The sixth-generation mobile communication (6G) networks will face more complex endogenous security problems, and it is urgent to propose new universal security theories and establish new practice norms to deal with the “unknown unknown” security threats in cyberspace. This paper first expounds the new paradigm of cyberspace endogenous security and introduces the vision of 6G cyberspace security. Then, it analyzes the security problems faced by the 6G core network, wireless access network, and emerging associated technologies in detail, as well as the corresponding security technology development status and the integrated development of endogenous security and traditional security. Furthermore, this paper describes the relevant security theories and technical concepts under the guidance of the new paradigm of endogenous security.
For optimal results, retrieving a relevant feature from a microarray dataset has become a hot topic for researchers involved in the study of feature selection (FS) techniques. The aim of this review is to provide a thorough description of various, recent FS techniques. This review also focuses on the techniques proposed for microarray datasets to work on multiclass classification problems and on different ways to enhance the performance of learning algorithms. We attempt to understand and resolve the imbalance problem of datasets to substantiate the work of researchers working on microarray datasets. An analysis of the literature paves the way for comprehending and highlighting the multitude of challenges and issues in finding the optimal feature subset using various FS techniques. A case study is provided to demonstrate the process of implementation, in which three microarray cancer datasets are used to evaluate the classification accuracy and convergence ability of several wrappers and hybrid algorithms to identify the optimal feature subset.
We digitally reproduce the process of resource collaboration, design creation, and visual presentation of Chinese seal-carving art. We develop an intelligent seal-carving art-generation system (Zhejiang University Intelligent Seal-Carving System, http://www.next.zju.edu.cn/seal/; the website of the seal-carving search and layout system is http://www.next.zju.edu.cn/seal/search_app/) to deal with the difficulty in using a visual knowledge guided computational art approach. The knowledge base in this study is the Qiushi Seal-Carving Database, which consists of open datasets of images of seal characters and seal stamps. We propose a seal character generation method based on visual knowledge, guided by the database and expertise. Furthermore, to create the layout of the seal, we propose a deformation algorithm to adjust the seal characters and calculate layout parameters from the database and knowledge to achieve an intelligent structure. Experimental results show that this method and system can effectively deal with the difficulties in the generation of seal carving. Our work provides theoretical and applied references for the rebirth and innovation of seal-carving art.
With the rapid development of data-driven intelligent transportation systems, an efficient route recommendation method for taxis has become a hot topic in smart cities. We present an effective taxi route recommendation approach (called APFD) based on the artificial potential field (APF) method and Dijkstra method with mobile trajectory big data. Specifically, to improve the efficiency of route recommendation, we propose a region extraction method that searches for a region including the optimal route through the origin and destination coordinates. Then, based on the APF method, we put forward an effective approach for removing redundant nodes. Finally, we employ the Dijkstra method to determine the optimal route recommendation. In particular, the APFD approach is applied to a simulation map and the real-world road network on the Fourth Ring Road in Beijing. On the map, we randomly select 20 pairs of origin and destination coordinates and use APFD with the ant colony (AC) algorithm, greedy algorithm (A*), APF, rapid-exploration random tree (RRT), non-dominated sorting genetic algorithm-II (NSGA-II), particle swarm optimization (PSO), and Dijkstra for the shortest route recommendation. Compared with AC, A*, APF, RRT, NSGA-II, and PSO, concerning shortest route planning, APFD improves route planning capability by 1.45%–39.56%, 4.64%–54.75%, 8.59%–37.25%, 5.06%–45.34%, 0.94%–20.40%, and 2.43%–38.31%, respectively. Compared with Dijkstra, the performance of APFD is improved by 1.03–27.75 times in terms of the execution efficiency. In addition, in the real-world road network, on the Fourth Ring Road in Beijing, the ability of APFD to recommend the shortest route is better than those of AC, A*, APF, RRT, NSGA-II, and PSO, and the execution efficiency of APFD is higher than that of the Dijkstra method.
This paper presents applications of the continuous feedback method to achieve path-following and a formation moving along the desired orbits within a finite time. It is assumed that the topology for the virtual leader and followers is directed. An additional condition of the so-called barrier function is designed to make all agents move within a limited area. A novel continuous finite-time path-following control law is first designed based on the barrier function and backstepping. Then a novel continuous finite-time formation algorithm is designed by regarding the path-following errors as disturbances. The settling-time properties of the resulting system are studied in detail and simulations are presented to validate the proposed strategies.
In this study, we discuss how multi-agent systems (MASs) with a leader can achieve distributed bipartite tracking consensus using asynchronous impulsive control strategies. The proposed asynchronous impulsive control approach does not require the impulse to occur simultaneously for all agents. The communication links between neighboring nodes of MASs are antagonistic. When the leader’s control input is non-zero, sufficient conditions are obtained to achieve bipartite asynchronous impulsive tracking consensus in closed-loop MASs. More extensive ranges of asynchronous impulsive effects are discussed, and the designed controller’s feedback can effectively work against adverse impulsive permutation. Simple algebraic conditions for estimating the impulsive gain boundary and asynchronous impulsive interval are presented. Theoretical results are demonstrated with illustrative examples.
Observability ensures that any two distinct initial states can be uniquely determined by their outputs, so the stream ciphers can avoid unobservable nonlinear feedback shift registers (NFSRs) to prevent the occurrence of equivalent keys. This paper discusses the observability of Galois NFSRs over finite fields. Galois NFSRs are treated as logical networks using the semi-tensor product. The vector form of the state transition matrix is introduced, by which a necessary and sufficient condition is proposed, as well as an algorithm for determining the observability of general Galois NFSRs. Moreover, a new observability matrix is defined, which can derive a matrix method with lower computation complexity. Furthermore, the observability of two special types of Galois NFSRs, a full-length Galois NFSR and a nonsingular Galois NFSR, is investigated. Two methods are proposed to determine the observability of these two special types of NFSRs, and some numerical examples are provided to support these results.
Non-orthogonal multiple access (NOMA) based fog radio access networks (F-RANs) offer high spectrum efficiency, ultra-low delay, and huge network throughput, and this is made possible by edge computing and communication functions of the fog access points (F-APs). Meanwhile, caching-enabled F-APs are responsible for edge caching and delivery of a large volume of multimedia files during the caching phase, which facilitates further reduction in the transmission energy and burden. The need of the prevailing situation in industry is that in NOMA-based F-RANs, energy-efficient resource allocation, which consists of cache placement (CP) and radio resource allocation (RRA), is crucial for network performance enhancement. To this end, in this paper, we first characterize an NOMA-based F-RAN in which F-APs of caching capabilities underlaid with the radio remote heads serve user equipments via the NOMA protocol. Then, we formulate a resource allocation problem for maximizing the defined performance indicator, namely network profit, which takes caching cost, revenue, and energy efficiency into consideration. The NP-hard problem is decomposed into two sub-problems, namely the CP sub-problem and RRA sub-problem. Finally, we propose an iterative method and a Stackelberg game based method to solve them, and numerical results show that the proposed solution can significantly improve network profit compared to some existing schemes in NOMA-based F-RANs.