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Third, we also investigate bi-directional transmission of carrierless amplitude and phase CAP modulation format signal. In this thesis we focus on the experimental demonstration of DSP channel estimation implementations with CAP signal in the bi-directional optical transmission system.
In the experimental demonstration, digital decision feed back equalizer DFE algorithms and a finite impulse response FIR equalizer algorithms are implemented to reduce the inter channel interference ICI. This PhD thesis also investigates a parallel block-divided overlapped chromatic dispersion DSP compensation algorithm. The essential benefit of using a parallel chromatic dispersion compensation algorithm is that it demands less hardware requirements than a conventional serial chromatic dispersion compensation algorithm.
In conclusion, the digital signal processing algorithms presented in this thesis have shown to improve the performance of digital assisted coherent receivers for the next generation of optical fiber transmission links. Projects Coherent detection for optical communication systems Project : PhD. Download as: Download as PDF.
Communications and Signal Processing
Download as HTML. Quantization means each amplitude measurement is approximated by a value from a finite set. Rounding real numbers to integers is an example. The Nyquist—Shannon sampling theorem states that a signal can be exactly reconstructed from its samples if the sampling frequency is greater than twice the highest frequency component in the signal.
In practice, the sampling frequency is often significantly higher than twice the Nyquist frequency. Theoretical DSP analyses and derivations are typically performed on discrete-time signal models with no amplitude inaccuracies quantization error , "created" by the abstract process of sampling. Numerical methods require a quantized signal, such as those produced by an ADC. The processed result might be a frequency spectrum or a set of statistics.
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But often it is another quantized signal that is converted back to analog form by a digital-to-analog converter DAC. In DSP, engineers usually study digital signals in one of the following domains: time domain one-dimensional signals , spatial domain multidimensional signals , frequency domain , and wavelet domains. They choose the domain in which to process a signal by making an informed assumption or by trying different possibilities as to which domain best represents the essential characteristics of the signal and the processing to be applied to it. A sequence of samples from a measuring device produces a temporal or spatial domain representation, whereas a discrete Fourier transform produces the frequency domain representation.
The most common processing approach in the time or space domain is enhancement of the input signal through a method called filtering. Digital filtering generally consists of some linear transformation of a number of surrounding samples around the current sample of the input or output signal. There are various ways to characterize filters; for example:.
A filter can be represented by a block diagram , which can then be used to derive a sample processing algorithm to implement the filter with hardware instructions. A filter may also be described as a difference equation , a collection of zeros and poles or an impulse response or step response. The output of a linear digital filter to any given input may be calculated by convolving the input signal with the impulse response. Signals are converted from time or space domain to the frequency domain usually through use of the Fourier transform. The Fourier transform converts the time or space information to a magnitude and phase component of each frequency.
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With some applications, how the phase varies with frequency can be a significant consideration. Where phase is unimportant, often the Fourier transform is converted to the power spectrum, which is the magnitude of each frequency component squared. The most common purpose for analysis of signals in the frequency domain is analysis of signal properties. The engineer can study the spectrum to determine which frequencies are present in the input signal and which are missing. Frequency domain analysis is also called spectrum- or spectral analysis. Filtering, particularly in non-realtime work can also be achieved in the frequency domain, applying the filter and then converting back to the time domain.
This can be an efficient implementation and can give essentially any filter response including excellent approximations to brickwall filters. There are some commonly-used frequency domain transformations. For example, the cepstrum converts a signal to the frequency domain through Fourier transform, takes the logarithm, then applies another Fourier transform. This emphasizes the harmonic structure of the original spectrum.
The Z-transform provides a tool for analyzing stability issues of digital IIR filters. It is analogous to the Laplace transform , which is used to design and analyze analog IIR filters. In numerical analysis and functional analysis , a discrete wavelet transform DWT is any wavelet transform for which the wavelets are discretely sampled.
As with other wavelet transforms, a key advantage it has over Fourier transforms is temporal resolution: it captures both frequency and location information. The accuracy of the joint time-frequency resolution is limited by the uncertainty principle of time-frequency.
Communications and Signal Processing • Electrical and Computer Engineering
Applications of DSP include audio signal processing , audio compression , digital image processing , video compression , speech processing , speech recognition , digital communications , digital synthesizers , radar , sonar , financial signal processing , seismology and biomedicine. Specific examples include speech coding and transmission in digital mobile phones , room correction of sound in hi-fi and sound reinforcement applications, weather forecasting , economic forecasting , seismic data processing, analysis and control of industrial processes , medical imaging such as CAT scans and MRI , MP3 compression, computer graphics , image manipulation , audio crossovers and equalization , and audio effects units.
DSP algorithms may be run on general-purpose computers and digital signal processors. Additional technologies for digital signal processing include more powerful general purpose microprocessors , field-programmable gate arrays FPGAs , digital signal controllers mostly for industrial applications such as motor control , and stream processors. For systems that do not have a real-time computing requirement and the signal data either input or output exists in data files, processing may be done economically with a general-purpose computer.
This is essentially no different from any other data processing , except DSP mathematical techniques such as the FFT are used, and the sampled data is usually assumed to be uniformly sampled in time or space. An example of such an application is processing digital photographs with software such as Photoshop. When the application requirement is real-time, DSP is often implemented using specialized or dedicated processors or microprocessors, sometimes using multiple processors or multiple processing cores. These may process data using fixed-point arithmetic or floating point. For more demanding applications FPGAs may be used.
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