The demand for cone-beam computed tomography (CBCT) imaging in clinics, particularly in dentistry, is rapidly increasing. Preoperative surgical planning is crucial to achieving desired treatment outcomes for imaging-guided surgical navigation. However, the lack of surface texture hinders effective communication between clinicians and patients, and the accuracy of superimposing a textured surface onto CBCT volume is limited by dissimilarity and registration based on facial features. To address these issues, this study presents a CBCT imaging system integrated with a monocular camera for reconstructing the texture surface by mapping it onto a 3D surface model created from CBCT images. The proposed method utilizes a geometric calibration tool for accurate mapping of the camera-visible surface with the mosaic texture. Additionally, a novel approach using 3D-2D feature mapping and surface parameterization technology is proposed for texture surface reconstruction. Experimental results, obtained from both real and simulation data, validate the effectiveness of the proposed approach with an error reduction to 0.32 mm and automated generation of integrated images. These findings demonstrate the robustness and high accuracy of our approach, improving the performance of texture mapping in CBCT imaging.

As an essential part of mobile networks, vehicular ad hoc networks (VANETs) are beneficial to the improvement of traffic efficiency and safety through real-time information sharing between vehicles. Digital Twins (DT) have been utilized to facilitate the design, testing, and deployment of VANETs. However, constructing Digital Twins still faces interference from malicious vehicles. Despite most vehicles following communication rules honestly, the reliability and authenticity of traffic messages cannot be guaranteed due to the network’s openness and vulnerability. Meanwhile, vehicles may suffer tracking attacks during the interaction without an effective privacy-preserving method, leading to the leakage of sensitive data. To address these issues, a decentralized trust management scheme embedded with blockchain that considers identity authentication is proposed to detect malicious DT-vehicles. In our method, each vehicle in the Digital Twin of VANETs (DT-VANETs) is equipped with a certificate recorded on the blockchain as a legal identity, which is also served as a pseudonym for security during message transmission. The trustworthiness of the vehicle is evaluated based on direct trust and recommendation trust. Direct interaction between vehicles consists of message authenticity verification and active detection, which are the basis of direct trust calculation. For other vehicles, these direct trust opinions are treated as second-hand information to obtain recommendation trust. Unreliable recommendations are filtered by our proposed RTF algorithm, further resisting cooperation attacks. Vehicles judged to be malicious will have their certificates revoked and removed from DT-VANETs, providing a guarantee for the establishment of trust in DT-VANETs. Experimental results show that the proposed scheme can effectively resist malicious attacks in DT-VANETs.

Recent advancement in propulsion technologies to reduce the need for travel or increase the share of sustainable unmanned devices has accelerated the shift toward sustainable transport. To achieve the optimization of route planning in the air-ground integrated network (AGIN), we design an optimization strategy of accompanying graph navigation for unmanned devices, which aims to reduce the power consumption and$CO_{2}$gas emissions. The optimization of accompanying graph navigation is composed of three strategies, namely, the navigation based on the complete maps, the navigation based on the partitioned maps, and the navigation without maps. We propose a Two-tiered Grid (TG) index and Distributed AGIN Navigation (DAN) to navigate on partitioned maps. The top layer of the TG-index is composed of the border vertices of the global road network, which reflects the overall traffic conditions of the global road network and provides coarse-grained navigation routes. The bottom layer is a grid index composed of subgraphs, which reflects traffic conditions in local areas and provides fine-grained navigation routes. The navigation optimization is implemented in several segments, which can be run by multi-processors and realize rapid response to a large number of concurrent queries.

Route planning over dynamic road networks is an increasingly fundamental problem of modern transportation systems for human society, especially in the field of Intelligent Supply Chain (ISC). Due to the high degree of urbanization and the high number of vehicles, longer response time caused by massive concurrent queries, as well as more attacks caused by malicious vehicles, results in low efficiency of the transportation system and huge waste of computation resources. Thus, it is necessary to provide an efficient and safe transportation service for intelligent transportation planning. To achieve it, we utilize and improve the trust model to prevent the waste of computation resources. Meanwhile, we introduce a Trusted Parallel Optimization on Route Planning (T-PORP) based on Dual-level Grid (DLG) index to continuously handle the process of route planning in parallel. Considering the evolving traffic condition, we employ an LSTM (Long Short-Term Memory) neural network to periodically predict the weights of roads. Experimental results indicate that T-PORP is effective to sorts of trust model attacks and reduces the response time by an average of about 46.7% and saves the processing time by an average of about 27.6% compared with CANDS (Continuous Optimal Navigation via Distributed Stream Processing) algorithm.

Near-infrared (NIR) data containing spectral response information for detecting target composition are sparsely implied in spectral frequency sequence. Spectral feature information should be extracted using computer-oriented chemometric methods. An Internet of Things (IoT) framework constructed with NIR calibration platform needs some advanced algorithm architectures to realize intelligent analysis. A feedback convolutional neural network (CNN) architecture, including three repeated segments of convolution, pooling, and flattening, is designed in this article for multiple extraction of spectral features from one-dimensional NIR data. An error-feedback iteration mechanism is proposed in the model training process to optimize convolution filters of each segment. Multisegment features are fused successively to ease the sparse information issue. Fusion data are further used to train the calibration models with a parametric-scaling fully connected network to determine the suitable numbers of hidden and output nodes. The adaptive network structure has the advantage of obtaining optimal prediction results from fused feature data. The proposed feedback CNN architecture based on feature information fusion is applied to the NIR rapid quantitative detection of selenium content in paddy rice samples. Experimental results showed that the fusion of multisegment features can enhance the ability of spectral information extraction. The optimal model based on fused feature data performs better than models based on separate feature data of each segment. The feedback convolutional network for information fusion can be applied in the NIR collaborative IoT framework for rapid detection spectroscopy to ensure high-confidence NIR analysis in the artificial intelligence performance of IoT.

Vehicular ad hoc network(VANET) is a special mobile ad hoc network (MANET) which plays an important role in the intelligent traffic system(ITS). Based on the high mobility of VANETs, the security problems have not been reasonably solved when we enjoy the convenience brought by the Location Based Service(LBS). We present a blockchain-based trust management model for location privacy preserving. The scheme allows vehicles to use certificate to request LBS without revealing their privacy information. We construct anonymous cloaking region to ensure the privacy security of vehicles. We propose a trust management algorithm to constrain and standardize the behavior of vehicles, and use blockchain to implement the data security of vehicles. In the experiments, we conduct the tests with various data sets. Security analysis and experiments show that the system is resilient to sorts of trust model attacks, which can better preserve the privacy security of vehicles. Simulation results reveal that the proposed system is effective and feasible in collect.