Dual-band dynamic reconfigurable intelligent surface for signal enhancement in millimeter wave

Millimeter wave with large bandwidth,high transmission rate,and low delay is considered a reliable alternative to cope with the spectrum shortage.However,the fast attenuation and narrow beam characteristics make it difficult to achieve long-distance or wide-range applications.Here,a 1-bit dual-band reflective reconfigurable intelligent surface(RIS)for signal enhancement in millimeter wave with 16×16 elements is designed,fabricated,and measured.Different from most existent RIS,dynamic programming is realized at two separate frequency bands by integrating the PIN diodes and field-programmable gate array(FPGA).Particularly,the beam deflection,dual-beam,and multi-beam are created based on the coding theory and convolution operation,proving the effectiveness of wavefront manipulation.Moreover,the far-field patterns and signal power with different coding sequences are measured and compared.It is indicated that the received signal power is 6–7 dB stronger than that without coding,which shows good agreement with the desired expectations.The proposed reconfigurable metasurface exhibits great potential in beam forming,making it a promising candidate for progressive wireless communication applications.

Modeling of Malicious Code Propagations in Internet of Things

Nowadays, the main communication object of Internet is human-human. But it is foreseeable that in the near future any object will have a unique identification and can be addressed and con- nected. The Internet will expand to the Internet of Things. IPv6 is the cornerstone of the Internet of Things. In this paper, we investigate a fast active worm, referred to as topological worm, which can propagate twice to more than three times faster tl^an a traditional scan-based worm. Topological worm spreads over AS-level network topology, making traditional epidemic models invalid for modeling the propagation of it. For this reason, we study topological worm propagation relying on simulations. First, we propose a new complex weighted network mod- el, which represents the real IPv6 AS-level network topology. And then, a new worm propagation model based on the weighted network model is constructed, which descries the topological worm propagation over AS-level network topology. The simulation results verify the topological worm model and demonstrate the effect of parameters on the propagation.

A Real-Time Multi-Vehicle Tracking Framework in Intelligent Vehicular Networks

In this paper,we provide a new approach for intelligent traffic transportation in the intelligent vehicular networks,which aims at collecting the vehicles’locations,trajectories and other key driving parameters for the time-critical autonomous driving’s requirement.The key of our method is a multi-vehicle tracking framework in the traffic monitoring scenario..Our proposed framework is composed of three modules:multi-vehicle detection,multi-vehicle association and miss-detected vehicle tracking.For the first module,we integrate self-attention mechanism into detector of using key point estimation for better detection effect.For the second module,we apply the multi-dimensional information for robustness promotion,including vehicle re-identification(Re-ID)features,historical trajectory information,and spatial position information For the third module,we re-track the miss-detected vehicles with occlusions in the first detection module.Besides,we utilize the asymmetric convolution and depth-wise separable convolution to reduce the model’s parameters for speed-up.Extensive experimental results show the effectiveness of our proposed multi-vehicle tracking framework.

In-Network Puncturing for Delay-Efficient Rate Control of Distributed Video Coding in Wireless Video Sensor Networks

Wyner-Ziv Video Coding （WZVC） is considered as a promising video coding scheme for Wireless Video Sensor Networks （WVSNs） due to its high compression efficiency and error resilience functionalities, as well as its low encoding complex- ity. To achieve a good Rate-Distortion （R-D） per- formance, the current WZVC paradi^prls usually a- dopt an end-to-end rate control scheme in which the decoder repeatedly requests the additional deco- ding data from the encoder for decoding Wyner-Ziv frames. Therefore, the waiting time of the additional decoding data is especially long in multihop WVSNs. In this paper, we propose a novel pro- gressive in-network rate control scheme for WZVC. The proposed in-network puncturing-based rate control scheme transfers the partial channel codes puncturing task from the encoder to the relay nodes. Then, the decoder can request the addition- al decoding data from the relay nodes instead of the encoder, and the total waiting time for deco- ding Wyner-Ziv frames is reduced consequently. Simulation results validate the proposed rate con- trol scheme.

Vulnerability Testing of HTTP based on Vulnerability-testing Oriented Petri Net(VOPN)

Vulnerability-testing Oriented Petri Net （VOPN）, a vulnerability testing model for communication protocol is brought forward first, which is combined Petri Net system with protocol Syntax analysis. Then vulnerability testing of implementation of HTTP protocol based on VOPN is made and the process is analyzed to prove the feasibility of the model.

Social network search based on semantic analysis and learning

Because of everyone＇s involvement in social networks, social networks are full of massive multimedia data, and events are got released and disseminated through social networks in the form of multi-modal and multi-attribute heterogeneous data. There have been numerous researches on social network search. Considering the spatio-temporal feature of messages and social relationships among users, we summarized an overall social network search framework from the perspective of semantics based on existing researches. For social network search, the acquisition and representation of spatio-temporal data is the basis, the semantic analysis and modeling of social network cross-media big data is an important component, deep semantic learning of social networks is the key research field, and the indexing and ranking mechanism is the indispensable part. This paper reviews the current studies in these fields, and then main challenges of social network search are given. Finally, we give an outlook to the prospect and further work of social network search.

An Enhanced and Extensible Data Structure for Flow Export Engine

Flow-based measurement is a popular method for various network monitoring usages.However, many flow exporting softwares have still low performance to collect all flows.In this paper, we propose a IPFIX-based flow export engine with an enhanced and extensible data structure, called XFix, on the basis of a GPL tool,-nProbe.In the engine, we use an extensible two-dimensional hash table for flow aggregation, which is able to improve the performance of the metering process as well as support bidirectional flow.Experimental results have shown its efficiency in multi-thread processing activity.

A Selection Algorithm of Service Providers for Optimized Data Placement in Multi-Cloud Storage Environment

The benefits of cloud storage come along with challenges and open issues about availability of services, vendor lock-in and data security, etc. One solution to mitigate the problems is the multi-cloud storage, where the selection of service providers is a key point. In this paper, an algorithm that can select optimal provider subset for data placement among a set of providers in multicloud storage architecture based on IDA is proposed, designed to achieve good tradeoff among storage cost, algorithm cost, vendor lock-in, transmission performance and data availability. Experiments demonstrate that it is efficient and accurate to find optimal solutions in reasonable amount of time, using parameters taken from real cloud providers.

Feedback iterative learning control for time-delay systems based on 2D analysis approach

This paper deals with the iterative learning control （ILC） design for multiple-input multiple-output （MIMO）,time-delay systems （TDS）.Two feedback ILC schemes are considered using the so-called two-dimensional （2D） analysis approach.It shows that continuous-discrete 2D Roesser systems can be developed to describe the entire learning dynamics of both ILC schemes,based on which necessary and sufficient conditions for their stability can be provided.A numerical example is included to validate the theoretical analysis.

Improved Prefix Based Format-Preserving Encryption for Chinese Names

In the era of big data, government, business and personal digital information will be possible for data mining. Data mining requires massive data as a support. However, the direct release of the original mass data, which usually contain some sensitive information of personal or analysis, will result in leakage of user privacy. Therefore, it is becoming increasingly important to protect privacy information in data publishing. In this paper, we focus on the multi-type self-identified format-preserving encryption. First, we introduce a multi-type self-identified format-preserving encryption system and discuss the encryption of various types of data in this system. Then, for the format preserving encryption(FPE) about Chinese name, we study from the encryption model construction and basic encryption scheme. The format-preserving encryption model about Chinese name is constructed and the concept of the name library is presented. Based on this, it is used to not only limit the message space to reduce complexity, but also ensure the cipher in accordance with the Chinese naming habits. In addition, according to the encryption and decryption model, format-preserving encryption process of Chinese name is designed. In order to add new names, the algorithm of name space expansion is proposed. Based on the Prefix, this paper put forward an algorithm named Cycle-Prefix, which enhances the security and dynamics of FPE by using two adjustment factors and the circular encryption. Compared with the traditional Prefix algorithm, experiments show that Cycle-Prefix can not only complete the task of FPE for Chinese name, but also encrypt same plain text into different ciphers under the premise of similar efficiency with Prefix.

Spatio-Temporal Adaptive Super-Resolution Reconstruction Model Based on Zemike Moment for Spatial Video Sequences

Video Super-Resolution （SR） reconstruction produces video sequences with High Resolution （HR） via the fusion of several Low-Resolution （LR） video frames. Traditional methods rely on the accurate estimation of subpixel motion, which constrains their applicability to video sequences with relatively simple motions such as global translation. We propose an efficient iterative spatio-temporal adaptive SR reconstruction model based on Zemike Moment （ZM）, which is effective for spatial video sequences with arbitrary motion. The model uses region correlation judgment and self-adaptive threshold strategies to improve the effect and time efficiency of the ZM-based SR method. This leads to better mining of non-local self-similarity and local structural regularity, and is robust to noise and rotation. An efficient iterative curvature-based interpolation scheme is introduced to obtain the initial HR estimation of each LR video frame. Experimental results both on spatial and standard video sequences demonstrate that the proposed method outperforms existing methods in terms of both subjective visual and objective quantitative evaluations, and greatly improves the time efficiency.

Cumulative Laws, Team Assembling Mechanisms Determining Network Structure

A number of researching works have shed light on the field of complex networks recently. We investigate a wide range of real-world networks and find several interesting phenomena. Firstly, almost all of these networks evolve by overlapping new small graphs on former networks. Secondly, not only the degree sequence of the mature network follows a power-law distribution, but also the distribution of the cumulative occurrence times during the growing process are revealed to have a heavy tail. Existing network evolving models do not provide interpretation to these phenomena. We suggest a model based on the team assembling mechanism, which is extracted from the growing processes of real-world networks and requires simple parameters, and produces networks exhibiting these properties observed in the present study and in previous works.

Privacy Protection through Appropriate Interaction Patterns

In network environments,before meaningful interactions can begin,trust may need to be established between two interactive entities in which an entity may ask the other to provide some information involving privacy.Consequently,privacy protection and trust establishment become important in network interactions.In order to protect privacy while facilitating effective interactions,we propose a trust-based privacy protection method.Our main contributions in this paper are as follows:(1)We introduce a novel concept of k-sensitive privacy as a measure to assess the potential threat of inferring privacy;(2)According to trust and k-sensitive privacy evaluation,our proposed method can choose appropriate interaction patterns with lower degree of inferring privacy threat;(3)By considering interaction patterns for privacy protection,our proposed method can overcome the shortcomings of some current privacy protection methods which may result in low interaction success rate.Simulation results show that our method can achieve effective interactions with less privacy loss.

Variable-length sequential dynamic features-based malware detection

In order to solve the problem that traditional signature-based malware detection systems are inefficacious in detecting new malware,a practical malware detection system is constructed to find out new malware. Application programming interface( API) call sequence is introduced to capture activities of a program in this system. After that,based on variable-length n-gram,API call order can be extracted from API call sequence as the malicious behavior feature of a software. Compared with traditional methods,which use fixed-length n-gram,the solution can find more new malware. The experimental results show that the presented approach improves the accuracy of malware detection.

A Personalized Cloud Services Recommendation Based on Cooperative Relationship between Services

A personalized recommendation for cloud services, which is based on usage history and the cooperative relationship of cloud services, is presented. According to service groups, a service group could be defined as several services that were used together by one user at a time, and cooperative relationship between each two services can be calculated. In the process of recommendation, the services which are highly related to the service that the user has selected would be obtained firstly, the result should then take the QoS (Quality of Service) similarity between service’s QoS and user’s preference into account, so the final result combining the cooperative relationship and similarity will meet the functional needs of users and also meet the user’s personalized non-functional requirements. The simulation proves that the algorithm works effectively.

Information tracing model based on PageRank

In social network, original publisher and important nodes in the diffusion process can be traced by analyzing the spreading network of a hot topic. The participated users and spreading network structure of a hot topic build an information tracing model, which mines the source and important diffusion nodes. Firstly, it analyzed the development trend of a hot topic and extracts the users involved. Secondly, it established a user network according to the following relationship of the users involved. Thirdly, the contribution rate of users on the development of the hot topic was initialized, and the Page Rank algorithm was used to construct the information tracing model. Finally, the Top k users were selected as the information publisher and important users of the hot topic according to the contribution rate. Experimental results showed that our model can effectively discover the hot topic of the publisher and important users.

Hashtag Recommendation Based on Multi-Features of Microblogs

Hashtag recommendation for microblogs is a very hot research topic that is useful to many applications involving microblogs. However, since short text in microblogs and low utilization rate of hashtags will lead to the data sparsity problem, it is difficult for typical hashtag recommendation methods to achieve accurate recommendation. In light of this, we propose HRMF, a hashtag recommendation method based on multi-features of microblogs in this article. First, our HRMF expands short text into long text, and then it simultaneously models multi-features （i.e., user, hashtag, text） of microblogs by designing a new topic model. To further alleviate the data sparsity problem, HRMF exploits hashtags of both similar users and similar microblogs as the candidate hashtags. In particular, to find similar users, HRMF combines the designed topic model with typical user-based collaborative filtering method. Finally, we realize hashtag recommendation by calculating the recommended score of each hashtag based on the generated topical representations of multi-features. Experimental results on a real-world dataset crawled from Sina Weibo demonstrate the effectiveness of our HRMF for hashtag recommendation.

Impact of Strangers on Opportunistic Routing Performance

Routing is one of the challenging tasks in Delay Tolerant Networks （DTNs）, due to the lack of global knowledge and sporadic contacts between nodes. Most existing studies take a greedy scheme in data forwarding process, i.e., only nodes with higher utility values than current carriers can be selected as relays. They lack an in-depth investigation on the main features of the optimal paths in Epidemic. These features are vital to any forwarding scheme that tends to make a trade-off between packet delivery delay and cost. This is mainly because Epidemic provides an upper bound on cost and a lower bound on delivery delay. Therefore, a deep understanding of these features is useful to make informed forwarding decisions. In this paper, we try to explore these features by observing the roles of different social relationships in the optimal paths through a set of real datasets. These datasets provide evidence that strangers have two sides in data forwarding process, and that the importance of strangers shows a decreasing trend along the forwarding paths. Using this heuristic knowledge, we propose STRON, a distributed and lightweight forwarding scheme. The distributed feature makes it very suitable for opportunistic scenarios and the low communication and computation features make it easy to be integrated with state-of-the-art work. The trace-driven simulations obviously confirm its effectiveness, especially in terms of packet delivery delay and cost.

Robust visual tracking based on scale invariance and deep learning

Visual tracking is a popular research area in com- puter vision, which is very difficult to actualize because of challenges such as changes in scale and illumination, rota- tion, fast motion, and occlusion. Consequently, the focus in this research area is to make tracking algorithms adapt to these changes, so as to implement stable and accurate vi- sual tracking. This paper proposes a visual tracking algorithm that integrates the scale invariance of SURF feature with deep learning to enhance the tracking robustness when the size of the object to be tracked changes significantly. Particle filter is used for motion estimation. The co^fidence of each parti- cle is computed via a deep neural network, and the result of particle filter is verified and corrected by mean shift because of its computational efficiency and insensitivity to external interference. Both qualitative and quantitative evaluations on challenging benchmark sequences demonstrate that the pro- posed tracking algorithm performs favorably against several state-of-the-art methods throughout the challenging factors in visual tracking, especially for scale variation.