Multimedia Systems development
Question One
The universal Turing machine compares and influences the modern day computing significantly. The Turing machine is the main inspiration towards the modern day Von Neumann architecture also referred to as the universal computing machine. The two devices operate on the concept of input and output where there exists a model that defines their relationship that the machines execute. In both machines, computations involve a change in state guided by the relation model of the input and outputs. In both the machines are programming and operate using stored program making it is possible to construct a machine that can computer any computer sequence with just reprogramming the machine.
The machines, however, differ in the sense that the Turing machine is a mathematical equivalent of the modern computers. The modern computers provide for faster execution of the complex algorithms that was a challenge in the Turing implementation of multi-tape computation. The Turing machine also lacks the concept of a compiler and the operating system prevalent in the modern computers.
The advancements in the artificial intelligence prove the concept developed by Alan Turing when he worked towards building a machine equivalent to the human brain. His achievement, especially in the Turing test, has led many to believe that Alan Turing is the father of computing, the test he conducted then is used even in the modern word measure the ability of a machine to learn.
Question Two
Digital data versus analog data
Analog data is data stored in waves; this is the form the signals both audio and video are transmitted. The frequencies continuously vary producing different signals.
Digital data is data stored in the form of binary numbers; these binary numbers can also be expressed as numerical values.
Sampling versus quantification
Sampling is the process of converting analog signals to a digital signal (Arnst, Soize & Ghanem, 2013). In analog to the digital convention, a signal is divided into equal intervals; during sampling, the rate determines the resolutions of the digitized signals. Quantification is the process of converting the digital signals to an analog signal. It involves putting together different mapping large set of input into small sets.
Sample resolution and sample rate
The sampling rate is founded in an average number of samples obtained in one second given by the reciprocals of the signal frequency. Sample resolutions on the other side are the number of bits used to represent each signal. In the digitization process, the sampling rate is used to determine the sample resolution of the resulting digital image.
Description based encoding versus command based encoding
In the description based encoding, the DSP is provided with information about the required output. The command based encoding on the other side involves providing the DSP with detailed instructions on what to be given. The instructions define the output of the signal encoding process.
Question Three
Signal compressor targets to reduce the size of a signal without losing the message carried by the compressed signal. It helps in decreasing the cost of transmission and storage required. The compression strategies are categorized into either the lossy and lossless techniques (Kontoyiannis, 2000). Lossless techniques suggest that the data being stored or transmitted is similar to the original data. It is the best approach for data that is highly sensitive where it loses its meaning if a portion is lost example is executable code and word processing.
In the case of the lossy compression, data do not have to be retained in the exact condition for transmission or storage, loss of a little part can be composited by the available data pixels to reconstruct the original data. Images are the best example of this category.
In Encyclopedia compression, one needs lossless data compression techniques whereas a photo compression uses the lossy method. Loss of data in Encyclopedia, which is a word document, may mean loss of the message while the loss of a part of it can be reconstructed without losing the image resolutions.
References
Arnst, M., Soize, C., & Ghanem, R. (2013). Hybrid Sampling/Spectral Method for Solving Stochastic Coupled Problems . SIAM/ASA Journal On Uncertainty Quantification, 1(1), 218-243.
Kontoyiannis, I. (2000). Pointwise redundancy in lossy data compression and universal lossy data compression. IEEE Trans. Inform. Theory, 46(1), 136-152.
