Measurement Science and Technology

Advanced three-dimensional (3D) tracking methods are essential for studying particle dynamics across a wide range of complex systems, including multiphase flows, environmental and atmospheric sciences, colloidal science, biological and medical research, and industrial manufacturing processes. This review provides a comprehensive summary of 3D particle tracking and flow diagnostics using digital holography (DH). We begin by introducing the principles of DH, accompanied by a detailed discussion on numerical reconstruction. The review then explores various hardware setups used in DH, including inline, off-axis, and dual or multiple-view configurations, outlining their advantages and limitations. We also delve into different hologram processing methods, categorized into traditional multi-step, inverse, and machine learning (ML)-based approaches, providing in-depth insights into their applications for 3D particle tracking and flow diagnostics across multiple studies. The review concludes with a discussion on future prospects, emphasizing the significant role of ML in enabling accurate DH-based particle tracking and flow diagnostic techniques across diverse fields, such as manufacturing, environmental monitoring, and biological sciences.

Optical flow methods have been developed over the past two decades for application to particle image velocimetry (PIV) images with the goal of acquiring higher resolution measurements of the velocity field than conventional cross-correlation (CC)-based techniques. Numerous optical flow velocimetry (OFV) algorithms have been devised to solve the ill-posed optical flow problem, with various physics-inspired strategies to tailor them to fluid flows. While OFV can be applied to continuous scalar fields, it has demonstrated the most success on images of tracer particles, i.e. traditional planar PIV images. Compared to state-of-the-art CC algorithms, OFV methods have demonstrated an order of magnitude increase in spatial resolution and up to a factor of two improvement in overall accuracy when evaluated on synthetic data, at the cost of increased computational time. The requirements for particle seeding density, inter-frame displacement, and image quality are also more stringent for OFV methods compared to CC. OFV has been applied sparingly in experiments to date, but appears to offer the same advantages demonstrated on synthetic data. At this stage, OFV seems best suited to planar velocity measurements, although extensions to stereoscopic measurements have been demonstrated.

This paper proposes an autonomous anti-disturbance control algorithm for vertical takeoff and landing unmanned aerial vehicle (VTOL-UAV) in satellite positioning rejection environment. In this kind of environment, the external satellite information is unavailable. This paper proposes a VTOL-UAV environment perception and precise positioning algorithm based on tightly coupled LiDAR and inertial navigation, on the basis of environment perception and precise positioning, the autonomous motion planning algorithm for VTOL-UAV is proposed. Motion planning is carried out according to the environmental information and the dynamic characteristics of the UAV, and the planning results include the spatial path information, the velocity information, the acceleration information and the JERK information of the UAV. Finally, the path tracking method of the VTOL-UAV with an extended state observer is proposed. Based on accurate estimation and feed-forward suppression of the external disturbances of the complex environment, the motion planning results can be accurately tracked, so as to realize the autonomous control of VTOL-UAV in complex environment of ultra-low altitude and improve the intelligence level of UAV.

Addressing the challenges of position information solution among multiple unmanned aerial vehicles (UAVs) and the issue of low relative navigation accuracy, an improved collaborative localization algorithm based on the cubature Kalman filter (CKF) is proposed. It utilizes a tightly coupled approach to construct a relative ranging model equipped with ultra-wideband and inertial measurement unit sensors, enabling the correction of self-navigation information. The cosine similarity function is utilized to determine the reliability of the observations, and the observation noise matrix is adaptively adjusted based on the innovation vector from each iteration, thereby refining the positioning trajectory for collaborative navigation. Simulation experiments results demonstrate that the improved CKF collaborative localization algorithm significantly enhances the relative positioning accuracy between UAVs, effectively reduces the maximum relative positioning error, and exhibits strong anti-interference capabilities. It is more suitable for indoor multi-UAV and multi-sensor collaborative localization.

In this Technical Note, we present a simple and low-cost production method for fluorescent particles perfectly suitable for velocimetry applications. We leverage the unique ability of Nile Red (NR) fluorophores to adsorb on the polymer surface and/or embed itself in between polymeric chains. The laboratory procedure to dye polyamide particles with NR and monetary advantages over commercially available fluorescent particles is outlined. Subsequently, the fluorescence behavior of the dyed particles is tested under a laser illumination source in polar and non-polar liquids. The distinct advantage of the emission spectrum of NR-dyed particles is demonstrated with sample test results.

Fault diagnosis in rotating machinery, especially in aviation, is an active research area. RF sensors have the potential to provide on-wing information for fault diagnosis for gas turbine engines. A dual-polarized, distributed RF sensor system was tested in an axial compressor to determine if the measurement system could detect and predict rotor offset and whirl. A magnetic bearing system was used to dynamically control the rotor position. The RF polarimetry data were used as the input data to simple, multilayer neural networks. Regression analysis was performed to predict the rotor shaft centerline position and rotor whirl orbits' major and minor axis lengths. Typical neural network error was within 5 μm for whirl orbits up to 175 μm. The quality of the neural network prediction as a function of the number of features was studied. The results suggest that a final configuration may only require a single transmit-receive antenna pair and one RF tone.

The growing complexity of machinery and the increasing demand for operational efficiency and safety have driven the development of advanced fault diagnosis techniques. Among these, convolutional neural networks (CNNs) have emerged as a powerful tool, offering robust and accurate fault detection and classification capabilities. This comprehensive review delves into the application of CNNs in machine fault diagnosis, covering its theoretical foundation, architectural variations, and practical implementations. The strengths and limitations of CNNs are analyzed in this domain, discussing their effectiveness in handling various fault types, data complexities, and operational environments. Furthermore, we explore the evolving landscape of CNN-based fault diagnosis, examining recent advancements in data augmentation, transfer learning, and hybrid architectures. Finally, the future research directions and potential challenges to further enhance the application of CNNs for reliable and proactive machine fault diagnosis are highlighted.

High-resolution reconstruction of flow-field data from low-resolution and noisy measurements is of interest due to the prevalence of such problems in experimental fluid mechanics, where the measurement data are in general sparse, incomplete and noisy. Deep-learning approaches have been shown suitable for such super-resolution tasks. However, a high number of high-resolution examples is needed, which may not be available for many cases. Moreover, the obtained predictions may lack in complying with the physical principles, e.g. mass and momentum conservation. Physics-informed deep learning provides frameworks for integrating data and physical laws for learning. In this study, we apply physics-informed neural networks (PINNs) for super-resolution of flow-field data both in time and space from a limited set of noisy measurements without having any high-resolution reference data. Our objective is to obtain a continuous solution of the problem, providing a physically-consistent prediction at any point in the solution domain. We demonstrate the applicability of PINNs for the super-resolution of flow-field data in time and space through three canonical cases: Burgers' equation, two-dimensional vortex shedding behind a circular cylinder and the minimal turbulent channel flow. The robustness of the models is also investigated by adding synthetic Gaussian noise. Furthermore, we show the capabilities of PINNs to improve the resolution and reduce the noise in a real experimental dataset consisting of hot-wire-anemometry measurements. Our results show the adequate capabilities of PINNs in the context of data augmentation for experiments in fluid mechanics.

For individual calibration and testing events, no third-party assessments are conducted on the validity of measuring instrument calibrations, personnel qualifications, or the appropriateness of measurement conditions in current calibration and testing practices, as is done during accreditation review audits. Since performing such assessments manually can be labor-intensive for human reviewers and prone to errors, we have developed a system for the automatic validation of digitized data, working alongside the current accreditation system. This system is designed for the documented records of the calibration of a reference photovoltaic device. The system incorporates three key functions: data packaging, automatic validation, and auditable log by the means of the blockchain system. Specifically, all data related to the calibration of an item is packaged by an application, the extracted data from the package is validated by programs, and the validation logs are stored securely without tampering. This system can support quality compliance in calibration and testing processes.

Spatial and temporal characteristics of a table-top scale laser produced plasma formed on the surface of a tungsten target are reported. The emitted radiation was spectrally filtered to a narrow band of ca. 1 nm full width half maximum (FWHM) centred on a wavelength of 13.6 nm using a combination of Zr thin film transmission filters and a Mo–Si multilayer mirror (MLM). The temporal profile of the 13.6 nm flux was measured for different laser fluences while the spatial profile of the emission was sampled in one region using a back illuminated charge coupled device (CCD), both done with the aid of a flat MLM. The emitting plasma was imaged at 13.6 nm by replacing the flat MLM with a curved mirror which returned an extreme ultraviolet (EUV) source size of up to ca. 130 µms. The peak flux was estimated to be ca. 10¹⁴ photons nm⁻¹ sr⁻¹. For comparison purposes the W target was replaced by a solid Sn target which produced, on average, almost double the EUV flux at 13.6 nm, albeit with greater shot to shot jitter.

High-speed imaging is central to the experimental investigation of fast phenomena, like flapping flags. Event-based cameras use new types of sensors that address typical challenges such as low illumination conditions, large data transfer, and the trade-off between increasing repetition rate and measurement duration more efficiently and at reduced costs compared to classical frame-based fast cameras. Event-based cameras output unstructured data that frame-based algorithms can not process. This paper proposes a general method to reconstruct the motion of a slender object similar to the centreline of a flapping flag from raw streams of event data. The method takes advantage of continuous illumination, and the reconstruction update rate is set after and independent of the data collection. Our algorithm relies on a coarse chain-like structure that encodes the current state of the line and is updated by the occurrence of new events. The algorithm is applied to synthetic data, generated from known motions, to demonstrate that the method is accurate up to one percent of error for tip-based, shape-based, and modal decomposition metrics. Degradation of the reconstruction accuracy due to simulated defects only occurs when the severity of the defects is more than two orders of magnitude larger than what we typically encounter in experiments. The algorithm is then applied to experimental data of flapping flags, and we obtain relative errors below one percent when comparing the results with the data from laser distance sensors. The reconstruction of line deformation from event-based data is accurate and robust, and unlocks the ability to perform autonomous measurements in experimental mechanics.

Rolling bearings in aero-engine rotor systems are prone to wear failure due to installation deviations and manufacturing defects. The rapid deterioration of bearing wear can significantly compromise the safety and reliability of the rotor system and the entire machine. Accurate online monitoring of the bearing wear state is crucial to ensuring the safe and stable operation of the rotor-bearing system. However, in rolling bearing condition monitoring, the state information provided by a single type of data is often limited and insufficient to accurately reflect the bearing fault state. Multi-source information, particularly oil metal debris signals, can be utilized to achieve more precise monitoring and assessment of the bearing condition. This paper investigates rolling bearing wear faults and their long-term operational evolution through experiments conducted on a custom rotor-bearing test rig. A multi-physical parameter monitoring approach is employed to analyze collected data, including bearing vibration, temperature, and lubricating oil debris. First, an online monitoring method for bearing wear state is proposed, utilizing oil debris analysis and a Kalman filter. Next, a multi-source fault feature fusion method is adopted, integrating vibration features, particle features, and temperature features. Principal component analysis is applied to extract key sensitive feature parameters from the feature parameter set. Finally, the factors influencing the accuracy of bearing wear fault diagnosis are analyzed and compared using SVM, KNN, and decision tree methods. This analysis determines the relative importance of debris and temperature in bearing wear diagnosis, offering a novel reference for fault detection.

Smart insoles enable real-time monitoring of physiological parameters and offer feedback on the health status. This study introduces a foot-monitoring smart insole system that uses flexible sensors and inertial sensor technologies to measure the pressure distribution and foot motion data in five plantar areas. This study involved material selection, structural design, insole fabrication, hardware design, and data analysis. The system prototype underwent thorough performance validation tests, including sensitivity (3.63 mV N⁻¹), linearity error (2.09%), hysteresis error (3.37%), accuracy error (4.6%), repeatability error (5.49%), and response time (3.05 ms). Moreover, to evaluate its applicability in real-world motion monitoring, the system was tested to distinguish between different movement patterns, such as walking, jumping, and running. The results demonstrate the stability and accuracy of the system across all metrics. This technology will significantly impact sports medicine, rehabilitation therapy, and performance optimization.

Photovoltaic (PV) power generation, known for its environmental benefits and renewability, plays a critical role in advancing sustainable energy. However, the inherent randomness and volatility of PV generation challenge the stable operation of power systems with high PV penetration. Accurate PV power prediction is essential for ensuring safe grid integration and reliable power system operation. This study introduces an advanced short-term PV power prediction framework, combining multi-scale similar days (MSSD) selection and a trend-aware bidirectional gated recurrent unit (TABiGRU). First, MSSD is employed to select historical data with meteorological conditions similar to the predicted day as training samples, reducing the impact of meteorological randomness on the model. Then, to enhance the model's ability to capture the trends in meteorological dynamics, a TABiGRU model is proposed, which introduces meteorological change rate features and dynamic weight adjustment to improve the model's adaptability to meteorological fluctuations. In addition, an energy valley optimization algorithm is used to tune the hyperparameters of TABiGRU, preventing performance degradation due to improper parameter settings. Furthermore, to mitigate the cumulative error issue of point prediction under uncertain meteorological conditions, adaptive bandwidth kernel density estimation is used to generate high-quality prediction intervals, providing more robust decision support for power system scheduling. Finally, experimental results demonstrate that the proposed method achieves high prediction accuracy and stability under various meteorological conditions, particularly showing significant advantages in complex meteorological fluctuation scenarios, providing strong support for the safe and stable operation of the power grid.

The inverse Preisach hysteresis model plays an important role in simulating the hysteresis characteristics of magnetic materials and calculating their losses. The uniformly distributed centered cycle (UDCC) method is commonly used to determine the distribution function of the inverse Preisach model, but its accuracy is low when simulating hysteresis characteristics at low magnetic densities. This paper proposes an accurate fitting method of Preisach model parameters using Gaussian mixture model (GMM) with non-uniform grid partition. This method includes two innovative works: first, a hybrid sensitivity function associated with the curvature and slope of the curve is constructed to determine key regions. Additionally, the method proposes using GMM to approximate the Preisach density function and fit the limit rotational curve. Genetic algorithms are used to optimize the GMM parameters. Compared to UDCC method, this approach does not require more measured hysteresis loop data and reduces the computational load of the distribution function matrix. Experimental and simulation results show that the proposed method achieves higher fitting accuracy in identifying the inverse Preisach model distribution function compared to UDCC method, validating the effectiveness of the proposed method.

To improve the accuracy of bearing state classification in multi-source monitoring environments, a novel decision fusion method based on probabilistic Jensen–Shannon (PJS) divergence and D-S evidence theory is proposed. Firstly, the initial state probability of each bearing is determined by the bearing state classification score value, and the basic probability assignment (BPA) value of each bearing state is set by obtaining the global credibility of each sensor based on the pre-network test results. Secondly, the PJS divergence is proposed on the basis of the Kullback Leibler (KL) divergence to measure the conflict between the data collected by each sensor, and the weight value of the BPA value of each sensor is reset according to the PJS divergence value, which improves the accuracy of the BPA value. Finally, the D-S evidence theory is used to fuse the BPA value of each sensor and obtain the final bearing state classification result. The experimental part shows the effectiveness using the bearing signals collected in the laboratory, and the generality of this paper's method is verified by using it in conjunction with other networks.

Advanced three-dimensional (3D) tracking methods are essential for studying particle dynamics across a wide range of complex systems, including multiphase flows, environmental and atmospheric sciences, colloidal science, biological and medical research, and industrial manufacturing processes. This review provides a comprehensive summary of 3D particle tracking and flow diagnostics using digital holography (DH). We begin by introducing the principles of DH, accompanied by a detailed discussion on numerical reconstruction. The review then explores various hardware setups used in DH, including inline, off-axis, and dual or multiple-view configurations, outlining their advantages and limitations. We also delve into different hologram processing methods, categorized into traditional multi-step, inverse, and machine learning (ML)-based approaches, providing in-depth insights into their applications for 3D particle tracking and flow diagnostics across multiple studies. The review concludes with a discussion on future prospects, emphasizing the significant role of ML in enabling accurate DH-based particle tracking and flow diagnostic techniques across diverse fields, such as manufacturing, environmental monitoring, and biological sciences.

Large volumes of light sources and their characteristics control devices are challenges to out-of-laboratory applications of quantum precision measurement devices, such as spin-exchange relaxation-free (SERF) inertial and extremely weak magnetic field measurement devices. High-Q whispering gallery mode (WGM) resonators have excellent optical field confinement capability, extremely narrow linewidth, and small mode volume, which can easily realize compact low noise and high-frequency stability light sources, having a great potential to realize more compact quantum precision measurement devices. This will broaden the use of quantum precision measurement devices in areas such as transportable SERF gyroscopes, wearable magnetometers, etc. In this review, the fundamentals and characteristics of WGM optical cavities are firstly introduced, as well as its fabrication techniques and built-up materials. Then, the laser stabilizing applications of WGM optical cavities including wavelength tuning, self-injection locking, and thermal stabilization are discussed. Finally, the application prospects of quantum precision measurement are discussed.

This paper presents a comprehensive review of recent advancements in bearing health monitoring and remaining useful life (RUL) prediction. It highlights key innovations in anomaly detection, health indicator construction, degradation modeling, and RUL estimation, examining developments across statistical, machine learning, and deep learning approaches while analyzing their strengths, limitations, and application contexts. Special emphasis is placed on the role of deep learning in capturing complex degradation patterns from multi-dimensional time series data and improving predictive accuracy in dynamic industrial settings. Additionally, this review explores multi-source data fusion techniques, which enhance anomaly detection robustness by integrating information from diverse sensor modalities. By identifying critical challenges and suggesting future research directions, this study aims to advance the development of robust and adaptive prediction models for intelligent maintenance in industrial applications.

This article reviews the state-of-the-art in remote bonding of fiber Bragg grating sensors, primarily for Lamb wave measurements in structures. The presence of damage in a structure modifies Lamb waves through reflection and scattering, as well as the potential conversion between Lamb modes. While FBG sensors have been applied to capture ultrasonic waves for the past 30 years, the mounting configuration called remote bonding has recently come to attention as an alternative method to capture guided waves from structures. In this case, the FBG is not located at the bond, but instead at a remote location along the optical fiber. The Lamb waves are converted into propagating acoustic waves along the fiber, which are measured by the FBG. This article presents a discussion of the primary benefits of remote bonding, including the higher sensitivity to low-amplitude Lamb waves, the fact that the FBG can situated in a less harsh environment than the sensing region, and the insensitivity to quasi-static strain. The properties of ultrasonic modes in optical fibers and their conversion from Lamb waves is first reviewed. Strategies to detect these waves with FBGs and the associated instrumentation is also presented. Recent examples from the literature utilizing remote bonded FBGs are then presented. Finally, acoustic couplers to transfer the ultrasonic modes from one or more optical fibers to another are also reviewed.

Abstract:To address the issue of the low amplitude of the second harmonic received during the nonlinear detection of carbon fiber composites, which is easily drowned by noise and results in poor detection sensitivity, a selective amplification nonlinear detection method based on the inverse phase principle is proposed. This study compares the efficiency of second harmonic generation and the sensitivity of nonlinear coefficients to crack parameters between traditional nonlinear detection methods and the proposed selective amplification nonlinear detection method. A mathematical model of the interaction between ultrasonic guided waves and microcracks was established based on nonlinear detection theory, and simulations and experiments were conducted according to the proposed method. The results demonstrate that the selective amplification nonlinear detection method enhances the amplitude of the second harmonic by more than 40% compared to traditional nonlinear detection, effectively improving the sensitivity of nonlinear detection. This provides a novel approach for characterizing the extent of damage in composites and holds significant practical engineering value.

The manufacturing accuracy of plunge shaving cutter directly determines the machining error of gear. The plunge shaving cutter has a complex, discontinuous tooth surface structure, with the land being a key feature on the tooth surface. Its boundary causes measuring force fluctuations of the probe and the probe from the land and falls into the serration slot in existing measurement methods, resulting in the measurement results cannot truly reflect the machining accuracy. Therefore, a precision measurement method for the manufacturing accuracy of plunge shaving cutters is proposed in this paper, fully considering the influence of the land. A linkage model for land measurement is established, enabling the measurement of the land across three-teeth three-sections. The parameters of land and its distribution on the tooth surface are accurately determined through land identification, which provides the basis for the measurement of plunge shaving cutter. A linkage model between the probe and the rotary table is constructed based on the dip of land, ensuring that the tooth profile is always measured along the centerline of land by the probe, and the detrimental effect of the probe falling into the serration slot during conventional measurement is avoided. The segment measurement of tooth direction is achieved by controlling the probe using the land parameters, such as the midpoint of land and land width, which the fluctuation of measurement data is effectively reduced. Finally, the correctness of the method is verified through measurement comparison experiments. Based on traditional measurement methods, the influence of the land on the measurement position and path is fully considered in the proposed approach. Accurate measurement and evaluation of the manufacturing accuracy of plunge shaving cutter's tooth profile and tooth direction are achieved, which provided technical support for the high-precision machining of gears.

Reliable operation of pneumatic control valves is essential for the safety and efficiency of industrial control systems. Existing data-driven anomaly detection methods are typically based on datasets containing faults from faulty valves. However, the scarcity of abnormal samples and tedious or time-consuming of data labeling in practice often lead to imbalanced datasets and unlabeled data, which pose challenges for the application of these methods. To address this problem, we propose a novel anomaly detection method for pneumatic control valves based on stacked re-optimized convolutional autoencoder (S-RCAE) and product quantization (PQ) technology. S-RCAE is designed as a feature extraction network to obtain the latent and essential feature representations of anomalies in the monitoring data. It is stacked by placing a sample selection layer with the DBSCAN clustering algorithm between the two convolutional autoencoders (CAEs). Furthermore, the two CAEs are modified by inserting a convolutional block attention module (CBAM) between every two layers. During the training of S-RCAE, the idea of few-shot learning and an improved cost-sensitive function are adopted for the labeling and feature extraction of anomaly samples in unlabeled and class-imbalanced datasets. Then, a feature fusion mechanism is implemented for the resulting representation from S-RCAE. The PQ is further introduced to enable dimensionality reduction and storage of the fused features and the accurate identification and assessment of anomalies using quantitative scores. The experimental results show that the proposed method can accurately identify and quantify the abnormal states of the valve from the unlabeled and class-imbalanced datasets.

Correlative imaging, integrating diverse observational methods, has become increasingly vital for comprehensive sample analysis by linking distinct datasets that are accessible only through individual techniques. This study combines cryogenic scanning electron microscopy (cryo-SEM) and cryogenic Raman micro-spectroscopy (cryo-Raman), which together offer extensive insight into sample analysis. Cryo-SEM effectively investigates the surface, structure, and morphology, while cryo-Raman excels in detecting and identifying chemical composition. To integrate these techniques, we developed a novel assembly compatible with commercial cryo-SEM sample holders. The assembly is designed for precise sample positioning and observation at cryogenic temperatures of liquid nitrogen. This setup, controlled by LabVIEW software, includes a Dewar vessel containing liquid nitrogen, a 3D-printed stage for mounting sample holders, and a piezoelectric stage for XY motion, with z axis adjustments connected to the Raman spectrometer table. A plexiglass cover minimizes contamination and preserves sample integrity during analysis.
The assembly was validated by first analyzing polystyrene at cryogenic temperatures to ensure its functionality. Subsequent tests on Cupriavidus necator H16 – bacteria capable of producing polyhydroxyalkanoates (PHAs), a type of biodegradable polyester –demonstrated the system's efficiency. Polyhydroxyalkanoates, which are of interest as a sustainable alternative to petrochemical plastics, can be produced from industrial waste streams. Our results show that the combination of cryo-SEM and cryo-Raman is suitable for studying these microorganisms and permits a deeper understanding of the properties of polymer granules in microbial cells. This integrated approach represents a significant advance in semi-correlative imaging, providing more efficient and detailed analyses for future research in microbial physiology production and related fields.

The vibration signals of bearings operating at time-varying speeds exhibit remarkably non-stationary and typically are accompanied by noise interference. The Time-Frequency Distribution (TFD) obtained by the traditional time-frequency analysis methods is blurred due to the resolution limitation. For accurately revealing instantaneous fault characteristic frequencies of signals, Parameterized Resampling Multisynchrosqueezing Transform is put forward, which combines the advantages of the Parameterized Resampling Time-Frequency transform and the Improved Multisynchrosqueezing Transforms. Firstly, instantaneous frequency components are extracted by means of the Parameterized Resampling Time-Frequency transform and its inverse transform to reduce noise interference. The time-frequency energy concentration is subsequently improved by the resampling operator and reassignment operation. Finally, the simulation signal and two real-world bearing fault signals are applied for validation. The results indicate that the TFD obtained by the proposed method exhibits the highest energy concentration and the best readability under noise interference, as well as excellent anti-noise robustness, in comparison with other time-frequency analysis methods.

High-speed imaging is central to the experimental investigation of fast phenomena, like flapping flags. Event-based cameras use new types of sensors that address typical challenges such as low illumination conditions, large data transfer, and the trade-off between increasing repetition rate and measurement duration more efficiently and at reduced costs compared to classical frame-based fast cameras. Event-based cameras output unstructured data that frame-based algorithms can not process. This paper proposes a general method to reconstruct the motion of a slender object similar to the centreline of a flapping flag from raw streams of event data. The method takes advantage of continuous illumination, and the reconstruction update rate is set after and independent of the data collection. Our algorithm relies on a coarse chain-like structure that encodes the current state of the line and is updated by the occurrence of new events. The algorithm is applied to synthetic data, generated from known motions, to demonstrate that the method is accurate up to one percent of error for tip-based, shape-based, and modal decomposition metrics. Degradation of the reconstruction accuracy due to simulated defects only occurs when the severity of the defects is more than two orders of magnitude larger than what we typically encounter in experiments. The algorithm is then applied to experimental data of flapping flags, and we obtain relative errors below one percent when comparing the results with the data from laser distance sensors. The reconstruction of line deformation from event-based data is accurate and robust, and unlocks the ability to perform autonomous measurements in experimental mechanics.

Correlative imaging, integrating diverse observational methods, has become increasingly vital for comprehensive sample analysis by linking distinct datasets that are accessible only through individual techniques. This study combines cryogenic scanning electron microscopy (cryo-SEM) and cryogenic Raman micro-spectroscopy (cryo-Raman), which together offer extensive insight into sample analysis. Cryo-SEM effectively investigates the surface, structure, and morphology, while cryo-Raman excels in detecting and identifying chemical composition. To integrate these techniques, we developed a novel assembly compatible with commercial cryo-SEM sample holders. The assembly is designed for precise sample positioning and observation at cryogenic temperatures of liquid nitrogen. This setup, controlled by LabVIEW software, includes a Dewar vessel containing liquid nitrogen, a 3D-printed stage for mounting sample holders, and a piezoelectric stage for XY motion, with z axis adjustments connected to the Raman spectrometer table. A plexiglass cover minimizes contamination and preserves sample integrity during analysis.
The assembly was validated by first analyzing polystyrene at cryogenic temperatures to ensure its functionality. Subsequent tests on Cupriavidus necator H16 – bacteria capable of producing polyhydroxyalkanoates (PHAs), a type of biodegradable polyester –demonstrated the system's efficiency. Polyhydroxyalkanoates, which are of interest as a sustainable alternative to petrochemical plastics, can be produced from industrial waste streams. Our results show that the combination of cryo-SEM and cryo-Raman is suitable for studying these microorganisms and permits a deeper understanding of the properties of polymer granules in microbial cells. This integrated approach represents a significant advance in semi-correlative imaging, providing more efficient and detailed analyses for future research in microbial physiology production and related fields.

High precision of real-time (RT) Low Earth Orbit (LEO) satellite clocks is crucial for enabling future LEO-enhanced positioning, navigation, and timing services. For filter-based RT processing, multi-GNSS observations from onboard LEO satellites can strengthen the observation model and improve the LEO precise orbit determination, but have limited improvement effect on the clock estimation. The time references for RT GNSS clocks of different constellations (even from the same analysis center) are different and vary with time. This requires estimating constellation-wise LEO satellite clocks or time-varying Inter-System Biases (ISBs). This contribution investigates multi-GNSS LEO satellite clock and the corresponding Signal-In-Space Ranging Errors (SISRE) using GPS/Galileo observations collected onboard Sentinel-6A satellite, and GNSS products from various analysis centers with different latencies, including the Centre National d'Etudes Spatiales (CNES) real-time (RT), the GeoForschungsZentrum Multi-GNSS Experimental (MGEX) (GBM) rapid, the European Space Agency MGEX (ESM) final, and the Wuhan University MGEX (WUM) final products. Three ISB stochastic models were implemented, i.e., the constant, the random walk, and the white noise models. Results show that the ISB estimates exhibit systematic effects of orbital periods (ca. 1.85 h) when applying the random walk or white noise models using the post-processing products. The white noise model shows the best performance when applying the RT GNSS products with large time-varying differences in the constellation-wise time references, with improvements of about 40.8% and 12.5% for clock precision, and about 38.1% and 7.9% for SISRE, respectively, compared to the constant and random walk models. When using the post-processing products with very similar time references for different constellations, the three ISB stochastic models do not deliver significant differences in clock precision and SISRE.

This paper proposes a prototype device engineered to measure the optical properties of semiconductor films deposited on Si substrates under controlled stress conditions. Utilizing a four-point bending method enhanced with a displacement conversion mechanism, the device ensures the preservation of necessary optical paths for both reflected and transmitted light, which is essential for optical measurements. The design incorporates an indenter with an inclination of less than 0.02° within the four-point bending apparatus, enabling application of the stress necessary to change optical properties without fracturing the Si substrate. The flexure observed in the Si substrate upon stress application was found to closely align with theoretical predictions for four-point bending. Furthermore, this study established a direct proportionality between the stress dependence of the Raman shift and applied stress, with the proportionality coefficient matching those documented in the literature. The device has been successfully demonstrated to facilitate Raman spectroscopy measurements at a stress level of 600 MPa, doubling the previously reported threshold, without resulting in fractures of the Si wafer. This development presents a powerful tool for the assessment of semiconductor films on Si substrates, offering advanced insights into stress-dependent optical properties.

Instrument calibration is essential to ensure measurement accuracy and reliability, particularly in wood characterization using non-destructive acoustic techniques. This study aims to develop and validate an improved calibration strategy for wood characterization tools. It focuses on integrating advanced algorithms into resource-constrained microcontroller systems. An optimized time-of-flight (ToF) detection algorithm based on the Akaike Information Criterion (AIC) was implemented. The algorithm incorporates adaptive intelligent windows to autonomously identify the onset of acoustic waves, eliminating user intervention and enhancing repeatability. A suitable calibration material compatible with commercial piezoelectric sensors was identified and adapted for testing. Experimental investigations were carried out on cylindrical rods of various materials and lengths to measure acoustic wave propagation velocity, comparing results from two commercial systems and a laboratorydeveloped prototype. Time of flight measurements obtained with the prototype showed a high level of agreement with theoretical propagation times, outperforming commercial systems in accuracy and computational efficiency. These findings support the use of an aluminium bar as the reference calibration material, alongside the intelligent AIC algorithm, to ensure consistent and reliable measurements. The proposed calibration strategy offers a robust and repeatable solution for wood characterization applications. By optimizing computational efficiency and accuracy, this approach enables the integration of advanced acoustic measurement techniques into cost-effective, microcontroller-based systems, paving the way for broader adoption in industrial and research settings.

Multi-instance 6D pose estimation is a fundamental task in processing depth images and point cloud data for industrial robots and automation. This task is often hindered by challenges such as a high number of pseudo outliers, instance occlusions, and low overlap between instances and models. The Point Pair Feature (PPF) is a widely recognized concept for addressing multi-instance pose estimation, characterized by its lack of training requirements, robustness to occlusion, and exceptional ease of use. However, existing PPF-based methods exhibit relatively poor performance, particularly when compared to machine learning-based approaches. In this paper, we propose a robust multi-view PPF-based method that specifically addresses the challenges of generating multi-view models and enhancing model generalization. Additionally, we introduce a comprehensive usage framework for multi-view models. This framework incorporates background removal and scene segmentation for preprocessing, a multi-view PPF-based approach for primary computation, and a multi-instance spatial structure to eliminate erroneous results during post-processing. When evaluated on the ITODD datasets from the BOP Challenge, our method achieves the SOTA performance among traditional methods for 3D point cloud data, with an average recall of 69.6%. These results demonstrate the following: (1) a significant performance improvement of 44.7% compared to the leading conventional method, and (2) performance that is nearly equivalent to that of the leading machine learning method. These results underscore the robustness and effectiveness of our method in advancing multi-instance pose estimation for industrial applications.

Currently, various smart manufacturing technologies are in operation in high-tech industries. However, this is still out of reach for many traditional manufacturing industries, particularly those in developing countries. Therefore, this study is dedicated to using new technologies to improve the process inspection in high-manpower production lines in a low-cost and efficient manner. In this experiment, the assembly process for 67 engines was continuously detected. Each engine has 11 locking points. After cleaning the data, 1249 coordinate values were recorded. Based on the monitoring results obtained during the experimental process, the probability of successful color recognition was extremely high. First, it was found that an air tubing pipe connected by pneumatic tools could effectively replace object and motion detection as the basis for judgment. Second, it was found that doing so could also avoid shadows caused by tools or actions, thereby reducing the chance of misjudgment. This discovery not only reduces the number of cameras used, but also more accurately detects whether the screws are locked. Moreover, the hardware specifications required for color recognition are not in strong demand, and the recognition rate can reach 1 μs. In the future, it can be widely used on various tools by changing the finger sleeves or marking the tool with a ribbon without hindering the operation. In the future, applying cost-effective and easy-to-maintain smart manufacturing methods to traditional manufacturing production lines will also effectively reduce labor shortages caused by factors such as the gradual decline in the workforce and the impact of disease and provide a safer and more efficient working environment.

Camera calibration is a key component of three-dimensional particle tracking velocimetry (PTV) experiments, and its proper implementation is key to the success of the method. In this paper, we review and compare four different camera calibration models used in PTV experiments without volumetric refinement. One of the calibration models is new and provides an analytical inversion of the Soloff polynomial. The other three calibration models are taken from three established open source PTV frameworks: OpenPTV, MyPTV and proPTV. In particular, we present a general formulation of calibration models that allows their rigorous comparison and evaluation with respect to their 3D-to-2D projection errors and 2D-to-3D reconstruction errors. We compare the models and the calibration errors in three different tasks, including extrapolation and interpolation of marker points, using a realistic calibration of an experimental camera setup. In the end, we conclude with the pros and cons of each method in order to be able to choose the most suitable one for individual needs.

Spurious measurements in surface data are common in technical surfaces. Excluding or ignoring these spurious points may lead to incorrect surface characterization if these points inherit features of the surface. Therefore, data imputation must be applied to ensure that the estimated data points at spurious measurements do not deviate strongly from the true surface and its characteristics. Traditional surface data imputation methods rely on simple assumptions and ignore existing knowledge of the surface, resulting in suboptimal estimates. In this paper, we propose the use of stochastic processes for data imputation. This approach, which originates from surface texture simulation, allows a straightforward integration of a priori knowledge. We employ Gaussian processes with both stationary and non-stationary covariance structures to address missing values in surface data. In addition, we apply the method to a real-world scenario in which a spurious turned profile is obtained from an actual measurement. 
Our results demonstrate that the proposed method fills the missing values by maintaining the surface characteristics, particularly when surface features are missing.

This paper investigates depolarization influences on detected filtered Rayleigh scattering (FRS) signals to understand, in general, when it is necessary to model both the polarized and depolarized portion of the signal, and when it is sufficient to just model the polarized portion. The effectiveness and robustness of spectroscopic FRS (i.e. the frequency scanning method) is driven by the ability to accurately model these signals across the full range of operating conditions for the instrument. Due to the molecular Rayleigh scattering process, the polarization of the scattered laser light will be slightly depolarized with respect to the incident light. The analysis in this work shows that for a subset of operating conditions (e.g. lower angles between laser propagation direction and observation direction, and near-ambient thermodynamic state), the molecular vapor filter for the FRS measurement suppresses most of the polarized signal, while allowing the depolarized signal to pass through. As such, there is a need to model this depolarized portion of the signal to obtain accurate measurements across the full FRS operating space. Applying the proposed signal model in this work to an extensive FRS database demonstrates vast improvement in residuals between measured and modeled FRS signals.