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In Internet-of-Things, downlink multi-device interference has long been considered as a harmful element deteriorating system performance, and thus the principle of the classic interference-mitigation based precoding is to suppress the multi-device interference by exploiting the spatial orthogonality. In recent years, a judicious interference utilization precoding has been developed, which is capable of exploiting multi-device interference as a beneficial element for improving device’s reception performance, thus reducing downlink communication latency. In this review paper, we aim to review the emerging interference utilization precoding techniques. We first briefly introduce the concept of constructive interference, and then we present two generic downlink interference-utilization optimizations, which utilizes the multi-device interference for enhancing system performance. Afterwards, the application of interference utilization precoding is discussed in multi-cluster scenario. Finally, some open challenges and future research topics are envisaged.

In real-world applications, there are many set-based fuzzy classifications . However, the current researches have some limitations in solving such classifications. Therefore, a method called possibility measure based fuzzy support function machine (PMFSFM) is discussed in this work. Firstly, two notes are provided as improvement of SFM in theoretical and experimental perspective. Secondly, a set-based fuzzy classification in Euclidean space R d is converted into a function-based task in Banach space C(S ) based on support function and membership degree. Thirdly, a fuzzy optimization problem based on possibility measure is derived and some properties are discussed. Subsequently, a PMFSFM for set-based fuzzy classification is constructed, and it can give both the fuzzy class and the membership degree of a given input to the fuzzy class. Experiment results concerning water quality evaluation in fuzzy environment show the effectiveness of PMFSFM.

The spectrum efficiency (SE) and security of the secondary users (SUs) in the cognitive radio networks (CRNs) have become two main issues due to the limitation interference to the primary users (PUs) and the shared spectrum with the PUs. Intelligent reflecting surface (IRS) has been recently proposed as a revolutionary technique which can help to enhance the SE and physical layer security of wireless communications. This paper investigates the application of IRS in an underlay CRN, where a multi-antenna cognitive base station (CBS) utilizes spectrum assigned to the PU to communicate with a SU via IRS in the presence of multiple coordinated eavesdroppers (Eves). To achieve the trade-off between the secrecy rate (SR) and energy consumption, we investigate the secrecy energy efficiency (SEE) maximization problem by jointly designing the transmit beamforming at the CBS and the reflect beamforming at the IRS. To solve the non-convex problem with coupled variables, we propose an iterative alternating optimization algorithm to solve the sub-problems alternately, by utilizing an iterative penalty function based algorithm for sub-problem 1 and the difference of two-convex functions method for sub-problem 2. Furthermore, we provide a second-order-cone-programming (SOCP) approximation approach to reduce the computational complexity. Finally, the simulation results demonstrate that IRS can help significantly improve the SE and enhance the physical layer security in the CRNs. Moreover, the effectiveness and superiority of our proposed algorithm in achieving the trade-off between the SR and energy consumption are verified., Comment: Accepted by IEEE Transactions on Cognitive Communications and Networking

At present, the epidemic situation of COVID-19 is raging rampantly in the whole world, affecting the hearts of billions of people. The new coronavirus has been detected in many foods and agricultural products. At the same time, vaccines and medicines to prevent or treat COVID-19 are also stepping up research and development and gradually put into use. The quality and safety of foods, medicines, and agricultural products are directly related to the lives and health of people. There are many potential dangers and hidden risks of accidents in the production, sale, and transportation of dangerous goods and special equipment. Therefore, it is necessary to effectively monitor and record the workflow of the above productions or goods. In this paper, we developed an important product traceability public service cloud platform (IPTPSCP) based on batch identification and record keeping with International Two-Dimensional Code Object Identifier System (IDcode) coding rules. Through a case study of the tea factory that produces and sells Xinyang Maojian tea, a test and implementation of IPTPSCP was shown by designing a colorful QR code to prevent the traceability information from being forged in batches. Judging from the overall effect of the practical application of more than a dozen settled enterprises, IPTPSCP has improved the efficiency of data collection and monitoring by about 13%. The results show that the IPTPSCP can be considered as an effective tool to guarantee the quality and safety of products. Besides, since it is not required for the enterprise to invest much money and manpower to develop software, IPTPSCP reduces the cost of implementing product traceability by about 36%. [ABSTRACT FROM AUTHOR] Copyright of Mathematical Problems in Engineering is the property of Hindawi Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Anonymous authentication has significant contribution to privacy protection in safety message dissemination for Vehicular Ad Hoc Networks (VANETs); however, it suffers from heavy workload of vehicle revocation check and message signature verification, which leads to unsatisfactory timeliness and high message loss rate in heavy traffic situations. To manage these challenging problems, this paper proposes an efficient safety message authentication protocol for VANETs by combining batch group signature verification and our proposed Group Session Key (GSK). Specifically, our signature verification method for safety message can both achieve computation efficiency by reducing the number of computation operations of bilinear pairing and resist impersonation attack by using tracking key implement. GSK is associated with the forward and backward keys and is only shared among unrevoked vehicle within a group. Performance analysis and simulations demonstrate that our protocol can provide promising security against various common types of attackers and is more efficient than traditional group-signature-based authentication protocols, in terms of computation time cost, authentication delay and message loss rate.

The electronic structure of van der Waals (vdW) TaS2/TaSe2 and TaSe2/TaTe2 heterostructures has been investigated by first-principle calculations at different electric fields. All the monolayer 1T-TaX2, TaS2/TaSe2 and TaSe2/TaTe2 heterostructures are metallic, where Fermi level goes across the conductance band at a zero electric field. Spin splitting appears in TaS2/TaSe2 heterostructure at an electric field from −0.1 to −0.5 V/A, and gradually decreases with the increase of electric field. Whereas the spin splitting slowly increases when the electric field increases from 0.1 to 0.3 V/A. Furthermore, the band structure of TaSe2/TaTe2 heterostructure is similar to TaSe2/TaTe2 one. However, at an electric field of 0.3 V/A, the TaSe2/TaTe2 vdW heterostructure is an indirect-band -gap semiconductor with a gap of 0.690 eV, suggesting that the electric field has a great effect on the band structures. Our results can bring more significant basis for nanoelectronic device.

The main characteristics of global climate change include significant temperature increases and uneven precipitation distributions, which are both limiting factors for the sustainable development of dryland agriculture in semiarid regions. Inner Mongolia, which occupies the largest area of the semiarid region in China, is a good representation of the region’s climate and dryland agricultural conditions. Spring wheat is widely grown in this region, but the negative impacts of climate change have seriously threatened spring wheat production in recent years. To adapt to these changes, it is imperative to study the influence of climate change on spring wheat production. We employed a vulnerability assessment method to quantitatively evaluate the impacts of climate change on spring wheat production in Inner Mongolia and recommended specific countermeasures based on the results. The average temperature and precipitation during the spring wheat growing season was 16.5 °C and 224 mm, respectively, from 1961 to 2012. Northeastern Inner Mongolia was characterized by lower temperature and higher precipitation; the eastern region had both higher temperature and precipitation; and the southwestern area had higher temperature and lower precipitation. The climate in this region showed a warming and drying trend from 1961 to 2012, with average temperature during the spring wheat growing season increasing with an average rate 0.3 °C/10a and average precipitation decreasing with an average rate 4.3 mm/10a. Over the study period (from 1996 to 2012), the vulnerability of spring wheat in the eastern, central, and southwestern areas of Inner Mongolia was high, whereas that in the northeast was relatively low. Assuming the adaptive capacity of spring wheat is stable, the comprehensive unit vulnerability of spring wheat production is expected to significantly increase under the investigated climate change scenarios (based on historical climate trends and RCP4.5, RCP8.5 scenarios) relative to average values over the study period. Exposure and regional vulnerability could be reduced by decreasing the proportion of spring wheat grown in vulnerable areas, such as the central and southwestern areas of Inner Mongolia.

In this paper, we investigate the multi-cluster flocking behavior of the hierarchical Cucker–Smale model parameterized by a constant β measuring the strength of the interaction between agents. The previous studies showed that when 0 ≤ β 1 / 2 unconditional flocking would occur, while for β ≥ 1 / 2 conditional flocking would occur provided the initial data satisfy some given conditions. We will show in this paper that, when β = 1 / 2 , unconditional flocking would occur for the model, while for β > 1 / 2 , we give a complete understanding for the asymptotic multi-cluster formation when the initial positions and velocities have the ordering relations: by computing the initial data we can specify how many clusters emerge and which agents are in the same cluster.

We consider the Cucker–Smale model consisting of interacted particles involving random failure. More precisely, each pair of neighboring agents can fail to interact with a certain probability. Two cases are considered: undirected connected interactions and directed rooted interactions. Our results show that the unconditional flocking for long-range communication rate is still true independently of the failure rate probability. For short-range communication rate, conditional flocking can be established. These results, together with the previous study of Dalmao and Mordecki (2012) [8] , suggest that the C–S flocking system under various interaction topologies can endure random failure in interactions.

Support vector machine (SVM) has shown prominent performance for binary classification. How to effectively apply it to massive datasets with large number of classes and instances is still a serious challenge. Instance selection methods have been proposed and shown significant efficacy for reducing the training complexity of SVM, but more or less trade off the generalization performance. This paper presents an instance selection method especially for multi-class problems. With cluster centers of positive class as reference points instances are selected for each one-versus-rest SVM model. The purpose of clustering here is to improve the efficiency of instance selection, other than to select instances directly from clusters as previous methods did. Experiments on a wide variety of datasets demonstrate that the proposed method selects fewer instances than most competitive algorithms and keeps the highest classification accuracy on most datasets. Additionally, experimental results show that this method also performs superiorly for binary problems.

There always are some vehicles (nodes) to demonstrate various misdemeanours, such as, modifying data, transmitting fraudulent data about road congestion or vehicle, due to the distinct characteristics of Vehicular Ad-Hoc Networks (VANETs). Those malicious data from safety message have the potential to damage in VANETs. Therefore, the received safety message should be verified before the message is accepted and relayed. In this study, we address the challenge by detecting incorrect safety message. We propose a novel approach to deal with evaluating the trustworthiness of safety message reported by neighbor vehicles by detecting the consistency between the actions and the words (safety message). If it is inconsistent, the safety message is recognized as incorrect message; otherwise, the message is accepted and relayed. Moreover, the safety precaution is considered to protect itself and minimize the losses. Simulation results show that our proposed method can detect effectively the incorrect safety message. The detection ration is about 90% when the number of malicious vehicles is small.

A new class of kernels has been designed to enhance the usability of prior knowledge. Prior knowledge is shown to improve the generalization ability of kernel algorithms for binary classification problems. The prior knowledge is expressed in natural language via fuzzy rules. First, the concepts of a fuzzy rule base and prior-confidence region are proposed to formulate the prior knowledge. Then, the new kernels, which are referred to as natural classification kernels (NCKs), are represented by fuzzy equivalence relations based on the formulation of prior knowledge. An NCK is interpreted as a measure of similarities between samples. It is proven that NCKs have two desired properties: 1) transitivity with respect to the triangular norms and 2) the ability to provide higher similarities to spatially closer samples from the same class. Using transitivity, a large number of NCKs may be directly obtained by means of triangular norms. Additionally, the theoretical results show that the second property makes it possible for the support vector machine (SVM) and convex hull separation algorithm to generalize from training samples to test samples in the prior-confidence region. Finally, some synthetic datasets and a benchmark dataset are employed to validate the proposed approach.

In this paper, we introduce a location-based privacy-preserving authentication protocol (LPA) in vehicular ad hoc networks (VANETs) to address the issue on conditional privacy preservation in which safety messages can be anonymously authenticated by peer vehicles while an authority can trace the sender in a dispute. Specifically, in the LPA protocol, we propose a top-down security system: the top authority (TA) authorizes the roadside units (RSUs) to issue group certificates to the onboard units (OBUs). In addition, we also introduce RSU neighbor set to enhance security and efficiency. We demonstrate the merits of the proposed scheme through extensive analysis and performance evaluation. Copyright © 2011 John Wiley & Sons, Ltd.

Software of railway system has high safety requirement. Formal method based on SCADE (safety-critical application development environment) was applied to the software development of onboard automatic train protection system to guarantee safety of the software. We built up a mathematical model of dynamic curves and transformed it into SCADE model in the form of dataflow and safe state machine. Simulation test, coverage test, and formal verification were performed. The results of the test and verification prove the model safe. Compared to the traditional software development method of onboard automatic train protection system, formal development method based on SCADE can improve reliability of software effectively.

Prior knowledge, such as wind speed probability distribution based on historical data and the wind speed fluctuation between the maximal value and the minimal value in a certain period of time, provides much more information about the wind speed, so it is necessary to incorporate it into the wind speed prediction. First, a method of estimating wind speed probability distribution based on historical data is proposed based on Bernoulli’s law of large numbers. Second, in order to describe the wind speed fluctuation between the maximal value and the minimal value in a certain period of time, the probability distribution estimated by the proposed method is incorporated into the training data and the testing data. Third, a support vector regression model for wind speed prediction is proposed based on standard support vector regression. At last, experiments predicting the wind speed in a certain wind farm show that the proposed method is feasible and effective and the model’s running time and prediction errors can meet the needs of wind speed prediction.

Background Localized birth weight references for gestational age serve as an essential tool in accurate evaluation of atypical birth outcomes (e.g. small for gestational age) in clinical diagnosis and region-specific epidemiological studies. Such standards are currently not available in Mainland China. Aims To construct up-to-date, sex- and parity-specific birth weight references based on 231,937 births in Taiyuan, China during years 2005–2011. Study design Population-based, cross-sectional. Subjects Hospital-registered, healthy infants with births dated between 11/01/2005 and 12/31/2011 within Taiyuan area. Outcome measures Birth weight in grams, and gestational age in complete weeks were calculated using a combination of last-menstrual-date-based estimation and ultrasound examination. Results Separate birth weight references are constructed for male and female infants born from primiparous and multiparous mothers. Male infants are found to weigh more than female infants in later gestational ages (appr. weeks 33–42), and infants born to multiparous mother are found to weigh more than infants born to primiparous mothers in later gestational ages (appr. weeks 36–42). Conclusions The Taiyuan birth weight reference curves display similar trends of growth as reference curves from other countries worldwide (Netherlands, Scotland, Australia, Canada, Hong Kong, Korea and Kuwait). However, growth of birth weight for Taiyuan infants tends to be slower compared to European and North American infants regardless of gender, but similar to infants from other Asian countries.

Formal software safety verification is an important issue for on-board ATP (Automation Train Protection) system. A SCADE-based model safety formal verification method is designed in this paper. The extracted safety properties of ATP are expressed by formal automaton machine, which is an unambiguous semantics of the formal method ensuring model-based formal verification mechanisms for system safety. Furthermore, the on-board ATP system and the safety properties module are modeled in SCADE suite, and the safety verification by combination of the two models is done in the Design Verifier using SAT-based Bounded model-checking. The advantages of this method are of completeness and can reduce verification costs.

Our research purpose is abstracting computational model from the structure and function as well as collaboration among the biolo58gical cells. Quorum sensing P system means bacteria produce specific signaling molecules spontaneously and sense the change in their concentration. In this paper, we use multicompartmental Gillespie algorithm to model and simulate Vibrio fischeri’s quorum sensing phenomenon. We put forward an algorithm considering communication and synchronization of each reaction space in a multi-environment p system, whose effectiveness is verified by simulation tools such as CellDesigner and Scilab. At last we propose an idea of using this P System into Multi-Agent System.

This paper surveys the concept, architecture, key characteristics and technologies of IoT, especially intelligent identifying and sensing technologies, data uncertainty, data representation methods, massive data storage and processing scheme, security and privacy. Based on existing research, we propose related open issues and future evolution direction of IoT. Keywords-Internet of Things, Architecture, Key Technologies, Intelligent Technology

In recent years, various nonlinear time series analysis methodologies have been applied to study cardiac arrhythmias electrocardiograph (ECG) signals. They exhibit competitive advantages in comparison with the conventional linear methods but most of them heavily rely on the phase space reconstruction. In this paper the arrhythmias electrocardiograph (ECG) signals is investigated from the perspective of networks so as to find another effective approach independent of the phase space reconstruction to deeply discover some unknown information underlying the signals. The ECG signal is thereby transformed to a network topology, where its R-R cycles are regarded as nodes in the network, and link weights between two nodes are determined by Euclidean distance of corresponding two cycles. We then employ the network statistical criteria to discover the distinction among different cardiac rhythms. We validate this idea with atrial fibrillation (AF) and normal sinus rhythm (NSR) ECG signals. The results demonstrate that the differences between them can be well revealed from this novel perspective. The described method provides an insight into cardiac arrhythmias studies.

In this paper, we examine one of the security issues in vehicular ad-hoc network (VANETs) routing: position-spoofing attack. We propose a trusted neighbor table (TNT) based location verification scheme to detect and prevent position-spoofing attack. The basic idea of our scheme is to establish a TNT for each node to record the latest location of its neighbors, and each neighbor has a trust value. Since our TNT is trusted, by choosing a next hop from it and considering its trust value, we can mitigate the influence of falsified position information in geographic routing protocols. Analysis shows that our scheme is secure and efficient.

This paper introduces a method of learning kernel by fuzzy equivalence relation (FER) based on prior knowledge. Firstly, prior knowledge is represented through fuzzy membership functions and fuzzy inference rules. Consequently features of prior knowledge are obtained by proper inference methods. Secondly, the learning rules of FER-kernel are obtained in terms of FER semantic interpretation and fuzzy inference. According to the proposed method, the representation of FER-kernel is incorporated with prior knowledge. Moreover, decision functions with the obtained FER-kernel generalize well with unseen examples corresponding to prior knowledge owing to the transitivity of FER-kernel with respect to triangular norm. Finally, some experiments of binary classification are conducted to demonstrate the performance of FRE-kernels in SVM.

The next generation wireless networks are envisioned as convergence of different radio access technologies, in which vertical handoff occurs when a mobile terminal switches from one network to another. To provide seamless vertical handoff and improve quality of service (QoS), the vertical handoff algorithm cannot be achieved with the use of traditional receive signal strength (RSS) as the vertical handoff criteria. At the same time, using the fuzzy logic alone can not eliminate ping-pong effect. In this paper, we propose an improved vertical handoff algorithm which uses RSS measurement as the first stage: determine the available networks first and execute pre-handoff decision, and then select the optimal network through a fuzzy logic decision algorithm. We give a vertical handoff (VHO) scenario to illustrate the performance of the proposed algorithm.

Several new conditions for exponential convergence of DNN were proposed in this paper. These conditions guarantee the existence and uniqueness of equilibrium of DNN with certain different activation functions. To demonstrate the differences and features of the new criteria, some remarks are presented.