Convolution kernels support the modeling of complex syntactic information in machinelearning tasks. However, such models are highly sensitive to the type and size of syntactic structure used. It is therefore an import...
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In this paper, we present a new method for the design of an n-bit synchronous binary up counter in quantum-dot cellular automata (QCA). This method is based on the JK flip-flop which almost always produces the simples...
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FPGA was a programmable chip with powerful high parallel computing capabilities. Through the FPAG&ARM collab.rative processing of neural networks, it can improve computing efficiency and reduce energy consumption....
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Virtualisation is a prevalent technology in current computing. Among the many aspects of virtualisation, it can be employed to reduce hardware costs by server consolidation, implement "green computing" by re...
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The number of Web services on the Internet has been steadily increasing in recent years due to their growing popularity. Under the big data environment, how to effectively manage Web services is of significance for se...
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The key issue of Peer Data Management Systems (PDMSs) is how to efficiently organize and manage distributed resources in P2P networks to accurately route queries from the peer initiating the query to appropriate peers...
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A knowledge flow is invisible but it plays an important role in ordering knowledge exchange in teamwork. It can help achieve effective team knowledge management by modeling, optimizing, monitoring and controlling the ...
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The electroencephalogram (EEG) is widely used by physicians for interpretation and identification of physiological and pathological phenomena. However, the EEG signals are often corrupted by power line interferences n...
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ISBN:
(纸本)9781424417483
The electroencephalogram (EEG) is widely used by physicians for interpretation and identification of physiological and pathological phenomena. However, the EEG signals are often corrupted by power line interferences noise and EMG induced noise. These artifacts strongly influence the utility of recorded EEGs and need to be removed for better clinical diagnosis. How to eliminate the effect of the noise is an important preprocessing problem in signal processing. In this paper, a novel and efficient power interferences reduction algorithm by the recently developed empirical mode decomposition (EMD) for the EEG signal is proposed. The principle of this method consists of decompositions of the EEG signal into a limited number of intrinsic mode function (IMF). This algorithm can effectively detect, separate and remove a wide variety of artifacts from EEG recording. Experimental results show that the proposed EMD-based algorithm is possible to achieve an excellent balance between suppresses power interference and EMG noise effectively and preserves as many target characteristics of original signal as possible.
Analysis of transforming matrices between Bezier basis functions and geometrically continuous basis functions is presented It is shown that G 2 transforming matrix has some relationship with G1 transforming matrix. Ba...
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Effective document classification is a long-pursued goal in knowledge management. This paper proposes a novel hybrid approach of semantic representation and statistical measurements. Document is divided into content s...
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