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Yayın High precision synthesis of a richards immittance via parametric approach(IEEE-INST Electrical Electronics Engineers Inc, 2014-04) Yarman, Bekir Sıddık Binboğa; Köprü, Ramazan; Kumar, Narendra G.; Prakash, ChackoA Richards immitance is a positive real function expressed in terms of the Richards variable lambda = tanh(pT) = Sigma + j Omega where p = sigma + j omega is the classical complex frequency. A Richards immittance can be synthesized as a lossless two port terminated in a resistance as in Darlington's synthesis such that the two- port consists of commensurate transmission lines. In this paper, a high precision method is presented to synthesize a Richards immittance as a lossless two- port constructed with cascade connections of equal length transmission lines, as well as short and open stubs. The new method of synthesis utilizes Bode procedure ( or Parametric Method) to correct an immitance function specified in the complex Richards variable lambda at each step of the synthesis. It is verified that new technique can synthesize a randomly generated Richards immitate function yielding 25 commensurate lines with the accumulated numerical error less than 10(-3.) A complete synthesis package is developed in MatLab and successfully integrated with the Real Frequency Technique to design broadband matching networks. Examples are presented to show the merits of the new Richards synthesis tool.Yayın FSRFT - Fast simplified real frequency technique via selective target data approach for broadband double matching(IEEE, 2017-02) Köprü, RamazanThis brief introduces a broadband double-matching (DM) solver called fast simplified real frequency technique (FSRFT). FSRFT is essentially a greatly accelerated variant of the well-known classical simplified real frequency technique (SRFT). The basic idea that turns the classical SRFT into a 'fast' SRFT relies on two main approaches: the selective target data approach (STDA) and the constraint optimization approach (COA). STDA constructs an optimization target data set formed of only critically selected target data whose element number is equal to or slightly greater than the order of the system unknowns n plus 1, {n}+1. In order to exhibit speed performance comparison between SRFT and FSRFT, an example design is considered. An exemplary DM problem, dealing with an {n}=6th order low-pass Chebyshev-type equalizer design to match the given generator and load impedances, has been solved by SRFT within 29 s using 90 target data in a typical computer - e.g., Intel 2.20-GHz i7 CPU with 8-GB RAM. On the other hand, the same problem has been solved by the newly proposed FSRFT within only 0.6 s using only n+1=7 critically selected target data in the same computer. FSRFT introduced herein works in any domain, i.e., lumped, distributed, and mixed.
