Emerging wireless technologies such as 6G and satellite communications need thorough testing and validation before deployment. While NFV (Network Function Virtualization) effectively supports network analysis, evaluation, and testing, it lacks the ability to facilitate research in wireless communication and the convergence of wired and wireless networks. In this paper, we aim to enhance the capabilities of NFV by integrating the ns-3 network simulator with the NFV system. Moreover, we utilize the rich wireless modules in ns-3 to expand wireless communication emulation functions for NFV. Firstly, we discuss the operational principles of integrating the ns-3 emulated wireless network with the NFV network, which implemented by utilizing the communication interface between ns-3 and Linux. Next, we discuss the technical details of expanding the wireless communication emulation function within the NFV system. Furthermore, we provide a case study on the convergence between the NFV-based wired network and the ns-3 emulated MANET (Mobile Ad-hoc NETwork). The experimental results demonstrate that the proposed method has extended the availability of NFV in wireless communication emulation.

Unmanned aerial vehicles (UAVs) can serve as mobile base stations (MBSs) to provide wireless communication for ground terminals (GTs). This paper proposes a novel polynomial-time method to place MBSs, in order to minimize the number of MBSs ensuring each GT is within the wireless coverage of at least one MBS. The proposed algorithm transforms the deployment problem of MBSs into a minimum clique partition problem with the minimum enclosing circle coverage constraint. Based on the distance between GTs and the coverage radius of MBSs, the algorithm constructs an undirected graph $G(V,E)$ to denote the adjacent information between GTs. In our algorithm, the GT with the minimum degree is given a higher priority to deploy MBSs, and the location of each MBS will be refined gradually to cover as many as possible GTs. Numerical results show that, in the case where there are no capacity constraints for MBSs, the proposed algorithm performs advantageously over other algorithms in terms of the required number of MBSs as well as runtime overhead. Besides, we also analyze the impact of the capacity constraint of MBSs on the number of required MBSs, and compare the proposed algorithm with the Edge-prior algorithm on the case with the capacity constraint, showing that our algorithm requires fewer MBSs especially when the capacity of MBSs is high.

This paper presents the first investigation into using the goal-crossing paradigm for object selection with virtual reality (VR) head-mounted displays. Two experiments were carried out to evaluate ray-casting crossing tasks with target discs in 3D space and goal lines on 2D plane respectively in comparison to ray-casting pointing tasks. Five factors, i.e. task difficulty, the direction of movement constraint (collinear vs. orthogonal), the nature of the task (discrete vs. continuous), field of view of VR devices and target depth, were considered in both experiments. Our findings are: (1) crossing generally had shorter or no longer time, and higher or similar accuracy than pointing, indicating crossing can complement or substitute pointing; (2) crossing tasks can be well modelled with Fitts' Law; (3) crossing performance depended on target depth; (4) crossing target discs in 3D space differed from crossing goal lines on 2D plane in many aspects such as time and error performance, the effects of target depth and the parameters of Fitts' models. Based on these findings, we formulate a number of design recommendations for crossing-based interaction in VR.

Human action recognition has became an important task in computer vision and has received a significant amount of research interests in recent years. Convolutional Neural Network (CNN) has shown its power in image recognition task. While in the field of video recognition, it is still a challenge problem. In this paper, we introduce a high-efficient attention-based convolutional network named ECPNet for video understanding. ECPNet adopts the framework that is a consecutive connection of 2D CNN and pseudo-3D CNN. The pseudo-3D means we replace the traditional 3 × 3 × 3 kernel with two 3D convolutional filters shaped 1 × 3 × 3 and 3 × 1 × 1. Our ECPNet combines the advantages of both 2D and 3D CNNs: (1) ECPNet is an end-to-end network and can learn information of appearance from images and motion between frames. (2) ECPNet requires less computing resource and lower memory consumption than many state-of-art models. (3) ECPNet is easy to expand for different demands of runtime and classification accuracy. We evaluate the proposed model on three popular video benchmarks in human action recognition task: Kinetics-mini (split of full Kinetics), UCF101 and HMDB51. Our ECPNet achieves the excellent performance on above datasets with less time cost.

We present a 3D densely connected convolutional networks(DenseNet3D) for the task of automatic 2D-to-3D video conversion. In contrast to previous automatic 2D-to-3D conversion algorithms, which have separate stages and need ground truth depth as supervision, our method uses spatial transformer networks to combine the separate stages so that our model can be trained end-to-end and directly use stereo image pairs as supervision. We further propose a 3D densely connected convolutional networks(DenseNet3D), which replace the original convolution layer with 3D convolution in densely connected convolutional networks to capture the spatiotemporal characteristics of videos. The experiment shows that the network has better results and faster speed than existing state-of-the-art methods.

With the popularity of mobile terminals and the convenience of mobile operating, more and more private data is stored in mobile phones, which makes users pay more attention to mobile security. The data of mobile motion sensors are used to construct the user's behavioral characteristics and biometric characteristics. The principle is to capture the subtle changes in the smartphones caused by the user holding smartphones and touching screens. These changes are unique to different users. This paper studies the effects of mobile phone orientations on continuous authentication based on mobile motion sensors. Additionally, this paper constructs an adaptive phone orientation method for continuous authentication. An orientation detection model is constructed using K-means and Random Forest. The authentication model is constructed by using one-class SVM. Meanwhile, our experiments show that the data of mobile motion sensors are great difference in different phone orientations. We believe considering the situation of multi-orientations data affecting authentication accuracy. The adaptive phone orientation method can better fit for the scenarios where users use mobile devices in different phone orientations. Considering the orientation of the data will improve the robustness of the system and the accuracy of authentication.

Crossing is a fundamental paradigm for target selection in human-computer interaction systems. This paradigm was first introduced to virtual reality (VR) interactions by Tu et al., who investigated its performance in comparison to pointing, and concluded that crossing is generally no less effective than pointing and has unique advantages. However, owing to the characteristics of VR interactions, there are still many factors to consider when applying crossing to a VR environment. Thus, this review summarizes the main techniques for object selection in VR and crossing-related studies. Then, factors that may affect crossing interactions are analyzed from the perspectives of the input space and visual space. The aim of this study is to provide a reference for future studies on target selection based on the crossing paradigm in virtual reality.

The exponential increase of the intelligent connected devices and the dramatic growth of the wireless data traffic have motivated the development of the green wireless networks as well as the Internet of Things (IoT). In this paper, we study the minimization problem of the total power to satisfy the required rate constraints in IoT, where the users simultaneously communicate through multiple independent channels. This problem is complicated due to the nonlinear data rate function based on the Shannon capacity formula. To this end, we first transfer the initial problem in power domain to an equivalent problem in rate domain instead of direct approximation for the high data rate. Then, we approximate it to a convex problem with the spectral radius constraints by the use of the Neumann expansion and nonlinear Perron-Frobenius theorem. By doing so, we achieve the close upper bound for this total power minimization problem. Moreover, we obtain the lower bound by making use of the convex relaxation technique, and finally get the global optimal solution by leveraging the branch-and-bound method. Simulation results verify that our proposed algorithms have a good approximation to the global optimal value for the power and rate allocations.

Pointing and gesturing with the dominant thumb (DT) and the non-dominant thumb (NT) are two common tasks for bimanual tablet gripping interaction. Understanding the differences between DT and NT is important to pointing and gesturing based interface design on tablets, but is overlooked by previous studies. We therefore conducted two experiments. In the first experiment, participants carried out pointing tasks with DT and NT, respectively, on a tablet. We found DT and NT input differed in pointing time (DT had average shorter time than NT) and required target sizes for fast and accurate pointing (7.7 mm and 9.4 mm in diameter for DT and NT). On the other hand, DT and NT were alike in pointing accuracy. They performed about the same for target size larger than 9.6 mm and distance shorter than 21 mm. Both DT and NT pointing can be modeled by Fitts’ law. In the second experiment, participants performed gesturing tasks with DT and NT respectively on a tablet. The collected data were analyzed using a set of gesture features. Results showed DT and NT were different in features such as articulation time, size ratio and indicative angle difference, but similar in features like aperture, axial symmetry and shape distance. The differences of time and accuracy between DT and NT depended on gesture complexity but not gesture sizes. We discuss these findings with implications for future bimanual thumb interaction design and research.

Privacy-preserving string equality test is a fundamental operation of many algorithms, including privacy-preserving authentication in Internet of Things (IoT). Existing secure equality test schemes can theoretically achieve string equality comparison and preserve the private strings. However, they suffer from heavy computation and communication cost, especially while the strings are of hundreds of bits or longer, which is not suitable for IoT applications. In this paper, we propose an approximate Fast privacy-preserving equality Test Protocol (FTP), which can securely complete string equality test and achieve high running efficiency at the cost of little accuracy loss. We strictly analyze the accuracy of our proposed scheme and formally prove its security. Additionally, we leverage extensive simulation experiments to evaluate the running cost, which confirms our high efficiency; for instance, our proposed FTP can securely compare two 256 -bit strings within 0.7 seconds on ordinary laptops.