Border Gateway Protocol (BGP) is the de facto inter-domain routing protocol. There have been many incidents of IP prefix hijacking by BGP protocol in the Internet. Attacks may hijack victim's address space to disr...
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Multi-hop time synchronization in wireless sensor networks (WSNs) is often time-consuming and error-prone due to random time-stamp delays for MAC layer access and unstable clocks of intermediate nodes. Constructive in...
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ISBN:
(纸本)9781479964703
Multi-hop time synchronization in wireless sensor networks (WSNs) is often time-consuming and error-prone due to random time-stamp delays for MAC layer access and unstable clocks of intermediate nodes. Constructive interference (CI), a recently discovered physical layer phenomenon, allows multiple nodes transmit and forward an identical packet simultaneously. By leveraging CI, we propose direct multihop (DMH) time synchronization by directly utilizing the time-stamps from the sink node instead of intermediate n-odes, which avoids the error caused by the unstable clock of intermediate nodes. DMH doesn't need decode the flooding time synchronization beacons. Moreover, DMH explores the linear regression technique in CI based time synchronization to counterbalance the clock drifts due to clock skews.
This paper presents a novel non-negative matrix factorization algorithm based on double sparsity K-SVD. It keeps the good parts-based representation. And meanwhile it has a well sparsity as sparse coding. The influenc...
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This paper presents a novel non-negative matrix factorization algorithm based on double sparsity K-SVD. It keeps the good parts-based representation. And meanwhile it has a well sparsity as sparse coding. The influences given by different initialization condition have been successfully overcome. Compared with other algorithms, the algorithm proposed is much faster. This dissertation demonstrates the advantages of the proposed algorithm by simulator experimentation.
Focusing on the protection of privacy and non- publicity of special networks, the CPN model is extended to PCPN by the protocol factor and the formal model. The FPCPN (Formal Protocol Colored Petri Net) is presented t...
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Focusing on the protection of privacy and non- publicity of special networks, the CPN model is extended to PCPN by the protocol factor and the formal model. The FPCPN (Formal Protocol Colored Petri Net) is presented to analyze the operation mechanism of protocols. It is introduced by four steps, such as the whole structure, the network element conversion, the conditions of transition triggers/executions and sub-system partitions. Finally, the feasibility and efficiency of FPCPN is validated by an example.
In this paper, a novel band-pass miniaturized FSS is proposed, in which a unit cell consists of four spiral etched slot lines arranged central-symmetrically on a dielectric substrate, and the spiral elements are place...
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In this work, a wide banded planar multi-input/output port Rotman lens for phase array antennas is investigated by simulation. The Rotman lens consists of lens and delay lines. With essential parameters, the outline o...
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In this work, a wide banded planar multi-input/output port Rotman lens for phase array antennas is investigated by simulation. The Rotman lens consists of lens and delay lines. With essential parameters, the outline of the lens is calculated by Matlab, in this way we can design a Rotman lens with certain functions, also the model is constructed by simulation software with the data imported from Matlab. The lens consists of the lens and the delay line. A useful segmented design of the delay line is applied to improve the transmission efficiency. A principle for dumpy port design is raised to improve the output characteristics. By this way, the magnitude error of different frequency is limited within 3 dB and below 5 dB when the port is changed. The lens offers a phase shift from -70 degree to +70 degree with a changeable step.
The importance of incremental learning in changing environments has been acknowledged in recent years. In this paper we present an ensemble learning method for supervised learning with drifting concepts. The method em...
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The importance of incremental learning in changing environments has been acknowledged in recent years. In this paper we present an ensemble learning method for supervised learning with drifting concepts. The method employs hypothesis test as mechanism for detecting concept drift and learns a base classifier for each new training data chunk. Former classifiers deemed as usable by the hypothesis test mechanism and the new classifiers are integrated to form the final classifiers ensemble for prediction. The main focus of the work is to identify the usability of base classifiers that representing the same or similar concept with the current one, make full use of the older valid information together with the newer examples to improve classification accuracy, and avoid the interference of classifiers representing conflictive concepts with the current one. Experiments with simulated concept drift scenarios compared the proposed method with other approaches. The results showed that the method could consistently recognize different types of drift, adapt quickly to these changes to maintain its performance level, and utilize the former knowledge to improve its performance for recurring context.
This paper proposes a method for classification of incomplete data using neural network ensembles. In the method, the incomplete data set is analyzed and projected into a group of complete data subsets that give a ful...
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This paper proposes a method for classification of incomplete data using neural network ensembles. In the method, the incomplete data set is analyzed and projected into a group of complete data subsets that give a full description of the known values in the data set by joining together. Those complete data subsets are then used as the training sets for the neural networks. Base classifiers are selected and integrated according to their classification accuracies and the support degrees of their training data sets to give the final predication. Compared with other methods dealing with missing data in classification, the proposed method can utilize all the information provided by the incomplete data, maintain maximum consistency of the incomplete data set and avoid the dependency on distribution or model assumptions. Experiments on two UCI datasets showed the superiority of the algorithm to other two typical treatments of missing data in ensemble learning.
To identify personality of a radio signal, a new method of multi-object optimization is proposed to solve a system of high order cumulants equations. The power amplifier model is first shown to be equivalent to a mult...
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To identify personality of a radio signal, a new method of multi-object optimization is proposed to solve a system of high order cumulants equations. The power amplifier model is first shown to be equivalent to a multi-input single-output system. A system of equations is derived from the cumulant relation between input and output. The system is solved with multi-object genetic optimization to obtain the features. The system of equations is verified by simulation, and the results of estimation are compared with computed values, showing that the proposed method can extract features from the received signal only with minor errors.
This paper presents a new approach for constructing normal maps that capture high-frequency geometric detail from dense models of arbitrary topology and are applied to the simplified version of the same models generat...
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