Real Images Research Articles

The Effects of Inquiry-Based Learning Environment with Augmented Reality Integration on Spatial Reasoning for Topic of Space / Kesan Persekitaran Pembelajaran Berasaskan Inkuiri dengan Integrasi Realiti Terimbuh terhadap Penaakulan Spatial bagi Tajuk Ruang

Students' mastery of the topic of Space in the field of Geometry is also influenced by their spatial reasoning. The use of technology such as augmented reality applications is necessary in assisting students in develop these skills through its ability to display real objects virtually that can be manipulated freely. However, the effectiveness of integrating augmented reality applications requires the implementation of suitable teaching strategies. This is because learning involving difficult concepts requires students not only to receive information directly but also to actively engage in learning activities to generate knowledge and skills. Therefore, this study aims to design an inquiry-based learning environment with the integration of augmented reality and examine its effects on students' spatial reasoning for the topic of Space in Year Three Mathematics. This study used a pre-experimental quantitative research design involving 25 Year Three students. The purposive sampling method was used to select the school as the location of the study and convenience sampling was used to select students as the study respondents. Pre-tests and post-tests were used to measure students' spatial reasoning after the intervention. The results of the descriptive analysis show that there is an increase in the mean score of 6.16 for students' spatial reasoning. The results of the inferential analysis also prove that there is a significant difference in the mean score (Asymp. Sig. (2-tailed) <0.001) between the pre-test and the post-test on students' spatial reasoning. Therefore, the results of this study have had a positive effect on students' spatial reasoning. In conclusion, this study is expected to encourage teachers to apply 21st-century learning strategies along with the integration of appropriate technology tools to design a learning environment of Mathematics learning among primary school students.

A Rapid Head Organ Localization System Based on Clinically Realistic Images: A 3D Two Step Progressive Registration Method with CVH Anatomical Knowledge Mapping.

Rapid localization of ROI (Region of Interest) for tomographic medical images (TMIs) is an important foundation for efficient image reading, computer-aided education, and well-informed rights of patients. However, due to the multimodality of clinical TMIs, the complexity of anatomy, and the deformation of organs caused by diseases, it is difficult to have a universal and low-cost method for ROI organ localization. This article focuses on actual concerns of TMIs from medical students, engineers, interdisciplinary researchers, and patients, exploring a universal registration method between the clinical CT/MRI dataset and CVH (Chinese Visible Human) to locate the organ ROI in a low-cost and lightweight way. The proposed method is called Two-step Progressive Registration (TSPR), where the first registration adopts "eye-nose triangle" features to determine the spatial orientation, and the second registration adopts the circular contour to determine the spatial scale, ultimately achieving CVH anatomical knowledge automated mapping. Through experimentation with representative clinical TMIs, the registration results are capable of labeling the ROI in the images well and can adapt to the deformation problem of ROI, as well as local extremum problems that are prone to occur in inter-subject registration. Unlike the ideal requirements for TMIs' data quality in laboratory research, TSPR has good adaptability to incomplete and non-thin-layer quality in real clinical data in a low-cost and lightweight way. This helps medical students, engineers, and interdisciplinary researchers independently browse images, receive computer-aided education, and provide patients with better access to well-informed services, highlighting the potential of digital public health and medical education.

UKRAINIANS IN POLAND AND UKRAINE: IS THE WAR CRISIS ABLE TO CHANGE PATTERNS OF FINANCIAL BEHAVIOUR

The russian war against Ukraine caused a large wave of displaced people both inside and outside the country. According to UNHCR as of 2023, more than 1.6 million Ukrainian citizens applied for protection status in Poland and 4.9 million people received internally displaced status in Ukraine. This paper aims to analyse the data from a 2023 survey of internally displaced Ukrainians and those who are considered refugees to identify how their financial patterns have changed and what measures can be applied for adjustment to a new environment. The results show that there are no significant changes in patterns of financial behaviour, however, both groups demonstrate coping strategies: more than 50% in both groups shorten their financial horizon plans. Their expenditures are focused mostly on essential needs. In both countries, Ukrainians invest less because of the decrease in their income. As well, the majority in both groups do not invest at all. Bank deposits remain the most popular investment for Ukrainians (in Ukraine is more than 25%, and in Poland is more than 15%). The real estate objects in Ukraine are less attractive because of security as before 2022. At the same time, Ukrainians are searching for alternative ways of investment. Ukrainians demonstrate a high level of using different digital technologies for different purposes, including personal finance. The fintech solutions are recommended to facilitate better decision-making in personal finance and change the patterns of financial behaviour. Fintech can increase the financial inclusion of IDPs in Ukraine and migrants in Poland and direct money flows for investment for further their pension payments.

Image inpainting algorithm based on inference attention module and two-stage network

Current image inpainting techniques often yield distorted structures or blurred textures that clash with the surrounding context, particularly when addressing extensive missing regions or highly textured images. These methods often struggle to reconstruct realistic and coherent image structures. To address this challenge, we introduce a two-stage network image restoration approach that leverages an inferential attention mechanism. In the first stage, an edge generation network is employed to produce plausible phantom edge information. Subsequently, an image complementation network is utilized to complete the image restoration process. To enhance the visual realism and restoration accuracy of the generated images, we incorporate an inferential attention mechanism within the image complementation network. This mechanism effectively mitigates inconsistencies in the generated features, leading to the production of more meaningful and effective information. We evaluate our proposed method on benchmark datasets, including Places2 dataset, CelebFaces Attributes dataset, and Paris StreetView dataset. The experimental results can demonstrate that the proposed method achieves better Structural Similarity Index and Peak Signal-to-noise Ratio than others. These metrics indicate superior performance compared to existing image inpainting methods, delivering higher image inpainting accuracy and more realistic image reconstruction effect.

DIGAN: distillation model for generating 3D-aware Terracotta Warrior faces

Utilizing Generative Adversarial Networks (GANs) to generate 3D representations of the Terracotta Warriors offers a novel approach for the preservation and restoration of cultural heritage. Through GAN technology, we can produce complete 3D models of the Terracotta Warriors’ faces, aiding in the repair of damaged or partially destroyed figures. This paper proposes a distillation model, DIGAN, for generating 3D Terracotta Warrior faces. By extracting knowledge from StyleGAN2, we train an innovative 3D generative network. G2D, the primary component of the generative network, produces detailed and realistic 2D images. The 3D generator modularly decomposes the generation process, covering texture, shape, lighting, and pose, ultimately rendering 2D images of the Terracotta Warriors’ faces. The model enhances the learning of 3D shapes through symmetry constraints and multi-view data, resulting in high-quality 2D images that closely resemble real faces. Experimental results demonstrate that our method outperforms existing GAN-based generation methods.

Analysis of students' mathematical communication skills in terms of learning styles

The purpose of this research is to obtain a description of the mathematical communication skills in terms of the learning styles of Grade XI TKR 3 students at SMK Negeri 1 Tanara for the 2023-2024 academic year. This research is qualitative in nature. Six subjects were selected, representing each learning style, from Grade XI TKR 3 students at SMK Negeri 1 Tanara, Tanara District, Serang Regency. The data collection techniques used were observation, interviews, documentation, and tests based on the indicators of mathematical communication skills, namely: (1) the ability to express mathematical symbols through everyday events, (2) the ability to determine the solution set by relating real objects to mathematical ideas, (3) the ability to explain mathematical problems related to elimination, (4) the ability to analyze mathematical problems related to substitution, and (5) the ability to evaluate mathematical thinking and strategies used in problems according to the observed object. The results showed that (1) subjects with a visual learning style have good mathematical concept comprehension on indicators 1, 2, 3, 4, and 5; (2) subjects with an auditory learning style have good mathematical communication on indicators 1, 2, and 5, but performed poorly on indicators 3 and 4; and (3) subjects with a kinesthetic learning style have good mathematical communication skills on indicators 1 and 2, but performed poorly on indicators 3, 4, and 5.

ConIQA: A deep learning method for perceptual image quality assessment with limited data

Effectively assessing the realism and naturalness of images in virtual (VR) and augmented (AR) reality applications requires Full Reference Image Quality Assessment (FR-IQA) metrics that closely align with human perception. Deep learning-based IQAs that are trained on human-labeled data have recently shown promise in generic computer vision tasks. However, their performance decreases in applications where perfect matches between the reference and the distorted images should not be expected, or whenever distortion patterns are restricted to specific domains. Tackling this issue necessitates training a task-specific neural network, yet generating human-labeled FR-IQAs is costly, and deep learning typically demands substantial labeled data. To address these challenges, we developed ConIQA, a deep learning-based IQA that leverages consistency training and a novel data augmentation method to learn from both labeled and unlabeled data. This makes ConIQA well-suited for contexts with scarce labeled data. To validate ConIQA, we considered the example application of Computer-Generated Holography (CGH) where specific artifacts such as ringing, speckle, and quantization errors routinely occur, yet are not explicitly accounted for by existing IQAs. We developed a new dataset, HQA1k, that comprises 1000 natural images each paired with an image rendered using various popular CGH algorithms, and quality-rated by thirteen human participants. Our results show that ConIQA achieves superior Pearson (0.98), Spearman (0.965), and Kendall’s tau (0.86) correlations over fifteen FR-IQA metrics by up to 5%, showcasing significant improvements in aligning with human perception on the HQA1k dataset.

Generative Adversarial Networks with Radiomics Supervision for Lung Lesion Generation.

Data-driven methods for lesion generation are quickly emerging due to the need for realistic imaging targets for image quality assessment and virtual clinical trials. We proposed a generative adversarial network (GAN) architecture for conditional generation of lung lesions based on user-specified classes of lesion size and solidity. The network consists of two discriminators, one for volumetric lesion data, and one for radiomics features derived from the lesion volume. A Wasserstein loss with gradient penalty was adopted for each discriminator. Training data were drawn from contoured and annotated lesions from a public lung CT database. Four quantitative evaluation methods were devised to assess the network performance: 1) overfitting (similarity between generated and real lesions), 2) diversity (similarity among generated lesions), 3) conditional consistency (capability of generating lesions according to user-specified classes), and 4) similarity in distributions of various lesion properties between the generated and real lesions. Ablation studies were also performed to investigate the importance of individual network component. The proposed network was found to generate lesions that resemble real lesions by visual inspection. Solid lesions are distinct from non-solid ones, and lesion sizes largely correspond to their specified classes. With a classifier trained on real lesions, the classification accuracies of generated and real lesions in both solid and non-solid classes are similar. Radiomics features of generated and real lesions were found to have similar distributions, indicated by the relatively low Kullback-Leibler (KL) divergence values. Furthermore, the correlations between pairwise radiomics features in generated lesions were comparable to those of real lesions. The proposed network presents a promising approach for generating realistic lesions with clinically relevant features crucial for the comprehensive assessment of medical imaging systems.

The Influence Mechanism of Internet Celebrity Phenomenon on Career Selection View of Generation Z in China--based on the Cultivation Theory

With the continuous development of information technology, television, as a mainstream media is gradually being replaced by new media, and this new mainstream media is also subtly influencing the career views of ‘Generation Z’ in China. This paper uses the cultivation theory proposed by American scholar Gerbner to analyze how the ‘symbolic reality’ constructed by the Internet celebrity industry affects the construction of ‘subjective reality’ in individuals' minds, and provides a brief overview of the ‘objective reality’ of the Internet celebrity industry in China.

Гражданско-правовое регулирование возникновения долевой собственности в объектах недвижимости

The article examines the features and mechanisms of the emergence of shared ownership in real estate objects based on an analysis of scientific literature and law enforcement practice. In the course of the study, the author revealed that for the emergence of legal relations of common shared ownership, it is necessary to comply with formal conditions and the direct expression of the will of the sole owner of the property, or the consent of all co-owners of such common property. In Russian law, the mechanism of commingling real estate is not possible, as it results in the legal demise of the property. Meanwhile, in doctrine and law enforcement practice, there are concepts of inseparable improvements in property, which also do not entail the emergence of common shared ownership. This pertains to changes made by a lessee to the landlord's real estate. Conclusion dwells upon the fact that the grounds for the emergence of shared ownership are clearly defined by current Russian legislation and do not imply any expansion.

Symmetrical Siamese Network for pose-guided person synthesis

Pose-Guided Person Image Synthesis (PGPIS) aims to generate a realistic person image that preserves the appearance of the source person while adopting the target pose. Various appearances and drastic pose changes make this task highly challenging. Due to the insufficient utilization of paired data, existing models face difficulties in accurately preserving the source appearance details and high-frequency textures in the generated images. Meanwhile, although current popular AdaIN-based methods are advantageous in handling drastic pose changes, they struggle to capture diverse clothing shapes imposed by the limitation of global feature statistics. To address these issues, we propose a novel Symmetrical Siamese Network (SSNet) for PGPIS, which consists of two synergistic symmetrical generative branches that leverage prior knowledge of paired data to comprehensively exploit appearance details. For feature integration, we propose a Style Matching Module (SMM) to transfer multi-level region appearance styles and gradient information to the desired pose for enriching the high-frequency textures. Furthermore, to overcome the limitation of global feature statistics, a Spatial Attention Module (SAM) is introduced to complement the SMM for capturing clothing shapes. Extensive experiments show the effectiveness of our SSNet, achieving state-of-the-art results on public datasets. Moreover, our SSNet can also edit the source appearance attributes, making it versatile in wider application scenarios.

A Smith Predictor Modified with a Pseudo Feedforward Control for the Charge-Coupled Device-Based Optoelectronic Tracking System.

In the high-precision optoelectronic tracking system (OTS) based on a charge-coupled device (CCD), the boresight error extracted from the tracking image contains an undeniable delay, which directly limits the control bandwidth of visual tracking. High bandwidth means high response speed and tracking accuracy. Generally, a model-based delay compensation control method called the Smith predictor is utilized to separate time delay from the closed loop to promote the control bandwidth. However, due to the existence of errors between the established model and the real object, the improvement in the bandwidth is still limited to ensure system stability, resulting in insufficient tracking performance. In this paper, to solve the problem, a Smith predictor modified with pseudo feedforward control for the OTS is proposed. The experimental results demonstrate that the proposed method achieves significant improvements in tracking performance, reducing the maximum residual error at 1 Hz from 365 arcseconds (using the classic Smith predictor) to 283 arcseconds, a 22.5% improvement. Across the main frequency band (0.2 Hz to 2 Hz), the residual errors were consistently lower using the proposed method.

Building Face Ageing Model Using Face Synthesis

Advancements in face synthesis technology have enabled innovative methods for modeling facial aging. This research paper focuses primarily on creating a robust face aging model using deep learning and Generative Adversarial Networks (GANs), trained on a diverse dataset of facial images. The proposed approach captures both global features and local textures to produce realistic age-progressed images while preserving the subject's identity. This paper also examines face synthesis techniques, with specific emphasis for the various practical usage of GANs. The key objective of our project is to upgrade both the discriminator and the generator parts of GANs to generate more realistic, age- progressed face images. We evaluated the model using quantitative metrics and qualitative assessments, demonstrating its effectiveness. Additionally, we address ethical considerations, proposing guidelines for responsible use. Our study offers a novel framework with significant applications in security, forensics, and entertainment, and suggests future research directions to improve accuracy and ethical standards.

A Learning Dendritic Neuron-Based Motion Direction Detective System and Its Application to Grayscale Images.

In recent research, dendritic neuron-based models have shown promise in effectively learning and recognizing object motion direction within binary images. Leveraging the dendritic neuron structure and On-Off Response mechanism within the primary cortex, this approach has notably reduced learning time and costs compared to traditional neural networks. This paper advances the existing model by integrating bio-inspired components into a learnable dendritic neuron-based artificial visual system (AVS), specifically incorporating mechanisms from horizontal and bipolar cells. This enhancement enables the model to proficiently identify object motion directions in grayscale images, aligning its threshold with human-like perception. The enhanced model demonstrates superior efficiency in motion direction recognition, requiring less data (90% less than other deep models) and less time for training. Experimental findings highlight the model's remarkable robustness, indicating significant potential for real-world applications. The integration of bio-inspired features not only enhances performance but also opens avenues for further exploration in neural network research. Notably, the application of this model to realistic object recognition yields convincing accuracy at nearly 100%, underscoring its practical utility.

Study on the Development of a Virtual Reality Application for Skeletal Anatomy Learning Using Rapid Application Development (RAD)

The use of innovative multimedia technology continues to evolve and has now become a primary technology tool in education. Currently, the utilization of virtual reality (VR) technology is being used as an immersive medium for learning, especially in the field of medicine. Immersive technology is presented in 3D visualization, approaching real and relevant objects in medical education as an alternative to traditional teaching aids such as cadavers. In this research, an exploration of the VR environment was conducted in a case study of the skeletal system using 3D skull bone objects. The exploration involved tasks such as preparing the 3D skull bone object data assets in the 3D development environment, exporting data assets to the 3D-VR environment, and configuring aspects related to visualization and object control based on visual C#. So far, documentation of the 3D-VR application development environment and the trial results of the case study object development on the Unity platform have been produced. Additionally, documentation of the interface design application using Rapid Application Development (RAD) has also been generated. The Android-based VR application has functioned well in accommodating the skull object, designed with 3 layers of the skeletal system adjusted to their respective depths. The skull consists of 22 components, including cranium.

Visual object detection system based on augmented reality and steady-state visual evoked potential

This study investigates a brain-computer interface (BCI) system based on an augmented reality (AR) environment and steady-state visual evoked potentials (SSVEP). The system is designed to facilitate the selection of real-world objects through visual gaze in real-life scenarios. By integrating object detection technology and AR technology, the system augmented real objects with visual enhancements, providing users with visual stimuli that induced corresponding brain signals. SSVEP technology was then utilized to interpret these brain signals and identify the objects that users focused on. Additionally, an adaptive dynamic time-window-based filter bank canonical correlation analysis was employed to rapidly parse the subjects' brain signals. Experimental results indicated that the system could effectively recognize SSVEP signals, achieving an average accuracy rate of 90.6% in visual target identification. This system extends the application of SSVEP signals to real-life scenarios, demonstrating feasibility and efficacy in assisting individuals with mobility impairments and physical disabilities in object selection tasks.

Generating realistic training images from synthetic data for excavator pose estimation

Computer vision-based 3D pose estimation for automated excavator operation monitoring requires numerous training images annotated with 3D pose labels. Owing to challenges in collecting such datasets in a field setting, using synthetic images from virtual environments has emerged recently. However, synthetic images lack the realism inherent in onsite images, potentially impacting pose estimation performance on real images. This paper thus proposes a generative model for generating realistic training excavator images with multiple backgrounds. The evaluation was conducted by comparing estimation models trained on synthetic images (Model #1), generated excavator images with single background (Model #2), and generated excavator images with multiple backgrounds (Model #3). Model #3 exhibited the lowest mean angular error of 5.96° on real data, implying its superiority in generalizing real patterns. The proposed model facilitates data acquisition for improving pose estimation without manual annotation, providing rich information on excavator movements for proactive safety and productivity management.

Methods for ice crossings load capacity calculation

Introduction. The article deals with a problem relevant to the territory of our country – the calculation of the bearing capacity of ice crossings and winter roads. This problem is becoming more and more urgent in connection with the development of the northern territories, an increase in the carrying capacity of road transport and the volume of cargo traffic. At the same time, the construction of permanent roads and bridge crossings requires very large capital expenditures, which are not yet available for the Russian Federation.Methods and materials. A critical analysis of methods for determining by calculation the bearing capacity of ice cover at crossings (the maximum permissible load on the axle of a single car or road train) was carried out.The recommendations of normative and methodological documents on this issue are considered. The presented analysis of domestic and foreign publications made it possible to evaluate mathematical models of different levels of complexity and detail, with a different set of factors influencing the final result.Results. As a result of the research, a summary table, which contains formulas for calculating the bearing capacity of the ice cover, the parameters that are taken into account in these formulas, as well as the values of the bearing capacity calculated using these formulas for two temperatures: 0 °C and minus 20 °C has been compiled.Conclusions. According to the results of the analysis, it can be seen that the largest number of parameters is taken into account in M.M. Kazansky-R.A. Shulman; Q. Wang; ODM 218.4.030-2016 Methodological recommendations for assessing the carrying capacity of ice crossings’ dependencies. At the same time, the value of the bearing capacity, calculated according to the 11th formulas, varies by 2-3 or more times presented. Consequently, the presented mathematical models for predicting the carrying capacity of ice crossings require experimental verification on real objects by dragging the control cargo (with some refinement of this method).

Some cases of explicit expression of the resulting field strength of magnets placed in the field of external sources

Formulas for calculating of the resulting magnetic field strength of magnets with a given magnetization distribution or given field of external sources are presented. We have collected exactly those cases where a user who does not have much experience in the field of computational mathematics and programming can perform calculations using these formulas. Namely, in these cases formulas for tension are mostly expressed in final form through elementary functions or require the use of one of the standard subroutines available in the library of any programming language. Such formulas can serve both as reference material and for solving corresponding problems with suitable real objects, as well as for determining optimal strategies and the optimal set of input parameters when using universal software packages and assessing the error of the results obtained.

EStatiG: Wearable Haptic Feedback with Multi-Phalanx Electrostatic Brake for Enhanced Object Perception in VR

Haptic gloves have enabled the immersive and realistic sense of interacting with objects in virtual reality (VR) by providing coordinated haptic sensation with visual information. However, previous approaches mainly focused on providing feedback on the fingertips or distal phalanges, with minimal attention paid to the other phalanges. We propose EStatiG, a haptic glove that delivers force feedback over all finger regions from the fingertip to the proximal phalanges. Here, we enrich the perception of object shape during grasping tasks in VR. To do this, we developed the double layer and multi-stacked electrostatic clutches (ES clutches) to form an electrostatic brake (ES brake) for each phalanx. With the lightweight structure (130 grams), we enabled high-resolution force feedback while maintaining wearability and usability. We validated the user perception performance when using the proposed device. Our results showed a significant improvement in the perception of phalanx angle positions and the overall experience of realism and immersion when interacting with various object shapes in VR.