Virtual Objects Research Articles

Virtual Sound Image Reconstruction Method for Multi-objective Optimization of Folk Music Based on Evolutionary Algorithm

At present, the exchanges in various fields such as culture, economy, and politics are becoming more and more close in the world. From the perspective of the relationship between national music and cultural development, the development of national music has also received more and more attention, and it has become an inevitable trend in the development of today's era. The purpose of this paper was to perform virtual reconstruction of the sound image of folk music through multiple objective optimizations, and to model the virtual sound image of folk music as a multi-objective optimization problem. According to the research on sound image positioning, the relevant noise factors were removed, so as to achieve the playback of ethnic music that enhanced the surround effect and visual enjoyment. The evolutionary algorithm in this paper was mainly based on the multi-objective optimized sound image localization technology. For the music virtual sound image, an improved FCM algorithm and an evolutionary multi-objective optimization algorithm combining local and non-local information of the sound image were proposed, respectively. Through the analysis of the traditional sound image algorithm method, the accuracy of the sound image localization on the horizontal plane could be effectively improved. After the conversion, the audience could feel the better stereo image and surround feeling of the folk music. Through experimental analysis, it can be seen that the system can not only perform virtual conversion of audio-visual signals of different frequencies, but also provide data for different audio playback systems. The feature point registration error is low, and the reconstruction effect is good, especially the random delay processing in the range of 0∼20m, and the performance is better than the traditional method. Finally, virtual left and right surround sound image signals were obtained, which effectively improved the three-dimensional surround feeling of folk music.

StarGAN Model to Translate Interior Style Trained with Virtual Images

StarGAN Model to Translate Interior Style Trained with Virtual Images

A seismic ambient noise data classification method based on waveform and frequency-wavenumber analysis: Application to reliable geological interpretation adjacent to Well Songke-2, Northeast China

Passive seismic interferometry techniques enable the retrieval of virtual shot gathers at receiver locations. However, it is difficult to distinguish body waves from surface waves in seismic ambient noise data, which leads to failed dispersion measurements, artificial deep reflections, and misleading geologic interpretations. We develop a new method that uses the waveforms and their corresponding f- k spectra to identify body waves and surface waves. Our technique functions as a data classification procedure before wavefield retrieval and subsequent imaging. A field data example is conducted to test the feasibility of this technique. The field data results demonstrate that our technique can reconstruct satisfactory high signal-to-noise-ratio wavefields for reliable imaging and interpretation purposes. The method can also serve as a validation tool for identifying surface wave-related artificial events in the obtained virtual source reflection image. The marker boundaries of the Songliao Basin in Northeast China are interpreted in the obtained profile and are in agreement with the well-logging data. In addition, two normal faults are detected, which correspond to the extensional rift environment during the early Cretaceous.

Photon-Counting Computed Tomography for Microstructural Imaging of Bone and Joints.

Recently, photon-counting computed tomography (PCCT) has been introduced in clinical research and diagnostics. This review describes the technological advances and provides an overview of recent applications with a focus on imaging of bone. PCCT is a full-body scanner with short scanning times that provides better spatial and spectral resolution than conventional energy-integrating-detector CT (EID-CT), along with an up to 50% reduced radiation dose. It can be used to quantify bone mineral density, to perform bone microstructural analyses and to assess cartilage quality with adequate precision and accuracy. Using a virtual monoenergetic image reconstruction, metal artefacts can be greatly reduced when imaging bone-implant interfaces. Current PCCT systems do not allow spectral imaging in ultra-high-resolution (UHR) mode. Given its improved resolution, reduced noise and spectral imaging capabilities PCCT has diagnostic capacities in both qualitative and quantitative imaging that outperform those of conventional CT. Clinical use in monitoring bone health has already been demonstrated. The full potential of PCCT systems will be unlocked when UHR spectral imaging becomes available.

Improving Teaching Using Artificial Intelligence and Augmented Reality

With the rapid advancements in technology, the educational landscape is witnessing significant transformations in pedagogy and classroom dynamics. Two prominent technologies, Artificial Intelligence (AI) and Augmented Reality (AR), are gaining prominence in the field of education, promising to revolutionize the way teaching and learning take place. This article explores the potential benefits, challenges, and practical applications of integrating AI and AR into the teaching process to enhance student engagement and learning outcomes. The integration of AI in education brings forth personalized learning experiences. AI-powered algorithms analyze vast amounts of student data, including learning patterns, strengths, and weaknesses, to create tailored learning paths. This individualized approach helps educators identify students' unique needs and provide targeted support, ensuring that no student is left behind. Moreover, AI-based chatbots and virtual teaching assistants are increasingly being used to address student queries promptly, providing real-time support and fostering a more interactive learning environment. AR, on the other hand, enables the overlay of virtual objects and information in the real-world environment.. Students can explore complex concepts through visualizations, simulations, and interactive demonstrations, facilitating a deeper understanding of abstract topics. AR also fosters collaboration and teamwork among students, promoting active learning and peer-to-peer knowledge sharing. Combining AI and AR technologies offers a powerful synergy in the educational realm. AI can analyze AR-generated data and adapt instructional strategies in real time, responding to individual students' progress. This synergy not only enhances learning outcomes but also empowers teachers with data-driven insights, enabling them to make informed decisions about their teaching methodologies. However, successfully implementing AI and AR in education comes with its challenges. Issues related to data privacy, ethical considerations, and the need for effective teacher training in utilizing these technologies require careful attention.

A dual-energy computed tomography-based radiomics nomogram for predicting time since stroke onset: a multicenter study.

We aimed to develop and validate a radiomics nomogram based on dual-energy computed tomography (DECT) images and clinical features to classify the time since stroke (TSS), which could facilitate stroke decision-making. This retrospective three-center study consecutively included 488 stroke patients who underwent DECT between August 2016 and August 2022. The eligible patients were divided into training, test, and validation cohorts according to the center. The patients were classified into two groups based on an estimated TSS threshold of ≤ 4.5 h. Virtual images optimized the visibility of early ischemic lesions with more CT attenuation. A total of 535 radiomics features were extracted from polyenergetic, iodine concentration, virtual monoenergetic, and non-contrast images reconstructed using DECT. Demographic factors were assessed to build a clinical model. A radiomics nomogram was a tool that the Rad score and clinical factors to classify the TSS using multivariate logistic regression analysis. Predictive performance was evaluated using receiver operating characteristic (ROC) analysis, and decision curve analysis (DCA) was used to compare the clinical utility and benefits of different models. Twelve features were used to build the radiomics model. The nomogram incorporating both clinical and radiomics features showed favorable predictive value for TSS. In the validation cohort, the nomogram showed a higher AUC than the radiomics-only and clinical-only models (AUC: 0.936 vs 0.905 vs 0.824). DCA demonstrated the clinical utility of the radiomics nomogram model. The DECT-based radiomics nomogram provides a promising approach to predicting the TSS of patients. The findings support the potential clinical use of DECT-based radiomics nomograms for predicting the TSS. Accurately determining the TSS onset is crucial in deciding a treatment approach. The radiomics-clinical nomogram showed the best performance for predicting the TSS. Using the developed model to identify patients at different times since stroke can facilitate individualized management.

The Neural Correlates of Embodied L2 Learning: Does Embodied L2 Verb Learning Affect Representation and Retention?

We investigated how naturalistic actions in a highly immersive, multimodal, interactive 3D virtual reality (VR) environment may enhance word encoding by recording EEG in a pre/post-test learning paradigm. While behavior data have shown that coupling word encoding with gestures congruent with word meaning enhances learning, the neural underpinnings of this effect have yet to be elucidated. We coupled EEG recording with VR to examine whether embodied learning improves learning and creates linguistic representations that produce greater motor resonance. Participants learned action verbs in an L2 in two different conditions: specific action (observing and performing congruent actions on virtual objects) and pointing (observing actions and pointing to virtual objects). Pre- and post-training participants performed a match-mismatch task as we measured EEG (variation in the N400 response as a function of match between observed actions and auditory verbs) and a passive listening task while we measured motor activation (mu [8-13 Hz] and beta band [13-30 Hz] desynchronization during auditory verb processing) during verb processing. Contrary to our expectations, post-training results revealed neither semantic nor motor effects in either group when considered independently of learning success. Behavioral results showed a great deal of variability in learning success. When considering performance, low performance learners showed no semantic effect and high performance learners exhibited an N400 effect for mismatch versus match trials post-training, independent of the type of learning. Taken as a whole, our results suggest that embodied processes can play an important role in L2 learning.

Fine structural human phantom in dentistry and instance tooth segmentation

In this study, we present the development of a fine structural human phantom designed specifically for applications in dentistry. This research focused on assessing the viability of applying medical computer vision techniques to the task of segmenting individual teeth within a phantom. Using a virtual cone-beam computed tomography (CBCT) system, we generated over 170,000 training datasets. These datasets were produced by varying the elemental densities and tooth sizes within the human phantom, as well as varying the X-ray spectrum, noise intensity, and projection cutoff intensity in the virtual CBCT system. The deep-learning (DL) based tooth segmentation model was trained using the generated datasets. The results demonstrate an agreement with manual contouring when applied to clinical CBCT data. Specifically, the Dice similarity coefficient exceeded 0.87, indicating the robust performance of the developed segmentation model even when virtual imaging was used. The present results show the practical utility of virtual imaging techniques in dentistry and highlight the potential of medical computer vision for enhancing precision and efficiency in dental imaging processes.

The Eye Stays in the Picture: Virtual Images in Early Modern and Modern Optics

Abstract In optics, real images can be projected onto a screen, while virtual ones always remain behind mirrors. This apparently straightforward distinction is based on complex premises which emerged in the Early Modern period, and its development went hand in hand with a transformation of the notion of image, which became detached from sensual perception. In this article I will outline this historical process, and argue that the distinction between a real and virtual image still implies a reference to visual perception which makes it problematic, yet useful for didactic purposes.

Understanding, Virtually: How Does the Synthetic Cell Matter?

Abstract This paper examines how scientific understanding is enhanced by virtual entities, focusing on the case of the synthetic cell. Comparing it to other virtual entities and environments in science, we argue that the synthetic cell has a virtual dimension, in that it is functionally similar to living cells, though it does not mimic any particular naturally evolved cell (nor is it constructed to do so). In being cell-like at most, the synthetic cell is akin to many other virtual objects as it is selective and only partially implemented. However, there is one important difference: it is constructed by using the same materials and, to some extent, the same kind of processes as its natural counterparts. In contrast to virtual reality, especially to that of digital entities and environments, the details of its implementation is what matters for the scientific understanding generated by the synthetic cell. We conclude by arguing for the close connection between the virtual and the artifactual.

Correlation between vertebral bone mineral density and multi-level virtual non-calcium imaging parameters from dual-layer spectral detector computed tomography

Correlation between vertebral bone mineral density and multi-level virtual non-calcium imaging parameters from dual-layer spectral detector computed tomography

The Ghost in the Machine: Metaphors of the ‘Virtual’ and the ‘Artificial’ in Post-WW2 Computer Science

Abstract Metaphors that compare the computer to a human brain are common in computer science and can be traced back to a fertile period of research that unfolded after the Second World War. To conceptualize the emerging “intelligent” properties of computing machines, researchers of the era created a series of virtual objects that served as interpretive devices for representing the immaterial functions of the computer. This paper analyses the use of the terms “artificial” and “virtual” in scientific papers, textbooks, and popular articles of the time, and examines how, together, they shaped models in computer science used to conceptualize computer processes.

The Use of Virtual Reflectance Transformation Imaging (V-RTI) in the Field of Cultural Heritage: Approaching the Materiality of an Ancient Egyptian Rock-Cut Chapel

The Use of Virtual Reflectance Transformation Imaging (V-RTI) in the Field of Cultural Heritage: Approaching the Materiality of an Ancient Egyptian Rock-Cut Chapel

Acoustic Assembly and Scanning of Superlens Arrays for High-Resolution and Large Field-of-View Bioimaging.

High-resolution and dynamic bioimaging is essential in life sciences and biomedical applications. In recent years, microspheres combined with optical microscopes have offered a low cost but promising solution for super-resolution imaging, by breaking the diffraction barrier. However, challenges still exist in precisely and parallelly superlens controlling using a noncontact manner, to meet the demands of large-area scanning imaging for desired targets. This study proposes an acoustic wavefield-based strategy for assembling and manipulating micrometer-scale superlens arrays, in addition to achieving on-demand scanning imaging through phase modulation. In experiments, acoustic pressure nodes are designed to be comparable in size to microspheres, allowing spatially dispersed microspheres to be arranged into arrays with one unit per node. Droplet microlenses with various diameters can be adapted in the array, allowing for a wide range of spacing periods by applying different frequencies. In addition, through the continuous phase shifting in the x and y directions, this acoustic superlens array achieves on-demand moving for the parallel high-resolution virtual image capturing and scanning of nanostructures and biological cell samples. As a comparison, this noncontact and cost-effective acoustic manner can obtain more than ∼100 times the acquisition efficiency of a single lens, holding promise in advancing super-resolution microscopy and subcellular-level bioimaging.

Dual-energy CT applications on liver imaging: what radiologists and radiographers should know? A systematic review.

This review aims to provide a comprehensive summary of DECT techniques, acquisition workflows, and post-processing methods. By doing so, we aim to elucidate the advantages and disadvantages of DECT compared to conventional single-energy CT imaging. A systematic search was conducted on MEDLINE/EMBASE for DECT studies in liver imaging published between 1980 and 2024. Information regarding study design and endpoints, patient characteristics, DECT technical parameters, radiation dose, iodinated contrast agent (ICA) administration and postprocessing methods were extracted. Technical parameters, including DECT phase, field of view, pitch, collimation, rotation time, arterial phase timing (from injection), and venous timing (from injection) from the included studies were reported, along with formal narrative synthesis of main DECT applications for liver imaging. Out of the initially identified 234 articles, 153 met the inclusion criteria. Extensive variability in acquisition parameters was observed, except for tube voltage (80/140 kVp combination reported in 50% of articles) and ICA administration (1.5mL/kg at 3-4mL/s, reported in 91% of articles). Radiation dose information was provided in only 40% of articles (range: 6-80mGy), and virtual non-contrast imaging (VNC) emerged as a common strategy to reduce the radiation dose. The primary application of DECT post-processed images was in detecting focal liver lesions (47% of articles), with predominance of study focusing on hepatocellular carcinoma (HCC) (27%). Furthermore, a significant proportion of the articles (16%) focused on enhancing DECT protocols, while 15% explored metastasis detection. Our review recommends using 80/140 kVp tube voltage with 1.5mL/kg ICA at 3-4mL/s flow rate. Post-processing should include low keV-VMI for enhanced lesion detection, IMs for tumor iodine content evaluation, and VNC for dose reduction. However, heterogeneous literature hinders protocol standardization.

Virtual reality modelling for planning of percutaneous first step palliation in a newborn with heterotaxy syndrome.

We report the first-stage percutaneous palliation in a newborn with a rare heterotaxy syndrome variant including interrupted inferior vena cava, partial anomalous pulmonary venous drainage, and restrictive interatrial communication. Virtual reality imaging aided visualisation, decision-making, and planning. Successful atrial septoplasty performed via the internal jugular vein and anomalous pulmonary vein was followed by stenting of ductus arteriosus.

Impact of scatter radiation on spectral quantification performance of first- and second-generation dual-layer spectral computed tomography.

To assess the impact of scatter radiation on quantitative performance of first and second-generation dual-layer spectral computed tomography (DLCT) systems. A phantom with two iodine inserts (1 and 2mg/mL) configured to intentionally introduce high scattering conditions was scanned with a first- and second-generation DLCT. Collimation widths (maximum of 4cm for first generation and 8cm for second generation) and radiation dose levels were varied. To evaluate the performance of both systems, the mean CT numbers of virtual monoenergetic images (MonoEs) at different energies were calculated and compared to expected values. MonoEs at 50 versus 150keV were plotted to assess material characterization of both DLCTs. Additionally, iodine concentrations were determined, plotted, and compared against expected values. For each experimental scenario, absolute errors were reported. An experimental setup, including a phantom design, was successfully implemented to simulate high scatter radiation imaging conditions. Both CT scanners illustrated high spectral accuracy for small collimation widths (1 and 2cm). With increased collimation (4cm), the second-generation DLCT outperformed the earlier DLCT system. Further, the spectral performance of the second-generation DLCT at an 8cm collimation width was comparable to a 4cm collimation on the first-generation DLCT. A comparison of the absolute errors between both systems at lower energy MonoEs illustrates that, for the same acquisition parameters, the second-generation DLCT generated results with decreased errors. Similarly, the maximum error in iodine quantification was less with second-generation DLCT (0.45 and 0.33mg/mL for the first and second-generation DLCT, respectively). The implementation of a two-dimensional anti-scatter grid in the second-generation DLCT improves the spectral quantification performance. In the clinical routine, this improvement may enable additional clinical benefits, for example, in lung imaging.

Low-contrast detectability of photon-counting-detector CT at different scan modes and image types in comparison with energy-integrating-detector CT.

We aim to compare the low-contrast detectability of a clinical whole-body photon-counting-detector (PCD)-CT at different scan modes and image types with an energy-integrating-detector (EID)-CT. We used a channelized Hotelling observer (CHO) previously optimized for quality control purposes. An American College of Radiology CT accreditation phantom was scanned on both PCD-CT and EID-CT with 10 phantom positionings. For PCD-CT, images were generated using two scan modes, standard resolution (SR) and ultra-high-resolution (UHR); two image types, virtual monochromatic images at 70keV and low-energy threshold (T3D); both filtered-back-projection (FBP) and iterative reconstruction (IR) reconstruction methods; and three reconstruction kernels. For each positioning, three repeated scans were acquired for each scan mode, image type, and CTDIvol of 6, 12, and 24mGy. For EID-CT, images acquired from scans (10 positionings × 3 repeats × 3 doses) were reconstructed using the closest counterpart FBP and IR kernels. CHO was applied to calculate the index of detectability () on both scanners. With the smooth Br44 kernel, the of UHR was mostly comparable with that of the SR mode (difference: -11.4% to 8.3%, to 0.956), and the T3D images had a higher (difference: 0.7% to 25.6%) than 70keV images on PCD-CT. Compared with the EID-CT, UHR-T3D of PCD-CT had non-inferior (difference: -2.7% to 12.9%) with IR and non-superior (difference: 0.8% to 11.2%) with FBP using the Br44 kernel. PCD-CT produced higher than EID-CT by 61.8% to 247.1% with the sharper reconstruction kernels. The comparison between PCD-CT and EID-CT was significantly influenced by the reconstruction method and kernel. With a smooth kernel that is typically used in low-contrast detection tasks, the PCD-CT demonstrated low-contrast detectability that was comparable to EID-CT with IR and showed no superiority when using FBP. With the use of sharper kernels, the PCD-CT significantly outperformed EID-CT in low-contrast detectability.

Improved noise reduction in photon-counting detector CT using prior knowledge-aware iterative denoising neural network.

We aim to reduce image noise in high-resolution (HR) virtual monoenergetic images (VMIs) from photon-counting detector (PCD) CT scans by developing a prior knowledge-aware iterative denoising neural network (PKAID-Net) that efficiently exploits the unique noise characteristics of VMIs at different energy (keV) levels. PKAID-Net offers two major features: first, it utilizes a lower-noise VMI (e.g., 70keV) as a prior input; second, it iteratively constructs a refined training dataset to improve the neural network's denoising performance. In each iteration, the denoised image from the previous module serves as an updated target image, which is included in the dataset for the subsequent training iteration. Our study includes 10 patient coronary CT angiography exams acquired on a clinical dual-source PCD-CT (NAEOTOM Alpha, Siemens Healthineers). The HR VMIs were reconstructed at 50, 70, and 100keV, using a sharp vascular kernel (Bv68) and thin (0.6mm) slice thickness (0.3mm increment). PKAID-Net's performance was evaluated in terms of image noise, spatial detail preservation, and quantitative accuracy. PKAID-Net achieved a noise reduction of 96% compared to filtered back projection and 65% relative to iterative reconstruction, all while preserving spatial and spectral fidelity and maintaining a natural noise texture. The iterative refinement of PCD-CT data during the training process substantially enhanced the robustness of deep learning-based denoising compared to the original method, which resulted in some spatial detail loss. The PKAID-Net provides substantial noise reduction while maintaining spatial and spectral fidelity of the HR VMIs from PCD-CT.

Interacting with virtual characters, objects and environments: investigating immersive virtual reality in rehabilitation

Purpose This pilot study aimed to (a) investigate opportunities for immersive Virtual Reality (VR) technology in communication, physical, and visual rehabilitation by examining the interaction of people without disabilities in a range of structured virtual environments; and (b) validate research protocols that might be used to evaluate the physical, visual, and verbal interaction of users in virtual worlds, and their safety while using the technology. Methods Thirteen adults identifying as people without disability were exposed to VR via a head-mounted display. A video-review method was used to qualitatively code and analyse each participant’s communication, movement, orientation, and support needs. Results All participants oriented to their virtual environments sufficiently to use applications. Their spoken language was effective for interaction, although unconventional social behaviours were also observed. Two participants reported minor adverse reactions consistent with mild cybersickness. Conclusion The results provide insight into the types of environments and characters that support the greatest communicative, physical, and visual interaction in immersive VR. The tested protocols are useful to assess safety when using VR, and to observe communicative, physical, and visual interaction with virtual environments and characters. Implications for future research and use of VR with people with communication, physical and visual disability are discussed.