Direct form I fiber-optic discrete-time signal processors using optical amplifiers and embedded Mach-Zehnder structures

Abstract The discrete time signal processing requires an anti-aliasing filter at the input and a reconstruction filter at output. Some filters of biquads structure are characterized by a decreasing of the attenuation at high frequencies, caused by the final value of the output resistance of the operational amplifier. In this paper we discuss a design of combined BP filter without mentioned decrease. The proposed filter structure was verified by SPICE simulation.

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A Glimpse at Discrete-Time Signal Processing

A Foundation in Digital Communication ◽

10.1017/9781316822708.033 ◽

2017 ◽

pp. 786-802

Author(s):

Amos Lapidoth

Keyword(s):

Signal Processing ◽

Discrete Time ◽

Time Signal

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Deterministic Discrete-Time Signal Transforms

10.1093/oso/9780198860792.003.0015 ◽

2021 ◽

pp. 714-796

Author(s):

Stevan Berber

Keyword(s):

Fourier Transform ◽

Discrete Time ◽

Linear Time ◽

Power Spectra ◽

Time Signal ◽

Linear Time Invariant ◽

Energy Spectral Density ◽

Analysis And Synthesis ◽

Spectral Density Functions ◽

Linear Time Invariant Systems

Chapter 15 presents a detailed analysis of discrete-time signals and systems in the frequency domain, including the theory of the discrete Fourier series, the discrete-time Fourier transform, and the discrete Fourier transform, and key examples relevant for the analysis and synthesis of signals processed in the discrete transceiver blocks of a communication system. Amplitude spectra, magnitude spectra, phase spectra, and power spectra are defined and calculated for typical signals. Using a unique notation that distinguishes between energy signals and power signals, the correlation function and power or energy spectral density functions are inter-related by proving the Wiener–Khintchine theorem. A comprehensive analysis of linear-time-invariant systems, using the notions of impulse responses, correlation functions, and power spectral densities for both power and energy signals, is presented. The basic theory of the z-transform is also presented.

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Digital Filters

Encyclopedia of Multimedia Technology and Networking ◽

10.4018/978-1-59140-561-0.ch027 ◽

2011 ◽

pp. 180-196

Author(s):

Gordana Jovanovic-Dolecek

Keyword(s):

Discrete Time ◽

Continuous Time ◽

Digital Filters ◽

Time Signal ◽

Digital Signals ◽

Video Signals ◽

Time Signals ◽

Time Distance ◽

Independent Variable ◽

Distance Position

A signal is defined as any physical quantity that varies with changes of one or more independent variables, and each can be any physical value, such as time, distance, position, temperature, or pressure (Oppenheim & Schafer, 1999; Elali, 2003; Smith, 2002). The independent variable is usually referred to as “time”. Examples of signals that we frequently encounter are speech, music, picture, and video signals. If the independent variable is continuous, the signal is called continuous-time signal or analog signal, and is mathematically denoted as x(t). For discrete-time signals the independent variable is a discrete variable and therefore a discrete-time signal is defined as a function of an independent variable n, where n is an integer. Consequently, x(n) represents a sequence of values, some of which can be zeros, for each value of integer n. The discrete–time signal is not defined at instants between integers and is incorrect to say that x(n) is zero at times between integers. The amplitude of both the continuous and discrete-time signals may be continuous or discrete. Digital signals are discrete-time signals for which the amplitude is discrete. Figure 1 illustrates the analog and the discrete-time signals.

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