Virtual Reality Intelligent Hardware最新文献

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.

Background

With the development of information technology, network traffic logs mixed with various kinds of cyber-attacks have grown explosively. Traditional intrusion detection systems (IDS) have limited ability to discover new inconstant patterns and identify malicious traffic traces in real-time. It is urgent to implement more effective intrusion detection technologies to protect computer security.

Methods

In this paper, we design a hybrid IDS, combining our incremental learning model (KAN-SOINN) and active learning, to learn new log patterns and detect various network anomalies in real-time.

Results & Conclusions

The experimental results on the NSLKDD dataset show that the KAN-SOINN can be improved continuously and detect malicious logs more effectively. Meanwhile, the comparative experiments prove that using a hybrid query strategy in active learning can improve the model learning efficiency.

Background

For static scenes with multiple depth layers, the existing defocused image deblurring methods have the problems of edge ringing artifacts or insufficient deblurring degree due to inaccurate estimation of blur amount, In addition, the prior knowledge in non blind deconvolution is not strong, which leads to image detail recovery challenge.

Methods

To this end, this paper proposes a blur map estimation method for defocused images based on the gradient difference of the boundary neighborhood, which uses the gradient difference of the boundary neighborhood to accurately obtain the amount of blurring, thus preventing boundary ringing artifacts. Then, the obtained blur map is used for blur detection to determine whether the image needs to be deblurred, thereby improving the efficiency of deblurring without manual intervention and judgment. Finally, a non blind deconvolution algorithm is designed to achieve image deblurring based on the blur amount selection strategy and sparse prior.

Results

Experimental results show that our method improves PSNR and SSIM by an average of 4.6% and 7.3%, respectively, compared to existing methods.

Conclusions

Experimental results show that our method outperforms existing methods. Compared with existing methods, our method can better solve the problems of boundary ringing artifacts and detail information preservation in defocused image deblurring.

Background

The recurrent recovery is one of the common methods for video super-resolution, which models the correlation between frames via hidden states. However, when we apply the structure to real-world scenarios, it leads to unsatisfactory artifacts. We found that, in the real-world video super-resolution training, the use of unknown and complex degradation can better simulate the degradation process of the real world.

Methods

Based on this, we propose the RealFuVSR model, which simulates the real-world degradation and mitigates the artifacts caused by the video super-resolution. Specifically, we propose a multi-scale feature extraction module(MSF) which extracts and fuses features from multiple scales, it facilitates the elimination of hidden state artifacts. In order to improve the accuracy of hidden states alignment information, RealFuVSR use advanced optical flow-guided deformable convolution. Besides, cascaded residual upsampling module is used to eliminate the noise caused by the upsampling process.

Results

The experiment demonstrates that our RealFuVSR model can not only recover the high-quality video but also outperform the state-of-the-art RealBasicVSR and RealESRGAN models.

Background

Opening design is a major consideration in architectural buildings during early structural layout specification. Decisions regarding the geometric characteristics of windows, skylights, hatches, etc., greatly impact the overall energy efficiency, airflow and appearance of a building, both internally and externally.

Methods

In this work, we employ a goal-based, illumination-driven approach to opening design using a Bayesian Optimization approach, based on Gaussian Processes. A method is proposed that allows a designer to easily set lighting intentions along with qualitative and quantitative characteristics of desired openings.

Results

All parameters are optimized within a cost minimization framework to calculate geometrically feasible, architecturally admissible and aesthetically pleasing openings of any desired shape, while respecting the designer's lighting constraints.

In this work, we focus on the label layout problem: specifying the positions of overlaid virtual annotations in Virtual/Augmented Reality scenarios. Designing a layout of labels that does not violate domain-specific design requirements, while at the same time satisfying aesthetic and functional principles of good design, can be a daunting task even for skilled visual designers. Presenting the annotations in 3D object space instead of projection space, allows for the preservation of spatial and depth cues. This results in stable layouts in dynamic environments, since the annotations are anchored in 3D space. In this paper we make two major contributions. First, we propose a technique for managing the layout and rendering of annotations in Virtual/Augmented Reality scenarios by manipulating the annotations directly in 3D space. For this, we make use of Artificial Potential Fields and use 3D geometric constraints to adapt them in 3D space. Second, we introduce PartLabeling: an open source platform in the form of a web application that acts as a much-needed generic framework allowing to easily add labeling algorithms and 3D models. This serves as a catalyst for researchers in this field to make their algorithms and implementations publicly available, as well as ensure research reproducibility. The PartLabeling framework relies on a dataset that we generate as a subset of the original PartNet dataset [17] consisting of models suitable for the label management task. The dataset consists of 1,000 3D models with part annotations.