Browsing by Author "Kazakos, D."

Now showing 1 - 4 of 4
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    Concentration of Binary FM Spectra
    (1974-11-20) Papantoni-Kazakos, P.; Kazakos, D.
    The spectrum of a digital FM signal can be considered as an indicator of the resistance of the signal to distortions caused by band-limitation. The study in this paper is oriented toward the design of a signaling pulses that will achieve a concentrated about the carrier frequency FM signal spectrum. Convenient spectrum expressions and general guarantees of optimality are found for FSK signals.
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    The Limiting Density of a Nonlinear System
    (1974-10-20) Papantoni-Kazakos, P.; Kazakos, D.
    The RC filter-hard limiter-RC filter nonlinear system shown in Fig. 1 is the subject of this paper. Because of computational difficulties implicated in the analysis of the above system, only its response to the zero mean Gaussian system input has been analytically investigated [2,3,5]. An approximate output density has also been found for nonzero mean Gaussian, while verified to be "close" to the real one for finite means [4]. In the present paper, a close form of the system output density is obtained when the input mean tends to infinity. for that, xi-upcrossing methods were used.
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    Moments and Error Expressions in Polynomial Minimum Mean Square Estimation
    (1975-05-20) Kazakos, D.; Papantoni-Kazakos, P.
    The mathematical complexity of the minimum mean square estimators made inevitable the consideration of suboptimal solutions, such as the linear minimum mean square estimators. The compromise between performance and complexity can be in general less serious if the estimator that will substitute the optimum one is polynomial. If the minimum mean square estimator happens to be equal to a polynomial one, the polynomial substitution does not involve any compromise with respect to performance. Balakrishnan [1] found a necessary and sufficient condition satisfied by the joint characteristic functions of observations and variable to be estimated, so that the m.m.s. estimiate is a polynomial. The equivalent relationships in this case were found in the present paper. A matrix expression of the error difference from two different m.m.s. polynomial estimators was also found. This form involves much fewer calculations than required for finding separately the two errors.
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    Optimal Choice of the Kernel Function for the Parzen Kernel-type Density Estimators
    (1975-04-20) Kazakos, D.
    Let Wp(2) be the Sobolev space of probability density functions f(X) whose first derivative is absolutely continuous and whose second derivative is in Lp(- ∞ ,+ ∞), for p ∈ [1, + ∞]. Using an upper bound to the mean square error for a fixed X E [f(X) - fn(X)0]2, found by G. Wahba, where fn(X) is the Parzen Kernel-type estimate of f(X), we find the finite support Kernel function K(X) that minimizes the said upper bound. The optimal Kernel funciton is: K(y) = (1+a-1) (2T)-1 [1-T-a |y|a], for |y|-1.