Speed Estimation Research Articles

Robust variable step P&O algorithm based MPPT for PMSG wind generation system using estimated wind speed compensation technique

To expedite the cost recovery of the wind energy conversion system (WECS), maximizing power availability at all times is of utmost importance. Therefore, the primary objective of Maximum Power Point Tracking (MPPT) algorithms is to optimize power output by ensuring the achievement of maximum power under varying wind speeds. The perturbation and observation algorithm are widely recognized as the most well-known technique used in wind energy conversion systems (WECS) due to its versatility. Although this algorithm is valued for its simplicity and cost-effectiveness, it does have a significant drawback. Peculiarly, it is insensitive to fluctuations in instantaneous wind speed conditions and lacks accurate step size estimation. To address these limitations, a novel approach called the Robust Variable Step P&O (RVS-P&O) technique has been proposed. The RVS-P&O technique aims to overcome the aforementioned drawbacks. The control strategy employed by the proposed RVS-P&O-MPPT algorithm is based on three fundamental concepts: a new normalization of the selection of the step size, a novel correction procedure to estimate the power variation and wind speed estimation (WSE) algorithm based on the artificial neural network (ANN) controller. Simulations have been carried out by means of MATLAB/Simulink environment. The findings indicate the superiority of the proposed RVS-P&O algorithm compared to several P&O implementations: fixed Large Step (LS), fixed Small Step (SS), Variable Step (VS) and Modified (M)-P&O. The developed algorithm allows reaching 99.79% of wind energy conversion system (WECS) efficiency. This allows obtaining an enhancement of 2.19% in comparison to VS-P&O technique which is the most competing algorithm. Further, the response speed is clearly improved compared to the various P&O versions, as the settling time (sec) is of 0.0074 while that of VS and M−P&O is of 0.0157 and 0.0079, respectively. Indeed, to enhance the quality of the injected power into the grid, a Second Order Sliding Mode Controller (SOSMC) based Super Twisting Algorithm (STA) is proposed. The results show the suggested technique outperforms the PI and First Order (FO)-SMC for current THD minimization by attenuating the odd harmonics without chattering phenomenon.

A Novel Control Scheme for Induction Generator Based Stand-alone Micro Hydro Power Plants

A system comprising of a hydraulic turbine (HT) driven induction generator with excitation capacitor (IGEC) has been proposed for providing electricity to the residents living in remote areas and steep terrains, wherein the grid connection is unviable. The available water resource in such locations is effectively utilized and the load on the generator terminals is set, based on the requirement of the consumer demand. A method has been formulated for the estimation of excitation capacitor and rotor speed for ensuring nominal voltage and frequency at the generator terminals, regardless of variation in the consumer load. This design procedure is based on the analysis of IGEC employing the binary search algorithm (BSA). The logical way of arriving at the range of per unit (pu) speed to start the BSA has also been illustrated. A closed-loop control scheme has also been formulated, by taking generator voltage as the feedback variable and comparing the voltage set limits Vmin and Vmax. Accordingly, a controller action is initiated to add or disconnect the flexible loads. An available mathematical modeling of HT has been modified suitably and by using this model, the functioning of the proposed system with respect to the HT characteristics and generated frequency of IGEC has also been investigated. Using a MATLAB/Simulink software, the successful functioning of the proposed system has been demonstrated with typical operating conditions. The predetermined values and simulated observations are amply supported with the laboratory results conducted on a 3-phase, 3.7 kW IGEC.

Multiparameter Estimation-Based Sensorless Adaptive Direct Voltage MTPA Control for IPMSM Using Fuzzy Logic MRAS

This paper introduces a parameter-estimation-based sensorless adaptive direct voltage maximum torque per ampere (MTPA) control strategy for interior permanent magnet synchronous machines (IPMSMs). In direct voltage control, the motor’s electrical parameters, speed, and rotor position are of great significance. Thus, any mismatch in these parameters or failure to acquire accurate speed or position information leads to a significant deviation in the MTPA trajectory, causing high current consumption and hence affecting the performance of the entire control system. In view of this problem, a fuzzy logic control-based cascaded model reference adaptive system (FLC-MRAS) is introduced to mitigate the effect of parameter variation on the tracking of the MTPA trajectory and to provide precise information about the rotor speed and position. The cascaded scheme consists of two parallel FLC-MRAS for speed and multiparameter estimation. The first MRAS is utilized to estimate motor speed and rotor position to achieve robust sensorless control. However, the speed estimator is highly dependent on time-varying motor parameters. Therefore, the second MRAS is designed to identify the quadratic inductance and permanent magnet flux and continuously update both the speed estimator and control scheme with the identified values to ensure accurate speed estimation and real-time MTPA trajectory tracking. Unlike conventional MRAS, which uses linear proportional-integral controllers (PI-MRAS), an FLC is adopted to replace the PI controllers, ensuring high estimation accuracy and enhancing the robustness of the control system against sudden changes in working conditions. The effectiveness of the proposed scheme is evaluated under different speed and torque conditions. Furthermore, a comparison against the conventional PI-MRAS is extensively investigated to highlight the superiority of the proposed scheme. The evaluation results and our quantitative assessment show the ability of the designed strategy to achieve high estimation accuracy, less oscillation, and a faster convergence rate under different working conditions. The quantitative assessment reveals that the FLC-MRAS can improve the estimation accuracy of speed, permanent magnet flux, and quadratic inductance by 19%, 55.8% and 44.55%, respectively.

Analysing trackway-based speed calculations to infer dinosaur locomotive capabilities and behaviours

ABSTRACT Dinosaur trackway studies provide valuable insights into extinct animals’ locomotor behaviour. Despite limitations arising from the inability to directly observe these trackmakers, speed and gait estimation formulae have become standard tools in the field of dinosaur ichnology. Stride length has traditionally been used to estimate dinosaur average speed and intra-trackway speed variations, but its unilateral focus can overlook some nuances within trackways. Alternatively, the contralateral measure pace length may not always align with the direction of travel. Analysing data from the Mancheon-ri tracksite and others, our results show that step length aligns advantageously along the trackway axis and captures nuanced speed variations. Incorporating additional parameters such as cadence and step duration enriches our understanding of the trackmaker’s movements. Cadence summarises the overall rhythm of movement across trackways, and step duration reveals temporal variations between individual steps, offering potential for 3D animated reconstructions of dinosaur behaviour. Despite these methodologies’ inherent limitations and assumptions, they provided a more refined understanding of dinosaur locomotion and behavioural patterns. Acknowledging the limitations of studying unobservable trackmakers, our estimations – based on established formulas for hip height and speed – are both rooted in accepted methodologies and remain informative, enhancing our understanding of dinosaur trackmaker behaviours.

Speed sensorless Adaptive Power Control for photovoltaic-fed water pump using Extended Kalman–Bucy filter

In this study, a speed sensorless control method is proposed for a photovoltaic water-pump system operating under variable atmospheric conditions. The developed system not only continuously determines the maximum power point of the photovoltaic system but also generates a reference speed appropriate for each power value produced. The MATLAB/Simulink model of the driver system incorporates Adaptive Power Control, Direct Torque Control, a 3-phase induction motor, and the power plant. The determination of the maximum power point of the photovoltaic system and the input reference speed information for the Direct Torque Control under varying atmospheric conditions is accomplished through the application of the Adaptive Power Control algorithm. Notably, the control process operates as a closed-loop system without the requirement of a speed sensor. Instead, the Extended Kalman–Bucy estimator is employed for speed estimation. Empirical evaluation of the proposed method was conducted under constant temperature conditions and varying irradiance values. The results clearly illustrate the consistent operation of the motor, with a constant torque production aligned with each identified maximum power point corresponding to distinct irradiance levels. The control system demonstrates robust performance and operational efficiency across the entire range of operating points. The efficacy of the Kalman estimator in monitoring the reference speed, the utilization of a single solar radiation sensor, and the successful implementation of sensorless operation are significant advantages of the developed adaptive approach. The demonstrated performance of the proposed system showcases its potential for enhancing the efficient and reliable control of photovoltaic water-pump systems in real-world applications.

A Novel Approach for Improving Gait Speed Estimation Using a Single Inertial Measurement Unit Embedded in a Smartphone: Validity and Reliability Study.

Gait speed is a valuable biomarker for mobility and overall health assessment. Existing methods to measure gait speed require expensive equipment or personnel assistance, limiting their use in unsupervised, daily-life conditions. The availability of smartphones equipped with a single inertial measurement unit (IMU) presents a viable and convenient method for measuring gait speed outside of laboratory and clinical settings. Previous works have used the inverted pendulum model to estimate gait speed using a non-smartphone-based IMU attached to the trunk. However, it is unclear whether and how this approach can estimate gait speed using the IMU embedded in a smartphone while being carried in a pants pocket during walking, especially under various walking conditions. This study aimed to validate and test the reliability of a smartphone IMU-based gait speed measurement placed in the user's front pants pocket in both healthy young and older adults while walking quietly (ie, normal walking) and walking while conducting a cognitive task (ie, dual-task walking). A custom-developed smartphone application (app) was used to record gait data from 12 young adults and 12 older adults during normal and dual-task walking. The validity and reliability of gait speed and step length estimations from the smartphone were compared with the gold standard GAITRite mat. A coefficient-based adjustment based upon a coefficient relative to the original estimation of step length was applied to improve the accuracy of gait speed estimation. The magnitude of error (ie, bias and limits of agreement) between the gait data from the smartphone and the GAITRite mat was calculated for each stride. The Passing-Bablok orthogonal regression model was used to provide agreement (ie, slopes and intercepts) between the smartphone and the GAITRite mat. The gait speed measured by the smartphone was valid when compared to the GAITRite mat. The original limits of agreement were 0.50 m/s (an ideal value of 0 m/s), and the orthogonal regression analysis indicated a slope of 1.68 (an ideal value of 1) and an intercept of -0.70 (an ideal value of 0). After adjustment, the accuracy of the smartphone-derived gait speed estimation improved, with limits of agreement reduced to 0.34 m/s. The adjusted slope improved to 1.00, with an intercept of 0.03. The test-retest reliability of smartphone-derived gait speed was good to excellent within supervised laboratory settings and unsupervised home conditions. The adjustment coefficients were applicable to a wide range of step lengths and gait speeds. The inverted pendulum approach is a valid and reliable method for estimating gait speed from a smartphone IMU placed in the pockets of younger and older adults. Adjusting step length by a coefficient derived from the original estimation of step length successfully removed bias and improved the accuracy of gait speed estimation. This novel method has potential applications in various settings and populations, though fine-tuning may be necessary for specific data sets.

Evaluation of HY-2C and CFOSAT Satellite Retrieval Offshore Wind Energy Using Weather Research and Forecasting (WRF) Simulations

This study simulated the spatial distribution of wind speeds and wind energy density by using the WRF model, and we used the WRF-simulated results to evaluate the sea surface wind speeds retrieved from the HY-2C and CFOSAT satellite-borne microwave scatterometers over the Yellow Sea region. The main conclusions were as follows: (1) The combination of the MRF boundary layer parameterization scheme, the MM5 near-surface parameterization scheme, and the Global Data Assimilation System (GDAS) initial field demonstrated the best performance in simulating the 10 m wind speed in the Yellow Sea region, with a root-mean-square error (RMSE) of 1.57, bias of 1.24 m/s, and mean absolute percentage error (MAPE) of 17%. (2) The MAPE of the HY-2C inversion data was 9%, while the CFOSAT inversion data had an MAPE of 6%. The sea surface wind speeds derived from the HY-2C and CFOSAT satellite scatterometer inversions demonstrated high accuracy and applicability in this region. (3) The wind speed was found to increase with altitude over the Yellow Sea, with higher wind speeds observed in the southern region compared to the northern region. The wind power density increased with altitude, and the wind power density in the southern area of the Yellow Sea was higher than in the northern region. (4) The CFOSAT satellite inversion products were in good agreement with the WRF simulation results under low wind speed conditions. In contrast, the HY-2C satellite inversion products showed better agreement under moderate wind speed conditions. Under high wind speed conditions, both satellite inversion products exhibited minor deviations, but the HY-2C product had an overall overestimation, while the CFOSAT product remained within the range of −1 to 1 m/s. (6) The wind power density increased with the satellite-inverted 10 m wind speed. When the 10 m wind speed was less than 9 m/s, the wind power density exhibited a roughly cubic trend of increase. However, when the 10 m wind speed exceeded 9 m/s, the wind power density no longer increased with the rise in 10 m wind speed. These findings provide valuable insights into wind energy resources in the Yellow Sea region and demonstrate the effectiveness of satellite scatterometer inversions for wind speed estimation. The results have implications for renewable energy planning and management in the area.

MVCV-Traffic: multiview road traffic state estimation via cross-view learning

Fine-grained urban traffic data are often incomplete owing to limitations in sensor technology and economic cost. However, data-driven traffic analysis methods in intelligent transportation systems (ITSs) heavily rely on the quality of input data. Thus, accurately estimating missing traffic observations is an essential data engineering task in ITSs. The complexity of underlying node-wise correlation structures and various missing scenarios presents a significant challenge in achieving high-precision estimation. This study proposes a novel multiview neural network termed MVCV-Traffic, equipped with a cross-view learning mechanism, to improve traffic estimation. The contributions of this model can be summarized into two parts: multiview learning and cross-view fusing. For multiview learning, several specialized neural networks are adopted to fit diverse correlation structures from different views. For cross-view fusing, a new information fusion strategy merges multiview messages at both feature and output levels to enhance the learning of joint correlations. Experiments on two real-world datasets demonstrate that the proposed model significantly outperforms existing traffic speed estimation methods for different types and rates of missing data.

A recursive system identification inertia estimator for traditional and converter-interfaced generators

This paper proposes a method to estimate the inertia constant of synchronous generators (SGs) and the virtual inertia provided by converter-interfaced generators (CIGs). It can be applied when the system is under normal operating conditions or after power imbalance events. The algorithm is based on a recursive system identification approach and uses the active power and bus frequency measurements proceeding from phasor measurement units. The measurements are preprocessed to improve the estimation accuracy. Subsequently, a model-independent rotor speed estimator is used to compute rotor speed variations (or internal frequency variations, in the case of CIGs). Afterwards, a recursive least-square equation error formulation is implemented to compute the SG and CIG parameters in the z-domain, which are later converted into their respective counterparts in the s-domain. The method is applied to several generation devices in the IEEE 39-bus and IEEE 118-bus test systems, including traditional SGs and CIGs providing virtual inertia. Numerical results show the accuracy and the low computational burden of the proposed method.

Assessment and improvement of the accuracy of tacholess instantaneous speed estimation

Accurately estimating instantaneous angular speed (IAS) is crucial for effective fault diagnosis under nonstationary operating conditions. Although numerous methods have been developed to improve the accuracy of IAS estimation, finding a suitable technique (method and vibration component) for a specific application remains challenging. This is because accurately assessing the accuracy of the estimated IAS without a tachometer is not straightforward. Thus, since the accuracy of the estimated IAS is unknown, its accuracy cannot be further improved.To address the challenge of accurately estimating and further improving the accuracy of IAS without a tachometer, this study proposes a method to enhance the accuracy of IAS obtained from different techniques (methods and vibration components). The study first investigates the influence of IAS error on order analysis. Next, it discusses three indicators that can assess the accuracy of the estimated IAS without a tachometer. These indicators are designed to compare the energy of the deterministic and random parts of the signal, assess the sparsity of the order spectrum, and quantify the smearing of the vibration component of interest on the order spectrum. The signal and all IASs are divided into windowed sections to determine the most accurate IAS in each section using the three indicators separately or a resultant indicator obtained by merging them using the entropy weighting method. The most accurate IAS are then merged together to obtain the resultant IAS.The study finds that the indicators can effectively evaluate the accuracy of the IAS without a tachometer. The proposed method also produces a more accurate IAS than the IAS candidates obtained by different techniques.

Forecast uncertainty and ensemble spread in surface currents from a regional ocean model

An operational ocean Ensemble Prediction System (EPS) for the coastal seas off Northern Norway is evaluated by comparing with high-frequency radar current speed estimates. The EPS is composed of 24 members for which the ocean current is not perturbed nor constrained but forced with an atmosphere ensemble. The ocean ensemble spread stems from (i) accumulated differences in wind-forcing history and (ii) constraints of sea surface temperature by data assimilation. The intention of the ensemble is to reflect the actual uncertainty in initial conditions, which are largely unknown in terms of mesoscale circulation. We find a low but pronounced predictive skill in surface currents along with a good statistic skill. Additionally, current speeds show deterioration of the validation metrics over the forecast range. Further, high-resolution wind forcing seems to provide better forecast skill in currents compared to lower resolution forcing. In general, the ensemble exhibits the ability to predict forecast uncertainty.

Instantaneous angular speed estimation in gas foil bearings from vibration signal

Instantaneous angular speed estimation in gas foil bearings from vibration signal

A sensory-motor decoder that transforms neural responses in extrastriate area MT into smooth pursuit eye movements.

Visual motion drives smooth pursuit eye movements through a sensory-motor decoder that uses multiple parallel neural pathways to transform the population response in extrastriate area MT into movement. We evaluated the decoder by challenging pursuit in monkeys with reduced motion reliability created by reducing coherence of motion in patches of dots. Our strategy was to determine how reduced dot coherence changes the population response in MT. We then predicted the properties of a decoder that transforms the MT population response into dot coherence-induced deficits in the initiation of pursuit and steady-state tracking. During pursuit initiation, decreased dot coherence reduces MT population response amplitude without changing the preferred speed at its peak. The successful decoder reproduces the measured eye movements by multiplication of 1) the estimate of target speed from the peak of the population response with 2) visual-motor gain based on the amplitude of the population response. During steady-state tracking, the decoder that worked for pursuit initiation failed to reproduce the paradox that steady-state eye speeds do not accelerate to the target speed despite persistent image motion. It predicted eye acceleration to target speed even when monkeys' eye speeds were steady at well below the target speed. To account for the effect of dot coherence on steady-state eye speed, we postulate that the decoder uses sensory-motor gain to modulate the eye velocity positive feedback that normally sustains perfect steady-state tracking. Then, poor steady-state tracking persists because of balance between eye deceleration caused by low positive feedback gain and acceleration driven by MT.NEW & NOTEWORTHY By challenging a sensory-motor system with degraded sensory stimuli, we reveal how the sensory-motor decoder transforms the population response in extrastriate area MT into commands for the initiation and steady-state behavior of smooth pursuit eye movements. Conclusions are based on measuring population responses in MT for multiple target speeds and different levels of motion reliability and evaluating a decoder with a biologically motivated architecture to determine the decoder properties that create the measured eye movements.

A Tight Coupling Algorithm for Strapdown Inertial Navigation System (SINS)/Global Positioning System (GPS) Adaptive Integrated Navigation Based on Variational Bayesian

Multi-source nonlinear noise exists in the process of multi-source navigation information fusion in long-endurance positioning systems in complex environments. In such engineering applications, the classical Kalman filter (KF) and the extended Kalman filter (EKF) have the phenomena of noise instability and parameter drift, which lead to the divergence of filtering results and reductions in accuracy over long periods of time. Aiming at the above problems, this paper proposes a fusion algorithm of the variational Bayesian (VB) and the cubature Kalman filter (CKF). Firstly, the system is modeled through nonlinear filtering, and the CKF error equation is established by taking the position difference and velocity difference between SINS and GPS as observation variables. Then, to address the problem of poor self-adaptation of the CKF algorithm, the variational Bayesian adaptive estimation method is introduced into the CKF algorithm, and a measurement noise variance estimation model is introduced to the process of time and measurement updates of the CKF algorithm to finally obtain the adaptive VB–CKF algorithm. The simulation results from the experimental platform show that the proposed fusion algorithm improves the combined SINS/GPS system by about 30% in terms of attitude angle accuracy and reduces speed and position estimation errors (RMSE) by about 45%. At the same time, comprehensive experiments on multiple types of sites show that compared with the CKF algorithm, the VB–CKF algorithm improves the positioning accuracy by 10% when the GPS signal is stable and improves the accuracy by about 38% when the GPS measurement noise changes dramatically in complex terrain, which effectively suppresses the accuracy divergence of the CKF algorithm and has high value for engineering applications.

Hand-Guiding Gesture-Based Telemanipulation with the Gesture Mode Classification and State Estimation Using Wearable IMU Sensors

This study proposes a telemanipulation framework with two wearable IMU sensors without human skeletal kinematics. First, the states (intensity and direction) of spatial hand-guiding gestures are separately estimated through the proposed state estimator, and the states are also combined with the gesture’s mode (linear, angular, and via) obtained with the bi-directional LSTM-based mode classifier. The spatial pose of the 6-DOF manipulator’s end-effector (EEF) can be controlled by combining the spatial linear and angular motions based on integrating the gesture’s mode and state. To validate the significance of the proposed method, the teleoperation of the EEF to the designated target poses was conducted in the motion-capture space. As a result, it was confirmed that the mode could be classified with 84.5% accuracy in real time, even during the operator’s dynamic movement; the direction could be estimated with an error of less than 1 degree; and the intensity could be successfully estimated with the gesture speed estimator and finely tuned with the scaling factor. Finally, it was confirmed that a subject could place the EEF within the average range of 83 mm and 2.56 degrees in the target pose with only less than ten consecutive hand-guiding gestures and visual inspection in the first trial.

On the distinction of seasonal internal solitary waves characteristics in the Lombok Strait based on multi-satellite data

ABSTRACT This study presents a multi-satellite approach utilizing synthetic aperture radar (SAR) and optical sensors to distinguish the characteristics of internal solitary waves (ISWs) during different seasons in the Lombok Strait. SAR and optical sensors are employed to estimate the dynamic parameters (soliton number, wavelength, and phase speed) of ISWs during different seasons based on the detected ISW patterns. ISW characteristics in the Lombok Strait during two seasons were observed using Sentinel-1/SAR, GCOM-C/SGLI, and Terra/MODIS sensors. Results indicated that Sentinel-1/SAR detected a higher soliton number for the northward-propagating ISWs in the Lombok Strait during the north-west monsoon (NWM) than the south-east monsoon (SEM) period. The identified wavelengths of ISWs were wider during the SEM than during the NWM whenever two packets were detected in one image. Similar variations were observed by optical sensors (SGLI and MODIS). Estimation of ISW phase speed derived from multi-satellite images and the Korteweg-de Vries (KdV) equation showed that the phase speed was faster during the NWM than the SEM. These results correlated with the seasonal variation of thermocline depth and density differences between the two layers. Because of the bias caused by turbid water, a pattern of high chlorophyll-a was observed during the NWM period in the SGLI, MODIS, and VIIRS products. Propagation of turbid water to the north was assumed to be a result of the intense ISW activity in this area. Highly variable currents due to ISW activity in the Lombok Strait area were assumed to cause the anomalous sediment transport in this study. The multi-satellite observation data used in this study enhanced understanding of the influence of ISWs on coastal interactions in the Lombok Strait.

Downscaling ERA5 wind speed data: a machine learning approach considering topographic influences

Energy system modeling and analysis can provide comprehensive guidelines to integrate renewable energy sources into the energy system. Modeling renewable energy potential, such as wind energy, typically involves the use of wind speed time series in the modeling process. One of the most widely utilized datasets in this regard is ERA5, which provides global meteorological information. Despite its broad coverage, the coarse spatial resolution of ERA5 data presents challenges in examining local-scale effects on energy systems, such as battery storage for small-scale wind farms or community energy systems. In this study, we introduce a robust statistical downscaling approach that utilizes a machine learning approach to improve the resolution of ERA5 wind speed data from around 31 km × 31 km to 1 km × 1 km. To ensure optimal results, a comprehensive preprocessing step is performed to classify regions into three classes based on the quality of ERA5 wind speed estimates. Subsequently, a regression method is applied to each class to downscale the ERA5 wind speed time series by considering the relationship between ERA5 data, observations from weather stations, and topographic metrics. Our results indicate that this approach significantly improves the performance of ERA5 wind speed data in complex terrain. To ensure the effectiveness and robustness of our approach, we also perform thorough evaluations by comparing our results with the reference dataset COSMO-REA6 and validating with independent datasets.

Basic Properties of Solutions of Boundary Value Problem for the Dirac-Wave Operator

We study local and global properties of solutions of the boundary value problem for the Dirac wave operator. This includes explicit formulas, domain of dependence, range of influence, finite propagation speed and energy estimates. We also introduce the space of Dirac wave homogeneous polynomials and the corresponding Dirac-wave conjugate functions.

Estimation of Wind Speed Based on Schlieren Machine Vision System Inspired by Greenhouse Top Vent.

Greenhouse ventilation has always been an important concern for agricultural workers. This paper aims to introduce a low-cost wind speed estimating method based on SURF (Speeded Up Robust Feature) feature matching and the schlieren technique for airflow mixing with large temperature differences and density differences like conditions on the vent of the greenhouse. The fluid motion is directly described by the pixel displacement through the fluid kinematics analysis. Combining the algorithm with the corresponding image morphology analysis and SURF feature matching algorithm, the schlieren image with feature points is used to match the changes in air flow images in adjacent frames to estimate the velocity from pixel change. Through experiments, this method is suitable for the speed estimation of turbulent or disturbed fluid images. When the supply air speed remains constant, the method in this article obtains 760 sets of effective feature matching point groups from 150 frames of video, and approximately 500 sets of effective feature matching point groups are within 0.1 difference of the theoretical dimensionless speed. Under the supply conditions of high-frequency wind speed changes and compared with the digital signal of fan speed and data from wind speed sensors, the trend of wind speed changes is basically in line with the actual changes. The estimation error of wind speed is basically within 10%, except when the wind speed supply suddenly stops or the wind speed is 0 m/s. This method involves the ability to estimate the wind speed of air mixing with different densities, but further research is still needed in terms of statistical methods and experimental equipment.

Noninvasive measurement of local temperature using ultrasound-switchable fluorescence.

Measuring the local background temperature in diseased and inflamed tissues is highly desirable, especially in a non-invasive way. In this work, ultrasound-switchable fluorescence (USF) technique was utilized to estimate the local background temperature for the first time by analyzing the temperature dependence of fluorescence emission from USF contrast agents induced by a focused ultrasound (FU) beam. First, temperature-sensitive USF agents with distinct temperature switching-on thresholds were synthesized, and their thermal switching characteristics were quantified using an independent spectrometer system. Second, the USF contrast agent suspension was injected into a microtube that was embedded into a phantom and the dynamic USF signal was acquired using a camera-based USF system. The differential profile of the measured dynamic USF signal was computed and compared with the thermal switching characteristics. This allowed for the calculation of the local background temperature of the sample in the FU focal volume based on the estimation of heating speed. An infrared (IR) camera was used to acquire the surface temperature of the sample and further compare it with the USF system. The results showed that the difference between the temperatures acquired from the USF thermometry and the IR thermography was 0.64 ± 0.43 °C when operating at the physiological temperature range from 35.27 to 39.31 °C. These results indicated the potential use of the USF system for measuring the local temperature in diseased tissues non-invasively. The designed USF-based thermometry shows a broad application prospect in high spatial resolution temperature imaging with a tunable measurement range in deep tissue.