Non-contact Technique Research Articles

A Review of Visual Estimation Research on Live Pig Weight.

The weight of live pigs is directly related to their health, nutrition management, disease prevention and control, and the overall economic benefits to livestock enterprises. Direct weighing can induce stress responses in pigs, leading to decreased productivity. Therefore, modern livestock industries are increasingly turning to non-contact techniques for estimating pig weight, such as automated monitoring systems based on computer vision. These technologies provide continuous, real-time weight-monitoring data without disrupting the pigs' normal activities or causing stress, thereby enhancing breeding efficiency and management levels. Two methods of pig weight estimation based on image and point cloud data are comprehensively analyzed in this paper. We first analyze the advantages and disadvantages of the two methods and then discuss the main problems and challenges in the field of pig weight estimation technology. Finally, we predict the key research areas and development directions in the future.

Label-Free Non-Contact Vascular Imaging using Photon Absorption Remote Sensing.

Functional vascular imaging is a critical method for early detection and prevention of disease. Established non-contact vascular imaging techniques capture predominantly structural information. In this study, a novel non-contact label-free in vivo Photon Absorption Remote Sensing (PARS) microscope is developed for structural and functional vascular imaging. The presented in vivo PARS microscope captures the endogenous absorption of green (532nm) light to form a complete picture of vasculature and surrounding tissues. Imaging system repeatability is enhanced through robust transient absorption signal extraction, and state-of-the-art real-time alignment methods. Detailed imaging of vascular structure is demonstrated through in vivo microscopy of two established animal models: mouse ear and chicken embryo. Preliminary functional contrast is realized through video rate imaging of red blood cell dynamics in the capillary networks of chicken embryos. The presented in vivo PARS microscope successfully captures detailed structural and functional vascular contrast. This innovative non-contact label-free imaging technique holds promise as a tool for preventative medical care, as functional change often precedes structural change.

Methyl ricinoleate droplet impact on a high temperature stainless steel surface: Dynamic behavior and heat transfer

The rapid and uniform heating of the feedstock plays a key role in pyrolysis processes. Dispersing methyl ricinoleate (MR) into spray droplets can improve the heating speed and uniformity in the pyrolysis process, which is of great benefit to the yield of the target product. This article describes the results and mechanisms of single MR droplet impacting on a heated stainless steel surface at different temperatures (385 ∼ 520 °C). Two non-contact measurement techniques, including a high-speed camera and a thermal infrared imager, were used to record the dynamic behavior and heating effects of the MR droplet on the heated surface at different temperatures, respectively. Four representative impingement states were observed during droplet impact: adhesion rebound, adhesion rebound with breakup, bouncing, and bouncing with breakup. As a result, a comprehensive map was created, based on a dimensionless analysis, describing the relationships between the typical states. Regarding the quantitative analysis, the maximum spreading factor and the dimensionless droplet residence time were linearly related to the 0.336 and 0.389 powers of the Weber number, respectively. By calculating the average heat flux of a droplet, it was found that it is related to the stainless steel surface temperature, the droplet saturation temperature, and the Reynolds number. The transient temperature at the surface of the MR droplet was measured to quantify the effectiveness of the heating. This study could provide a theoretical basis for the regulation and optimization of MR pyrolysis conditions.

Exploring the Frontiers of Cell Temperature Measurement and Thermogenesis.

The precise measurement of cell temperature and an in-depth understanding of thermogenic processes are critical in unraveling the complexities of cellular metabolism and its implications for health and disease. This review focuses on the mechanisms of local temperature generation within cells and the array of methods developed for accurate temperature assessment. The contact and noncontact techniques are introduced, including infrared thermography, fluorescence thermometry, and other innovative approaches to localized temperature measurement. The role of thermogenesis in cellular metabolism, highlighting the integral function of temperature regulation in cellular processes, environmental adaptation, and the implications of thermogenic dysregulation in diseases such as metabolic disorders and cancer are further discussed. The challenges and limitations in this field are critically analyzed while technological advancements and future directions are proposed to overcome these barriers. This review aims to provide a consolidated resource for current methodologies, stimulate discussion on the limitations and challenges, and inspire future innovations in the study of cellular thermodynamics.

Trapping light, revealing properties: Laser trapping as a powerful tool for photoluminescence spectroscopy

Laser trapping, a non-contact technique for manipulating microscopic objects, has gained prominence in scientific research. When coupled with fluorescence spectroscopy, it offers a powerful tool for exploring material properties. This study demonstrates the application of laser trapping in the crystallization of MAPbBr3 perovskites and its simultaneous use for photoluminescence imaging. MAPbBr3 perovskites are a class of materials with exceptional optical properties, making them attractive for various optoelectronic applications. However, conventional excitation methods using UV light can lead to phase segregation and mechanical distortion of these materials. Two-photon excitation, on the other hand, offers advantages such as deeper penetration and reduced scattering interference. In this research, we utilize a 1064 nm continuous wave laser for both trapping and excitation purposes. The MAPbBr3 perovskite, with its absorption band ranging from 400 to 540 nm, exhibits two-photon excitation at 532 nm. By focusing the laser beam at the air-solution interface, we successfully crystallize MAPbBr3 perovskites from an unsaturated precursor solution. Simultaneously, the same laser source is used for photoluminescence imaging, allowing for real-time analysis of the crystal's emission properties. This approach eliminates the need for additional excitation sources and simplifies the experimental setup. The combination of laser trapping and two-photon excitation opens up new possibilities for studying perovskite materials. It provides a gentle and non-invasive method for manipulating and characterizing hybrid perovskites materials, paving the way for advancements in various fields such as optoelectronics and energy harvesting.

MDAR: A Multiscale Features-Based Network for Remotely Measuring Human Heart Rate Utilizing Dual-Branch Architecture and Alternating Frame Shifts in Facial Videos.

Remote photoplethysmography (rPPG) refers to a non-contact technique that measures heart rate through analyzing the subtle signal changes of facial blood flow captured by video sensors. It is widely used in contactless medical monitoring, remote health management, and activity monitoring, providing a more convenient and non-invasive way to monitor heart health. However, factors such as ambient light variations, facial movements, and differences in light absorption and reflection pose challenges to deep learning-based methods. To solve these difficulties, we put forward a measurement network of heart rate based on multiscale features. In this study, we designed and implemented a dual-branch signal processing framework that combines static and dynamic features, proposing a novel and efficient method for feature fusion, enhancing the robustness and reliability of the signal. Furthermore, we proposed an alternate time-shift module to enhance the model's temporal depth. To integrate the features extracted at different scales, we utilized a multiscale feature fusion method, enabling the model to accurately capture subtle changes in blood flow. We conducted cross-validation on three public datasets: UBFC-rPPG, PURE, and MMPD. The results demonstrate that MDAR not only ensures fast inference speed but also significantly improves performance. The two main indicators, MAE and MAPE, achieved improvements of at least 30.6% and 30.2%, respectively, surpassing state-of-the-art methods. These conclusions highlight the potential advantages of MDAR for practical applications.

3D micromorphological reconstruction and roughness characterization of wood surface based on sequence images

A novel non-contact optical inspection technique is proposed to precisely reconstruct the three-dimensional surface structure of wood and evaluate its roughness. The feasibility of using image fusion techniques to characterize the 3D roughness of wood surfaces is explored by combining high-resolution serial image acquisition techniques with advanced data processing methods. The work focuses on examining various cut surfaces of three distinct wood species: Larch, Catalpa, and Toona sinensis. The proposed 3D roughness parameters are verified using 3D reconstruction and precise measurements. The experimental results demonstrate that the proposed method exhibits strong adaptability in measuring wood surface roughness and achieves a high degree of consistency with the results obtained from traditional digital microscope systems. This study not only validates the possible use of multilayer sequence image fusion technology in measuring wood roughness, but also establishes a solid basis for the creation of 3D assessment criteria for wood surface roughness and associated inspection systems.

Algorithm Analysis and Optimization of a Digital Image Correlation Method Using a Non-Probability Interval Multidimensional Parallelepiped Model

Digital image correlation (DIC), a widely used non-contact measurement technique, often requires empirical tuning of several algorithmic parameters to strike a balance between computational accuracy and efficiency. This paper introduces a novel uncertainty analysis approach aimed at optimizing the parameter intervals of a DIC algorithm. Specifically, the method leverages the inverse compositional Gauss–Newton algorithm combined with a prediction-correction scheme (IC-GN-PC), considering three critical parameters as interval variables. Uncertainty analysis is conducted using a non-probabilistic interval-based multidimensional parallelepiped model, where accuracy and efficiency serve as the reliability indexes. To achieve both high computational accuracy and efficiency, these two reliability indexes are simultaneously improved by optimizing the chosen parameter intervals. The optimized algorithm parameters are subsequently tested and validated through two case studies. The proposed method can be generalized to enhance multiple aspects of an algorithm’s performance by optimizing the relevant parameter intervals.

Unveiling full-dimensional distribution of trap states toward highly efficient perovskite photovoltaics

To gain a deep understanding and address key issues in perovskite photovoltaics, such as power conversion efficiency (PCE) and long-term stability, defect passivation and analysis of the device performance are required. Here, we proposed a non-contact characterization technique named scanning photocurrent measurement system (SPMS) for device surface detection, having conducted signal analysis and method adjustments based on perovskite photovoltaic devices. This technique enables the monitoring of minority carriers in the device, allowing for the investigation of carrier behavior based on photocurrent signals. By integrating SPMS with thermal conductance spectroscopy (TAS) and drive-level capacitance profiling (DLCP), we further simulated a spatial three-dimensional (3D) distribution of trap states in the device and analyzed the distribution of trap states matched with energy space. Through extensive case studies, we have validated the universality and accuracy of this method. The integration of trap state characterization techniques provides strong support for targeted defect passivation and performance evaluation of perovskite photovoltaic devices, yielding a highly efficient perovskite solar cell with PCE as high as 25.74%.

Full-Field Measurement of Lightweight Nonlinear Structures Using Three-Dimensional Scanning Laser Doppler Vibrometry

Modern noncontact measurement techniques such as the three-dimensional scanning laser Doppler vibrometry (3D SLDV) are advantageous in measuring vibrations of lightweight, thin-walled aerospace structures, which were conventionally deemed as difficult or not feasible to apply using attached transducers. Nevertheless, the full-field measurements using 3D SLDV are still limited to extracting modal properties of linear structures, while measurements of complex nonlinear structures are rarely reported. This paper aims to extend the full-field measurement capability of 3D SLDV and combines it with a multiple-input–single-output vibration controller to deal with nonlinear structures. An advanced test strategy is introduced, which is capable of obtaining amplitude-dependent resonant frequencies, modal damping ratios, and full-field, multiharmonic mode shapes of nonlinear normal modes (NNMs). Conflicting parameters such as the frequency resolution and measurement time are optimized by combining phase separation and phase resonance testing techniques in a coherent strategy. The capabilities of the proposed nonlinear modal testing strategy are demonstrated on a realistic, large-scale fan blade that exhibits softening behaviors. Two of its NNMs were investigated at larger vibration amplitudes. Its nonlinear modal parameters were successfully extracted and validated, highlighting the time efficiency and data accuracy of the proposed strategy for measuring industrial-scale, lightweight nonlinear structures.

Analisis Karakteristik Material Baja dengan Metode Digital Image Correlation (DIC)

The use of steel materials in building construction opens new opportunities for sustainable development, as steel exhibits corrosion resistance, durability, and reliability in terms of strength and ductility. Digital Image Correlation (DIC) is non-contact technique in which digital images of the surface of a test object are captured using high-resolution cameras. This study conducted measurements of strain distribution on the specimen's surface using the DIC method throughout the entire tensile testing process. The study particularly focuses on examining changes in strain distribution during the melting phase and the local deformation phase leading to fracture. In this research, a comparison will be made between the load-displacement curves obtained from experimental laboratory testing and the results analyzed using the DIC method for SS400-grade steel material. Based on the results of the tensile test and DIC analysis that have been conducted, conclusions have been drawn in the research. The tensile test results of SS400 steel material with a thickness of 6 mm, 8 mm, 10 mm, and 12 mm meet the quality requirements in the tested specification standards, and the results of the force-displacement curve between the experimental test results and the DIC method obtained a minimum deviation with a value below 10%,. Therefore, it can be concluded that the DIC method exhibits a reasonably good level of accuracy, making it suitable for validating the results of experimental tests.

Label-free functional imaging of vagus nerve stimulation-evoked potentials at the cortical surface

Vagus nerve stimulation (VNS) is an FDA-approved stimulation therapy to treat patients with refractory epilepsy. In this work, we use a coherent holographic imaging system to characterize vagus nerve-evoked potentials (VEPs) in the cortex in response to VNS stimulation paradigms without electrode placement or any genetic, structural, or functional labels. We analyze stimulation amplitude up to saturation, pulse width up to 800 μs, and frequency from 10 Hz to 30 Hz, finding that stimulation amplitude strongly modulates VEPs response magnitude (effect size 0.401), while pulse width has a moderate modulatory effect (effect size 0.127) and frequency has almost no modulatory effect (effect size 0.009) on the evoked potential magnitude. We find mild interactions between pulse width and frequency. This non-contact label-free functional imaging technique may serve as a non-invasive rapid-feedback tool to characterize VEPs and may increase the efficacy of VNS in patients with refractory epilepsy.

Multilevel Printed Wearable Radio-Frequency Intelligent Identification Platform for Object Recognition.

As a noncontact target recognition technique, radio-frequency identification (RFID) technology demonstrates attractive potential in constructing human-machine interaction (HMI) systems. However, the current development of RFID technologies in HMI systems is hampered by critical challenges in manufacturing high-performance RFID readers with superior flexibility and wearing comfort. Hence, we propose a multilevel printing strategy to overcome the difficulties in manufacturing high-performance large-scale microwave systems. Compared to traditional processes, the RFID system fabricated by the hybrid additive manufacturing technique exhibits equivalent electromagnetic performance and has obvious advantages in terms of manufacturing cost and environmental friendliness. A printed reconfigurable antenna with intelligent radiation mode is seamlessly integrated with the reader circuit via a "one-step" printing technology. Additionally, through chemical doping and artificial intelligence (AI) prediction, we have developed a modified polydimethylsiloxane (PDMS) encapsulation to miniaturize the system volume and enhance reliability. Electromagnetic and mechanical measurements demonstrated that our flexible RFID platform offers superior reliability and stability during long-term daily use. The RFID platform possesses exceptional capabilities in target positioning and accurate identification, demonstrating unique potential in noncontact sensing and recognition, which are highly demanded by flexible and wearable HMI systems.

Statistical blade tip timing measurement, Part II: High-order cumulant architecture

Blade tip timing (BTT) is a potential non-contact vibration measurement technique for rotating blades owing to its high efficiency and long service life. However, probe layout design and vibration parameter identification are critical challenges in BTT due to its inherent undersampling. To address this issue, we have proposed the concept of statistical BTT measurement which promotes efficient layouts and parameter identification methods by shifting the recovery target from the signal to itself to its statistics. In the first paper of series papers, we have developed covariance architecture for BTT measurement and signal post-processing. In this second part of series papers, we set out to develop BTT measurement from a higher-order statistic perspective. Specifically, we find that the 2qth-order cumulant retains the vibration information (frequency and amplitude) of the signal itself and it can be recovered at lower sampling rates than what is prescribed by the covariance architecture. On this basis, we propose a higher-order cumulant architecture for BTT measurement, which contains two aspects: higher-order difference layout (HODL) and higher-order cumulant-based parameter identifications. HODL is a special family of layouts that physically guarantee the availability of consecutive higher-order cumulant estimations and thus leads to a satisfactory parameter identification. To facilitate application, a series of concrete HODLs are provided for users, including a four-level nested layout and its enhanced version, and optimal fourth-order difference layout. The quantitative comparison in the simulations and experiments shows the effectiveness of the proposed higher-order cumulant architecture. Compared with the architectures developed from covariance and signal itself perspective, the proposed higher-order cumulant architecture is not only more robust to noise but also further reduces the number of required probes.

Nondestructive evaluation of debonding in composites using air-coupled coda wave analysis and local defect resonance techniques

Abstract Low acoustic energy conversion efficiency is a major challenge for air-coupled ultrasonic technology. In the determination of the lift-off distance of air-coupled sensors, there is a balance between the acoustic energy attenuation and the difficulty of extracting defect information. In this study, an air-coupled local defect resonance (LDR) technique with coda wave analysis is proposed for the nondestructive evaluation of debonding in composites. A sensor consisting of 19 elements was used to simultaneously excite and receive ultrasonic waves. Air-coupled LDR experiments were conducted on the two types of composite structures. The effects of sensor lift-off distance and coda wave analysis on the performance of the LDR technique were investigated. It was found that the sensor lift-off distance and the coda wave analysis had a significant effect on the defect detection capability of the LDR technique. For composites, the optimal sensor lift-off distance was found to be between 3.5λ and 5.5λ, where λ is the wavelength. Compared to multiple reflection echoes, the coda waves are more suitable for identifying the damage in composites. The proposed non-contact ultrasonic technique effectively reduces the required incident acoustic energy and can be used for efficient detection of debonding in composites.

Observation of enhanced heat transfer between a nanotip and substrate at nanoscale distances via direct temperature probing with Raman spectroscopy

Nanoscale heat transfer between two nanostructured surfaces holds paramount significance in the realms of extreme manufacturing and high-density data storage. However, experimental probing of heat transfer encounters significant challenges, primarily due to limitations in current instrumentation. Here, we report a method based on Raman spectroscopy to directly probe the temperature difference between a Si nanotip and SiC substrate. Results indicate a decrease in substrate temperature, while the temperature of the nanotip remains relatively stable as the nanotip moves away from the substrate from approximately 82.5 to 1320 nm. We trace this enhanced heat transfer to a significant augmentation, by one order of magnitude, in air conduction and thermal radiation energy exchange theoretically, with air conduction being the dominant mode over thermal radiation. This work advances the direct observation of surface temperatures with gaps smaller than 1 μm, utilizing a noncontact and nondestructive Raman technique, which can be extended to studying near-field heat transfer across various Raman-active surfaces.

A comparative study of principal component analysis and kernel principal component analysis for photogrammetric shape-based turbine blade damage analysis

Photogrammetry is popular as a non-contact full-field data acquisition technique for machine condition monitoring purposes. Two popular implementations thereof are Digital Image Correlation (DIC) and 3D Point Tracking (3DPT). Since these two approaches require surface preparation in the form of applying speckle patterns or discrete markers, their use is negated in several industrial applications, such as the online condition monitoring and/or assessment of horizontal axis wind turbines. A shape-based photogrammetric approach that focuses on analysing changes in the form of a rotating blade’s boundary contour is a viable alternative that does not require any surface preparation. 3D shapes of a blade at a particular instance can be extracted from a pair of stereoscopic images of the blade. Shape Principal Component Descriptors (SPCDs) determined from applying Principal Component Analysis (PCA) to Fourier coefficients of chain-code shape signatures of the blade contour can be determined. These can be regarded as indicators of the form of the shape, and as the blade rotates, variations in the SPCDs can be investigated to better understand the dynamics of the blades. This paper focuses on the application and performance evaluation of data reduction and classification techniques for a novel shape-based photogrammetry condition monitoring technique. Investigations are conducted to show that applying data reduction methods to raw time domain SPCDs can result in successful classification of differently damaged blades. Post-processing strategies are developed to ensure a more robust shape based condition monitoring tool for analysing turbine blades. Kernel Principal Component Analysis (KPCA) is implemented and it is shown that it out-performs the conventional PCA in terms of classifying different blade damage modes. The feasibility of using multi-domain statistical features as feature vectors to which PCA or KPCA is applied for classification purposes is also investigated. Results indicating how well differently damaged blades can be distinguished are provided, and it is clearly illustrated that multi-domain statistical features are more robust to noise contamination in the signals compared to using the raw SPCDs data.

Relating the ultrasonic and aerosol filtration properties of filters

Non-contact methods are useful to improve the quality control of particle filtration media. The purpose of this paper is to investigate the correlation between the filtration efficiency of a porous sheet and its ultrasonic properties obtained using a non-contact technique. An air-coupled ultrasonic technique is used to obtain rapid measurements without affecting the integrity of the material. High frequencies (from 0.1 to 2.5 MHz) are used to improve technique sensitivity, and transmitted waves are measured to probe the internal properties of the material. Measurements of transmission coefficient spectra (amplitude and phase) and the corresponding ultrasound velocity and attenuation coefficient at different frequencies are obtained for a set of filtration media with well-characterized properties. Results show that the ultrasonic properties of filtration media vary as a function of basis weight, and therefore filtration efficiency, for a given charge state. However, the effect of electrostatic charge on ultrasonic propagation is almost negligible, as expected. We conclude that ultrasonic transmission may provide a valuable tool for the continuous online monitoring of material quality during fabrication and as a method to tease apart mechanical and electrostatic contributions to particle filtration.

A survey of vision-based condition monitoring methods using deep learning: A synthetic fiber rope perspective

Computer vision technology has attracted significant interest in the condition monitoring (CM) community due to its potential to automate visual inspection and analysis of structures and components. By facilitating the processing and interpretation of visual information, including images and video data, computer vision holds promise for CM applications. However, it is essential to distinguish computer vision from non-contact CM techniques regarding their underlying principles and methods. While computer vision enables non-contact, remote monitoring, and condition assessment with minimal disruption to daily operations, it is distinct from non-contact CM techniques, which utilize various sensors to assess the condition of assets without physical contact or interference. Building upon the potential of computer vision technology, this survey paper presents a comprehensive overview of the current state-of-the-art CM methods based on computer vision and deep learning (DL) techniques, focusing on their application in monitoring synthetic fiber ropes (SFRs). SFRs are a viable alternative to steel wire ropes for underwater equipment and cranes that handle heavy loads. This is due to their high resistance to frictional wear, high tensile strength, lightweight, and flexibility. New materials, technologies, and processes for CM are being developed to meet the growing demand for SFRs. The paper explores ongoing research in applications that monitor the wear and aging of materials, as well as estimate their remaining useful life. The survey briefly discusses the traditional non-destructive testing and machine learning (ML) methods for CM applications. More importantly, DL-based methods, including supervised, unsupervised, semi-supervised, and self-supervised methods, are discussed in detail, together with the use of deep generative models and the recently developed diffusion models in the generation of synthetic datasets. Furthermore, the paper addresses the difficulties present in DL-based CM applications, including the scarcity of labeled data and the complexity and variety of the models used. The article ends by discussing the benefits of employing DL-based visual methods to understand SFR degradation processes, particularly in monitoring and maintenance.

Anthropometric Study of Correlation between the Selected Craniofacial Measurements with the Stature in Adult Bangladeshi Manipuri Males

Cephalofacial anthropometry can be useful for racial identification, forensic scientist, physical anthropologist, genetic counsellors as well as reconstructive surgery for the purposes of identification of an individuals and understanding human physical variation, gender and ethnicity especially with facial recognition as a tool in recent advances in biometrics. Stature is one of the important criteria for personal identification which has a proportional biological relationship with every parts of the human body like head, face, trunk, extremities and vertebral column. In this study, our main goal is find out the correlation between the selected craniofacial measurements with the stature in Adult Bangladeshi Manipuri males. This cross-sectional study was carried out in 100 healthy adult males from March 2017 to February 2018 in the Department of Anatomy, Bangabandhu Sheikh Mujib Medical University (BSMMU), Dhaka, Bangladesh. Descriptive statistics and testing of hypotheses were used for the analysis using SPSS software (version 22.0). Here non- significant positive correlations of the stature with the mandible height and significant positive correlations of the stature with mandible breadth was found. The Mean (±SD) of Mandible height (sto-gn) was 4.44 (±0.69), Mandible breadth (go-go) was 11.45 (±0.57). This study will be anticipated to provide baseline quantitative data on the linear craniofacial measurements and the stature of adult Bangladeshi Manipuri males. And using larger samples with non-contact measurement technique will help in defining craniofacial anthropometric profiling of the adult Bangladeshi Manipuri males. Mugda Med Coll J. 2024; 7(1): 19-25