2D-3D cross-dimensional registration for spinal surgery navigation, which aims to achieve real-time visual navigation of preoperative 3D vertebrae based on intraoperative 2D fluoroscopy images, faces significant challenges due to semantic and dimensional gaps. Traditional 2D-3D registration methods often require fine adjustment steps and have low computational efficiency. In this paper, we propose a self-geometry-guided direct regression method based on dual perspective images. Firstly, an effective mechanism for unifying the dual view coordinate system was proposed. Secondly, a novel feature extraction module based on a face-graph convolutional network (F-GCN) is proposed to effectively extract 3D vertebra posture features. Finally, a posture direct regression network guided by self-vertebral geometry based on 2D-3D fusion features was constructed. Experimental results show that our method has made significant progress in solving the problem of 2D-3D cross-dimensional registration for spinal surgery navigation.

The Earth observation era has bestowed dividends upon supervised land cover classification based on deep learning and optical data. However, limitations, such as insufficient spectral information and reduced quality during inclement weather for optical data, coupled with the need for extensive labeled samples, impede accurate classification. This letter harnesses multimodal images with deep contrastive learning to reduce reliance on labeled data and classify land covers. By employing a well-designed contrastive learning method with triangular similarity loss, our model can learn effective multimodal features without labeled samples. Moreover, the learned features are fused at the early feature level and used for the downstream classification task with fewer labeled samples. Experimental results demonstrate the benefits of incorporating multiple modalities, highlighting the potential of combining multimodal image analysis and contrastive learning for land cover classification with limited labeled samples.

Accurate and timely monitoring of the functional urban impervious surfaces (FUIS), e.g., ports, roads, residential and non-residential buildings, is vital but challenging for coastal cities due to their diverse land covers and complicated weather. Synthetic aperture radar (SAR), with its all-weather working capability, provides a unique opportunity to monitor coastal cities promptly and regularly. This study develops a scattering texture hierarchical fusion network (STHFN) to integrate SAR scattering characteristics for more accurate recognition of FUIS based on the scattering texture index (STI). Experimental results verify the effectiveness of STHFN with up to 8% improvement in the accuracy of FUIS classification, demonstrating the promising application of STHFN in utilizing SAR backscattering features for coastal urban monitoring.

Urban land cover (ULC) is a fundamental indicator of urbanization, while cloud cover hinders accurate and timely ULC monitoring. The fusion of synthetic aperture radar (SAR) and cloud-free optical data has shown good performance in previous studies, while there is a lack of investigation in cloud-prone areas where optical data is contaminated by clouds. This study proposes a cloud-oriented framework for fusing the two data sources for ULC classification in cloud-prone areas. For alleviating cloud interference, the framework proposes a cloud probability weighting strategy and a pixel-wise cloud dictionary learning algorithm considering the interference difference in different cloud probability levels. Experiments show that all algorithms using fused data improve the overall accuracy (OA) of above 6% and 20% compared with using single SAR and optical data, respectively. Compared with traditional SVM, RF, and dictionary learning methods which ignore cloud interference and directly concatenate optical and SAR features, the proposed method shows a significant improvement of 3% in OA. It improves almost all land covers' producer accuracy (PA) and user accuracy (UA), up to 9%. Further experiments with three cloud probability level samples find that the higher the cloud probability, the lower the classification accuracy of the sample. At each probability level, the proposed pixel-wise cloud dictionary learning method improves more than 2% in OA, improves up to 4% to 10% in PA and UA.

Mapping the land cover/use type of urban area surface plays a vital role in many remote sensing applications. The performance of classification is inevitably limited by the finite amount of information available from a single data source, the restricted atmosphere condition and the complex landscape of the urban areas. Even when multiple sources of data are used, the fusion strategy is relatively homogeneous. In this paper, we aim to explore the potential of transformer based fusion method in mapping the urban regions with optical and synthetic aperture radar images. Specifically, we propose a hybrid fusion transformer network that simultaneously implements multi-source data fusion at both the feature and the decision levels. The experiments are conducted on the high resolution multiple remote sensing images, and the results show that the hybrid fusion based on transformer can achieve 82.17% in overall accuracy (OA) and 76.91 % in kappa coefficient. Moreover, compared with convolution neural network based methods, the transformer based methods are on average 2% higher in OA and 3.6% higher in kappa coefficient.

Conversational recommender systems discover users' preferences through dialog and make proper recommendations. Previous works fall into task-oriented and sociable conversational recommender systems. However, these works are not interpretable and sociable simultaneously. To address this problem, we propose a conversational recommender system with topic-related preference graph (CRTPG), consisting of a topic-related preference graph (TP-Graph) construction module, a key entity prediction module, and a dialog generation module. The TP-Graph recognizes the user's entity-level preference and keeps preference information for the recent topics. The key entity prediction module provides key entities as explicit content guidance for dialog generation based on TP-Graph. The dialog generation module generates appropriate responses based on the TP-Graph and knowledge related to key entities. TP-Graph and key entity help humans understand the precise information the system makes decisions based on, improving the interpretability of the system. We conduct automatic and human evaluations on the DuRecDial dataset. Experimental results show that CRTPG achieves state-of-the-art results on recommendation and dialog generation.

Lossy compression causes the degradation of the classification accuracy of remote sensing (RS) images due to the introduced distortion by compression. In this letter, a convolutional sparse coding (CSC)-based method is proposed to quantitatively measure such an effect. In detail, the filters used in CSC are learned by online convolutional dictionary learning (OCDL) to construct the dictionary. Thereafter, the sparse coefficient maps are obtained based on the alternating direction method of multipliers (ADMM) algorithm. In addition, multiple kernel learning (MKL) is used to estimate the corresponding classification accuracy. The experimental results demonstrate that our method performs better in predicting the classification accuracy of RS images compared with the other state-of-the-art algorithms.

An increasing number of Industry Internet of Things (IIoT) applications put forward the requirements for strict time synchronization and accurate time service from providers. In order to indicate the time service performance of the Internet Time Service Providers (TSPs) on user sides, we build an Internet time service monitoring system which can obtain the real-time data of time service through the Internet. The monitoring system records the time service’s performance from three major categories: NTP pool projects, National Metrology Institutes and Commercial Organizations. After that, we propose three service metrics, in terms of Availability, Stability and Accuracy, to evaluate the performance of time service provided by TSPs. On the other hand, a novel anomaly detection algorithm is proposed to gather the statistics of abnormal data and then remove the abnormal data. Experimental results show that 56 TSPs’ availability are more than 95%. It indicates that a longer transmission path results in a lower availability. The further analyzed results also denote that the link hops have no correlations of Availability, Stability and Accuracy. Moreover, the relationship between Stability and Accuracy is positively correlated.

Dictionary learning has emerged as a powerful tool for a range of image processing applications and a proper dictionary always plays a key issue to the final achievable performance. In this paper, a class-oriented discriminative dictionary learning (CODDL) method is presented for image classification applications. It takes a comprehensive consideration of multiple optimization objectives, emphasizing class discrimination of both dictionary atoms and representation coefficients. The atoms of the learned dictionary should be grouped into class level sub-dictionaries. Meanwhile, the sparse representation coefficients of an input sample should be concentrated on the sub-dictionary of the class it belongs to. Then, based on the learned class-oriented discriminative dictionary, the structured representation coefficients can thus be used for image classification with a simple and efficient classification scheme. The superior performance of the proposed algorithm is demonstrated through extensive experiments.

In order to decrease the spectral distortion using the IHS-based fusion method and more effectively inject the spatial detail information of high spatial resolution images into the fused image, this paper presents a new image fusion method that combines the IHS transform with dual-tree complex wavelet transform. The proposed method can overcome the distortion of spectrum. Firstly, intensity-hue-saturation (IHS) transform of multispectral(MS) image is implemented. Then panchromatic (PAN) image and the intensity component of MS images are decomposed into three scales using dual-tree complex wavelet transform respectively. After that, the region-based fusion operator is adopted to select the wavelet coefficients by fusing the wavelet coefficients of PAN image and the intensity component of MS at each scale in wavelet domain. Further, the fused intensity component is obtained by inverse dual-tree complex wavelet transform. Finally, the fused image is obtained by inverse IHS transform. The experiment results demonstrate our proposed method is effective.

We study the delay performance of all-optical packet communication networks configured as ring and bus topologies employing crossconnect switches (or wavelength routers). Under a cross-connect network implementation, a packet experiences no (or minimal) internal queueing delays. Thus, the network can be implemented by high speed all-optical components. We further assume a packet-switched network operation, such as that using a slotted ring or bus access methods. We also derive the delay performance for networks under a store-and-forward network operation. We show these implementations to yield very close average end-to-end packet queueing delay performance. We note that a cross-connect network operation can yield a somewhat higher queueing delay variance levels. However, the mean end-to-end queueing delay for all traffic flows are the same for a cross-connect network operation (under equal nodal traffic loading), while that in a store-and-forward network increases as the path length increases. Similar delay performance results are obtained for bus networks.

We study the survivability of a meshed-ring communication network that employs cross-connect switches. For WDM networks, the cross-connect switches are implemented as wavelength routers. Nodes can also provide cross-connection at the ATM VP (virtual path) level. By meshing the ring, the nodal degree of connectivity is increased as compared to a ring topology, and thus more alternative (protection) paths are available. For routing purposes, wavelength subnetworks are embedded in the topology. Nodes communicate with each other across one of the subnetworks to which both belong. We consider two types of subnetwork topologies to simplify the routing in a normal (nonfailure) situation. For each type of subnetwork, different protection methods are proposed to protect against a single link and/or nodal failure. The throughput performance of such meshed-ring networks under failure conditions is clearly superior to that achieved by (SONET) ring networks. We show that certain protection methods even result in lower values of the protection capacity as well as the protection capacity ratio (i.e., the overall capacity used under a failure divided by the total capacity) as compared to ring networks. We also present methods for constructing wavelength subnetworks to achieve single-failure protection using the minimal number of wavelengths.

We introduce a meshed ring communications network which employs cross-connect switches. The cross-connect switches can be implemented as wavelength routers resulting in WDM networks, or as ATM virtual path (VP) switches leading to ATM compatible network systems. We show in the paper that this network architecture results in a significant increase in throughput performance in comparison with SONET ring networks. For a certain class of meshed rings, under a uniform traffic matrix, we derive the optimal topology which achieves maximum throughput efficiency. For practical implementation reasons, we investigate the performance of networks which employ a reduced number of identifiers (e.g., using fewer wavelengths or fewer VPIs). We demonstrate that by increasing the bandwidth allocated to wavelength subnetworks, the required number of wavelengths is reduced. Furthermore, we show that by modifying the topological layout of the meshed ring network, we can reduce substantially the number of required wavelengths (or identifiers), while incurring just a modest reduction in throughput efficiency.