Digital Signal Processing References 225
Part A | 9
with its value at any other time instant in the past or in
the future. Therefore, if both the information signal and
the additive noise are modeled as white noise, (9.144)
becomes
H.!/ D
X 0
V 0
G
.!/ :
(9.146)
The ratio .X 0 =V 0 / with the PSD of the information signal x.t/ in the numerator and that of the additive noise
in the denominator is known as signal-to-noise ratio
(SNR) and is a very important parameter to characterize a system’s sensitivity to external noise, as well as
exogenous disturbance and crosstalk interference [9.1,
5].
9.6 Concluding Remarks
In this chapter, an account of digital signal processing
concepts, methods and techniques employed in ocean
engineering is given. After looking into the fundamental processes of continuous signal sampling and
reconstruction, the Z-transform is introduced as the tool
of convenience to analyze difference equations just like
the Laplace transform is suited to analyze differential
equations. Digital filters are presented then as well as
the Fast Fourier Transform algorithm. Fundamentals of
waveform analysis and stochastic processes are presented last as employed for system identification and
signal estimation.
References
9.1 J.G. Proakis: Digital Communications, 4th edn.
(McGraw-Hill, New York 2000)
9.2 J.G. Proakis, D. Manolakis: Digital Signal Processing: Principles, Algorithms and Applications, 3rd edn.
(Prentice Hall, Upper Saddle River 1995)
9.3 W.A. Kuperman, J.F. Lynch: Shallow-water acoustics,
Phys. Today 57(10), 55 (2004)
9.4 L. Ljung: System Identification: Theory for the
User, 2nd edn. (Prentice Hall, Upper Saddle River
1999)
9.5 H.V. Poor, G.W. Wornell: Wireless Communications:
Signal Processing Perspectives (Prentice Hall, Upper
Saddle River 1998)
9.6 P.S.R. Diniz: Adaptive Filtering: Algorithms and Practical Implementation, 2nd edn. (Springer, Berlin, Heidelberg 2002)
9.7 T. Kohonen: Self Organization and Associative Memory, 3rd edn. (Springer Verlag, Berlin, Heidelberg 1989)
9.8 R.E. Schapire: The Design and Analysis of Efficient
Learning Algorithms (MIT Press, Cambridge 1992)
Part A | 9
with its value at any other time instant in the past or in
the future. Therefore, if both the information signal and
the additive noise are modeled as white noise, (9.144)
becomes
H.!/ D
X 0
V 0
G
.!/ :
(9.146)
The ratio .X 0 =V 0 / with the PSD of the information signal x.t/ in the numerator and that of the additive noise
in the denominator is known as signal-to-noise ratio
(SNR) and is a very important parameter to characterize a system’s sensitivity to external noise, as well as
exogenous disturbance and crosstalk interference [9.1,
5].
9.6 Concluding Remarks
In this chapter, an account of digital signal processing
concepts, methods and techniques employed in ocean
engineering is given. After looking into the fundamental processes of continuous signal sampling and
reconstruction, the Z-transform is introduced as the tool
of convenience to analyze difference equations just like
the Laplace transform is suited to analyze differential
equations. Digital filters are presented then as well as
the Fast Fourier Transform algorithm. Fundamentals of
waveform analysis and stochastic processes are presented last as employed for system identification and
signal estimation.
References
9.1 J.G. Proakis: Digital Communications, 4th edn.
(McGraw-Hill, New York 2000)
9.2 J.G. Proakis, D. Manolakis: Digital Signal Processing: Principles, Algorithms and Applications, 3rd edn.
(Prentice Hall, Upper Saddle River 1995)
9.3 W.A. Kuperman, J.F. Lynch: Shallow-water acoustics,
Phys. Today 57(10), 55 (2004)
9.4 L. Ljung: System Identification: Theory for the
User, 2nd edn. (Prentice Hall, Upper Saddle River
1999)
9.5 H.V. Poor, G.W. Wornell: Wireless Communications:
Signal Processing Perspectives (Prentice Hall, Upper
Saddle River 1998)
9.6 P.S.R. Diniz: Adaptive Filtering: Algorithms and Practical Implementation, 2nd edn. (Springer, Berlin, Heidelberg 2002)
9.7 T. Kohonen: Self Organization and Associative Memory, 3rd edn. (Springer Verlag, Berlin, Heidelberg 1989)
9.8 R.E. Schapire: The Design and Analysis of Efficient
Learning Algorithms (MIT Press, Cambridge 1992)