Search Results (813)

This paper presents a thermal gas flow sensing system, from surface acoustic wave (SAW) temperature sensor to oscillation circuit and multi-module miniaturization integration. A single-port GaN/Si SAW resonator with single resonant mode and excellent characteristics was fabricated. Combined with an in-house-developed SiGe HBT, a temperature-sensitive high-frequency oscillator was constructed. Under constant temperature control, system-level flow measurement was achieved through dual-oscillation configuration and modular integration. The fabricated SAW device shows a temperature coefficient of frequency (TCF) −28.29 ppm/K and temperature linearity 0.998. The oscillator operates at 1.91 GHz with phase noise of −97.72/−118.62 dBc/Hz at 10/100 kHz offsets. The system demonstrates excellent dynamic response and repeatability, directly measuring 0–50 sccm flows. For higher flows (>50 sccm), a shunt technique extends the test range based on the 0–10 sccm linear region, where response time is <1 s with error <0.9%. Non-contact operation ensures high stability and long lifespan. The sensor shows outstanding performance and broad application prospects in flow measurement. Full article

To increase semiconductor production yield and meet the growing global demand, air bearings offering higher processing speeds and reduced friction losses have been proposed as an ideal solution. However, due to the non-contact support characteristic of air bearings, challenges such as shaft displacement caused by processing resistance inevitably arise. As an engineering requirement, the shaft must restrict lateral deflection to within 30 μm under transverse force. In our previous research, a compensation system using a nozzle–flapper mechanism as a displacement sensor was proposed to address shaft displacement. The effectiveness of the nozzle–flapper system in measuring shaft displacement was validated at rotational speeds up to 20,000 rpm. Furthermore, the compensation system’s ability to maintain the shaft’s initial position under a 5 N external force was verified in related collaborative research. In this study, building upon prior work, we further analyze the system characteristics of the cylindrical nozzle–flapper. This includes modeling the geometric space formed by the specific shape of the cylindrical flapper and nozzle and proposing an airflow hypothesis based on this geometry. The hypothesis is incorporated into the theoretical model of a standard nozzle–flapper system, resulting in an optimized theoretical method applicable to cylindrical configurations. Experimental results validating the effectiveness of the proposed model are also presented. Full article

Non-mechanical contact distance measurement solutions are becoming more and more necessary in various industries, including building monitoring, automotive, and aviation industries. Inductive proximity sensor (IPS) technology is becoming a more popular solution in the field of short distances. Because of its small size, dependability, and measurement capabilities, IPS is a good option. Separate circuits are used in the classical structures to generate the excitation signal for the sensor coil and measure the response signal. The response signal’s amplitude is typically measured. This article proposes an IPS model that uses frequency response as its basis for operation. A microcontroller and embedded technology are used to implement a small IPS structure. This includes the circuit for determining distance, as well as the signal generator used to excite the sensor coil. In essence, an LC circuit is employed, which at the unit step has a damped oscillatory response by nature. Periodically injecting energy into the LC circuit, however, causes it to enter a persistent oscillatory state. The full experimental model is implemented and presented in the article, illustrating how the distance can be measured with a 33 µm accuracy within the 10 mm range with the help of the nonlinear relationship between frequency and distance and the linear drift of frequency with temperature. Full article

This article presents a principled framework for selecting and tuning classical computer vision algorithms in the context of optical displacement sensing. By isolating key factors that affect algorithm behavior—such as feed window size and motion step size—the study seeks to move beyond intuition-based practices and provide rigorous, repeatable performance evaluations. Computer vision-based optical odometry sensors offer non-contact, high-precision measurement capabilities essential for modern metrology and robotics applications. This paper presents a systematic comparative analysis of three classical tracking algorithms—phase correlation, template matching, and optical flow—for 2D surface displacement measurement using synthetic image sequences with subpixel-accurate ground truth. A virtual camera system generates controlled test conditions using a multi-circle trajectory pattern, enabling systematic evaluation of tracking performance using 400 × 400 and 200 × 200 pixel feed windows. The systematic characterization enables informed algorithm selection based on specific application requirements rather than empirical trial-and-error approaches. Full article

To break through the bottleneck in the mapping of the mechanoluminescent (ML) intensity field to the strain field, a quantification method for full-field strain measurement based on pixel-level data fusion is proposed, integrating ML imaging with digital image correlation (DIC) to achieve precise reconstruction of the strain field. Experiments are conducted using aluminum alloy specimens coated with ML film sensor on their surfaces. During the tensile process, ML images of the films and speckle images of the specimen backsides are simultaneously acquired. Combined with DIC technology, high-precision full-field strain distributions are obtained. Through spatial registration and region matching algorithms, a quantitative calibration model between ML intensity and DIC strain is established. The research results indicate that the ML intensity and DIC strain exhibit a significant linear correlation (R² = 0.92). To verify the universality of the model, aluminum alloy notched specimen tests show that the reconstructed strain field is in good agreement with the DIC and finite element analysis results, with an average relative error of 0.23%. This method enables full-field, non-contact conversion of ML signals into strain distributions with high spatial resolution, providing a quantitative basis for studying ML response mechanisms under complex loading. Full article

This study aimed to develop a non-contact, marker-less machine learning model to estimate forearm pronation–supination angles using 2D hand landmarks derived from MediaPipe Hands, with inertial measurement unit sensor angles used as reference values. Twenty healthy adults were recorded under two camera conditions: medial (in-camera) and lateral (out-camera) viewpoints. Five regression models were trained and evaluated: Linear Regression, ElasticNet, Support Vector machine (SVM), Random Forest, and Light Gradient Boosting Machine (LightGBM). Among them, LightGBM achieved the highest accuracy, with a mean absolute error of 5.61° in the in-camera setting and 4.65° in the out-camera setting. The corresponding R² values were 0.973 and 0.976, respectively. The SHAP analysis identified geometric variations in the palmar triangle as the primary contributors, whereas elbow joint landmarks had a limited effect on model predictions. These results suggest that forearm rotational angles can be reliably estimated from 2D images, with an accuracy comparable to that of conventional goniometers. This technique offers a promising alternative for functional evaluation in clinical settings without requiring physical contact or markers and may facilitate real-time assessment in remote rehabilitation or outpatient care. Full article

Non-contact, automated health monitoring is a cornerstone of modern precision livestock farming, crucial for enhancing animal welfare and productivity. Infrared thermography (IRT) offers a powerful, non-invasive means to assess physiological status. However, its practical use on farms is limited by a key challenge: accurately locating regions of interest (ROIs), like the eyes and face, in the blurry, low-resolution thermal images common in farm settings. To solve this, we developed a new framework called ID-APM, which is designed for robust ROI registration in agriculture. Our method uses a trinocular system and our RAP-CPD algorithm to robustly match features and accurately calculate the target’s 3D position. This 3D information then enables the precise projection of the ROI’s location onto the ambiguous thermal image through inverse disparity estimation, effectively overcoming errors caused by image blur and spectral inconsistencies. Validated on a self-built dataset of farmed sika deer, the ID-APM framework demonstrated exceptional performance. It achieved a remarkable overall accuracy of 96.95% and a Correct Matching Ratio (CMR) of 99.93%. This research provides a robust and automated solution that effectively bypasses the limitations of low-resolution thermal sensors, offering a promising and practical tool for precision health monitoring, early disease detection, and enhanced management of semi-wild farmed animals like sika deer. Full article

Smart manufacturing demands ever-increasing equipment reliability and continuous availability. Traditional fault diagnosis relies on attached sensors and complex wiring to collect vibration signals. This approach suffers from poor environmental adaptability, difficult maintenance, and cumbersome preprocessing. This study pioneers the use of high-temporal-resolution dynamic visual information captured by an event camera to fine-tune a multimodal large model for the first time. Leveraging non-contact acquisition with an event camera, sparse pulse events are converted into event frames through time surface processing. These frames are then reconstructed into a high-temporal-resolution video using spatiotemporal denoising and region of interest definition. The study introduces the multimodal model Qwen2.5-VL-7B and employs two distinct LoRA fine-tuning strategies for bearing fault classification. Strategy A utilizes OpenCV to extract key video frames for lightweight parameter injection. In contrast, Strategy B calls the model’s built-in video processing pipeline to fully leverage rich temporal information and capture dynamic details of the bearing’s operation. Classification experiments were conducted under three operating conditions and four rotational speeds. Strategy A and Strategy B achieved classification accuracies of 0.9247 and 0.9540, respectively, successfully establishing a novel fault diagnosis paradigm that progresses from non-contact sensing to end-to-end intelligent analysis. Full article

This study analyzed the physical performance (via field tests) and in-match physical responses (via wearable inertial sensors) of national and international cerebral palsy (CP) football players competing in Spain’s First Division. A total of 80 players (FT1: n = 22; FT2: n = 48; FT3: n = 10) completed sprinting, change of direction, and dribbling tests. In-match data from 74 players were collected across 56 official matches. Players were classified as “international” (candidates for the national team) or “national” (non-candidates). Statistical analyses identified performance differences and predictors of international selection using multiple discriminant analysis. International players outperformed national ones in sprinting, agility, and dribbling, especially in FT1 and FT2 classes (p < 0.05; large effect sizes). In-match data (analyzed for FT2 only) showed that international players covered more distance at all intensities and executed more high-intensity actions (e.g., maximal velocity, ball contacts). High-intensity running was the strongest predictor of international status (74.5%, Wilks’ λ = 0.86, p = 0.01). Change of direction and dribbling were key discriminators in FT1 and FT2, while no clear predictor emerged in FT3. These findings support the use of physical tests and wearable technology for evidence-based talent identification and selection in CP football. Full article

A layerwise laminate FE model capable of predicting the dynamic response of delaminated composite beams with piezoelectric actuators and sensors encompassing local non-linear contact and sliding at the delamination interfaces was formulated. The kinematic assumptions of the layerwise model enabled the representation of opening and sliding of delamination interfaces as generalized strains, thereby allowing the introduction of interfacial contact and sliding effects through constitutive relations at the interface. This realistic FE model, assisted by representative experiments, was used to study the time response of delaminated active sensory composite beams with predefined delamination extents. The time response was measured and simulated for narrowband actuation signals at two distinct frequency levels using a surface-bonded piezoceramic actuator, while signal acquisition was performed with a piezopolymer sensor. Four different composite specimens, each containing a different delamination size, were used for this study. Experimental results were directly compared with model predictions to evaluate the performance of the proposed analytical approach. Damage signatures were identified in both the signal amplitude and the time of flight, and the sensitivity to delamination size was examined. Finally, the distributions of axial and interlaminar stresses at various time snapshots of the transient analysis are presented, along with contour plots across the structure’s thickness, which illustrate the delamination location and wave propagation patterns. Full article

Capacitive-coupling non-contact voltage sensors face a key challenge: their probe-conductor coupling capacitance varies, making it hard to accurately determine the division ratio. This capacitance is influenced by factors like the conductor’s insulation material, radius, and relative position. To address this challenge, this paper proposes a sensor gain self-calibration method based on switching capacitors. This method obtains multiple sets of real-time measurement outputs by connecting and switching different standard capacitors in parallel with the sensor’s structural capacitance, and then simultaneously solves for the coupling capacitance and the voltage under test, thereby achieving on-site autonomous calibration of the sensor gain. To effectively suppress interference from stray electric fields in the surrounding space, a shielded coaxial probe structure and corresponding back-end processing circuitry were designed, significantly enhancing the system’s anti-interference capability. Finally, an experimental platform incorporating insulated conductors of various diameters was built to validate the method’s effectiveness. Within the 100–300 V power-frequency range, the reconstructed voltage amplitude shows a maximum relative error of 1.06% and a maximum phase error of 0.76°, and harmonics are measurable up to the 50th order. Under inter-phase electric field interference, the maximum relative error of the reconstructed voltage amplitude is 1.34%, demonstrating significant shielding effectiveness. For conductors with diameters ranging from 6 mm² to 35 mm², the measurement error is controlled within 1.57%. These results confirm the method’s strong environmental adaptability and broad applicability across different conductor diameters. Full article

Accurate temperature regulation is essential in microfluidic apparatus, particularly for procedures such as polymerase chain reaction (PCR), cellular analysis, and chemical reactions that rely on stable thermal conditions. However, achieving temperature uniformity at the microscale remains challenging due to rapid heat dissipation, small thermal mass, and intricate flow–heat interactions. This work reviews contemporary methodologies to enhance thermal control in microfluidic systems, including proportional–integral–derivative (PID) and fuzzy PID controllers, liquid metal-based sensing, thermoelectric cooling (TECs), and evaporation or integrated heating elements for precise thermal output management. Emerging fabrication technologies, such as additive manufacturing, enable the direct integration of heating elements and sensors within microchips, improving thermal efficiency and device compactness. Advanced materials, including carbon nanotubes infused with gallium and temperature-sensitive quantum dots, offer innovative, non-contact thermal monitoring capabilities. Furthermore, artificial intelligence-driven feedback systems present opportunities for adaptive, real-time thermal optimization. By consolidating these strategies, this review highlights pathways to develop more dependable, efficient, and application-ready microfluidic devices, with implications for diagnostics, research, and other practical uses. Full article

This study examined the influence of physiological parameters on peak velocity (Vpeak) and of kinematic variables on running economy (RE) during an outdoor incremental VAM-EVAL test completed by eleven national-level triathletes. Maximal oxygen uptake (VO₂max), ventilatory thresholds, RE, and minimum muscle oxygen saturation (SmO₂min) were obtained with a portable gas analyzer and near-infrared spectroscopy (NIRS), while cadence, stride length, vertical oscillation, and contact time were recorded with a foot-mounted inertial sensor. Multiple linear regression showed that VO₂max and SmO₂min together accounted for 86% of the variance in Vpeak (VO₂max: r = 0.76; SmO₂min: r = −0.68), whereas RE at 16 km·h⁻¹ displayed only a moderate association (r = 0.54). Links between RE and kinematic metrics were negligible to weak (r ≤ 0.38). These findings confirm VO₂max as the primary determinant of Vpeak and suggest that SmO₂min can be used as a complementary, non-invasive marker of endurance capacity in triathletes, measurable in the field with portable NIRS. Additionally, inter-individual differences in cadence, stride length, vertical oscillation, and contact time suggest that kinematic adjustments are not universally effective but rather highly individualized, with their impact on RE likely depending on each athlete’s specific characteristics. Full article

For the measurement of electrical conductivity of metal materials, the traditional contact measurement method has a limited test range and requires periodic electronic calibration. In order to overcome the above shortcomings, this paper takes the inductive response of an RLC circuit driven by alternating sources as the research object and proposes a non-contact method for conductivity measurement of non-ferromagnetic metals engaged by a single-layer solenoid sensor. The effect of the circuit parameters on the inductive sensor characteristics has been described with different resonant modes, and the electric conductivities of different metals can be theoretically calculated based on eddy current. Moreover, the Comsol Multiphysics software is used to conduct finite element analysis to compare the experimental results and the simulation, which is consistent with the theoretical analysis. The measured accuracy of the inductive sensor is verified to be higher than 91% in parallel resonance, which exhibits higher stability and precision than that of series mode. The implementation of this project will provide the theoretical basis and data reference for the detection of electromagnetic properties of unknown metals and has a wide range of applications in non-destructive testing, engineering construction detection, and other fields. Full article

Motion artifacts (MA) cause great variability in a measured arterial pulse signal, and treatment of MA solely as a baseline drift (BD) fails to eliminate its effect on the measured signal. This paper presents a study on the effect of MA at rest (<0.7 Hz) on measured arterial pulse signals using a microfluidic-based tactile sensor. By taking full account of the dynamic behavior of the transmission path from the true pulse signal in an artery to a measured pulse signal at the sensor, the tissue-contact-sensor (TCS) stack, an analytical model of MA in a measured pulse signal is developed. In this model, the TCS stack is treated as a 1DOF system for its dynamic behavior; MA is quantified as the displacement (i.e., BD) and time-varying system parameters (TVSP) of the TCS stack. The mathematical expression of MA in a measured pulse signal reveals that while BD remains as low-frequency additive noise, TVSP causes time-varying harmonics in a measured pulse signal. Further time-frequency analysis (TFA) of measured pulse signals validates the existence of TVSP and, for the first time, reveals its effect on a measured pulse signal: time-varying amplitude in each harmonic and non-flat harmonic-MA-coupled baseline. Full article