This work introduces a probabilistic approach for aperiodic time-modulated arrays tailored to simultaneous multi-beam applications. Specifically, they include on-off RF switches that contribute to amplitude tapering, as in <italic>“classic”</italic> time-modulated arrays, while the spacing is aperiodic, so it is possible to obtain large distances between the elements, in terms of wavelength, without the appearance of grating lobes. The significant spacing allows also a reduction of the deleterious impact of mutual coupling. The proposed aperiodic time-modulated arrays are broadband in the sense that the carrier frequency can potentially span a wide bandwidth without the appearance of grating lobes, independently on the element placement, and they are high-resolution in the sense that the array aperture can be made <italic>“electrically”</italic> very large regardless the number of elements. The array factor can be controlled simultaneously and independently via switching timing and element positioning. In addition, the proposed arrays are reconfigurable in the sense that a change in the duty cycles of the switches can relatively easily change the shape of the radiation pattern. Furthermore, the presented mathematical modeling allows for obtaining simultaneous beams.
Telecommunication, Electric apparatus and materials. Electric circuits. Electric networks
Abstract Aiming at the problems of slow speed and poor accuracy of traditional millimeter wave sparse imaging, a sparse imaging algorithm based on graph convolution model is proposed from the perspective of sparse signal recovery. The graph signal model is constructed by combining the low‐rank and piecewise smoothing(LRPS) regular terms, based on which the proximal operator is replaced by the denoising graph convolution network, and the graph convolution sparse reconstruction network LRPS‐GCN is constructed, and the recovered target image is obtained by iterating with the optimal non‐linear sparse variation. For the proposed algorithm, simulation experiments are carried out using synthetic datasets under different target densities, iteration times and noise environments, and compared with the traditional graph signal reconstruction algorithm and the deep compressed sensing reconstruction algorithm, and then use the measured data with varying degrees of sparsity to validate. The experimental results show that the reconstructed images by this algorithm have better performance in terms of normalised mean square error, target to background ratio, reconstruction time and memory usage.
The fast development of communication technologies and computer systems brings several challenges from a security point of view. The increasing number of IoT devices as well as other computing devices make network communications more challenging. The number, sophistication, and severity of network-related attacks are growing rapidly. There are a variety of different attacks including remote-to-user (R2L), user-to-remote (U2R), denial of service (DoS), distributed DDoS, and probing. Firewalls, antivirus scanners, intrusion detection systems (IDSs), and intrusion prevention systems (IPSs) are widely used to prevent and stop cyber-related attacks. Especially, IDPSs are used to stop and prevent intrusions on communication networks. However, traditional IDSs are no longer effective in detecting complicated cyber attacks from normal network traffic. Because of this, new promising techniques, which specifically utilize data mining, machine learning, and deep learning, need to be proposed in order to distinguish intrusions from normal network traffic. To effectively recognize intrusions, the feature generation, feature selection, and learning processes must be performed delicately before the classification stage. In this study, a new feature selection method called FSAP (Feature Selection Approach) is proposed. In addition, a hybrid attack detection model called SABADT (Signature- and Anomaly-Based Attack Detection Technique) is suggested, which utilizes different classification metrics to recognize attacks. The proposed general method FSACM (Feature Selection and Attack Classification Method) is tested on KDD ’99, UNSW-NB15, and CIC-IDS2017 datasets. According to the experiment results, the proposed method outperformed the state-of-the-art methods in the literature in terms of detection, accuracy, and false-alarm rates.
Based on the Chinese Family Panel Studies (CFPS) 2010–2018, this article investigates how relative deprivation influences household consumption in rural China. High-dimensional fixed-effects (HDFE), the instrumental variable (IV), and causal mediation analysis (CMA) are leveraged to estimate the causal effect and mechanisms. Results show that relative deprivation reduces survival-oriented consumption of food, development-oriented consumption of transportation, telecommunication, and education, as well as enjoyment-oriented of durable goods, and increases survival-oriented consumption of residence and development-oriented consumption of healthcare and medical services. Mechanism analysis indicates that relative deprivation decreases household consumption through the anticipated effect and increases it through a cognitive trap effect. On the whole, the anticipated effect prevails over the cognitive trap effect.
Abstract In this paper, the physical layer security of an indoor visible light communication system, that consists of two transmission light‐emitting diodes to broadcast the information towards the legitimate user, is investigated. Further, a secure LED selection scheme is proposed to select an LED that can perform secure information broadcasting, in the presence of an active and/or passive eavesdropper. The probability of secure information broadcasting is obtained in terms of the positive secrecy rate which is defined under the constraints of known or unknown imperfect channel state information of both legitimate and eavesdropping links. The channel state information (CSI) knowledge of each transmitting link is estimated with the use of a minimum mean square error technique. The performance metrics of the system are defined in terms of the average secrecy capacity and secrecy outage probability parameters. Further, the performance of the system is compared with the conventional stand‐alone VLC system by varying various physical parameters. Further, analytical results are corroborated with the computer simulation results.
Đukanović Slaviša, Mladenović Vladimir, Gligorijević Milan
et al.
This paper deals with the general technical, spatial, and temporal characteristics of satellite telecommunications systems. Particular attention was paid to the peculiarities of the territory of the Republic of Serbia in terms of implementation and use of modern satellite telecommunication infrastructure. The overview shows how and on which way to use various satellite telecommunication systems to configure and exploit next-generation networks, especially modern communications such as 5G technology and IoT. Their work cannot be imagined without the high speeds and high frequencies that allow us to transmit a wealth of information - from short messages/news to HD video on a mobile phone. We need either a base station network or in the case of the high seas or areas where the base station system is difficult to imagine, we need satellite communication. The paper presents data that give a numerical and graphical overview of geostationary satellites visible from the territory of the city of Belgrade, depending on the orbital position and the associated angle of azimuth and elevation, which would also be valid for the leading territory of Serbia.
Abstract Neural networks provide new possibilities to uncover semantic relationships between words by involving contextual information, and further a way to learn the matching pattern from document-query word contextual similarity matrix, which has brought promising results in IR. However, most neural IR methods rely on the conventional word-word matching framework for finding a relevant document for a query. Its effect is limited due to the wide gap between the lengths of query and document. To address this problem, we propose a salient context-based semantic matching (SCSM) method to build a bridge between query and document. Our method locates the most relevant context in the document using a shifting window with adapted length and then calculates the relevance score within it as the representation of the document. We define the notion of contextual salience and the corresponding measures to calculate the relevance of a context to a given query, in which the interaction between the query and the context is modeled by semantic similarity. Experiments on various collections from TREC show the effectiveness of our model as compared to the state-of-the-art methods.
According to the problem of poor sensing performance caused by the existing spectrum sensing algorithm, the covariance matrix opportunistic cooperative (CMOC) spectrum sensing algorithm was proposed. Based on the traditional covariance matrix spectrum sensing algorithm, odd and even slots were divided by the new proposed algorithm, then opportunistic cooperation was added. The relationship between the false alarm probability and the threshold of the new algorithm was also analyzed, and the analytic detection probability was deduced for the actualization of the more accurate spectrum sensing detection. The simulation results verify that the proposed algorithm obviously improves the detection probability. In Rayleigh fading channel, the detection probability is increased by 0.19 and 0.13, which compared with the existing energy detection spectrum sensing algorithm without relay cooperative and the covariance matrix spectrum sensing algorithm without relay cooperation, when the SNR is −10 dB. Moreover, the proposed algorithm has high energy efficiency as well as the moderate computational complexity, so it is especially suited for the spectrum cognition applications in the new generation wireless communication.
Researches on the spectrum handover in distributed cognitive radio network,and the method of proactive spectrum handoff is proposed.The model is established by spectrum sensing and the information before which shows the law authorized user follows,to predict the usage of spectrum.The cognitive user can perform spectrum handoff in advance without interference on the authorized user.At the same time,the method of spectrum selection based on the task allocation algorithm of ant colony is proposed,considering more communication parameters,making cognitive user can perform spectrum handoff as required,to ensure the uninterrupted transmission.Simulation and analysis show that,the proposed scheme can perform spectrum handoff in advance,complete transmission of the traffic in a short time and have a high flexibility for distributed cognitive radio network.
Zuo-yong TANG, Yi-jia YUAN, Yong-qiang DONG
et al.
The existence of selfish nodes seriously affects the routing performance of opportunistic networks(OppNet).To protect the OppNet against the nodes’ selfish behavior,a credit-based selfish nodes detection mechanism was proposed to make it possible to keep away from such nodes during the process of message forwarding.The mechanism leverages 2-ACK messages to observe the nodes’behavior.Then the credit value was calculated based on the observation information and accordingly acts as the metric to distinguish the selfish nodes.Simulation results show that,when coupled with various routing algorithms,the mechanism could detect selfish nodes out accurately,and improve network performance effectively in terms of delivery rate and traffic load.
To deal with the threat of malicious Web pages,a distributed defending scheme against malicious Web pages based on social trust was proposed.Besides the malicious URL list from third-party professional organizations,the direct and indirect trust relations between friends in social network were used to obtain evaluations of Web pages.The experiences about Web surfing from a user’s friends were collected to result in synthetical evaluations on his computer.Each user cooperated with his friends,so that a defending system was formed on the overall perspective,which can improve the defending ability of the social network against malicious Web pages.The experiment results indicate that the visits of malicious Web pages under the scheme decrease obviously than the methods without social trust.