Real Images Research Articles

Filling the Holes on 3D Heritage Object Surface Based on Automatic Segmentation Algorithm

ABSTRACTReconstructing and processing 3D objects are activities in the research field of computer graphics, image processing and computer vision. The 3D objects are processed based on geometric modelling (a branch of applied mathematics and computational geometry) or machine learning algorithms based on image processing. The computation of geometrical objects includes processing the curves and surfaces, subdivision, simplification, meshing, holes filling or reconstructing the 3D surface's objects on both point cloud data and triangular mesh. While the machine learning methods are developed using deep learning models. With the support of 3D laser scan devices and LiDAR techniques, the obtained dataset is close to the original shape of the real objects. Besides, photography and its application based on modern techniques in recent years help us collect data and process the 3D models more precisely. This article proposes a new method for filling holes on the 3D object's surface based on automatic segmentation. Instead of filling the hole directly as the existing methods, we now subdivide the hole before filling it. The hole is first determined and segmented automatically based on the computation of its local curvature. It is then filled on each part of the hole to match its local curvature shape. The method can work on both 3D point cloud surfaces and triangular mesh surfaces. Compared to the state‐of‐the‐art (SOTA) methods, our proposed method obtained higher accuracy of the reconstructed 3D objects.

The individual and the city: Two models of interaction

Two conditionally opposite types of human interaction with society are considered: extroverted and introverted. The process of extroverted interaction is aimed at direct contact with the urban community, and introverted – at the imaginary reality, the artistic image of what is happening. The first type is supported by the majority cultural practice and the rules of the system, while the second type is associated with a minimal presence in the system and a playful interpretation of the context. The key elements of architectural interpretation of the interaction under consideration are the house and the city – simultaneously as real objects and as metaphors of opposite meanings. Architecture in its essential manifestation is embodied as a city in the extroverted project, and as a house in the introverted one.

Advanced Model with a Trajectory Tracking Stabilisation System and Feasible Solution of the Optimal Control Problem

In this study, we consider the extended optimal control problem and search for a control function in the class of feasible functions for a real control object. Unlike the classical optimal control problem, the control function should depend on the state, not time. Therefore, the control synthesis problem for the initial-state domain should be solved, instead of the optimal control problem with one initial state. Alternatively, an optimal trajectory motion stabilisation system may be constructed. Both approaches—control and trajectory motion stabilisation system syntheses—cannot be applied to real-time control, as the task is too complex. The minimum threshold of quality criteria is searched for in the space of mathematical expression codes. Among other problems, the search space is difficult to define and the gradient is hard to determine. Therefore, the advanced control object model is used to obtain a feasible control function. The advanced model is firstly obtained before solving the optimal control problem and it already includes a trajectory motion stabilisation system; in particular, this stabilisation system is synthesised in advance at the control system design stage. When the optimal control problem appears, it is solved in real time in the classical statement, and a control function is searched for as a function of time. The advanced control object model also uses the reference model to generate the optimal trajectory. The search for the optimal control function is performed in real time and considers the synthesised stabilisation system of motion along a determined trajectory. Machine learning control via symbolic regression, namely, the network operator method, is used to directly solve the control synthesis problem. An example solution of the optimal control problem, with an advanced model moving in the environment with obstacles for a group of two mobile robots, is presented. The obtained solution is a control function for a reference model that generates a trajectory from a class of trajectories stabilised with the object’s control system.

The ground for necessary defence: analysis of legal positions of the Supreme Court

The article analyzes and summarizes the positions of the Supreme Court on the ground for necessary defence. It is noted that the Supreme Court has identified two elements of the ground for necessary defence (the commission of a socially dangerous trespass and the need to immediately avert or stop it) and has repeatedly referred to the interpretation of various aspects of each of them. The article mentions various definitions of “socially dangerous trespass” provided by the Supreme Court in its rulings. It is noted that the Supreme Court has not addressed the possibility of recognizing a socially dangerous act of an insane person or a person under the age of criminal liability as a socially dangerous trespass as a ground for necessary defence, but in one of its rulings it admitted that such a trespass was committed by a mentally ill victim. The author mentions the conclusion of the Supreme Court that the right to necessary defence cannot arise against lawful acts of another person, even if such acts cause damage to legally protected interests. It is mentioned that, in general, the Supreme Court does not recognize provoked violence as a trespass within the meaning of Article 36 of the Criminal Code of Ukraine, but the exception is when the degree of provoked violence is clearly higher than what a person could have predicted. It is noted that the Supreme Court identifies the objective reality of a socially dangerous trespass as its mandatory characteristic for necessary defence. The author quotes the Supreme Court’s explanation of when it is necessary to immediately avert or stop a socially dangerous trespass. The conclusions of the Supreme Court regarding the time limits of the right to necessary defence are analyzed. In particular, the Supreme Court identifies the initial and final moments of lawful defence. The ruling is mentioned, in which the Supreme Court reminds of the legal prohibition to take into account the possibility of a person to avoid a socially dangerous trespass or to request assistance of other persons or authorities when deciding on the existence or absence of a state of necessary defence.

Значение категории «земля» как отправная точка правового регулирования

The legally established meaning of land is the starting point not only for land law, but also for public policy in general. The modern meaning of land covers three aspects: land as a natural object, land as a natural resource and land as a real estate object. Ways to improve land legislation from the point of view of specifying the meaning of land are seen in ensuring the rational use and protection of land, the priority of protecting human life and health when making decisions on the use of land, and the participation of citizens in resolving issues related to the use of land in their place of residence. It is proposed to expand the meaning of land in the future by recognizing the emotional and moral connection of a person with the land as a value subject to protection, as well as regulating the rights of future generations in relation to the land.

Generation of NIR Spectral Band from RGB Image with Wavelet Domain Spectral Extrapolation Generative Adversarial Network

Near-infrared (NIR) imaging exhibits outstanding penetration capabilities and robust anti-interference characteristics. However, acquiring high-resolution and high-fidelity NIR images is difficult due to the limited mobility, high cost, and low resolution of NIR imaging hardware. In this paper, we proposed a new end-to-end Wavelet Domain Spectral Extrapolation Generative Adversarial Network (WSEGAN) to generate highly realistic NIR images from RGB images. Since RGB and NIR images have different types of noise and artifacts, which affects the quality of NIR images generation. We design a generator with a discrete wavelet transform and an attention mechanism to capture multi-resolution contextual information, and a multi-scale discriminator to capture detailed and global features. Then, the proposed approach is evaluated by three different datasets to achieve optimal results regarding both visual effects and quantitative evaluation. The normalized difference vegetation index (NDVI) is utilized to validate the effectiveness of the generated NIR images. The result demonstrates a strong correlation between the generated images and the actual vegetation distribution. More importantly, the proposed network is testified to generate NIR images to be fused with the RGB image source for agricultural target detection tasks. In dark conditions, results show that using multi-modal data instead of RGB images improves mAP0.5 detection accuracy by 4% on the CAPSICUM dataset and by 8% on the KIWI dataset across five object detection methods. This follows the physical significance of the NIR imaging and demonstrates the potential use of the NIR images generated by the proposed method.

Overview of textile tensile surface structures with the largest uniform area

The innovative design of tensile surface structures is often a distinguishing feature of the entire construction project. The use of membranes gives buildings a sense of lightness and often a futuristic look. The construction materials used in the design of membrane roofing are, in addition to the steel cables tensioning and supporting the roof membrane, technical fabrics. This article focuses on describing structures made of continuous tensioned membranes spanning a rope or bar network with a total uniform area of more than 20,000 m2. Five structures with the largest uniform load-bearing area are described. The most commonly used yarn materials and coatings are described. The basic strength parameters of the most commonly used fabrics are summarized. The principles of shaping membrane surfaces were described. Attention was drawn to the difficulty of transferring laboratory test results to real objects. It is very likely that, for reasons of usability and manufacturing technology, continuous membrane surfaces with larger areas than described will no longer be designed.

Faceify- Human Face Sketch to Real

The Human Face Sketch to Real Image Application using deep learning presents the development of an innovative application designed to convert human face sketches into realistic image using advanced deep learning techniques. The primary objective of this research is to bridge the gap between manual sketching and digital realism, offering a powerful tool for artists, law enforcement, and digital content creators. The proposed system leverages generative adversarial networks (GANs), particularly a variant known as the Sketch-to-Image GAN (SI-GAN), which is optimizes for translating sparse and abstract sketch lines into high-fidelity facial representations. The architecture consists of a generator network that refines sketches into realistic images, and a discriminator network that distinguishes between generated images and real photographs, continuously enhancing the model’s output. To ensure high accuracy and detail preservation, the model is trained on a diverse dataset comprising thousands of paired sketch and real face images. Advanced techniques such as attention mechanisms and multi-scale feature extraction are incorporated to maintain the integrity of facial features and textures. Additionally, a user friendly interface has been developed, allowing users to input sketches and receive high-quality realistic images in real-time. Evaluation metrics, including structural similarity index (SSIM) and Frechet Inception Distance (FID), Indicate that the proposed model outperforms existing state-of-the-art methods in both quality and realism. Keywords: Image Translation, Conditional Generative Adversarial Networks (CGANs), Deep Learning, Mobile Application Development, Backend Integration, Face Sketch, Photorealistic Image.

An Image-Based Sensor System for Low-Cost Airborne Particle Detection in Citizen Science Air Quality Monitoring.

Air pollution poses significant public health risks, necessitating accurate and efficient monitoring of particulate matter (PM). These organic compounds may be released from natural sources like trees and vegetation, as well as from anthropogenic, or human-made sources including industrial activities and motor vehicle emissions. Therefore, measuring PM concentrations is paramount to understanding people's exposure levels to pollutants. This paper introduces a novel image processing technique utilizing photographs/pictures of Do-it-Yourself (DiY) sensors for the detection and quantification of PM10 particles, enhancing community involvement and data collection accuracy in Citizen Science (CS) projects. A synthetic data generation algorithm was developed to overcome the challenge of data scarcity commonly associated with citizen-based data collection to validate the image processing technique. This algorithm generates images by precisely defining parameters such as image resolution, image dimension, and PM airborne particle density. To ensure these synthetic images mimic real-world conditions, variations like Gaussian noise, focus blur, and white balance adjustments and combinations were introduced, simulating the environmental and technical factors affecting image quality in typical smartphone digital cameras. The detection algorithm for PM10 particles demonstrates robust performance across varying levels of noise, maintaining effectiveness in realistic mobile imaging conditions. Therefore, the methodology retains sufficient accuracy, suggesting its practical applicability for environmental monitoring in diverse real-world conditions using mobile devices.

The Effectiveness of Manipulating Real Objects in Enhancing Retention Among Grade 7 Students Struggling With Concepts of Angles

The Effectiveness of Manipulating Real Objects in Enhancing Retention Among Grade 7 Students Struggling With Concepts of Angles

Two-stage deep learning framework for occlusal crown depth image generation

The generation of depth images of occlusal dental crowns is complicated by the need for customization in each case. To decrease the workload of skilled dental technicians, various computer vision models have been used to generate realistic occlusal crown depth images with definite crown surface structures that can ultimately be reconstructed to three-dimensional crowns and directly used in patient treatment. However, it has remained difficult to generate images of the structure of dental crowns in a fluid position using computer vision models. In this paper, we propose a two-stage model for generating depth images of occlusal crowns in diverse positions. The model is divided into two parts: segmentation and inpainting to obtain both shape and surface structure accuracy. The segmentation network focuses on the position and size of the crowns, which allows the model to adapt to diverse targets. The inpainting network based on a GAN generates curved structures of the crown surfaces based on the target jaw image and a binary mask made by the segmentation network. The performance of the model is evaluated via quantitative metrics for the area detection and pixel-value metrics. Compared to the baseline model, the proposed method reduced the MSE score from 0.007001 to 0.002618 and increased DICE score from 0.9333 to 0.9648. It indicates that the model showed better performance in terms of the binary mask from the addition of the segmentation network and the internal structure through the use of inpainting networks. Also, the results demonstrated an improved ability of the proposed model to restore realistic details compared to other models.

Using immersive virtual environments for teaching and socializing children with autism spectrum disorder

The authors' research interests are focused on the development of preschool children with autism spectrum disorder and the potential of virtual reality technologies for teaching and developing children in a gaming environment. The article reviews articles published in peer-reviewed journals and posted in the Scopus database and the Google Scholar search engine for scientific publications over the past 10 years. The objective of the review is to identify international research experience regarding the impact of virtual environments and hardware on children with autism spectrum disorder, their potential effectiveness, recommended conditions and limitations of using virtual reality for social adaptation, development of communication, cognitive and motivational skills. The articles analyzed in the review focus on the following issues: the potential and results of using different types of virtual reality technologies for developing various skills and abilities in children with autism; using virtual reality to work with problems and risks; as well as the features, limitations and safety of using virtual reality headsets by children with autism spectrum disorder. It is recommended to use immersive virtual environments for children with autism under the following conditions: the presence of close and trusted people (caregivers or parents), the presence of real game objects for switching children after exiting the virtual environment and distracting attention from possible negative effects of using gadgets, continuing communication with the child after a virtual reality session in a normal physical space to practice the skill being taught, specific time limits for staying in the virtual environment, etc. It is concluded that the results of using virtual reality technologies remain contradictory, but valid data from scientific research allow us to assert that the use of immersive virtual environments is acceptable, can be effective for teaching children with autism spectrum disorder and, if certain conditions are met, is adequately perceived by them.

Science, Fiction, and Contemporary Media: Blurred Borders in Knowledge Dissemination in Michael Crichton’s The Andromeda Strain

This study examines the technical methods used by Michael Crichton to blur the lines between factual information and fictional elements in his novel The Andromeda Strain. The creative use of scientific terminology and artistic documentation that supports a sense of reality plays a role in creating a certain blurring that works to enhance the blurring of the boundaries between reality and imagination. The study aims to explore the novel within the framework of postmodern literary theory, questioning the concept of absolute truth, and emphasizing the role of language and literary discourse in constructing reality. Through a critical analysis of the novel, this study seeks to clarify the methods followed by Crichton to employ language and discourse to create an atmosphere of authenticity that contributes greatly to blurring the line between reality and imagination. In addition, the study comprehensively analyses modern media platform technologies that are used to erode the boundaries between objective reality and fictional narratives, and their consequent effects on the dissemination of information. The study concluded that Crichton's novel The Andromeda Strain embodies the successful integration of scientific concepts with fantasy elements, which resulted in a captivating and intellectually stimulating narrative.

Application of improved deep learning algorithm in obstacle avoidance of UAV

Abstract To enhance the intelligent obstacle avoidance capabilities of unmanned aerial vehicles (UAVs), we introduce a refined version of the You Only Look Once (YOLO) algorithm, termed Improved YOLO (I-YOLO). This novel algorithm not only maintains the swift detection speed inherent in the original YOLO but also augments its feature enhancement network. Specifically, we propose a three-branch parallel feature pyramid network (TBPFP) to capture richer semantic information pertaining to the context of small obstacles. Furthermore, we incorporate a generative adversarial network (GAN) in a cascaded manner to synthesize more realistic super-resolution images, thereby bolstering detection accuracy. Experimental evaluations demonstrate that, in comparison to the baseline YOLO algorithm, our I-YOLO variant exhibits superior detection precision and feature extraction capabilities in the perception of intricate environmental targets. While there is a modest decrement in detection speed, it remains adequate to fulfill real-time requirements, particularly in the context of video stream processing.

Re-imagen: Generating coherent background activity in synthetic scenario-based forensic datasets using large language models

Due to legal and privacy-related restrictions, the generation of synthetic data is recommended for creating datasets for digital forensic education and training. One challenge when synthesizing scenario-based forensic data is the creation of coherent background activity besides evidential actions. This work leverages the creative writing abilities of large language models (LLMs) to generate personas and actions that describe the background usage of a device consistent with the created persona. These actions are subsequently converted into a machine-readable format and executed on a virtualized device using VM control automation. We introduce Re-imagen, a framework that combines state-of-the-art LLMs and a recent unintrusive GUI automation tool to produce synthetic disk images that contain arguably coherent “wear-and-tear” artifacts that current synthesis platforms lack. While, for now, the focus is on the coherence of the generated background activity, we believe that the proposed approach is a step toward more realistic synthetic disk image generation.

DSViT: An Enhanced Transformer Model for Deepfake Detection

The rapid development of artificial intelligence and deep learning models has enabled the creation of highly realistic fake images and videos, posing significant threats to information security and safety. Accurate detection of these forged contents is crucial to prevent the spread of misinformation and to protect the integrity of digital media. Although several advanced studies in this field, such as Vision Transformer (ViT) and Convolutional Vision Transformer (CViT), have been conducted, there remain limitations that need to be addressed. In this paper, we introduce a novel model, improved from CViT, designed to optimize the process of deepfake detection, named DSViT (Deepfake Detection with SC-based Convolutional Vision Transformer). This model judiciously integrates Convolutions and a SCConvolution block with the ViT architecture. We conducted experiments on the Deepfake Detection Challenge (DFDC) dataset and compared the results with the CViT model to demonstrate the effectiveness of the proposed model

Evolution of realistic neutron star in the framework of [formula omitted] gravity

This work analyses and evaluates a few realistic compact objects in the presence of a gravitational interaction between two particles with a nonmetricity Q. In the f(Q) gravity framework, we have selected the anisotropic equation of motion and have determined f(Q) to be a linear function of nonmetricity Q. To evaluate the field equations in our work, we have opted to employ the Krori-Barua metric. We calculated the anisotropic factor for each of the four compact objects and found that the anisotropic component is positive and increases monotonically and interpreted that the nuclear force can oppose the gravitational attraction. At last, the relationship between mass and radius has been determined and illustrated visually. We have noted that the compactness of the pulsars LMC X-4, SMC X-4, Cen X-3, and Vela X-1 is inside the Buchdahl’s limit for varying values of a. This has led to the interpretation that these pulsars are neutron stars in a modified gravity background of f(Q). In addition, we calculated the model mass and, using thirty distinct choices of a, ran the Chi-Square test to see if there was a noticeable difference between the observed and model-generated masses. We have also looked at how the surface redshift has changed over time and whether the compact objects in our model that were previously described are compact.

Theory of similarity and ultrasonic testing

Application of similarity theory (dimensional analysis) to ultrasonic (UT) testing is discussed using as an illustration the example of concrete blocks. The concept of dimensional analysis makes it possible to represent a physical system via a minimal set of variables, the so-called π groups, which are the dimensionless quantities. These dimensionless parameters, characterizing the respective phenomenon, should have approximately equal values for the real object and for the model. Small concrete samples have been tested in the lab conditions using various UT probes and techniques. Two dimensionless criteria, valid and similar for the real field case and for the laboratory samples, have been introduced: total attenuations of UT waves and ratios of void size to the UT wavelength. To get total attenuations in the field conditions for thick concrete block, similar to the total attenuations measured in lab experiments on small samples, one should apply the low-frequency UT waves 60-100kHz. However, these UT waves will be able to reliably detect only the large inhomogeneities (e.g. voids), whose size is about half of the used UT wave, i.e. inhomogeneities with dimensions larger than 20-40mm.

Model for atomization droplet size and energy distribution ratio at the distal end of an electrostatic nozzle

Electrostatic atomization minimum quantity lubrication (EMQL) employs the synergistic effect of multiple physical fields to atomize minute quantities of lubricant. This innovative methodology is distinguished by its capacity to ameliorate the atomization attributes of the lubricant substantially, which subsequently augments the migratory and infiltration proficiency of the droplets within the complex and demanding milieu of the cutting zone. Compared with the traditional minimum quantity lubrication (MQL), the EMQL process is further complicated by the multiphysical field influences. The presence of multiple physical fields not only increases the complexity of the forces acting on the liquid film but also induces changes in the physical properties of the lubricant itself, thus making the analysis of atomization characteristics and energy distribution particularly challenging. To address this objective reality, the current study has conducted a meticulous measurement of the volume average diameter, size distribution span, and the percentage concentration of inhalable particles of the charged droplets at various intercept positions of the EMQL nozzle. A predictive model for the volume-averaged droplet size at the far end of the EMQL nozzle was established with the observed statistical value F of 825.2125, which indicates a high regression accuracy of the model. Furthermore, based on the changes in the potential energy of surface tension, the loss of kinetic energy of gas, and the electric field work at different nozzle orifice positions in the EMQL system, an energy distribution ratio model for EMQL was developed. The energy distribution ratio coefficients under operating conditions of 0.1 MPa air pressure and 0 to 40 kV voltage on the 20 mm cross-section ranged from 3.094‰ to 3.458‰, while all other operating conditions and cross-sections had energy distribution ratios below 2.06‰. This research is expected to act as a catalyst for the progression of EMQL by stimulating innovation in the sphere of precision manufacturing, providing theoretical foundations, and offering practical guidance for the further development of EMQL technology.

Recognition of inheritance as escheated

The features of recognition of inheritance as escheated are determined. The author analyses the peculiarities of interest construction in the system of legal relations of inheritance escheat. It is determined that the institution of inheritance escheat is aimed at satisfying the public interest in maintaining the stability of property relations in society. The property (economic) interest in acquiring ownership of inherited property is derived from the main public interest. By its legal nature, an escheated inheritance is a legal succession. The author emphasises that the procedure for transferring escheated property to municipal ownership is established in local acts of local self-government bodies which have a procedural nature regarding the acquisition of municipal ownership of escheated property. It is argued that depending on the type of real estate object, it is advisable to determine the legal regime of escheated property, in particular, land and residential real estate. It is proved that the construction of interest in the system of legal relations of escheatment of inheritance is complex and combines public (social) and economic (property) interest. The public (social) interest is to preserve the stability of property relations in society. The economic (property) interest has a public and private orientation, in particular, it satisfies the interest of the territorial community as both the acquirer of the property right and the creditor to satisfy the testator’s obligations at the expense of the deceased property. At the same time, a public entity assumes the burden of the testator’s obligations sometimes not in its own interests, but in order to ensure the stability of property relations in society. The improvement of civil legislation on the possibility of a territorial community to waive the obligation to file an application to the court for recognition of inheritance as escheated in case of destruction of real estate or its significant damage in view of martial law is proposed.