Non-contact Way Research Articles

Chrysanthemum classification method integrating deep visual features from both the front and back sides.

Chrysanthemum morifolium Ramat (hereinafter referred to as Chrysanthemum) is one of the most beloved and economically valuable Chinese herbal crops, which contains abundant medicinal ingredients and wide application prospects. Therefore, identifying the classification and origin of Chrysanthemum is important for producers, consumers, and market regulators. The existing Chrysanthemum classification methods mostly rely on visual subjective identification, are time-consuming, and always need high equipment costs. A novel method is proposed to accurately identify the Chrysanthemum classification in a swift, non-invasive, and non-contact way. The proposed method is based on the fusion of deep visual features of both the front and back sides. Firstly, the different Chrysanthemums images are collected and labeled with origins and classifications. Secondly, the background area with less available information is removed by image preprocessing. Thirdly, a two-stream feature extraction network is designed with two inputs which are the preprocessed front and back Chrysanthemum images. Meanwhile, the incorporation of single-stream residual connections and cross-stream residual connections is employed to extend the receptive field of the network and fully fusion the features from both the front and back sides. Experimental results demonstrate that the proposed method achieves an accuracy of 93.8%, outperforming existing methods and exhibiting superior stability. The proposed method provides an effective and dependable solution for identifying Chrysanthemum classification and origin while offering practical benefits for quality assurance in production, consumer markets, and regulatory processes. Code and data are available at https://github.com/dart-into/CCMIFB.

In Situ Raman Spectroscopy for Early Corrosion Detection in Coated AA2024-T3.

Here we describe the synthesis and evaluation of a molecular corrosion sensor that can be applied in situ in aerospace coatings, then used to detect corrosion after the coating has been applied. A pH-sensitive molecule, 4-mercaptopyridin (4-MP), is attached to a gold nanoparticle to allow surface-enhanced Raman-scattering (SERS) for signal amplification. These SERS nanoparticles, when combined with an appropriate micron-sized carrier system, are incorporated directly into an MIL-SPEC coating and used to monitor the process onset and progression of corrosion using pH changes occurring at the metal-coating interface. The sensor can track corrosion spatially as it proceeds underneath the coating, due to the mobility of the proton front generated during corrosion and the homogeneous distribution of the sensor in the coating layer. To our knowledge, this report is the first time a 4-MP functionalized gold nanoparticle has been used, along with SERS spectroscopy, to monitor corrosion in an applied commercial coating in a fast, non-contact way.

Spectroscopic ellipsometry utilizing frequency division multiplexed lasers

Spectroscopic ellipsometry (SE), which measures the thickness of thin films in a non-contact way with an accuracy of angstroms, has been widely used for optical metrology. Several types of SE are available both commercially and in research, although they require specific implementations depending on the application. Here, we theoretically and experimentally demonstrate the Frequency Division Multiplexing Spectroscopic Ellipsometry (FDM-SE) technique. With respect to conventional rotating polarizing element ellipsometry, our variant uses discrete-wavelength intensity-modulated laser diodes. This modification enables the measurement of optical properties of materials at multiple wavelengths simultaneously. We further compare the performance of the FDM-SE to a commercial instrument by measuring the thickness of SiO2 films on a Si wafer, obtaining a difference between the measured thicknesses with both methods of less than 5 Å. The proposed FDM-SE technique therefore provides a more efficient alternative to conventional SE with a high accuracy for thickness measurements.

Optical interferometry based acoustic field characterization using physics informed neural networks

The acousto-optic effect offers a non-contact way of measuring and visualizing acoustic fields in transparent fluids. Acoustic density fluctuations induce changes in the media's refractive index that can be measured using interferometry. While sensing methods based on acousto-optics have gathered increasing attention, many of such methods are only sensitive to projections of the acoustic field along the optical path, limiting the fields that can be characterized to one or two dimensions, or requiring complex experimental setups. Recently, reconstruction methods that abide by the physics of acoustic wave propagation showed unparalleled performance compared with classical reconstruction methods, reducing the sampling requirements and enabling to characterize three-dimensional acoustic fields. Nonetheless, such reconstruction methods rely on discrete approximations which might be poor at describing spatially complex fields. In this work we use physics-informed neural networks (PINNs) to estimate the acoustic pressure over space from interferometer measurements. The network is flexible enough to represent complex acoustic fields, while its output is constrained to fulfill the wave equation. Once trained on the data available for a given experiment, the PINN can estimate the acoustic pressure at arbitrary positions. The approach is tested in an experimental study, showing an improved performance compared with state-of-the-art reconstruction methods.

Development and Validation of a Mathematical Model for Pyroelectric Temperature Measurement Sensors for Application in Mobile Robotic Systems

A pyroelectric temperature sensor for measuring human body temperature with increased accuracy and speed for application in mobile robotic systems has been developed. This pyroelectric temperature sensor for measuring human body temperature is intended for use in various educational institutions. Its usage will allow for identifying sick or potentially ill people and providing them with preliminary advice and avoid infecting other people. This is particularly important considering the seasonality of dangerous infectious diseases and the emergence of new ones (e.g., COVID-19). It is also advisable to use this pyroelectric sensor in hospitals, where temperature measurement is very crucial for monitoring the course of various diseases. The proposed pyroelectric temperature sensor is based on a nonlinear oscillatory system, which provides high sensitivity and allows for solving the problem of increasing the accuracy of measuring the human body temperature in a non-contact way. Measurement error is ±0.1% in the operating range (32–43) °C, measurement time—1 s, and the frequency instability is 3·10−4.

Non-contact defect imaging of carbon fiber composites using laser excited acoustic shearography

Non-contact defect imaging of carbon fiber composites using laser excited acoustic shearography

Non-contact measurement of firmness properties of fruits and vegetables using a parabolic-reflector airborne ultrasonic transducer and laser Doppler velocimeter

The requirement for high-quality fruits and vegetables have been increased. The sorting and grading of them in a non-contact way is important, and firmness is an important criterion. In this study, we developed a non-contact measurement system for the firmness of fruits and vegetables by using airborne ultrasonic waves and a parabolic reflector. Focused airborne ultrasound produced vibration on the surfaces of samples and the vibrations were detected by a laser Doppler velocimeter to characterize the elastic properties. First, we designed and fabricated an offset-type parabolic reflector. Then, the elastic parameters (maximum displacements, center frequencies, and damping factors) were determined based on the Kelvin⎯Voigt model. Finally, individual differences in the elastic properties of twelve kinds of samples and temporal changes in the maturity state and elastic properties of three kinds of samples were observed. From the results, we confirm the developed system could be potentially used as an evaluation system for the firmness of fruits and vegetables.

Моделирование гидродинамики жидкого металла в ячейке МГД-сепаратора

Ensuring the purity of metals and alloys is a key problem in both the metallurgical and nuclear industries. The difference in the electrical conductivity of the molten metal and impurity particles allows their separation from each other in a non-contact way using an electromagnetic force. In addition to the implementation of phase separation, such force inevitably sets the liquid metal in motion, changing the velocity distribution in the separation cells. This effect is most pronounced in channels of rectangular geometry and therefore should be studied extensively. In this paper, we numerically study the hydrodynamics in a separation cell in the presence of partitions of various configurations and in the presence or absence of an external weighting electromagnetic force with the aim to determine the cell geometry providing the most efficient separation and accumulation of impurity particles in it during the cyclic pumping of liquid metal. It has been found that the intense force action leads to the occurrence of parasitic vortices in the channel, which reduce the efficiency of the separation process. The initial flow velocity does not practically affect the topology of the flow in the channel, whereas the height and configuration of the baffles inside the channel, together with the magnitude of the external weighting force have a significant effect on the flow of liquid metal. It is shown that in the zones between the baffles, the parasitic vortices that could potentially facilitate the washout of impurity from this space are not formed. Based on the results of numerical study the optimum configuration of the MHD-separator cell is selected. It corresponds to the mode with the most effective separation according to four parameters: partition height, magnitude of the external weighting electromagnetic force, flow velocity, and topology of intermediate partitions.

Ternary nano-composite wireless ammonia sensor based on Polyaniline/CuTsPc/AgNPs for food intelligent packaging

Ternary nano-composite wireless ammonia sensor based on Polyaniline/CuTsPc/AgNPs for food intelligent packaging

Review of researches and technologies applicable to digitalization of the process of assessing the exterior of meat and dairy animals

To increase the efficiency of livestock farming, scientists are developing information and communication technologies aimed at digitalizing the process of assessing the exterior of animals. This review should improve understanding of the development steps of systems applicable to the digitalization of animal conformation assessment using computer vision and deep learning neural networks. The search focused on several topics: computer vision systems; training datasets; image acquisition systems; deep learning models; neural networks for training; performance parameters and system evaluation. Machine vision is an innovative solution by combining sensors and neural networks, providing a non-contact way to assess livestock conditions as cameras can replace human observation. Two approaches are used to obtain three-dimensional images for digitalization tasks in animal husbandry: shooting animals using one 3D camera fixed in one place, and shooting from different points using several 3D cameras that record images of animals and individual parts of their bodies, such like an udder. The features extracted from the images, called dorsal features, are used as input to the models. The reviewed publications used a variety of deep learning models, including CNN, DNN, R-CNN, and SSD, depending on the task. Similarly, neural networks such as EfficientNet, ShapeNet, DeepLabCut and RefineDet have been mainly used for animal health monitoring, while GoogleNet, AlexNet, NasNet, CapsNet, LeNet and ERFNet are mainly used for identification purposes.

Novel Photoluminescence and Optical Thermometry of Solvothermally Derived Tetragonal ZrO2:Ti4+,Eu3+ Nanocrystals

In this paper, we report on the solvothermal preparation and detailed characterization of pristine and intentionally doped zirconium dioxide (ZrO2) nanocrystals (NCs, ~5 nm) with Eu3+ or Ti4+/Eu3+ ions using alkoxide precursors. The results indicated that the ZrO2 NCs were dominantly of a tetragonal phase (t-ZrO2) with a small proportion of monoclinic ZrO2 (m-ZrO2). The high purity of t-ZrO2 NCs could be synthesized with more Eu3+ doping. It was found that the as-obtained ZrO2 NCs contain some naturally present Ti4+ ions originating from precursors, but were being overlooked commonly, and some carbon impurities produced during synthesis. These species showed distinct photoluminescence (PL) properties. At least two types of Eu3+, located at low- and high-symmetry sites (probably sevenfold and eightfold oxygen coordination), respectively, were demonstrated to build into the lattice structure of t-ZrO2 NCs together. The cationic dopants were illustrated to be distributed non-randomly over the sites normally occupied by Zr, while Ti impurities preferentially occupied the sites near the low-symmetry site of Eu3+, yielding efficient energy transfer from the titanate groups to the neighboring Eu3+. Luminescence nanothermometry could measure temperature in a non-contact and remote way and could find great potentials in micro/nano-electronics, integrated photonics, and biomedicine. On the basis of the dual-emitting combination strategy involving the white broadband CT (Ti3+→O−) emissions of the titanate groups and red sharp Eu3+ emissions, t-ZrO2:Eu3+ nanophosphors were demonstrated to be ratiometric self-referencing optical thermometric materials, with a working range of 130–230 K and a maxima of relative sensitivity of ~1.9% K−1 at 230 K.

Switchable Adhesive Based on Shape Memory Polymer with Micropillars of Different Heights for Laser-Driven Noncontact Transfer Printing.

Switchable adhesive is essential to develop transfer printing, which is an advanced heterogeneous material integration technique for developing electronic systems. Designing a switchable adhesive with strong adhesion strength that can also be easily eliminated to enable noncontact transfer printing still remains a challenge. Here, we report a simple yet robust design of switchable adhesive based on a thermally responsive shape memory polymer with micropillars of different heights. The adhesive takes advantage of the shape-fixing property of shape memory polymer to provide strong adhesion for a reliable pick-up and the various levels of shape recovery of micropillars under laser heating to eliminate the adhesion for robust printing in a noncontact way. Systematic experimental and numerical studies reveal the adhesion switch mechanism and provide insights into the design of switchable adhesives. This switchable adhesive design provides a good solution to develop laser-driven noncontact transfer printing with the capability of eliminating the influence of receivers on the performance of transfer printing. Demonstrations of transfer printing of silicon wafers, microscale Si platelets, and micro light emitting diode (μ-LED) chips onto various challenging nonadhesive receivers (e.g., sandpaper, stainless steel bead, leaf, or glass) to form desired two-dimensional or three-dimensional layouts illustrate its great potential in deterministic assembly.

Anomaly sound detection of industrial devices by using teacher-student incremental continual learning

Normal production processes will be substantially impacted by industrial devices in abnormal working conditions. Anomaly sound detection (ASD) model can monitor the working condition of devices by the non-contact and non-invasive way. When new device data is introduced, traditional ASD models are trained using data from all devices, to accommodate every device. However, in real-world settings, the kinds and amounts of devices are constantly changing, which raises difficulties for the current ASD models. This paper proposes a teacher-student incremental learning method for ASD models, aiming to solve ASD model scalability problem. In this paradigm, teacher model has knowledge of all the old devices. The objective of student model is to learn new device knowledge, while avoiding the forgetting of old device knowledge. When student model learns new device data, teacher model transfers the acoustic feature knowledge of old devices to student model via knowledge distillation. Furthermore, the imbalance between old and new knowledge causes challenges, such as knowledge forgetting or lower learning efficiency for student model. This paper presents a dual-teacher-student (DTS) model to solve the problem of knowledge imbalance. Different teacher models for new and old devices in DTS, directing student model to accomplish continuous and deep integration of knowledge. Evaluation for proposed method on the DCASE 2020 Task2 dataset. The results show, the proposed method outperforms other methods in terms of learning capability and robustness during the incremental learning process. Analysis of significance test on the experimental results demonstrates that the method outperforms other methods statistically.

Multi-scalar and multi-modal wide-field imaging of artworks with a novel hyperspectral system

Hyperspectral imaging (HSI) is a very powerful tool to study artworks in a non-contact way. Nevertheless, typical HSI systems, which rely on spatial-scanning and dispersive spectrometers, suffer from high-light losses and are difficult to operate for analysing complex artworks. Here we review the capabilities of a HSI system based on TWINS, an innovative Fourier Tranform (FT) spectrometer, which allows wide-field imaging. We demonstrate how, by coupling the TWINS to different imaging systems, it is possible to achieve multi-scalar configurations from very large field-of-view (FOV) acquisition to microscopy. Further, we show how the high-collection throughput of the device allows for the sequential and fast detection of multi-modal signals’, as diffuse reflectance, transmittance, photoluminescence (PL) and Raman.

Enhanced photocatalytic CO<sub>2</sub> reduction performance in Ni-doped perovskite nanocrystals controlled by magnetic fields

In recent years, magnetic fields have been widely applied in catalysis to increase the performance of electrocatalysis, photocatalysis, and thermocatalysis through an important noncontact way. This work demonstrated that doping CsPbCl<sub>3</sub> halide perovskite nanocrystals with nickel ions (Ni<sup>2+</sup>) and applying an external magnetic field can significantly enhance the performance of the photocatalytic carbon dioxide reduction reaction (CO<sub>2</sub>RR). Compared with its counterpart, Ni-doped CsPbCl<sub>3</sub> exhibits a sixfold increase in CO<sub>2</sub>RR efficiency under a 500 mT magnetic field. Insights into the mechanism of this enhancement effect were obtained through photogenerated current density measurements and X-ray magnetic circular dichroism. The results illustrate that the significant enhancement in catalytic performance by the magnetic field is attributed to the synergistic effects of magnetic element doping and the external magnetic field, leading to reduced electron‒hole recombination and extended carrier lifetimes. This study provides an effective strategy for enhancing the efficiency of the photocatalytic CO<sub>2</sub>RR by manipulating spin-polarized electrons in photocatalytic semiconductors via a noncontact external magnetic field.

In vitro toxicity of fine and coarse particulate matter on the skin, ocular and lung microphysiological cell-culture systems

Particulate matter (PM) has been associated with adverse effects on human health, causing allergies, skin and eye irritation and corrosion, respiratory tract irritation, headaches, bronchoconstriction, cardiopulmonary diseases such as asthma, chronic obstructive pulmonary disease (COPD), lung cancer, reproductive problems, premature deaths, and epigenetic changes that lead to a wide variety of cancers, among other health conditions. The air quality in the Medellín - Colombia presents fluctuations that oscillate between the maximum permissible levels established at the national level and by the WHO, which represents a latent risk to people's health. Although important efforts have been made to quantify the different levels of pollution and administrative measures have been established to mitigate air pollution, little research work has been done to establish the relationship between these levels of pollutants and the effects on biological systems. The objective of the present research was to make a morphological and chemical characterization of particulate matter (PM) captured with a commercial air filter and a electrospun nanofiber membrane and evaluate the cytotoxicity of the each PM extracts in monolayer and co-culture models which recreate microphysiological systems of lung, skin and cornea and propose the possible cellular interactions that lead the cytotoxic response of the chemical compounds found in particulate matter in cities. The morphology and elemental chemical characterization were done with scanning electron microscopy coupled with energy dispersive X-ray spectroscopy (SEM - EDS). For the polycyclic aromatic hydrocarbons detection was made with a chromatographic method accoupled to mass spectrometer. Finally, the cytotoxicity was made in monolayers of A549, HEK001, and SIRC cell lines and microphysiological systems consisting of two-cell layer construct to resemble the interaction between fibroblast and epithelial cells that comprises naturally the corneal, skin and lung tissue. We performed three different cocultures models with BALB/3T3 clone A31 as a feeder layer, using porous Transwell® inserts in the in-contact and non-contact way. Monolayer and co-culture models were exposed to coarse and fine PM (1, 2, and 5mg/mL) and the cell viability was evaluated at 24h using an MTT assay. The electrospun nanofibers membranes demonstrates higher efficiency to capture PM with different sizes and high concentration of polycyclic aromatic hydrocarbons, heavy metals, and other chemical compounds responsible of many human diseases. Cytotoxic effects of MP were observed in all models at higher concentration; however, models exposed to fine PM exhibited a significant reduction in cell viability compared to those exposed to coarse PM. In addition, multilayer models are more resistant to PM exposure than monolayer models. Furthermore, the study indicated that, depending on the seeding strategy, different results might be observed: the non-contact model showed higher resistance to PM exposure than in-contact for SIRC and HEK001, but A549 monolayers showed the highest viability response. This study demonstrates the usefulness of applying co-culture models to assess environmental pollutant toxicity, in addition to being a potential alternative method to animal testing for risk assessment.

Intelligent wireless sensing driven metaverse: A survey

Intelligent wireless sensing driven metaverse: A survey

Innovation in Green Materials for the Non-Contact Stabilization of Sensitive Works of Art: Preliminary Assessment and the First Application of Ultra-Low Viscosity Hydroxypropyl Methylcellulose (HPMC) by Ultrasonic Misting to Consolidate Unstable Porous and Powdery Media

Paintings and other works of art created with fragile and mechanically unstable powdery media present challenges to conservators. Frequently, powdery media is water-sensitive, extremely fragile, tends to delaminate, and may be altered by even the slightest physical action or interaction with liquids. Materials that can provide an efficient stabilization without unacceptably altering the optical characteristics of the delicate substrate are extremely limited. Among these, Funori, Isinglass, and Methocel A4C have become established for this use. In bench practice, consolidants are frequently applied in a non-contact way, using ultrasonic and pneumatic aerosol generators to minimize the impact of the consolidant on sensitive substrates. However, nebulizing the available materials is problematic in bench practice, because of their high viscosity and, only extremely low concentrations can be nebulized using low kinetic impact ultrasonic or pressure-based misting systems adopted from the healthcare industry. As a potential innovative solution, this study introduces novel ultra-low viscosity (ULV) cellulose ethers (ULV-HPMC) for stabilisation of unstable porous and powdery surfaces, which have been successfully applied in bench practice for the pilot treatment of Edvard Munch painting on canvas and two 19th c. Thai gouache paintings on panel. Novel ULV-HPMC materials have multiple desirable qualities for consolidation treatments in conservation, and in accelerated aging tests marginally outperformed Methocel A4C, considered to be one of the most stable consolidants in the practice of conservation. Because of the ultra-low viscosity, higher concentrations of ULV-HPMC materials can be applied as water-based aerosols in a non-contact way and in fewer applications, which is a significant advantage in the treatment of delicate water-sensitive surfaces. Notably, novel ULV biopolymers are low-cost, derive from sustainable and renewable sources, and do not raise health and environmental concerns. Such novel materials and methods seamlessly resonate with the ICOM-CC’s Melbourne 2014 declaration, EU Green Deal, and the UN’s Sustainable Development goals and show potential adding new sustainable materials with the exceptionally low viscosity to the conservator’s tool box.

Adapting the Time-Domain Synthetic Aperture Focusing Technique (T-SAFT) to Laser Ultrasonics for Imaging the Subsurface Defects.

Traditional ultrasonic testing uses a single probe or phased array probe to investigate and visualize defects by adapting certain imaging algorithms. The time-domain synthetic aperture focusing technique (T-SAFT) is an imaging algorithm that employs a single probe to scan along the test specimen in various positions, to generate inspection images with better resolution. Both the T-SAFT and phased array probes are contact methods with limited bandwidth. This work aims to combine the advantages of the T-SAFT and phased array in a noncontact way with the aid of laser ultrasonics. Here, a pulsed laser beam is employed to generate ultrasonic waves in both thermoelastic and ablation regimes, whereas the laser Doppler vibrometer is used to acquire the generated signals. These two lasers are focused on the test specimen and, to avoid the plasma and crater influence in the ablation regime, the transmission beam and reception beam are separated by 5 mm. By moving the test specimen with a step size of 0.5 mm, a 1D linear phased array (41 and 43 elements) with a pitch of 0.5 mm was synthesized, and three side-drilled holes (Ø 8 mm-thermoelastic regime, Ø 10 mm and Ø 2 mm-ablation regime) were introduced for inspection. The A-scan data obtained from these elements were processed via the T-SAFT algorithm to generate the inspection images in various grid sizes. The results showed that the defect reflections obtained in the ablation regime have better visibility than those from the thermoelastic regime. This is due to the high-amplitude signals obtained in the ablation regime, which pave the way for enhancing the pixel intensity of each grid. Moreover, the separation distance (5 mm) does not have any significant effect on the defect location during the reconstruction process.

Machine learning-assisted wearable triboelectric-electromagnetic vibration sensor for monitoring human rehabilitation training

Machine learning-assisted wearable triboelectric-electromagnetic vibration sensor for monitoring human rehabilitation training