Virtual Reality Intelligent Hardware最新文献

Background

Most existing chemical experiment teaching systems lack solid immersive experiences, making it difficult to engage students. To address these challenges, we propose a chemical simulation teaching system based on virtual reality and gesture interaction.

Methods

The parameters of the models were obtained through actual investigation, whereby Blender and 3DS MAX were used to model and import these parameters into a physics engine. By establishing an interface for the physics engine, gesture interaction hardware, and virtual reality (VR) helmet, a highly realistic chemical experiment environment was created. Using code script logic, particle systems, as well as other systems, chemical phenomena were simulated. Furthermore, we created an online teaching platform using streaming media and databases to address the problems of distance teaching.

Results

The proposed system was evaluated against two mainstream products in the market. In the experiments, the proposed system outperformed the other products in terms of fidelity and practicality.

Conclusions

The proposed system which offers realistic simulations and practicability, can help improve the high school chemistry experimental education.

Background

A task assigned to space exploration satellites involves detecting the physical environment within a certain space. However, space detection data are complex and abstract. These data are not conducive for researchers' visual perceptions of the evolution and interaction of events in the space environment.

Methods

A time-series dynamic data sampling method for large-scale space was proposed for sample detection data in space and time, and the corresponding relationships between data location features and other attribute features were established. A tone-mapping method based on statistical histogram equalization was proposed and applied to the final attribute feature data. The visualization process is optimized for rendering by merging materials, reducing the number of patches, and performing other operations.

Results

The results of sampling, feature extraction, and uniform visualization of the detection data of complex types, long duration spans, and uneven spatial distributions were obtained. The real-time visualization of large-scale spatial structures using augmented reality devices, particularly low-performance devices, was also investigated.

Conclusions

The proposed visualization system can reconstruct the three-dimensional structure of a large-scale space, express the structure and changes in the spatial environment using augmented reality, and assist in intuitively discovering spatial environmental events and evolutionary rules.

Background

Considerable research has been conducted in the areas of audio-driven virtual character gestures and facial animation with some degree of success. However, few methods exist for generating full-body animations, and the portability of virtual character gestures and facial animations has not received sufficient attention.

Methods

Therefore, we propose a deep-learning-based audio-to-animation-and-blendshape (Audio2AB) network that generates gesture animations andARK it’s 52 facial expression parameter blendshape weights based on audio, audio-corresponding text, emotion labels, and semantic relevance labels to generate parametric data for full- body animations. This parameterization method can be used to drive full-body animations of virtual characters and improve their portability. In the experiment, we first downsampled the gesture and facial data to achieve the same temporal resolution for the input, output, and facial data. The Audio2AB network then encoded the audio, audio- corresponding text, emotion labels, and semantic relevance labels, and then fused the text, emotion labels, and semantic relevance labels into the audio to obtain better audio features. Finally, we established links between the body, gestures, and facial decoders and generated the corresponding animation sequences through our proposed GAN-GF loss function.

Results

By using audio, audio-corresponding text, and emotional and semantic relevance labels as input, the trained Audio2AB network could generate gesture animation data containing blendshape weights. Therefore, different 3D virtual character animations could be created through parameterization.

Conclusions

The experimental results showed that the proposed method could generate significant gestures and facial animations.

Background

Functional mapping, despite its proven efficiency, suffers from a “chicken or egg” sce- nario, in that, poor spatial features lead to inadequate spectral alignment and vice versa during training, often resulting in slow convergence, high computational costs, and learning failures, particularly when small datasets are used.

Methods

A novel method is presented for dense-shape correspondence, whereby the spatial information transformed by neural networks is combined with the projections onto spectral maps to overcome the “chicken or egg” challenge by selectively sampling only points with high confidence in their alignment. These points then contribute to the alignment and spectral loss terms, boosting training, and accelerating convergence by a factor of five. To ensure full unsupervised learning, the Gromov–Hausdorff distance metric was used to select the points with the maximal alignment score displaying most confidence.

Results

The effectiveness of the proposed approach was demonstrated on several benchmark datasets, whereby results were reported as superior to those of spectral and spatial-based methods.

Conclusions

The proposed method provides a promising new approach to dense-shape correspondence, addressing the key challenges in the field and offering significant advantages over the current methods, including faster convergence, improved accuracy, and reduced computational costs.

Background

A medical content-based image retrieval (CBIR) system is designed to retrieve images from large imaging repositories that are visually similar to a user′s query image. CBIR is widely used in evidence- based diagnosis, teaching, and research. Although the retrieval accuracy has largely improved, there has been limited development toward visualizing important image features that indicate the similarity of retrieved images. Despite the prevalence of3D volumetric data in medical imaging such as computed tomography (CT), current CBIR systems still rely on 2D cross-sectional views for the visualization of retrieved images. Such 2D visualization requires users to browse through the image stacks to confirm the similarity of the retrieved images and often involves mental reconstruction of 3D information, including the size, shape, and spatial relations of multiple structures. This process is time-consuming and reliant on users’ experience.

Methods

In this study, we proposed an importance-aware 3D volume visualization method. The rendering parameters were automatically optimized to maximize the visibility of important structures that were detected and prioritized in the retrieval process. We then integrated the proposed visualization into a CBIR system, thereby complementing the 2D cross-sectional views for relevance feedback and further analyses.

Results

Our preliminary results demonstrate that 3D visualization can provide additional information using multimodal positron emission tomography and computed tomography (PET- CT) images of a non-small cell lung cancer dataset.

Wireless sensor networks (WSN) gather information and sense information samples in a certain region and communicate these readings to a base station (BS). Energy efficiency is considered a major design issue in the WSNs, and can be addressed using clustering and routing techniques. Information is sent from the source to the BS via routing procedures. However, these routing protocols must ensure that packets are delivered securely, guar- anteeing that neither adversaries nor unauthentic individuals have access to the sent information. Secure data transfer is intended to protect the data from illegal access, damage, or disruption. Thus, in the proposed model, secure data transmission is developed in an energy-effective manner. A low-energy adaptive clustering hierarchy (LEACH) is developed to efficiently transfer the data. For the intrusion detection systems (IDS), Fuzzy logic and artificial neural networks (ANNs) are proposed. Initially, the nodes were randomly placed in the network and initialized to gather information. To ensure fair energy dissipation between the nodes, LEACH randomly chooses cluster heads (CHs) and allocates this role to the various nodes based on a round-robin management mechanism. The intrusion-detection procedure was then utilized to determine whether intruders were present in the network. Within the WSN, a Fuzzy interference rule was utilized to distinguish the malicious nodes from legal nodes. Subsequently, an ANN was employed to distinguish the harmful nodes from suspicious nodes. The effectiveness of the proposed approach was validated using metrics that attained 97% accuracy, 97% specificity, and 97% sensitivity of 95%. Thus, it was proved that the LEACH and Fuzzy-based IDS approaches are the best choices for securing data transmission in an energy-efficient manner.

Background

Virtual reality technology has been widely used in surgical simulators, providing new opportunities for assessing and training surgical skills. Machine learning algorithms are commonly used to analyze and evaluate the performance of participants. However, their interpretability limits the personalization of the training for individual participants.

Methods

Seventy-nine participants were recruited and divided into three groups based on their skill level in intracranial tumor resection. Data on the use of surgical tools were collected using a surgical simulator. Feature selection was performed using the Minimum Redundancy Maximum Relevance and SVM-RFE algorithms to obtain the final metrics for training the machine learning model. Five machine learning algorithms were trained to predict the skill level, and the support vector machine performed the best, with an accuracy of 92.41% and Area Under Curve value of0.98253. The machine learning model was interpreted using Shapley values to identify the important factors contributing to the skill level of each participant.

Results

This study demonstrates the effectiveness of machine learning in differentiating the evaluation and training of virtual reality neurosurgical per- formances. The use of Shapley values enables targeted training by identifying deficiencies in individual skills.

Conclusions

This study provides insights into the use of machine learning for personalized training in virtual reality neurosurgery. The interpretability of the machine learning models enables the development of individualized training programs. In addition, this study highlighted the potential of explanatory models in training external skills.

Background

Intelligent garments, a burgeoning class of wearable devices, have extensive applications in domains such as sports training and medical rehabilitation. Nonetheless, existing research in the smart wearables domain predominantly emphasizes sensor functionality and quantity, often skipping crucial aspects related to user experience and interaction.

Methods

To address this gap, this study introduces a novel real-time 3D interactive system based on intelligent garments. The system utilizes lightweight sensor modules to collect human motion data and introduces a dual-stream fusion network based on pulsed neural units to classify and recognize human movements, thereby achieving real-time interaction between users and sensors. Additionally, the system in- corporates 3D human visualization functionality, which visualizes sensor data and recognizes human actions as 3D models in realtime, providing accurate and comprehensive visual feedback to help users better understand and analyze the details and features of human motion. This system has significant potential for applications in motion detection, medical monitoring, virtual reality, and other fields. The accurate classification of human actions con- tributes to the development of personalized training plans and injury prevention strategies.

Conclusions

This study has substantial implications in the domains of intelligent garments, human motion monitoring, and digital twin visualization. The advancement of this system is expected to propel the progress of wearable technology and foster a deeper comprehension of human motion.

In modern society, digital signage installed in many large-scale facilities supports our daily life. However, with a limited screen size, it is difficult to provide different types of information for many viewers at varying distances from the screen simultaneously. Therefore, in this study, we extend existing research on the use of hybrid images for tiled displays. To facilitate smoother information selection, a new interactive display method is proposed that incorporates a touchactivated widget as a high-frequency part of the hybrid image; these widgets are novel in that they are more visible to the viewers near to the display. We develop an authoring tool that we call the Hybrid Image Display Resolutions Optimizer (HYDRO); it features two kinds of control functions by which to optimize the visibility of the touch-activated widgets in terms of placement and resolution. The effectiveness of the present method is shown empirically via a quantitative user study and an eye-tracking-based qualitative evaluation.

Cross-modal retrieval has attracted widespread attention in many cross-media similarity search applications, especially image-text retrieval in the fields of computer vision and natural language processing. Recently, visual and semantic embedding (VSE) learning has shown promising improvements on image-text retrieval tasks. Most existing VSE models employ two unrelated encoders to extract features, then use complex methods to contextualize and aggregate those features into holistic embeddings. Despite recent advances, existing approaches still suffer from two limitations: 1) without considering intermediate interaction and adequate alignment between different modalities, these models cannot guarantee the discriminative ability of representations; 2) existing feature aggregators are susceptible to certain noisy regions, which may lead to unreasonable pooling coefficients and affect the quality of the final aggregated features. To address these challenges, we propose a novel cross-modal retrieval model containing a well-designed alignment module and a novel multimodal fusion encoder, which aims to learn adequate alignment and interaction on aggregated features for effectively bridging the modality gap. Experiments on Microsoft COCO and Flickr30k datasets demonstrates the superiority of our model over the state-of-the-art methods.