Object Surface Research Articles

Solution to the issue of high-order diffraction images for cylindrical computer-generated holograms

The cylindrical computer-generated hologram (CCGH), featuring a 360° viewing zone, has garnered widespread attention. However, the issue of high-order diffraction images due to pixelated structure in CCGH has not been previously reported and solved. For a cylindrical model offering a 360° viewing zone in the horizontal direction, the high-order diffraction images always overlap with the reconstruction image, leading to quality degradation. Furthermore, the 4f system is commonly used to eliminate high-order diffraction images in planar CGH, but its implementation is predictably complex for a cylindrical model. In this paper, we propose a solution to the issue of high-order diffraction images for CCGH. We derive the cylindrical diffraction formula from the outer hologram surface to the inner object surface in the spectral domain, and based on this, we subsequently analyze the effects brought by the pixel structure and propose the high-order diffraction model. Based on the proposed high-order diffraction model, we use the gradient descent method to optimize CCGH accounting for all diffraction orders simultaneously. Furthermore, we discuss the issue of circular convolution due to the periodicity of the Fast Fourier Transform (FFT) in cylindrical diffraction. The correctness of the proposed high-order diffraction model and the effectiveness of the proposed optimization method are demonstrated by numerical simulation. To our knowledge, this is the first time that the issue of high-order diffraction images in CCGH has been proposed, and we believe our solution can offer valuable guidance to practitioners in the field.

Glossiness perception and its pupillary response

Recent studies have revealed that pupillary response changes depend on perceptual factors such as subjective brightness caused by optical illusions and luminance. However, the manner in which the perceptual factor that is derived from the glossiness perception of object surfaces affects the pupillary response remains unclear. We investigated the relationship between the glossiness perception and pupillary response through a glossiness rating experiment that included recording the pupil diameter. We prepared general object images (original) and randomized images (shuffled) that comprised the same images with randomized small square regions as stimuli. The image features were controlled by matching the luminance histogram. The observers were asked to rate the perceived glossiness of the stimuli presented for 3,000 ms and the changes in their pupil diameters were recorded. Images with higher glossiness ratings constricted the pupil size more than those with lower glossiness ratings at the peak constriction of the pupillary responses during the stimulus duration. The linear mixed-effects model demonstrated that the glossiness rating, image category (original/shuffled), variance of the luminance histogram, and stimulus area were most effective in predicting the pupillary responses. These results suggest that the illusory brightness obtained by the image regions of high-glossiness objects, such as specular highlights, induce pupil constriction.

S-LIGHT: Synthetic Dataset for the Separation of Diffuse and Specular Reflection Images.

Several studies in computer vision have examined specular removal, which is crucial for object detection and recognition. This research has traditionally been divided into two tasks: specular highlight removal, which focuses on removing specular highlights on object surfaces, and reflection removal, which deals with specular reflections occurring on glass surfaces. In reality, however, both types of specular effects often coexist, making it a fundamental challenge that has not been adequately addressed. Recognizing the necessity of integrating specular components handled in both tasks, we constructed a specular-light (S-Light) DB for training single-image-based deep learning models. Moreover, considering the absence of benchmark datasets for quantitative evaluation, the multi-scale normalized cross correlation (MS-NCC) metric, which considers the correlation between specular and diffuse components, was introduced to assess the learning outcomes.

Development of Kirigami‐Patterned Stretchable Tactile Sensor Array with Soft Hinges for Highly Sensitive Force Detection

AbstractFlexible and stretchable tactile sensors are attracted in the fields of soft robotics, wearable electronics, and healthcare monitoring. The sensing performance of tactile sensors is commonly affected by external deformations like stretching, bending, and twisting, thus they may fail to function on deformable object surfaces. This paper presents a stretchable tactile sensor array using kirigami‐patterned structural design and soft hinges to reduce the influences of deformation. The kirigami pattern of sensor array is parametrically studied to achieve the required expansion patterns. Laser engraving is employed to modify the micropillars on the force‐sensitive rubber surface to increase the sensitivity. Characterization tests show that the sensor array has high sensitivity (≈1.49 × 10−1 kPa−1) for force sensing, and the stretching and bending deformation have almost negligible effects on sensing performance. Under 40% stretching or 180° bending conditions, the measured resistance changes (ΔR/R0) is ≈0.03 and 0.06, respectively. To demonstrate the capability of developed sensor array, it is mounted on an expandable balloon surface for force detection. The recorded signals changed less than 1.5% during expanding process while rapidly rose under applied force, which indicated that the sensor array has the potential to effectively function on complex and deforming surfaces.

High-performance, large-area flexible SERS substrates prepared by reactive ion etching for molecular detection

In the realm of molecular detection, the surface-enhanced Raman scattering (SERS) technique has garnered increasing attention due to its rapid detection, high sensitivity, and non-destructive characteristics. However, conventional rigid SERS substrates are either costly to fabricate and challenging to prepare over a large area, or they exhibit poor uniformity and repeatability, making them unsuitable for inspecting curved object surfaces. In this work, we present a flexible SERS substrate with high sensitivity as well as good uniformity and repeatability. First, the flexible polydimethylsiloxane (PDMS) substrate is manually formulated and cured. SiO2/Ag layer on the substrate can be obtained in a single process by using ion beam sputtering. Then, reactive ion etching is used to etch the upper SiO2 layer of the film, which directly leads to the desired densely packed nanostructure. Finally, a layer of precious metal is deposited on the densely packed nanostructure by thermal evaporation. In our proposed system, the densely packed nanostructure obtained by etching the SiO2 layer directly determines the SERS ability of the substrate. The bottom layer of silver mirror can reflect the penetrative incident light, the spacer layer of SiO2 and the top layer of silver thin film can further localize the light in the system, which can realize the excellent absorption of Raman laser light, thus enhancing SERS ability. In the tests, the prepared substrates show excellent SERS performance in detecting crystalline violet with a detection limit of 10−11 M. The development of this SERS substrate is anticipated to offer a highly effective and convenient method for molecular substance detection.

SUPPOSe 3Dge: A Method for Super-Resolved Detection of Surfaces in Volumetric Fluorescence Microscopy

We present SUPPOSe 3Dge, a method for the detection of surfaces in volumetric fluorescence microscopy images with resolution better than the diffraction limit. The method works by approximating an arbitrary object surface as a polyhedron formed by small triangular faces, which are thought as virtual point like sources of signal gradient. Then, the super resolution arises from solving a least square problem only on the positions of the sources, by comparing the measured image gradient with a synthetic one built from the approximated surface and the known point spread function of the instrument. We present the formulation of the method and its characterization using simulated measurements of two different types. We show that the method is able to identify with high precision and accuracy the sizes and shapes of objects smaller than the instrumental resolution. Using different quantifiers that measure the distance between the solution and the target object, we report reconstructions with an accuracy 100 times better than the axial resolution and 40 times the lateral resolution of the instrument.

ON THE FEATURES OF REFLECTION OF SHEAR ELASTIC WAVE PULSES FROM A VERTICAL CRACK

A computer simulation of the characteristics of pulse signals of shear waves reflected from the edge of a vertical crack coming to the surface of the control object and a bottom crack using radiated pulses of various lengths with narrow, medium width and wide spectra was carried out. Within the limits of changing the calculation parameters, a feature of pulse reflection is established – the ratio of the maxima of the envelope pulses when reflected from a crack coming to the surface of the OK and the bottom crack is approximately 3.40 (10.6 dB), which coincides with the corresponding ratio of the maxima of the spectra. Based on the results of calculations of pulse shapes with frequency spectrum of different widths when waves are reflected from a vertical crack, the recommendation is justified not to use long pulses with narrow spectra with reduced beam resolution in the time of flight diffraction method.

SEB: a computational tool for symbolic derivation of the small-angle scattering from complex composite structures.

Analysis of small-angle scattering (SAS) data requires intensive modeling to infer and characterize the structures present in a sample. This iterative improvement of models is a time-consuming process. Presented here is Scattering Equation Builder (SEB), a C++ library that derives exact analytic expressions for the form factors of complex composite structures. The user writes a small program that specifies how the sub-units should be linked to form a composite structure and calls SEB to obtain an expression for the form factor. SEB supports e.g. Gaussian polymer chains and loops, thin rods and circles, solid spheres, spherical shells and cylinders, and many different options for how these can be linked together. The formalism behind SEB is presented and simple case studies are given, such as block copolymers with different types of linkage, as well as more complex examples, such as a random walk model of 100 linked sub-units, dendrimers, polymers and rods attached to the surfaces of geometric objects, and finally the scattering from a linear chain of five stars, where each star is built up of four diblock copolymers. These examples illustrate how SEB can be used to develop complex models and hence reduce the cost of analyzing SAS data.

Расчет режимов релаксации обезвоженного поверхностного слоя путассу в процессе сушки

During cold drying of fish, the surface layers, which have lost a significant part of the water, become compacted and prevent the diffusion of water from the inner layers of the fish to the surface. In order to restore the diffusion properties of the surface layer of fish, it is necessary to create conditions for the redistribution of moisture along the thickness of the object, for example, temporarily limit external mass transfer. To do this, reduce the temperature, the speed of air movement in the drying chamber and increase its relative humidity. Due to diffusion, water from the central layers of the fish will flow to the dehydrated surface layer and moisten it. As a result, the mass-conducting properties of the surface layer will increase. The purpose of this work is to study the effect of relaxation processes on changes in the thickness of the dehydrated surface layer of fish for the subsequent development of scientifically sound rational drying regimes. The calculation of moisture redistribution in this work was carried out under the condition (assumption) that there is no external mass transfer of moisture from the surface of the processing object. The kinetics curve and water distribution curves in the processing facility were constructed during the production of dried fish products in 2; 2.5; 13.5; 55 hours respectively. The average humidity of the whiting after dehydration lasting 2 hours was 333% in terms of dry matter. The thickness of the dehydrated layer S = 1.6 mm. After 1.5 hours of relaxation, the moisture on the surface of the fish increased from 272 to 330% and approached the average humidity of the sample. During dehydration of fish lasting 13.5 hours, the average moisture content of the sample per dry mass was 217%, the thickness of the dehydrated surface layer S = 0.9 mm. After 2.5 hours of relaxation, the moisture content on the surface of the layer was 174%. It was found that at the beginning of the drying process, the dry dehydrated layer has 
 a positive tendency to restore the lost moisture-conducting (diffusion) properties, but at the end of the drying process it takes more time to restore them. The paper proposes a method for calculating relaxation curves, which can be used to study the processes of moisture redistribution in the processing object in order to create rational drying modes.

Determining the trend of geometrical changes of a hydrotechnical object based on data in the form of LiDAR point clouds

Monitoring the technical condition of hydrotechnical facilities is crucial for ensuring their safe usage. This process typically involves tracking environmental variables (e.g., concrete damming levels, temperatures, piezometer readings) as well as geometric and physical variables (deformation, cracking, filtration, pore pressure, etc.), whose long-term trends provide valuable information for facility managers. Research on the methods of analyzing geodetic monitoring data (manual and automatic) and sensor data is vital for assessing the technical condition and safety of facilities, particularly when utilizing new measurement technologies. Emerging technologies for obtaining data on the changes in the surface of objects employ laser scanning techniques (such as LiDAR, Light Detection, and Ranging) from various heights: terrestrial, unmanned aerial vehicles (UAVs, drones), and satellites using sensors that record geospatial and multispectral data. This article introduces an algorithm to determine geometric change trends using terrestrial laser scanning data for both concrete and earth surfaces. In the consecutive steps of the algorithm, normal vectors were utilized to analyze changes, calculate local surface deflection angles, and determine object alterations. These normal vectors were derived by fitting local planes to the point cloud using the least squares method. In most applications, surface strain and deformation analyses based on laser scanning point clouds primarily involve direct comparisons using the Cloud to Cloud (C2C) method, resulting in complex, difficult-to-interpret deformation maps. In contrast, preliminary trend analysis using local normal vectors allows for rapid threat detection. This approach significantly reduces calculations, with detailed point cloud interpretation commencing only after detecting a change on the object indicated by normal vectors in the form of an increasing deflection trend. Referred to as the cluster algorithm by the authors of this paper, this method can be applied to monitor both concrete and earth objects, with examples of analyses for different object types presented in the article.

Photonic crystal nanolasers in polydimethylsiloxane thin film for sensing quantities leading to strain

We propose and realize a 1D photonic crystal nanocavity laser embedded in a polydimethylsiloxane (PDMS) thin film. The nanolaser in PDMS exhibits a significant optical response to structural deformation. It can be attached to object surfaces or integrated into different configurations, enabling the detection of different quantities that induce strain in the film. In experiments, this nanolaser can detect temperature variations or micrometer-scale bending degrees by attaching it to a temperature-controllable or bendable plate, respectively. Moreover, we further utilize the film as a diaphragm of a chamber to demonstrate its potential as a highly sensitive pressure gauge and chemical sensor. By adjusting the thickness of the PDMS thin film and the position of the nanolaser, we experimentally achieved a minimum detectable gas pressure variation of 0.12 kPa and a sensing dynamic range of 46 dB. We also investigate the optical response of the nanolaser to the swelling of the PDMS thin film induced by different organic solvents in experiments. The experimental wavelength shift rates over time are proportional to different chemical vapors’ PDMS swelling ratios, which can be used to identify specific chemical vapors within the chamber that induce PDMS swelling. Based on the experimental results and the capability of reattaching to different objects or configurations, we believe that our PhC nanolaser demonstrated herein holds significant potential as a highly sensitive mechanical and chemical sensor.

Learning Deformable Hypothesis Sampling for Accurate PatchMatch Multi-View Stereo

This paper introduces a learnable Deformable Hypothesis Sampler (DeformSampler) to address the challenging issue of noisy depth estimation in faithful PatchMatch multi-view stereo (MVS). We observe that the heuristic depth hypothesis sampling modes employed by PatchMatch MVS solvers are insensitive to (i) the piece-wise smooth distribution of depths across the object surface and (ii) the implicit multi-modal distribution of depth prediction probabilities along the ray direction on the surface points. Accordingly, we develop DeformSampler to learn distribution-sensitive sample spaces to (i) propagate depths consistent with the scene's geometry across the object surface and (ii) fit a Laplace Mixture model that approaches the point-wise probabilities distribution of the actual depths along the ray direction. We integrate DeformSampler into a learnable PatchMatch MVS system to enhance depth estimation in challenging areas, such as piece-wise discontinuous surface boundaries and weakly-textured regions. Experimental results on DTU and Tanks & Temples datasets demonstrate its superior performance and generalization capabilities compared to state-of-the-art competitors. Code is available at https://github.com/Geo-Tell/DS-PMNet.

MFOS: Model-Free & One-Shot Object Pose Estimation

Existing learning-based methods for object pose estimation in RGB images are mostly model-specific or category based. They lack the capability to generalize to new object categories at test time, hence severely hindering their practicability and scalability. Notably, recent attempts have been made to solve this issue, but they still require accurate 3D data of the object surface at both train and test time. In this paper, we introduce a novel approach that can estimate in a single forward pass the pose of objects never seen during training, given minimum input. In contrast to existing state-of-the-art approaches, which rely on task-specific modules, our proposed model is entirely based on a transformer architecture, which can benefit from recently proposed 3D-geometry general pretraining. We conduct extensive experiments and report state-of-the-art one-shot performance on the challenging LINEMOD benchmark. Finally, extensive ablations allow us to determine good practices with this relatively new type of architecture in the field.

SAB-YOLOv5: An Improved YOLOv5 Model for Permanent Magnetic Ferrite Magnet Rotor Detection

Surface defects on the permanent magnetic ferrite magnet rotor are the primary cause for the decline in performance and safety hazards in permanent magnet motors. Machine-vision methods offer the possibility to identify defects automatically. In response to the challenges in the permanent magnetic ferrite magnet rotor, this study proposes an improved You Only Look Once (YOLO) algorithm named SAB-YOLOv5. Utilizing a line-scan camera, images capturing the complete surface of a general object are obtained, and a dataset containing surface defects is constructed. Simultaneously, an improved YOLOv5-based surface defect algorithm is introduced. Firstly, the algorithm enhances the capability to extract features at different scales by incorporating the Atrous Spatial Pyramid Pooling (ASPP) structure. Then, the fusion of features is improved by combining the tensor concatenation operation of the feature-melting network with the Bidirectional Feature Pyramid Network (BiFPN) structure. Finally, the introduction of the spatial pyramid dilated (SPD) convolutional structure into the backbone network and output end enhances the detection performance for minute defects on the target surface. In the study, the SAB-YOlOv5 algorithm shows an obvious increase from 84.2% to 98.3% in the mean average precision (mAP) compared to that of the original YOLOv5 algorithm. The results demonstrate that the data acquisition method and detection algorithm designed in this paper effectively enhance the efficiency of defect detection permanent magnetic ferrite magnet rotors.

Multi-Dimensional Fusion of Spectral and Polarimetric Images Followed by Pseudo-Color Algorithm Integration and Mapping in HSI Space

Spectral–polarization imaging technology plays a crucial role in remote sensing detection, enhancing target identification and tracking capabilities by capturing both spectral and polarization information reflected from object surfaces. However, the acquisition of multi-dimensional data often leads to extensive datasets that necessitate comprehensive analysis, thereby impeding the convenience and efficiency of remote sensing detection. To address this challenge, we propose a fusion algorithm based on spectral–polarization characteristics, incorporating principal component analysis (PCA) and energy weighting. This algorithm effectively consolidates multi-dimensional features within the scene into a single image, enhancing object details and enriching edge features. The robustness and universality of our proposed algorithm are demonstrated through experimentally obtained datasets and verified with publicly available datasets. Additionally, to meet the requirements of remote sensing tracking, we meticulously designed a pseudo-color mapping scheme consistent with human vision. This scheme maps polarization degree to color saturation, polarization angle to hue, and the fused image to intensity, resulting in a visual display aligned with human visual perception. We also discuss the application of this technique in processing data generated by the Channel-modulated static birefringent Fourier transform imaging spectropolarimeter (CSBFTIS). Experimental results demonstrate a significant enhancement in the information entropy and average gradient of the fused image compared to the optimal image before fusion, achieving maximum increases of 88% and 94%, respectively. This provides a solid foundation for target recognition and tracking in airborne remote sensing detection.

Proses pembuatan flywheel (roda gila) mesin Hammer Mill dengan metode Drilling, Milling, dan Turning di PT. Raja Ampat Indotim

The research is aimed at the manufacture of flaywheel for hammer mill machines using the method of drilling, milling and turning, which aims to keep the engine rotation stable on the machine due to excess load and is a mass that rotates and is used as a storage of energy inside the machine. This research focuses on drilling, milling, and turning processes on low-carbon steels. (ST 40 Steel). The Flywheel is a component that is a plate that because of its weight can withstand drastic speed changes so that the rotation movement of the machinery shaft becomes more smooth. The drilling, milling and turning methods are machining methods that are widely used in the manufacturing industry. From the results of the research using the methods of drilling, milling and turning the following are on the process of making flywheel i.e. with the time of the drilling process of the production of sepie holes 62.82 minutes, the milling process of smoothing 2 surfaces of the work object 242.06 hours and the turning of the profile of 2 surface work object 117.6 hours with a total production time of about 15 days. Therefore, the results of this research provide useful guidance for the industry to optimize the drilling, milling and turning processes in flywheel manufacturing.

Stereo Camera Setup for 360° Digital Image Correlation to Reveal Smart Structures of Hakea Fruits.

About forty years after its first application, digital image correlation (DIC) has become an established method for measuring surface displacements and deformations of objects under stress. To date, DIC has been used in a variety of in vitro and in vivo studies to biomechanically characterise biological samples in order to reveal biomimetic principles. However, when surfaces of samples strongly deform or twist, they cannot be thoroughly traced. To overcome this challenge, different DIC setups have been developed to provide additional sensor perspectives and, thus, capture larger parts of an object's surface. Herein, we discuss current solutions for this multi-perspective DIC, and we present our own approach to a 360° DIC system based on a single stereo-camera setup. Using this setup, we are able to characterise the desiccation-driven opening mechanism of two woody Hakea fruits over their entire surfaces. Both the breaking mechanism and the actuation of the two valves in predominantly dead plant material are models for smart materials. Based on these results, an evaluation of the setup for 360° DIC regarding its use in deducing biomimetic principles is given. Furthermore, we propose a way to improve and apply the method for future measurements.

Fingerprint-inspired biomimetic tactile sensors for the surface texture recognition

A bionic tactile device is designed for object surface texture recognition, taking inspiration from the microstructure of human fingerprints. The sense of touch in humans is achieved through the frictional vibration and generation of electrical potential signals by subcutaneous receptors. Constructing a tactile sensing device involves generating distinct signals upon interacting with different materials. The piezoelectric film PVDF is particularly suitable as a sensitive material for sensors due to its excellent flexibility, strong mechanical strength, excellent dynamic response and cost-effectiveness. This paper presents the design of a PVDF-based fingerprint-inspired tactile sensor capable of differentiating various textures. By combining the collected signals with machine learning algorithms, diverse textures can be effectively identified. To demonstrate the sensor’s superior performance, two experiments were conducted—one focused on recognizing different material textures, and the other on recognizing Braille characters. The accuracy achieved in these experiments was 97.4% and 96.5%, respectively, highlighting the technology’s significant potential in intelligent robotics and human-computer interaction.

Condition Assessment of Electrical Power Devices using Infrared Thermography: A Review

Departures from the stable operation of electrical equipment usually appear as changes in parameters such as vibrations, pressure, temperature, power, and flow. Excessive changes in these parameters will indicate significant damage, leading to total or partial breakdown of equipment. This paper reviews the use of infrared thermography (IRT) for the operating condition temperature monitoring of electrical equipment. Infrared thermography is a contactless method that is based on thermal radiation principles and used for determining the temperature distribution on the surface of objects by measuring the intensity of radiation emitted by the object. The basic principles underlying IRT are presented in this review, showing its pros and cons, the various IRT methods, processes involved in IRT image acquisition, intelligent techniques for IR image analysis, and factors that affect the accuracy of results, including technical, environmental, and procedural factors. Also presented is an overview of common applications of IRT condition monitoring to solar energy, medicine, civil infrastructure, and aerospace equipment. Common faults in electrical equipment, including those in transformers, circuit breakers, feeder pillars and arrestors, are discussed with illustrations from recent research studies. These studies have demonstrated the effectiveness of IRT in the predictive maintenance of electrical installations. Hence, the replication of the techniques reviewed in this work is recommended so as to reduce current costs incurred in maintaining and replacing damaged equipment, and the associated time losses.

Enhancing View Synthesis with Depth-Guided Neural Radiance Fields and Improved Depth Completion.

Neural radiance fields (NeRFs) leverage a neural representation to encode scenes, obtaining photorealistic rendering of novel views. However, NeRF has notable limitations. A significant drawback is that it does not capture surface geometry and only renders the object surface colors. Furthermore, the training of NeRF is exceedingly time-consuming. We propose Depth-NeRF as a solution to these issues. Specifically, our approach employs a fast depth completion algorithm to denoise and complete the depth maps generated by RGB-D cameras. These improved depth maps guide the sampling points of NeRF to be distributed closer to the scene's surface, benefiting from dense depth information. Furthermore, we have optimized the network structure of NeRF and integrated depth information to constrain the optimization process, ensuring that the termination distribution of the ray is consistent with the scene's geometry. Compared to NeRF, our method accelerates the training speed by 18%, and the rendered images achieve a higher PSNR than those obtained by mainstream methods. Additionally, there is a significant reduction in RMSE between the rendered scene depth and the ground truth depth, which indicates that our method can better capture the geometric information of the scene. With these improvements, we can train the NeRF model more efficiently and achieve more accurate rendering results.