Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 5

Information Theory Related Abstracts

5 Markov Characteristics of the Power Line Communication Channels in China

Authors: Ming-Yue Zhai


Due to the multipath and pulse noise nature, power line communications(PLC) channel can be modelled as a memory one with the finite states Markov model(FSMC). As the most important parameter modelling a Markov channel,the memory order in an FSMC is not solved in PLC systems yet. In the paper, the mutual information is used as a measure of the dependence between the different symbols, treated as the received SNA or amplitude of the current channel symbol or that of previous symbols. The joint distribution probabilities of the envelopes in PLC systems are computed based on the multi-path channel model, which is commonly used in PLC. we confirm that given the information of the symbol immediately preceding the current one, any other previous symbol is independent of the current one in PLC systems, which means the PLC channels is a Markov chain with the first-order. The field test is also performed to model the received OFDM signals with the help of AR model. The results show that the first-order AR model is enough to model the fading channel in PLC systems, which means the amount of uncertainty remaining in the current symbol should be negligible, given the information corresponding to the immediately preceding one.

Keywords: Information Theory, Channel Model, Power Line Communication, markovian, first-order

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4 Using Information Theory to Observe Natural Intelligence and Artificial Intelligence

Authors: Lipeng Zhang, Limei Li, Yanming Pearl Zhang


This paper takes a philosophical view as axiom, and reveals the relationship between information theory and Natural Intelligence and Artificial Intelligence under real world conditions. This paper also derives the relationship between natural intelligence and nature. According to communication principle of information theory, Natural Intelligence can be divided into real part and virtual part. Based on information theory principle that Information does not increase, the restriction mechanism of Natural Intelligence creativity is conducted. The restriction mechanism of creativity reveals the limit of natural intelligence and artificial intelligence. The paper provides a new angle to observe natural intelligence and artificial intelligence.

Keywords: Artificial Intelligence, Information Theory, Creativity, natural intelligence, restriction of creativity

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3 AI Peer Review Challenge: Standard Model of Physics vs 4D GEM EOS

Authors: David A. Harness


Natural evolution of ATP cognitive systems is to meet AI peer review standards. ATP process of axiom selection from Mizar to prove a conjecture would be further refined, as in all human and machine learning, by solving the real world problem of the proposed AI peer review challenge: Determine which conjecture forms the higher confidence level constructive proof between Standard Model of Physics SU(n) lattice gauge group operation vs. present non-standard 4D GEM EOS SU(n) lattice gauge group spatially extended operation in which the photon and electron are the first two trace angular momentum invariants of a gravitoelectromagnetic (GEM) energy momentum density tensor wavetrain integration spin-stress pressure-volume equation of state (EOS), initiated via 32 lines of Mathematica code. Resulting gravitoelectromagnetic spectrum ranges from compressive through rarefactive of the central cosmological constant vacuum energy density in units of pascals. Said self-adjoint group operation exclusively operates on the stress energy momentum tensor of the Einstein field equations, introducing quantization directly on the 4D spacetime level, essentially reformulating the Yang-Mills virtual superpositioned particle compounded lattice gauge groups quantization of the vacuum—into a single hyper-complex multi-valued GEM U(1) × SU(1,3) lattice gauge group Planck spacetime mesh quantization of the vacuum. Thus the Mizar corpus already contains all of the axioms required for relevant DeepMath premise selection and unambiguous formal natural language parsing in context deep learning.

Keywords: Neural Networks, Information Theory, Automated Theorem Proving, constructive quantum field theory

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2 Methodology for Obtaining Static Alignment Model

Authors: Lely A. Luengas, Pedro R. Vizcaya, Giovanni Sánchez


In this paper, a methodology is presented to obtain the Static Alignment Model for any transtibial amputee person. The proposed methodology starts from experimental data collected on the Hospital Militar Central, Bogotá, Colombia. The effects of transtibial prosthesis malalignment on amputees were measured in terms of joint angles, center of pressure (COP) and weight distribution. Some statistical tools are used to obtain the model parameters. Mathematical predictive models of prosthetic alignment were created. The proposed models are validated in amputees and finding promising results for the prosthesis Static Alignment. Static alignment process is unique to each subject; nevertheless the proposed methodology can be used in each transtibial amputee.

Keywords: Information Theory, prediction model, prosthetic alignment, transtibial prosthesis

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1 Application of Entropy Concept for Discharge Estimation: An Experimental Investigation in a Laboratory Flume

Authors: Gurpinder Singh, Manoj K. Jain


River flow measurement is an essential practice in hydraulic engineering for water resources planning and management, water availability analysis, flood forecasting. However, conventional methods (Prandtl-Von Karman law and power-law) of discharge measurement are costly, time-consuming, cumbersome, dangerous during high floods and rough weather. These laws are valid for wide-open channels only. Considering the limitations of traditional methods, Chiu (1987) presented the probability approach for finding velocity distribution at a river section with the help of the principle of maximum entropy, which provides better results in numerous situations like sediment-laden flows. The entropy theory relies on an entropy parameter which remains constant in different conditions of flow. Hence, it can be surmised as an intrinsic parameter. Experimental investigations on laboratory flume under controlled conditions were conducted to collect precise data at different discharge rates to record corresponding velocity distribution data, which was used to apply the concept of entropy theory for estimating the entropy parameter and discharge. Analysis of the collected data depicts that the entropy parameter remains constant with varying discharge rates. Results obtained based on analysis of collected data revealed that the two-dimensional entropy model was a quick and accurate technique for estimation of mean cross-sectional velocity and discharge.

Keywords: Information Theory, Shannon Entropy, POME, river

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