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.

Automated vehicle(AV) based on reinforcement learning is an important part of the intelligent transportation system. However, currently, the performance of AV heavily that relies on the quality of maps and mapless navigation is one potential method for navigation in a strange and dynamic changing environment. Although many efforts are made on mapless navigation, they either need prior knowledge, rely on an exceptional constructed environment or simple feature fusion mechanism in the networks. In this paper, we proposed a deep reinforcement learning method, namely TC-DDPG, which is consisted of DDPG, multi-challenge deep learning networks and time-critical reward function. By comparing to existing approaches, TC-DDPG takes the cost of time into consideration and achieves better performance and converges more easily. A new open source simulator is proposed and extensive experiments are conducted to demonstrate the performance of the TC-DDPG, which outperforms comparing methods and achieves 62.9% less in time cost, 12.0% less in distance cost and about 90% fewer in numbers of model parameters.

In Internet of Vehicles (IoV), the vehiclead hocnetwork (VANET) provides the location-based service (LBS) when vehicles communicate with the dynamic environment. As an integration of satellite systems and terrestrial communications, the space–air–ground integrated network (SAGIN) provides a reliable and efficient way for LBS. But the privacy protection in SAGIN cannot meet LBS security requirements well, so we present a blockchain-based LBS security preserving trust model.$K$-anonymous location privacy protection algorithm is used to hide users’ real position so that users can avoid personal privacy disclosure when requesting LBSs. We propose a trust management algorithm which can detect the malicious behaviors when constructing anonymous regions and clear the malicious users out of the system. Besides, we use blockchain to implement the transparency and conditional anonymity of the system. Missive experiments indicate that our scheme is feasible and outperforms part of state-of-the-art privacy protection approaches.

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.

With the increasing demand for intelligent transportation systems, short-term traffic flow prediction has become an important research direction. The memory unit of a Long Short-Term Memory (LSTM) neural network can store data characteristics over a certain period of time, hence the suitability of this network for time series processing. This paper uses an improved Gate Recurrent Unit (GRU) neural network to study the time series of traffic parameter flows. The LSTM short-term traffic flow prediction based on the flow series is first investigated, and then the GRU model is introduced. The GRU can be regarded as a simplified LSTM. After extracting the spatial and temporal characteristics of the flow matrix, an improved GRU with a bidirectional positive and negative feedback called the Bi-GRU prediction model is used to complete the short-term traffic flow prediction and study its characteristics. The Rectified Adaptive (RAdam) model is adopted to improve the shortcomings of the common optimizer. The cosine learning rate attenuation is also used for the model to avoid converging to the local optimal solution and for the appropriate convergence speed to be controlled. Furthermore, the scientific and reliable model learning rate is set together with the adaptive learning rate in RAdam. In this manner, the accuracy of network prediction can be further improved. Finally, an experiment of the Bi-GRU model is conducted. The comprehensive Bi-GRU prediction results demonstrate the effectiveness of the proposed method.

Effective querying of data in the road networks is an important problem in the Internet of vehicles. Aggregate nearest neighbor queries can return the objects that minimizes an aggregate distance function on road networks considering a set of query points in the Internet of vehicles. And${k}$aggregate nearest neighbor query (${k}$- ANN) is a complicate version for the basic one. The existing${k}$- ANN queries lack effective model to deal with the uncertain data and the existing query methods cannot be applied to solve the problem of (${k}$- ANN) query on uncertain data directly. Therefore, in this paper, a probabilistic threshold${k}$- ANN query method based on uncertain Voronoi diagram is proposed. The method includes three phases: processing phase, pruning phase and refinement phase. The processing phase is to compute the minimum covered circle of the query dataset which is prepared for the pruning phase. In pruning phase, the different pruning algorithms are proposed for the corresponding three aggregate function of aggregate nearest neighbor query. The data points that cannot be the result are pruned and the candidate set is obtained. In refinement phase, the sets composed of${k}$data points in candidate set whose probabilities are not less than the user-specified threshold are stored into the result set and returned to the user. Experiments are performed to evaluate the effectiveness and superiority of these algorithms on probabilistic thresholdk-ANNquery.

To explore the electric vehicle networks in smart cities through big data analysis technology, this study utilizes K-means and fuzzy theory in big data analysis technology to construct an objective function-based fuzzy mean clustering algorithm theory (FCM). Then, the FCM algorithm is improved, and the electric vehicle network is simulated. The results show that in the analysis of network data transmission performance, when the probability of successful propagation is 100% and the λ value is between 0.01-0.05, it is closest to the actual result, and the data delay is the smallest. In the analysis of the route guidance effects, when facing congested road sections, the route guidance strategy of this study can restrain the spread of congestion effectively and achieve timely evacuation of traffic congestion. In the further analysis of the impact of different factors on traffic conditions, under route guidance, with the increase in market penetration rate (MPR) of devices, following rate (FR) of vehicles, and congestion level (CL), the improvement of the induction strategy becomes clearer, and greater economic benefits are achieved. This study has found that utilizing big data analysis technology to improve the electric vehicle transportation networks can reduce the network data transmission performance delay significantly and change the path to suppress the spread of congestion effectively, which has provided experimental references for the development of electric vehicle transportation networks.

Spectral analysis is a practical technology used for rapid analysis of fruit maturity. Least-squares support vector regression (LSSVR) with radial basis function (RBF) kernel is an effective nonlinear method for the quantitative calibration in the visible-near-infrared (Vis-NIR) spectral region. However, the nonlinear effect and high-dimensional spectrum data influence the prediction accuracy and complexity of modeling procedures. This article presents a fuzzy optimization strategy to improve the performance of the RBF LSSVR model in Vis-NIR quantitative determination of pomelo maturity. In the proposed strategy, a linguistic iterative mode is introduced for optimizing RBF kernel parameters. The input variables of the model are the informative features extracted through fuzzy transform principal component analysis algorithm, and the output was a polynomial equation of the inputs. In this article, pomelo maturity is recorded using the quantitative indices of L*, a*, and b*. The Vis-NIR spectral data of Pomelo samples are first converted to a set of fuzzy-transformed principal components and inputted into a fuzzy-optimized LSSVR model for calibration, validation, and test. Parameter uncertainty is evaluated to verify the effectiveness of the proposed strategy. Experimental results show that the proposed fuzzy optimization strategy is feasible for reducing the computational complexity of the BRF LSSVR model, and the predictions on L*, a*, and b* are much appreciable. The proposed fuzzy optimization strategy based on the linguistic iteration mode is considered as the effective implementation for calibration models in the spectroscopy technology for rapid prediction of fruit maturity.