Cyclostationary Processes and Time Series: Theory, Applications, and Generalizations - Tapa blanda

Acerca del autor

Antonio Napolitano is Full Professor of Telecommunications at the University of Napoli Parthenope (Italy). In 1995 he received the Best Paper of the Year Award from the European Association for Signal Processing (EURASIP) for a paper on higher-order cyclostationarity. In 2007 was recipient of the EURASIP Best Paper Award for a paper on the functional approach in signal analysis. In 2008 he received from Elsevier the Most Cited Paper Award for a review article on cyclostationarity.
In 2016 he became an IEEE Fellow. He has been Associate Editor of the IEEE Transactions on Signal Processing and is on the Editorial Board of Signal Processing (Elsevier) and Digital Signal Processing (Elsevier). He has been in the Signal Processing Theory and Methods Technical Committees (SPM-TC) and is now in the Sensor Array and Multichannel Technical Committee (SAM-TC) of the IEEE Signal Processing Society.

De la contraportada

Cyclostationary processes provide an appropriate model for signals occurring in many fields of application such as communications engineering, radar/sonar, telemetry, mechanics, acoustics, econometrics, meteorology, hydrology, astronomy, and biology. The combination of these models with signal processing algorithms enables better performance than classical algorithms based on stationary models.

Cyclostationarity: Theory and Applications is a comprehensive reference combining classical cyclostationarity theory with the most recent theoretical advances and their application.

Content includes:

• The underlying theoretical framework, accompanied by details of their practical application

• The mathematical models of cyclostationary processes and their generalization.

• Second- and higher-order characterization of cyclostationary processes and their generalizations in time and frequency domains.

• Continuous- and discrete-time estimators of statistical functions of cyclostationary processes and their generalizations.

• The analysis of mean-square consistency and asymptotic Normality of statistical function estimators

• Applications where the adoption of these models with signal processing algorithms enables better performance than classical algorithms based on stationary models for signals.

• Signal analysis using both the classical stochastic-process approach and the functional approach, where statistical functions are built starting from a single function of time.