This paper decribes a novel method for detecting nonstationary signals in colored noise. A first order complex autoregressive, or AR(l), signal model is used which restricts the application of the detector to low order signals, i.e., those which are well modeled by a low order AR process and have only a single spectral peak. The detector assumes the noise covariance is stationary and known. The...

We will treat a task of statistical detection using discrete alphabet in this paper. It is well known that the likelihood ratio detector is the optimal one, provided that “measurements” taken on the detected object are mutually independent and that we know the feature distributions of target and background objects precisely. The detector presented here is optimal using only the independence ass...

The rapid detection of attackers within firewalls of enterprise computer networks is of paramount importance. Anomaly detectors address this problem by quantifying deviations from baseline statistical models of normal network behavior and signaling an intrusion when the observed data deviates significantly from the baseline model. However, many anomaly detectors do not take into account plausib...

the technical efficiency involved in cocoa production in cross river state was estimated using the stochastic frontier production function analysis. the effects of some selected socio- economic characteristics of the farmers on the efficiency indices were also estimated. the study relied upon primary data generated from interviewing cocoa farmers using a set of structured questionnaire. a multi...

Cognitive radio network is one of the prime solutions of band width crises problem. To resolve band width crises firstly we have to sense primary user (PU) licensed signal. To sense PU signal there are various sensing techniques which have been proposed by researchers, but each of them are having some limitations. In this paper we present a two stage detector for spectrum sensing (SS) in cognit...