Non-contact Nature Research Articles

An improved subpixel measurement method for piezoelectric ceramic driving characteristics based on particle filter

Microscopic vision displacement calculation is crucial for its high precision and non-contact nature in measuring piezoelectric ceramic displacement. However, the inefficient global search limits its effectiveness. This paper proposes an improved sub-pixel algorithm (GKF-PFBM) based on particle filtering. Firstly, particle filtering (PF) is combined with block matching (BM) to enhance matching efficiency and accuracy by replacing global search with particle state prediction and update. Then, subpixel interpolation using a Gaussian kernel function (GKF) is better adapted to nonlinear variations, handling high-frequency variations while preserving clear edges, thereby improving interpolation accuracy. Finally, a high-precision experimental platform is used to measure the driving characteristics of piezoelectric ceramics. Experimental results show that the mean errors of the GKF-PFBM algorithm and the bilinear interpolation algorithm (BI-BM) are 0.0032pixels and 0.1269pixels, respectively. The average hysteresis displacement error was measured to be approximately 12 nm by this method, verifying its precise non-contact measurement capability.

Advancements in Key Technologies for Vibration Isolators Utilizing Electromagnetic Levitation

With the advancement of manufacturing, the precision requirements for various high-precision processing equipment and instruments have further increased. Due to its noncontact nature, simple structure, and controllable performance, electromagnetic levitation has broad application prospects in ultra-precision instruments and ground testing of aerospace equipment. Research on vibration isolation technology using electromagnetic levitation is imperative. This paper reviews the latest research achievements of three types of passive isolators and five active isolation actuators. It also summarizes the current research status of analytical methods for passive isolators and the impact of isolator layout. This study explores current isolators’ achievements, such as the development of passive isolators that generate negative stiffness and require mechanical springs for uniaxial translational vibrations, single-function actuators, and control systems focused on position and motion vibration control. Based on the current isolators’ characteristics, this review highlights future developments, including focusing on passive isolators for heavy loads and multi-axis isolation, addressing complex vibrations, including rotational ones, and developing methods to calculate forces and torques for arbitrary six-DOF movements while improving speed. Additionally, it emphasizes the importance of multifunctional actuators to simplify system structures and comprehensive control systems that consider more environmental factors. This provides significant reference value for vibration isolation technology using electromagnetic levitation.

Enhancing LSPIV accuracy in low-speed flows and heterogeneous seeding conditions using image gradient

Flow measurement in rivers and channels is crucial for water resource management and infrastructure planning, especially under the context of climate change. However, traditional methods like mechanical current meters and hydroacoustic instruments face limitations in terms of cost, intrusiveness, and accessibility. In recent years, image-based velocimetry techniques have emerged as promising alternatives due to their non-contact nature and cost-effectiveness. Nevertheless, persistent challenges remain, particularly concerning the uniform distribution of surface tracers necessary for precise measurements. These challenges are particularly pronounced in cases involving artificial seeding, where ensuring uniform distribution poses a significant obstacle. To address this issue, this study presents a novel methodology for filtering Large Scale Particle Image Velocimetry (LSPIV) data based on indicators of pixel intensity gradients. The methodology was evaluated across various field measurements under low flow conditions, encompassing a wide range of seeding characteristics. The evaluations demonstrated improvements in mean surface velocity profile estimation, showing reductions of up to 70 % in normalized root mean square error compared to not using filters. Additionally, the results were compared with filters typically employed by experienced LSPIV users, such as background subtraction and cross-correlation coefficient thresholds, showing improvements with the proposed filter. Implementation of the proposed strategy reduces the subjectivity in LSPIV implementation, particularly for users with limited knowledge of the technique, but also require minimal post-processing efforts. The methodology is anticipated to be integrated into existing software tools, thereby enhancing the accessibility of LSPIV for individuals with limited expertise in image velocimetry. Overall, this methodology facilitates cost-effective expansion of hydrological information availability, particularly in resource-constrained regions.

Multi-mode high resolution TFM imaging of microdefects based on laser ultrasonic full matrix capture

The identification and visualization of submillimeter microdefects have long been a critical focus within the realm of ultrasonic testing. The utilization of high-frequency signals is imperative due to the small acoustic interaction structure. An additional benefit of using lasers is their non-contact nature and ability to utilize multiple imaging modes, effectively reducing false or missed detections and enhancing credibility. This study proposes an advanced optimization algorithm for imaging and quantitative evaluation of submillimeter microdefects using laser ultrasonic full matrix capture (FMC) and total focusing method (TFM). The analysis of acoustic directionality under laser excitation and detection provides a foundation for mode selection. In preprocessing, the multi-scale principal component analysis (MSPCA) method is introduced to suppress coherent noise and blind spots. Moreover, phase coherence methods are implemented to further reduce mode artifacts, background noise, and residual surface acoustic wave (SAW). Additionally, multi-mode imaging and mode fusion are achieved using reflected longitudinal waves, reflected shear waves, converted longitudinal waves, and converted shear waves. Experiments were carried out on two different types of microdefects, namely side drilled holes (SDHs) and blind holes (BHs), resulting in high resolution TFM imaging with a minimum BH size of 0.3 mm. The outcomes demonstrate the efficacy of MSPCA in suppressing coherent noise, with further enhancement in imaging quality after phase coherence weighting. In addition, the −6 dB threshold of multi-mode images also provided the size information. This research is poised to contribute to the resolution of quality assessment challenges in powder metallurgy, welding, and additive manufacturing processes.

Three-dimensional particulate volume fraction reconstruction in the fluid based on the Lambert–Beer physics information neural network

The measurement of particle volume fraction in flow fields is of great significance in scientific research and engineering applications. As one of the particle detection techniques, the light extinction method is widely used in measuring nano-particles volume fraction in flow fields due to its simplicity and non-contact nature. In particular, in complex reactive flow fields like combustion reactions, the volume fraction of soot particulate and other particles can be accurately measured and reconstructed via the light extinction method that based on the Beer–Lambert law. This is crucial for exploring combustion phenomena, understanding their internal mechanisms, and reducing pollutant emissions. However, due to the enormous computational burden, current algebra reconstruction techniques struggle to achieve high-precision three-dimensional (3D) reconstruction of particles. Therefore, this paper originally proposes a 3D reconstruction algorithm based on the Beer–Lambert law physical information neural networks (LB-PINNs). By incorporating physical information as constraints into the particle reconstruction process, it is possible to achieve high-precision 3D reconstruction of particles in complex flow field environments with low computational cost. Meanwhile, to address the trade-off issues of reconstruction accuracy and smooth noise resistance in previous reconstruction algorithms, i.e., Tikhonov regularization, this paper employs dynamically adjusted regularization parameters in the LB-PINN algorithm. This approach ensures smooth noise-resistant processing while maintaining reconstruction accuracy, significantly reducing computation time and resource consumption. According to the experimental results, LB-PINNs demonstrate superior performance compared to previous reconstruction algorithms when reconstructing the soot volume fraction in complex reacting flow fields, i.e., combustion flame scenarios.

Visual Feature-Guided Diamond Convolutional Network for Finger Vein Recognition.

Finger vein (FV) biometrics have garnered considerable attention due to their inherent non-contact nature and high security, exhibiting tremendous potential in identity authentication and beyond. Nevertheless, challenges pertaining to the scarcity of training data and inconsistent image quality continue to impede the effectiveness of finger vein recognition (FVR) systems. To tackle these challenges, we introduce the visual feature-guided diamond convolutional network (dubbed 'VF-DCN'), a uniquely configured multi-scale and multi-orientation convolutional neural network. The VF-DCN showcases three pivotal innovations: Firstly, it meticulously tunes the convolutional kernels through multi-scale Log-Gabor filters. Secondly, it implements a distinctive diamond-shaped convolutional kernel architecture inspired by human visual perception. This design intelligently allocates more orientational filters to medium scales, which inherently carry richer information. In contrast, at extreme scales, the use of orientational filters is minimized to simulate the natural blurring of objects at extreme focal lengths. Thirdly, the network boasts a deliberate three-layer configuration and fully unsupervised training process, prioritizing simplicity and optimal performance. Extensive experiments are conducted on four FV databases, including MMCBNU_6000, FV_USM, HKPU, and ZSC_FV. The experimental results reveal that VF-DCN achieves remarkable improvement with equal error rates (EERs) of 0.17%, 0.19%, 2.11%, and 0.65%, respectively, and Accuracy Rates (ACC) of 100%, 99.97%, 98.92%, and 99.36%, respectively. These results indicate that, compared with some existing FVR approaches, the proposed VF-DCN not only achieves notable recognition accuracy but also shows fewer number of parameters and lower model complexity. Moreover, VF-DCN exhibits superior robustness across diverse FV databases.

Machine Learning Applied to Terahertz Signal Analysis For Hygrothermal Aging Characterization of Composites

Given their noncontact nature, ease of inspecting nonmetallic materials, and ability to detect small structural changes, terahertz waves were chosen as the study methodology for this project. Initially, characterization data was pretreated and standardized. The data were then reduced in dimensionality using principal component analysis and classified using other machine learning methods. The results demonstrated that the terahertz wave technique could accurately distinguish between different temperature and aging time scenarios. They also indicated that nondestructive in-service characterization of composite repairs is feasible and can provide invaluable information for decision-making. The main benefits of this approach include ensuring the safe operation of offshore piping and optimizing resources when deciding whether to replace the repair or keep it in service.

High-performance color and fluorescence switching of cotton fabrics via light-induced proton transfer strategy of spiropyran sulfonate molecule

Light-responsive color-switching materials have garnered persistent interest recently due to their passive response to external stimuli and manifestation of notable visual color variations. Nonetheless, their development into high-end products has been hindered by unsatisfactory overall performance, characterized by challenges such as the inability to combine color contrast and retention time, poor repeatability, and reliance on a single coloring mode. Herein, we present a negative photochromic cotton fabric (NPCF) capable of real-time dual output of optical signals (color change or fluorescence emission), featuring high color contrast and resolution, excellent reversibility, and facile multicolor printing. These attributes stem from the utilization of a unique photoinduced proton transfer (PPT) strategy exhibited by the designed and synthesized spiropyran sulfonate molecule (MC-NO2). Guided by this strategy, NPCF demonstrates significant color changes and fluorescence switching in a real-time and highly reversible manner, leveraging the remote non-contact and non-destructive nature of visible light. Moreover, direct blending of MC-NO2 with commercially available water-soluble non-stimuli-responsive dyes enables customization of various photosensitive color systems for straightforward multi-color printing, aligning well with the environmental principles of green printing. This study serves as a valuable reference for the advancement and design of diverse high-performance, switchable materials suitable for the production of high-end products.

Light induced self-assembly of one-dimensional PT-symmetric optical system exhibiting pulling force.

Light induced self-assembly's non-contact and non-invasive nature, along with its versatility and dynamic assembly capabilities, make it particularly well-suited for the self-organization of particles. Previous self-assembly configurations are either in a static equilibrium state or in a dynamic equilibrium state driven by a pushing force. In this study, we introduce a one-dimensional parity-time symmetric (PT-symmetric) multilayer optical system consisting of balanced gain and loss, enabling the generation of a total pulling force on the structure. By conducting molecular dynamics simulations, we achieve the self-organized structure exhibiting pulling force. Furthermore, by reversing the direction of the incident light, we realized pushing force induced binding. The stability of the bound structure is also analyzed using linear stability analysis. Additionally, the light induced self-assembly exhibiting pulling and pushing force is achieved in the one-dimensional multilayer system with unbalanced gain and loss. This work provides an additional degree of freedom in the self-organization of particles.

In vivo biological safety investigation of Yb-CALGO femtosecond laser dental surgery.

While lasers have found their successful applications in various clinical specialties, in clinical dental practice, traditional mechanical drills are still predominantly utilized. Although erbium-doped lasers have been demonstrated for dental therapy, their clinical performance is still not satisfactory due to the long pulse width, low peak power, and small repetition rate. To attain a smaller thermal diffusion thus better biological safety and surgical precision, as well as more rapid ablation, the advancement of femtosecond laser techniques has opened another route of dental surgery; however, no biological safety investigation has been reported. Here, we present a systematic study of dental ablation by a Yb:CaAlGdO4 regenerative amplifier with a central wavelength of 1040 nm and pulse width of 160 fs. The in vivo experiment of dental surgery investigating the inflammatory response has been reported, for the first time to the best of our knowledge. It is demonstrated that dental surgery by Yb:CaAlGdO4 femtosecond laser ablation has better biological safety compared to the turbine drilling, thanks to its non-contact and ultrafast heat dissipation nature.

Pulsed Laser Ablation Characteristics of Light-Absorbing Mask Layer Based on Coating Thicknesses under Laser Lift-Off Patterning Process.

Thin transparent oxide layers are typically patterned for use in electronic products including semiconductors, displays, and solar cells for applications such as transparent electrodes, insulating films, and encapsulation films. Conventional patterning methods have traditionally been used in photolithography and lift-off processes. Photolithography employs the wet development process, which has disadvantages such as potential undercut effects, swelling, chemical contamination, and high process costs. On the other hand, laser ablation, which has the advantages of high accuracy, high speed, a noncontact nature, and selective processing, can be used to pattern thin films. However, absorption in transparent oxide films is usually low. In this study, experiments were conducted to determine the ablation characteristics of mask layers. The factors affecting ablation, including beam radii, fluences, overlap ratios, and coating thicknesses, were examined; and the parameters characteristic of residue-free ablation, namely the ablation threshold, minimum fluence, and minimum ablation linewidth, were also examined. The experimental results revealed that the beam radius was an important parameter in determining the resolutions of transparent films and substrates.

Enhancing the thermographic diagnosis of maxillary sinusitis using deep learning approach

ABSTRACT Recent studies witnessed the potential benefits of using deep learning (DL) based approaches in drug discovery, disease diagnosis, treatment planning and many more applications. In addition to software approaches, researchers and clinicians are substantially focusing to standardise the thermography in the medical realm due to its non-contact and non-ionising nature. The promising results of using thermography for sinusitis detection have been remarked in many studies, which have given new directions to conduct further research. In this study, the DL-based approach has been implemented to enhance the thermography-based diagnosis of various classes of maxillary sinusitis, including 1080 thermograms (Dataset: EIE, SLIET, India). More specifically, the study is comprised of three steps: (a) Pre-processing: face extraction using instance segmentation; (b) Classification: four different classes using Inception Network V3 model; and (c) performance evaluation. The major highlight of this study is the classification into four classes: control, unilateral left maxillary sinusitis (ULMS), unilateral right maxillary sinusitis (URMS), and bilateral maxillary sinusitis (BMS). Consequently, an overall accuracy of 99.5% with 99% sensitivity has been achieved by applying the proposed approach. Furthermore, the integration of automatic segmentation of facial features augments the efficiency and accuracy of the diagnostic process by filtering out the unwanted data.

Novel Surface Topographic Assessment of Lung Volume and Pulmonary Function Tests in Idiopathic Scoliosis: A Preliminary Study.

Severe spinal deformity results in restrictive pulmonary disease from thoracic distortions and lung-volume limitations. Though spirometry and body plethysmography are widely accepted tests for pulmonary function tests (PFTs), they are time-consuming and require patient compliance. This study investigates whether surface topographic [surface topography (ST)] measurements of body volume difference (BVD) and torso volume difference between maximum inhale and exhale correlate to values determined on PFTs. This study included patients with idiopathic scoliosis and thoracic/thoracolumbar curves ≥40 degrees. Patients received ST scans, clinical examinations, and EOS biplanar radiographs on the same day. PFTs were performed within 3 months of ST/radiographic analysis. Univariate linear regression analysis was used to examine relationships between BVD, PFT values, and mean curves. Sixteen patients (14.6 ± 2.2 y, 69% females) with idiopathic scoliosis and mean thoracic/thoracolumbar curves of 62 degrees ± 15˚ degrees (45 degrees to 93 degrees) were assessed. BVD displayed statistically high-positive positive correlations with forced vital capacity ( R = 0.863, P < 0.0001), forced expiratory volume in 1 second ( R = 0.870, P < 0.001), vital capacity ( R = 0.802, P < 0.0001), and TLC ( R = 0.831, P < 0.0001. Torso volume difference showed similarly high positive correlations to forced vital capacity, forced expiratory volume in 1 second, vital capacity, and TLC, but not residual volume. No correlations emerged between the mean thoracic/thoracolumbar curve and BVD or PFT values. This study strongly endorses further investigation into ST scanning as an alternative to traditional PFTs for assessing pulmonary volumes. The noncontact and noninvasive nature of ST scanning presents a valuable alternative method for analyzing thoracic volume, particularly beneficial for patients unable to cooperate with standard PFTs. Level II-prognostic.

Deep learning in fringe projection: A review

Fringe projection is widely recognized as a prominent technique for 3D measurement, owing to its non-contact nature, high precision, and exceptional spatial resolution. However, it faces challenges in achieving a delicate equilibrium between speed and accuracy, conducting measurements on intricate optical surfaces, and capturing objects requiring a high dynamic range. Deep learning, with its robust capacity to comprehensively learn and automatically extract features, holds great promise in effectively addressing the challenges encountered in fringe projection. This article offers a comprehensive analysis, discussion, and summary of the historical development, current state, and emerging trends in the applications of deep learning in fringe projection. It covers various applications, network structures, datasets, challenges, and potential future directions of utilizing deep learning in fringe projection. The aim of this paper is to provide researchers in this field with a comprehensive understanding of the latest advancements, research trends, and practical applications of this technology, thus fostering its further development and application.

On the sensitivity and noise of acousto-optic sensing: Exploring the detection limits

Acousto-optic sensing has garnered increasing attention in recent years due to its non-contact nature, gaining importance in a range of acoustic measurement applications. Although its potential is widely recognized, the signal-to-noise ratio often requires enhancement in practical applications, which can impede further development. While more sensitive measurement methods have been evolving, the discussion around noise and detection limits in acousto-optic sensing remains limited. This presentation will highlight the author's recent findings on sensitivity and noise in acousto-optic sensing. It aims to illuminate the achievable detection limits through a detailed analysis of optical and acoustic noises. The insights provided will offer a deeper understanding of the complexities involved and potentially guide future advancements in this field.

A correction method for radial distortion and nonlinear response of infrared cameras.

The key feature of non-contact temperature measurement provided by infrared (IR) cameras underpins their versatility. However, the accuracy of temperature measurements with IR cameras depends on imaging quality due to their non-contact nature, such as the lens, body temperature, and measurement environment. This paper addresses the correction of radial distortion and nonlinear response issues in IR cameras. To address radial distortion, we have designed a passive checkerboard calibration board specifically for infrared cameras. This board is used to calibrate the IR camera and derive the necessary camera parameters. Subsequently, these parameters are applied during the actual measurement process to rectify radial distortion effectively. Building on the radial distortion correction method mentioned above, we propose a multi-point segmented calibration approach that considers different temperature ranges and imaging regions. This method alleviates the issue of reduced temperature measurement accuracy due to variations in camera responses by computing gain and offset coefficient matrices for each temperature range. Experimental results demonstrate the effectiveness of the calibration board in correcting radial distortion in IR cameras, with a mean reprojection error of less than 0.16 pixels. Regarding the nonlinear response problem, the introduced method significantly reduces the relative error in temperature measurement. In the verification phase, spanning from 100 to 500 °C, the average relative error in temperature measurement decreases by 0.49% from 1.61% before and after correction, which highlights a substantial improvement in temperature measurement accuracy. This work gives a useful reference to improve the imaging quality and temperature measurement accuracy using infrared cameras.

RETRACTED: Impact of High-Frequency Traveling-Wave Magnetic Fields on Low-Conductivity Liquids: Investigation and Potential Applications in the Chemical Industry.

High-frequency traveling-wave magnetic fields refer to alternating magnetic fields that propagate through space in a wave-like manner at high frequencies. These magnetic fields are characterized by their ability to generate driving forces and induce currents in conductive materials, such as liquids or metals. This article investigates the application and approaches of a unique form of high-frequency traveling-wave magnetic fields to low-conductivity liquids with conductivity ranging from 1 to 102 S/m. Experiments were conducted using four representative electrolytic solutions commonly employed in the chemical industry: sulfuric acid (H2SO4), sodium hydroxide (NaOH), sodium chloride (NaCl), and ionic liquid ([Bmim]BF4). The investigation focuses on the impact of high-frequency magnetic fields on these solutions at the optimal operating point of the system, considering the effects of Joule heating. The findings reveal that the high-frequency traveling magnetic field exerts a significant volumetric force on all four low-conductivity liquids. This technology, characterized by its non-contact and pollution-free nature, high efficiency, large driving volume, and rapid driving speeds (up to several centimeters per second), also provides uniform velocity distribution and notable thermal effects. It holds considerable promise for applications in the chemical industry, metallurgy, and other sectors where enhanced three-phase transfer processes are essential.

Gridless compressed covariance sensing for parameter identification of nonuniform and undersampled blade tip timing data

Due to the harsh working environments, rotating blades are susceptible to failures, which endanger the operational safety of turbomachinery; thus, it is important to monitor rotating blades to promptly detect incipient failure. Blade tip timing (BTT) is a promising approach for monitoring the health of rotating blades owing to its noncontact nature, high measurement efficiency, and long service life. However, the large gap between the high vibration frequency and low sampling frequency results in severe undersampling of the BTT signal. Although advanced methods have been proposed to reconstruct the spectrum or identify parameters from BTT signal, almost all of them face basis mismatch issue caused by frequency discretization and suffers from inevitable reconstruction errors. To overcome these limitations, we developed a gridless compressed covariance sensing (GCCS) approach for the sparse spectral estimation and parameter identification of BTT data. Unlike existing methods relying on frequency discretization, GCCS directly performs a continuous range of spectrum reconstruction and parameter estimation. We derived that GCCS is equivalent to a weighted atomic norm minimization (ANM) with the weight function linked to the Capon spectrum, which implies the advantages of GCCS over methods based on conventional ANM. GCCS involves two core steps, first, covariance (matrix) recovery from compressive samples is formulated as a semidefinite programming; second, the extended Prony’s method is employed as a postprocessing technique to retrieve vibration parameters from the covariance estimations, thereby achieving continuous parameter estimation. The quantitative comparison in the simulations and experiments shows the effectiveness of GCCS in overcoming basis mismatch and improving the accuracy and consistency of the parameter estimation.

ИССЛЕДОВАНИЕ МАКРОПОРИСТОЙ СТРУКТУРЫ ЯЧЕИСТОГО БЕТОНА

The method of image analysis of the front surface of cellular concrete is an innovative way to obtain detailed and reliable data on the structure of the material at both the macro and micro levels. This characteristic is key to a deep understanding of the characteristics of the material. The main advantage of this method is its non-contact nature. Unlike traditional methods, which may require destruction of the sample during analysis, image analysis allows examination without any impact on the integrity of the material, which is especially important since this approach ensures that the sample is preserved for subsequent studies. The article presents a method developed by the authors for determining the porosity of cellular concrete. A feature of this method is the filling of the pores of the front surface with crushed amorphous carbon, followed by obtaining an image of the front surface using a digital camera, and then its processing and analysis. The results obtained using the developed method show high convergence with the results obtained using traditional methods for analyzing images of porous structures. Thus, the use of image analysis method represents a promising approach to determining the porosity of cellular concrete, providing high accuracy of data with minimal impact on the object of study.

CSI-F: A Human Motion Recognition Method Based on Channel-State-Information Signal Feature Fusion.

The recognition of human activity is crucial as the Internet of Things (IoT) progresses toward future smart homes. Wi-Fi-based motion-recognition stands out due to its non-contact nature and widespread applicability. However, the channel state information (CSI) related to human movement in indoor environments changes with the direction of movement, which poses challenges for existing Wi-Fi movement-recognition methods. These challenges include limited directions of movement that can be detected, short detection distances, and inaccurate feature extraction, all of which significantly constrain the wide-scale application of Wi-Fi action-recognition. To address this issue, we propose a direction-independent CSI fusion and sharing model named CSI-F, one which combines Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU). Specifically, we have introduced a series of signal-processing techniques that utilize antenna diversity to eliminate random phase shifts, thereby removing noise influences unrelated to motion information. Later, by amplifying the Doppler frequency shift effect through cyclic actions and generating a spectrogram, we further enhance the impact of actions on CSI. To demonstrate the effectiveness of this method, we conducted experiments on datasets collected in natural environments. We confirmed that the superposition of periodic actions on CSI can improve the accuracy of the process. CSI-F can achieve higher recognition accuracy compared with other methods and a monitoring coverage of up to 6 m.