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Bestsellers

Linear Algebra and Optimization with Applications to Machine Learning
Linear Algebra and Optimization with Applications to Machine Learning

Volume I: Linear Algebra for Computer Vision, Robotics, and Machine Learning
by Jean Gallier and Jocelyn Quaintance
Linear Algebra and Optimization with Applications to Machine Learning
Linear Algebra and Optimization with Applications to Machine Learning

Volume II: Fundamentals of Optimization Theory with Applications to Machine Learning
by Jean Gallier and Jocelyn Quaintance

 

  • articleNo Access

    Network Intrusion Feature Map Node Equalization Algorithm Based on Modified Variable Step-Size Constant Modulus

    When the network is subject to intrusion and attack, the node output channel equalization will be affected, resulting in bit error and distortion in the output of network transmission symbols. In order to improve the anti-attack ability and equalization of network node, a network intrusion feature map node equalization algorithm based on modified variable step-size constant modulus blind equalization algorithm (MISO-VSS-MCMA) is proposed. In this algorithm, the node transmission channel model after network intrusion is constructed, and sequential processing is performed to intruded nodes with the variable structure feedback link control method. With diversity spread spectrum technology, the channel loss after network intrusion is compensated and the network intrusion map feature is extracted. According to the extracted feature amount, channel equalization processing is performed for the cost function with the MISO-VSS-MCMA method to reduce the damage of network intrusion to the channel. Simulation results show that in node transmission channel equalization after network intrusion, this algorithm can reduce the error bit rate of signal transmission in network, and provide a good ability of correcting phase deflection in the output constellation, thus avoiding the error bit distortion and channel damage caused by network intrusion to the signal with a good equalization effect. This algorithm provides stronger convergence and map concentration, which demonstrates that its anti-interference and signal recovery capabilities are better, so it improves the anti-attack ability of the network.

  • chapterNo Access

    THE REPRESENTATION, COMPARISON, AND PREDICTION OF PROTEIN PATHWAYS

    A pathway is a collection of two or more proteins/molecules connected by their interactions within and around a cell. We study the informatics and evolutionary issues of pathways. Similar to the definition of homology in the comparison of nucleotide and protein sequences, we define homologous pathways as pathways that are evolved from the same ancestral pathway. We first present a survey of existing pathway databases and discuss their format of pathway representation. Then, our pathway representation, the SLIPR format, is presented. It is a semilinear graphic representation of nodes (proteins) and modes (interactions). Pathways in SLIPR format enable pathway comparisons for evolutionary relationship and large-scale pathway database searches. We also discuss how one can map out orthologous pathways, achieving a predictive power on functional assignment of novel genes, once the pathway is understood well-enough in a closely-related species.