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- Creators: Arizona State University
- Creators: Kosut, Oliver
- Resource Type: Text
Description
Lossy compression is a form of compression that slightly degrades a signal in ways that are ideally not detectable to the human ear. This is opposite to lossless compression, in which the sample is not degraded at all. While lossless compression may seem like the best option, lossy compression, which is used in most audio and video, reduces transmission time and results in much smaller file sizes. However, this compression can affect quality if it goes too far. The more compression there is on a waveform, the more degradation there is, and once a file is lossy compressed, this process is not reversible. This project will observe the degradation of an audio signal after the application of Singular Value Decomposition compression, a lossy compression that eliminates singular values from a signal’s matrix.
ContributorsHirte, Amanda (Author) / Kosut, Oliver (Thesis director) / Bliss, Daniel (Committee member) / Electrical Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
I propose a new communications scheme where signature signals are used to carry digital data by suitably modulating the signal parameters with information bits. One possible application for the proposed scheme is in underwater acoustic (UWA) communications; with this motivation, I demonstrate how it can be applied in UWA communications. In order to do that, I exploit existing parameterized models for mammalian sounds by using them as signature signals. Digital data is transmitted by mapping vectors of information bits to a carefully designed set of parameters with values obtained from the biomimetic signal models. To complete the overall system design, I develop appropriate receivers taking into account the specific UWA channel models. I present some numerical results from the analysis of data recorded during the Kauai Acomms MURI 2011 (KAM11) UWA communications experiment.
It is shown that the proposed communication scheme results in approximate channel models with amplitude-limited inputs and signal-dependent additive noise. Motivated by this observation, I study capacity of amplitude-limited channels under different transmission scenarios. Specifically, I consider fading channels, signal-dependent additive Gaussian noise channels, multiple-input multiple-output (MIMO) systems and parallel Gaussian channels under peak power constraints.
I also consider practical channel coding problems for channels with signal-dependent noise. I consider two specific models; signal-dependent additive Gaussian noise channels and Z-channels which serve as binary-input binary-output approximations to the Gaussian case. I propose a new upper bound on the probability of error, and utilize it for design of codes. I illustrate the tightness of the derived bounds and the performance of the designed codes via examples.
It is shown that the proposed communication scheme results in approximate channel models with amplitude-limited inputs and signal-dependent additive noise. Motivated by this observation, I study capacity of amplitude-limited channels under different transmission scenarios. Specifically, I consider fading channels, signal-dependent additive Gaussian noise channels, multiple-input multiple-output (MIMO) systems and parallel Gaussian channels under peak power constraints.
I also consider practical channel coding problems for channels with signal-dependent noise. I consider two specific models; signal-dependent additive Gaussian noise channels and Z-channels which serve as binary-input binary-output approximations to the Gaussian case. I propose a new upper bound on the probability of error, and utilize it for design of codes. I illustrate the tightness of the derived bounds and the performance of the designed codes via examples.
ContributorsElMoslimany, Ahmad (Author) / Duman, Tolga M. (Thesis advisor) / Papandreou-Suppappola, Antonia (Committee member) / Tepedelenlioğlu, Cihan (Committee member) / Kosut, Oliver (Committee member) / Arizona State University (Publisher)
Created2015
Description
There has been a substantial development in the field of data transmission in the last two decades. One does not have to wait much for a high-definition video to load on the systems anymore. Data compression is one of the most important technologies that helped achieve this seamless data transmission experience. It helps to store or send more data using less memory or network resources. However, it appears that there is a limit on the amount of compression that can be achieved with the existing lossless data compression techniques because they rely on the frequency of characters or set of characters in the data. The thesis proposes a lossless data compression technique in which the data is compressed by representing it as a set of parameters that can reproduce the original data without any loss when given to the corresponding mathematical equation. The mathematical equation used in the thesis is the sum of the first N terms in a geometric series. Various changes are made to this mathematical equation so that any given data can be compressed and decompressed. According to the proposed technique, the whole data is taken as a single decimal number and replaced with one of the terms of the used equation. All the other terms of the equation are computed and stored as a compressed file. The performance of the developed technique is evaluated in terms of compression ratio, compression time and decompression time. The evaluation metrics are then compared with the other existing techniques of the same domain.
ContributorsGrewal, Karandeep Singh (Author) / Gonzalez Sanchez, Javier (Thesis advisor) / Bansal, Ajay (Committee member) / Findler, Michael (Committee member) / Arizona State University (Publisher)
Created2021