Autor: Shankar, Ravi - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Signal Superposition Model with Mineralogy Based Spectroscopic Dielectric Model in Wireless Underground Sensor Networks
Autorzy:: Nethravathi, K. A.
Shankar, S. Ravi
Powiązania:: https://bibliotekanauki.pl/articles/226695.pdf
Data publikacji:: 2019
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: Minerology Based Spectroscopic Dielectric Model
Undegroud-Underground
Underground-Aboveground
Aboveground-Underground
Opis:: The propagation of EM waves in soil is defined by permittivity and permeability which are in turn affected by the soil parameters such as soil moisture and texture. Therefore, a suitable Dielectric Model like MBSDM is required for the channel characterization of WUSN. Effect of soil parameters and environmental conditions on signal propagation is modelled using Superposition Model. The simulation of these stages is done in MATLAB for UG-UG, UG-AG and AG–UG scenarios. The system is further implemented on the ZYNQ ZC–702 hardware platform.
Źródło:: International Journal of Electronics and Telecommunications; 2019, 65, 4; 693-699
2300-1933
Pojawia się w:: International Journal of Electronics and Telecommunications
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Instantaneous Frequency Estimation of Multi-Component Non- Stationary Signals using Fourier Bessel series and Time-Varying Auto Regressive Model
Autorzy:: Ravi Shankar Reddy, G.
Rao, R.
Powiązania:: https://bibliotekanauki.pl/articles/226382.pdf
Data publikacji:: 2015
Wydawca:: Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:: basis functions
Fourier-Bessel expansion
instantaneous frequency
multi component non stationary signal
Time-varying Auto Regressive model
Opis:: In this paper, we propose a novel technique for Instantaneous frequency (IF) estimation of multi component non stationary signals using Fourier Bessel Series and Time-Varying Auto Regressive (FB-TVAR) model. In the proposed technique, the Fourier-Bessel (FB) expansion decomposes the multicomponent non stationary signal into a number of monocomponent signals and TVAR model is used to model each monocomponent signal. In TVAR modeling approach the time varying parameters are expanded as a linear combination of basis functions. In this paper, the TVAR parameters are expanded by a discrete cosine basis functions. The maximum likelihood estimation algorithm for model order selection in TVAR models is also discussed. The Instantaneous frequency (IF) is extracted from the time-varying parameters by calculating the angles of the estimation error filter polynomial roots. The estimation of the TVAR parameters of a multicomponent signal requires the inversion of a large covariance matrix, while the projected technique (FB-TVAR) requires the inversion of a number of comparatively small covariance matrices with better numerical stability properties. Simulation results are presented for three component discrete Amplitude and Frequency modulated (AM-FM)signal.
Źródło:: International Journal of Electronics and Telecommunications; 2015, 61, 4; 365-376
2300-1933
Pojawia się w:: International Journal of Electronics and Telecommunications
Dostawca treści:: Biblioteka Nauki

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