Digital Representation Research Articles

Eigenfemora—Age-at-Death Estimation in the Proximal Femur through an Image Processing Approach

Estimating age at death is essential to establish biological profiles from human skeletal remains in both forensic and archeological settings. Imaging studies of skeletal age changes in adults have described the metamorphosis of trabecular bone structure and bone loss in the proximal femur as well as changes in morphology during different stages of life. This study aims to assess the utility of a digital representation of conventional X-ray films of the proximal femur for the estimation of age at death in a sample of 91 adult individuals (47 females and 44 males) of the Coimbra Identified Skeletal Collection. The proposed approach showed a root mean squared error (RMSE) of 17.32 years (and mean absolute error of 13.47 years) for females and an RMSE of 14.06 years (mean absolute error of 11.08 years) for males. The main advantage of this approach is consistency in feature detection and extraction, as X-ray images projected on the femora space will always produce the same set features to be analyzed for age estimation, while more traditional methods rely heavily on operator experience that can lead to inconsistent age estimates among experts.

Building a fashion influencer image on Instagram

This article examines the shift from analogue to digital transmission of information; the metaphor of virtual community, and the everyday life of social networks. It also studies the construction of virtual identity taking place under the influence of the influencer system on the Internet. Viewing fashion as a process of constant production and reproduction of the social, of imitation as a means of social adaptation, the text examines the transformation of fashion into a primarily digital representation through influencer marketing. The analogue and digital evolution of codes is distinct, and the need of media and digital literacy are a growing challenge for modern society. This work will provide a clearer delineation of the fluid nature of identity and social belonging as well-articulated behaviors.

Parametric modeling and fabrication as capturing knowledge: A design computation workflow for historical brick surfaces in Anatolia

Using computational design tools to create meaningful digital representations of architectural heritage delivers both challenges and opportunities. On one hand, digital tools aid the fast and detailed three-dimensional modeling of architectural elements. On the other hand, these models do not sufficiently document the materials and techniques of making. This research proposes a workflow to use computational design tools to analyze historic Anatolian brick elements while integrating their geometry, construction, and part-whole relations in parametric modeling and robotic fabrication processes. Our approach demonstrates a correlation between the design of the surface pattern and material application in historical bricklaying. The proposed workflow can be applied to formalize implicit design knowledge, integrating it into the digital environment and numeric control production codes. This holistic approach to heritage prioritizes both the tangible aspects, such as form and material, and intangible aspects such as the knowledge base of applied techniques.

The Representation of the Body in new media, TikTok

The representation of the body in new media appears to be focused on navigating gendered norms of the body image, particularly those of masculinity and femininity, and perceived ideals of beauty as a sign of sexual attraction. The goal of this study is to examine how social networks contribute to the development of stereotypes that rely on body visibility. TikTok is one of the most current social networking applications (SNAs) that allow users to create, modify, and share short form films. The research technique offers a self-representation focused content analysis of sample videos. Such analysis examines the impact SNAs have on the formation and expression of users’ notions of beauty and gender through their digital representations of the body.

Attention De-sparsification Matters: Inducing diversity in digital pathology representation learning

We propose DiRL, a Diversity-inducing Representation Learning technique for histopathology imaging. Self-supervised learning (SSL) techniques, such as contrastive and non-contrastive approaches, have been shown to learn rich and effective representations of digitized tissue samples with limited pathologist supervision. Our analysis of vanilla SSL-pretrained models’ attention distribution reveals an insightful observation: sparsity in attention, i.e, models tends to localize most of their attention to some prominent patterns in the image. Although attention sparsity can be beneficial in natural images due to these prominent patterns being the object of interest itself, this can be sub-optimal in digital pathology; this is because, unlike natural images, digital pathology scans are not object-centric, but rather a complex phenotype of various spatially intermixed biological components. Inadequate diversification of attention in these complex images could result in crucial information loss. To address this, we leverage cell segmentation to densely extract multiple histopathology-specific representations, and then propose a prior-guided dense pretext task, designed to match the multiple corresponding representations between the views. Through this, the model learns to attend to various components more closely and evenly, thus inducing adequate diversification in attention for capturing context-rich representations. Through quantitative and qualitative analysis on multiple tasks across cancer types, we demonstrate the efficacy of our method and observe that the attention is more globally distributed.

Performance Analysis of a Drone Development Kit-derived Digital Elevation Model

Surface modeling constitutes is a crucial aspect in numerous engineering inquiries and earth observation endeavors. In contemporary times, the acquisition of geospatial data essential for the digital representation of local regions is increasingly facilitated through drone-based methodologies, supplanting conventional terrestrial data gathering techniques. The market presently hosts a plethora of cost-effective, "ready-to-fly" unmanned aerial vehicles (UAVs), offering users the capability to generate photogrammetric outputs, including high geometric precision Digital Elevation Models (DEMs). Moreover, modularly structured drone development kits, designed for multifarious applications, are readily accessible for purchase. These drone kits offer an economically advantageous platform that users can customize to suit their specific needs. Nevertheless, the geometric precision of DEMs created using these kits hinges upon the capabilities of the imaging and navigation systems, in addition to the stabilization of the platform during autonomous flight. In this study, using a drone development kit and a commercial drone, simultaneous image acquisition was performed for the same study area and two different DEMs were produced. The efficacy of the DEM generated using the drone development kit was assessed through a comparative analysis with the DEM obtained from a commercial drone. In addition, geometric accuracy assessment was conducted for both DEMs using ground control points. The findings reveal the usability of drone development kits in precision DEM production, as well as their limitations.

Comparison of the suitability of the otsu method thresholding and multilevel thresholding for flower image segmentation

The digital representation of flowers, characterized by their vivid chromatic attributes, establishes them as viable candidates for deployment as input imagery within the object recognition paradigm. Within the context of object recognition, the imperative of a proficient image segmentation process is underscored, serving to effectively discern the object from its background and, consequently, optimizing the efficacy of the object recognition process. This research unfolds through a methodologically structured tripartite framework, encompassing the initial stage involving input imagery, the subsequent intermediate phase dedicated to image segmentation, and a conclusive stage centered on the quantitative evaluation of methodological outcomes. The second stage, focusing on image segmentation, employs the Otsu thresholding and multilevel thresholding methods. The subsequent third stage involves a thorough assessment of segmentation outcomes through the application of quantitative metrics, including Peak signal-to-oise ratio (PSNR) and Root Mean Square Error (RMSE). Empirical investigations, incorporating a diverse array of floral input images, reveal a conspicuous inclination towards a specific segmentation methodology. Specifically, the Otsu Thresholding method emerges as the more judicious choice relative to multilevel Thresholding, demonstrating superior performance with a diminished RMSE value and an augmented PSNR value, substantiated by an average RMSE value. This research is propelled by the overarching objective of discerning the most optimal method for the segmentation of flower images, particularly in the face of diverse input images. Its significant contribution lies in providing nuanced insights into the discerning selection of segmentation methodologies, attuned to the variability inherent in diverse forms of input imagery, thereby culminating in optimized outcomes within the domain of flower image recognition. Where did these results come from? please show it in the sub-discussion.

3D Remote Assistance for NDT Inspections

In this work, we present a system architecture that is especially designed for remote ultrasound testing inspections (UT). The system itself is realized using a real-time session service and a web service based on a REST API (REST: Representational State Transfer; API: application programming interface). This web service is used to store data persistently, e.g., sample geometries, raw nondestructive testing (NDT) data and derived inspection results in a shared dataspace. In the current development state, the results consist of textures mapped onto the sample geometry. This approach allows us to display the UT results directly on the real sample using mixed reality technologies. We also implemented a feature to assist the inspector remotely by making use of the availability of this digital representation. Hence, it is necessary to share additional data like the current UT-signal, temporary position marks, user and device positions, etc. between the different participants. A real-time distribution of this highly dynamic data is required to create an effective assistance environment. Therefore, a separate session service is used. The inspection data generated in this temporary session can also be transferred to the afore mentioned dataspace to be saved persistently. The system features mixed reality visualization for the inspector and optionally a virtual reality or a 3D Desktop environment for one or more remote assistants.

The Perils of Leveraging Evil Digital Twins as Security-Enhancing Enablers

Seeking more secure and effective digital representations.

Green Urban Scenarios: A Framework for Digital Twin Representation and Simulation for Urban Forests and Their Impact Analysis

Abstract Background Trees are a critical part of urban infrastructure. Cities worldwide are pledging afforestation objectives due to net-zero targets; however, their realisation requires a comprehensive framework that combines science, policy, and practice. Methods The paper presents the Green Urban Scenarios (GUS) framework for designing and monitoring green infrastructures. GUS considers weather, maintenance, tree species, diseases, and spatial distributions of trees to forecast their impacts. The framework uses agent-based modelling (ABM) and simulation paradigm to integrate green infrastructure into a city’s ecological, spatial, economic, and social context. ABM enables the creation of digital twins for urban ecosystems at any level of granularity, including individual trees, to accurately predict their future trajectories. Digital representation of trees is created using a combination of datasets such as earth observations from space, street view images, field surveys, and qualitative descriptions of typologies within existing and future projects. Machine learning and statistical models calibrate biomass growth patterns and carbon release schemes. Results The paper examines various green area typologies, simulating several hypothetical scenarios based on Glasgow’s urban forests. It exhibits the emergence of heterogeneity features of the forests due to interactions among trees. The growth trajectory of trees has a non-linear transition phase toward stable growth in its maturity. Reduced maintenance deteriorates the health of trees leading to lower survival rate and increased CO2emissions, while the stormwater alleviation capacity may differ among species. Conclusions The paper demonstrates how GUS can facilitate policies and maintenance of urban forests with environmental, social, and economic benefits.

Self-encoded chimera transform for digital image representation

Self-encoded chimera transform for digital image representation

The GIF that Cries: Digital Representations of Peru’s Post-conflict Condition

The GIF that Cries: Digital Representations of Peru’s Post-conflict Condition

Cyber-WISE: A Cyber-Physical Deep Wireless Indoor Positioning System and Digital Twin Approach.

In recent decades, there have been significant research efforts focusing on wireless indoor localization systems, with fingerprinting techniques based on received signal strength leading the way. The majority of the suggested approaches require challenging and laborious Wi-Fi site surveys to construct a radio map, which is then utilized to match radio signatures with particular locations. In this paper, a novel next-generation cyber-physical wireless indoor positioning system is presented that addresses the challenges of fingerprinting techniques associated with data collection. The proposed approach not only facilitates an interactive digital representation that fosters informed decision-making through a digital twin interface but also ensures adaptability to new scenarios, scalability, and suitability for large environments and evolving conditions during the process of constructing the radio map. Additionally, it reduces the labor cost and laborious data collection process while helping to increase the efficiency of fingerprint-based positioning methods through accurate ground-truth data collection. This is also convenient for working in remote environments to improve human safety in locations where human access is limited or hazardous and to address issues related to radio map obsolescence. The feasibility of the cyber-physical system design is successfully verified and evaluated with real-world experiments in which a ground robot is utilized to obtain a radio map autonomously in real-time in a challenging environment through an informed decision process. With the proposed setup, the results demonstrate the success of RSSI-based indoor positioning using deep learning models, including MLP, LSTM Model 1, and LSTM Model 2, achieving an average localization error of ≤2.16 m in individual areas. Specifically, LSTM Model 2 achieves an average localization error as low as 1.55 m and 1.97 m with 83.33% and 81.05% of the errors within 2 m for individual and combined areas, respectively. These outcomes demonstrate that the proposed cyber-physical wireless indoor positioning approach, which is based on the application of dynamic Wi-Fi RSS surveying through human feedback using autonomous mobile robots, effectively leverages the precision of deep learning models, resulting in localization performance comparable to the literature. Furthermore, they highlight its potential for suitability for deployment in real-world scenarios and practical applicability.

“Common Denominator” in Solving Multi-Factory Problems by Intelligent Systems

The most important property, a distinctive feature of any intelligent system, is its decision-making ability. In this case, the more complex the problem to be solved, the more and more diverse the initial data, and the more critical it is that the decision to be made was comprehensively considered and evaluated. In many cases, simultaneously arriving various initial data, if considered separately, and decisions based on such consideration lead to completely different results, often contradicting each other. Therefore, in the process of development and implementation of artificial intelligence (AI), it is especially important to investigate the “mechanism” of decision-making in conditions of the inconsistency of incoming initial data and the need to establish some generalizing rule, according to which it is possible to find a harmonizing solution taking into account various influencing factors. It is evident that when establishing the rules of decision-making, it is necessary to strive for a “positive” result from the point of view of the problem being solved. This undoubtedly requires analyzing the consequences of the decision made in a set time scale, which can be provided by appropriate feedback that will allow us to make the necessary corrective actions. Artificial intelligence in modern forms of practical realization has, as a rule, a digital embodiment. It should be taken into account that the digital representation of data inevitably shows an inaccurate display of initial values when processes of a continuous nature are considered and analyzed. Since a digital model has certain limitations and characteristic properties when analyzing and processing initial data, it is logical to assume that for this reason, there can be some general approach, some general rule, according to which a decision is made in the conditions of diverse initial data and the need to take into account the relevant consequences after the decision is made. This paper attempts to find a decision-making mechanism, harmonizing it according to the incoming external and available internal input data.

OBJECT DETECTION AND LOCALISATION FOR BIM ENRICHMENT

Abstract. The use of Building Information Models (BIM) during the entire life cycle of a building or facility requires an up-to-date and detailed digital representation. Nowadays the BIM focus mostly lies on the design and construction phase of the building and is rarely used for the rest of the life cycle. For further use the BIM must be updated after construction and enriched with appliance objects such as safety equipment, heating elements to enhance its usability. In this work a new approach is presented to detect and locate appliance objects in a three dimensional environment using object detection in images created by indoor mobile mapping devices. By detecting and locating these appliance objects in the three dimensional world they can be included in the BIM model. This approach enables the detection and localization needed for the placement of placeholders or detailed geometric models of those appliances in the BIM model or other digital representations. Resulting in an increased level of detail and usability of the digital representation of the building or facility during its further life cycle. Overall, this work demonstrates and examines the accuracy of the detection and localization of appliance objects.

REALITY CAPTURE METHODS FOR REMOTE INSPECTION OF BUILDING WORK

Abstract. Conventional building inspection, which requires in-person visits by a qualified inspector, can be costly, time-consuming, and even pose health and safety risks. The travel restrictions of the global Covid-19 pandemic further highlighted the need for remote inspection methods. Using reality capture techniques to create a digital 3D representation of the site offers promise for remote inspection of building work. This paper aims to assess different reality capture methods and their visualizations for remote inspection of building work through a case study conducted in Melbourne, Australia. The reality capture methods included terrestrial and mobile laser scanning, RGB-D imaging, and aerial photogrammetry using a UAV. Professional building inspectors participated in the evaluation process by conducting remote inspection based on the captured data and comparing it to the on-site experience. The assessment involved different visualizations of the data, including 3D panoramas, point clouds, and triangulated surface meshes. The results indicate that image visualisation and the ability to make measurement on images are desirable by professional inspectors. These findings support the adoption of reality capture techniques for remote inspection of building work, which can enhance the safety and efficiency of the process.

Tunnel performance prediction through degradation inspection and digital twin construction

Civil infrastructures inevitably undergo a degradation process due to extended periods of operation, frequent service requests, and weathering. This paper proposes a methodology for predicting necessary interventions and presents a database of inspection information for tunnels. The model developed correlates performance loss, measured by the tunnel serviceability index (TSI), to defects detected through a 2D grid composed of classified unit cells converted into a virtual representation. To support the framework developed, intervention time estimates were compared to stochastic degradation models presented in the literature. A limit analysis was performed using inverse exponential equations, utilizing data from different sources and supported by a digital representation of the analysis structure. This was done to estimate times with unitary inspection data, deterioration mapping control, and data connection to the developed inspection model. A railway tunnel for ore transport in northern Brazil was selected as a case study to apply the forecast model, as it showed an accelerated deterioration process within a few years of service. The tunnel was divided into four sections that were analyzed using the methodology developed through inspection-sensitive analysis. The section with the lowest service level (TS 1) matched the geological-geotechnical mapping, which indicated a rock mass with high metamorphic grade and susceptibility to weathering agents. The digital model resulted in an inspection digital twin (IDT) prototype that maps the zones with the greatest problems regarding the degradation process. Metadata is assigned to control elements, and there is online connection to a visual database of the tunnel. The target intervention time was set at 16 years by the developed structure, which falls within all forecast intervals calculated by the limit analyses. The inspection DT enables digital monitoring of the structure under analysis, allowing for efficient and optimized management.

Application of a stand-alone RTI measuring system with an integrated camera in cultural heritage digitisation

The Multi-Light Image Collection (MLIC) data, in most cases processed with the Reflectance Transformation Imaging (RTI) technique, is one of the popular methods of documentation and digital representation of movable finds from an archaeological excavation. The manual (hand-held) approach to collecting this data is technically simple and still popular in the case of archaeology. However, an extensive amount of work during image collection is a strong motivation for building RTI acquisition systems, which are also popular, in most cases, in the shape of domes. The research presented in the article focuses on two aspects of building such a device. First, the study examines the effect of the placement and number of lights on the resulting RTI model for a system based on LED stripe-shaped circuits. Secondly, the camera component was studied.Recent development in the area of low-cost accessories for microcomputers resulted in a few new industrial-grade USB cameras with the possibility to take high-resolution images. Cameras designed for use with microcomputers offer broad control of the focus or exposition parameters and provide the possibility to build RTI domes with the fully integrated camera, therefore, almost complete automatization of the capturing and processing of MLIC data.This paper presents the main assumptions and elements of the dome-shaped measurement system, which enables automatic RTI data acquisition and processing together with the evaluation of light configuration used to build relightable models. Amphoras handles with stamps and ancient pottery were used as an objects to evaluate the quality of the relightable models generated from the images acquired by a programmable miniature Arducam camera. Results of comparison with professional high-quality mirrorless cameras show that the quality of images from miniature cameras can be satisfactory for some archaeological objects.

Digital polycrystalline microstructure generation using diffusion probabilistic models

Accurate micromechanical simulation of polycrystalline materials requires a realistic digital representation of the grain scale microstructure. This work demonstrates the use of a generative diffusion probabilistic model for synthesizing single phase polycrystalline realizations. The model performs well and is capable of producing realistic microstructures consisting of not just simple equiaxed structures but also structures exhibiting more complex spatial arrangements. Masked microstructure generation reveals that the model is context aware of morphological descriptors which may be encoded in the latent space. Training on more diverse data sets, with scaled up architectures, may enable development of future models capable of synthesizing even more complex microstructural features.

Digital Threads – benefits, challenges, and best practices

AbstractThe premise of Digital Engineering (DE) – a term coined by the US DoD, is to introduce efficiency and agility into engineering practices based on digital representations of complex systems under development. DE aims to foster early and virtual validations, virtual exploration of solutions and design robustness by automating current document centric engineering processes. As stated by the DoD, DE is expected to result in shortening delivery time, reduced cost of development driving increased quality and trust that the intended system will indeed satisfy the stakeholder needs. The core technological pillars of digital engineering are model based engineering, designation of authoritative sources of truth to avoid ambiguity and inconsistency and establishing technological platforms to facilitate collaboration, security, and IP protection across all stakeholders. “Digital Threads” is an information architecture that aims to establish digital continuity across product lifecycle activities, foster engineering integrity, effective change management, tradeoff analysis, and dissemination of operational data. Digital threads are an essential part of the DoD Digital Engineering strategy. There are technological challenges to implement digital threads, as it requires to establish a digital fabric across various modeling and engineering operational environments. As of today, this topic is still in its infancy and the industry is seeking solutions and standards for it.