributes to the integration and portability of the light source. Based on the FIR, the temperature information could be demodulated through tracking the fluorescence spectral changes. The basic temperature sensing characteristics of this sensor were evaluated, which showed that the maximum stability deviation and reproducibility error were within ±1.1 K and ±0.8 K, respectively. Notably, it has a maximum measurement error of only 0.61 K when applied to the temperature monitoring of transformers. And a fast time response of 2.1 s was exhibited in the cyclic exhalation experiment. This proposed sensor is suitable for mass production due to its resistance to electromagnetic interference, low pumping power, fast time response, excellent stability and reproducibility.

Application on Intelligent Technology of Electrical Engineering and Automation

In the electrical industry, the complexity and construction scale of electrical engineering are increasing, the traditional technology has been difficult to efficiently adapt to the development needs of the new era industry, deepening the popularization of the application of intelligent technology in electrical engineering and automation, is an important development direction of the electrical industry. This paper describes the basic connotation of electrical engineering and automation and intelligent technology, analyzes the electrical engineering and automation intelligent technology application in improving the level of system control, improve data processing accuracy and optimize the significance of system design, studied the electrical engineering and automation system in fault response processing, automation control, system design, the specific application of real-time monitoring.

Developing a Portable Spectrometer to Detect Chemical Contaminants in Irrigation Water

Water pollution is a critical issue since it can severely affect health and the environment. The purpose of the study is to develop a portable spectrometer (ESP32-based spectrometer) to detect chemical contaminants in irrigation water by observing the light absorbance of contaminants. ESP32 and a light sensor (photodiode) were respectively, used as the main controller and detector of the portable spectrometer. It was developed based on optical dispersion and Beer–Lambert law theory. The light absorbance of different types of contaminants was displayed in a Blynk application for real-time monitoring. The samples were also tested using a lab-based spectroscopy method, ultraviolet-visible (UV-Vis) spectrometer. The spectral range of the measurement is from 350 nm to 700 nm and the standard error of the ESP32-based spectrometer is from 0.01 to 0.05. Five water samples were tested, consisting of ammonium nitrate, organic pesticide, zinc oxide and two different reservoirs used for irrigation. The absorption peaks of the ammonium nitrate and organic pesticide are 363 nm and 361 nm, respectively. Zinc oxide shows the absorbance peak at 405 nm, whereas both reservoirs show absorbance peaks lie in the region from 300 nm to 370 nm. Therefore, this study shows that different types of contaminants can absorb light only at specific wavelength regions by considering the concentration of samples. The developed ESP32-based spectrometer can be applied for on-site water quality monitoring as it is portable, light, simple and can be monitored in real time using multiple devices.

Optimal Sleep Scheduling for Energy-Efficient AoI Optimization in Industrial Internet of Things

Keeping sensor data fresh is desired for industrial Internet of Things (IIoT) especially in real-time monitoring applications. However, this may require sensors always in active mode and thus incur low energy efficiency. In this paper, we consider that a wireless sensor monitors a dynamical system and reports real-time measurements to a processing center through an unreliable wireless channel. We study the problem of optimizing the sensor data freshness in terms of Age of Information (AoI) while saving energy by scheduling the sensor to sleep when needed. The problem is formulated as a Markov decision process that takes both AoI and energy consumption into account, to which we theoretically prove that the optimal scheduling policy forms a cyclic sleep-wake pattern. The optimal sleep period is also analyzed. Simulation results demonstrate that the proposed scheduling policy outperforms other existing policies.

Acoustic-Signal-Based Damage Detection of Wind Turbine Blades-A Review.

Monitoring and maintaining the health of wind turbine blades has long been one of the challenges facing the global wind energy industry. Detecting damage to a wind turbine blade is important for planning blade repair, avoiding aggravated blade damage, and extending the sustainability of blade operation. This paper firstly introduces the existing wind turbine blade detection methods and reviews the research progress and trends of monitoring of wind turbine composite blades based on acoustic signals. Compared with other blade damage detection technologies, acoustic emission (AE) signal detection technology has the advantage of time lead. It presents the potential to detect leaf damage by detecting the presence of cracks and growth failures and can also be used to determine the location of leaf damage sources. The detection technology based on the blade aerodynamic noise signal has the potential of blade damage detection, as well as the advantages of convenient sensor installation and real-time and remote signal acquisition. Therefore, this paper focuses on the review and analysis of wind power blade structural integrity detection and damage source location technology based on acoustic signals, as well as the automatic detection and classification method of wind power blade failure mechanisms combined with machine learning algorithm. In addition to providing a reference for understanding wind power health detection methods based on AE signals and aerodynamic noise signals, this paper also points out the development trend and prospects of blade damage detection technology. It has important reference value for the practical application of non-destructive, remote, and real-time monitoring of wind power blades.

A selective analysis of sulfamethoxazole – Trimethoprim in tablet formulations using graphene oxide-zinc oxide quantum dots based nanocomposite modified glassy carbon electrode

In this study, simultaneous analysis on electrochemical detection of SMX and TMP in tablet formulation has been made using graphene oxide (GO) and ZnO QDs (GO-ZnO QDs) based nanocomposite modified glassy carbon electrode (GCE). The functional group presence was observed using FTIR study. The electrochemical characterization for GO, ZnO QDs and GO-ZnO QDs was studied using cyclic voltammetry using [Fe(CN)6]3- medium. In order to estimate the electrochemical redox behavior of SMX and TMP from tablet, the developed electrodes GO/GCE, ZnO QDs/GCE and GO-ZnO QDs/GCE are initially tested for electrochemical activity towards the SMX tablet in BR pH 7 medium. Later their electrochemical sensing has been monitored using square wave voltammetry (SWV). On observing the characteristic behavior of developed electrodes, GO/GCE exhibited detection potential of +0.48 V for SMX and +1.37 V for TMP whereas, ZnO QDs/GCE with +0.78V for SMX and for TMP 1.01 V respectively. Similarly, for GO-ZnO QDs/GCE, its 0.45 V for SMX and 1.11 V for TMP are observed using cyclic voltammetry. The obtained potential results on detecting SMX and TMP are in good agreement with previous results. Under optimized conditions, the response has been monitored with linear concentration range 50 μg/L to 300 μg/L for GO/GCE, ZnO QDs/GCE and GO-ZnO QDs/GCE in SMX tablet formulations. Their detection limits for the individual detection using GO-ZnO/GCE for SMX and TMP are found to be 0.252 ng/L and 19.10 μg/L and for GO/GCE it was 0.252 pg/L and 2.059 ng/L respectively. It was observed that ZnO QDs/GCE could not provide the electrochemical sensing towards SMX and TMP which may be due to the ZnO QPs can act as a blocking layer impeding the electron transfer process. Thus, the sensor performance lead to promising biomedical applications in real-time monitoring on evaluating selective analysis with SMX and TMP in tablet formulations.

Real-Time Monitoring of Tire Condition with Fast Detection Passive and Wireless TPMS

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Accurate tire pressure monitoring system (TPMS) is of great practical importance and the reliability and safety of its power supply module has great concern. The piezoelectric-based surface acoustic wave (SAW) sensor is considered to have great potential in this field because of its passive, wireless and small size advantages. This paper presents the application of passive and wireless SAW sensors for real-time tire condition monitoring. The pressure sensitive structure is optimized and a three-resonator structure is also designed sensing temperature and pressure. Furthermore, a fast detection system is developed to realize high-speed signal acquisition. At last, experiments are executed and the SAW temperature and pressure sensor property is measured. The results show that the designed SAW sensor can realize real-time monitoring of tire condition; the temperature measurement range can reach -40~120°C with an accuracy of ±1°C; the pressure measurement range can reach 0~2MPa with an accuracy of 0.2MPa. This paper indicates the wireless and passive SAW temperature and pressure sensor system has the potential for application in real-time monitoring of tire condition.&lt;/div&gt;&lt;/div&gt;