Browsing by Subject "Statistical characterization"

Now showing 1 - 4 of 4

Open Access
Birleşik sezim ve kestirim sistemlerinin gürültü ile geliştirilmesi
(IEEE, 2014-04) Akbay, Abdullah Başar; Gezici, Sinan
Belirli koşullar altında, optimal olmayan bazı sezici ve kestiricilerin performansını girdilerine gürültü ekleyerek geliştirmek mümkündür. Bu çalışmada, birleşik bir sezim ve kestirim sisteminin gürültü eklenerek geliştirilmesi incelenmektedir. Sistem performansının maksimizasyonu bir optimizasyon problemi olarak tanımlanmaktadır. Optimal toplanır gürültü dağılımının istatiksel özellikleri belirlenmektedir. Sistem performansının gürültü ile iyileştirilemeyeceği bir koşul elde edilmektedir.Önerilen optimizasyon probleminin, bir doğrusal programlama (DP) problemi olarak yaklaşımı sunulmaktadır. Bir sayısal örnek üzerinde, kuramsal bulguları desteklemek amacıyla, gürültü eklenmiş sistem ile orijinal sistemin performansları karşılaştırılmaktadır.
Open Access
Effects of additional independent noise in binary composite hypothesis-testing problems
(IEEE, 2009-09) Bayram, Suat; Gezici, Sinan
Performance of some suboptimal detectors can be improved by adding independent noise to their observations. In this paper, the effects of adding independent noise to observations of a detector are investigated for binary composite hypothesistesting problems in a generalized Neyman-Pearson framework. Sufficient conditions are derived to determine when performance of a detector can or cannot be improved via additional independent noise. Also, upper and lower limits are derived on the performance of a detector in the presence of additional noise, and statistical characterization of optimal additional noise is provided. In addition, two optimization techniques are proposed to calculate the optimal additional noise. Finally, simulation results are presented to investigate the theoretical results. © 2009 IEEE.
Open Access
On the optimality of stochastic signaling under an average power constraint
(IEEE, 2010-09-10) Göken, Çağrı; Gezici, Sinan; Arıkan, Orhan
In this paper, stochastic signaling is studied for scalar valued binary communications systems over additive noise channels in the presence of an average power constraint. For a given decision rule at the receiver, the effects of using stochastic signals for each symbol instead of conventional deterministic signals are investigated. First, sufficient conditions are derived to determine the cases in which stochastic signaling can or cannot outperform the conventional signaling. Then, statistical characterization of the optimal signals is provided and it is obtained that an optimal stochastic signal can be represented by a randomization of at most two different signal levels for each symbol. In addition, via global optimization techniques, the solution of the generic optimal stochastic signaling problem is obtained, and theoretical results are investigated via numerical examples. ©2010 IEEE.
Open Access
Stochastic resonance in binary composite hypothesis-testing problems in the Neyman-Pearson framework
(Elsevier, 2012-02-20) Bayram, S.; Gezici, Sinan
Performance of some suboptimal detectors can be enhanced by adding independent noise to their inputs via the stochastic resonance (SR) effect. In this paper, the effects of SR are studied for binary composite hypothesis-testing problems. A Neyman-Pearson framework is considered, and the maximization of detection performance under a constraint on the maximum probability of false-alarm is studied. The detection performance is quantified in terms of the sum, the minimum, and the maximum of the detection probabilities corresponding to possible parameter values under the alternative hypothesis. Sufficient conditions under which detection performance can or cannot be improved are derived for each case. Also, statistical characterization of optimal additive noise is provided, and the resulting false-alarm probabilities and bounds on detection performance are investigated. In addition, optimization theoretic approaches to obtaining the probability distribution of optimal additive noise are discussed. Finally, a detection example is presented to investigate the theoretical results.