Thesis (Ph. D.)--University of Wisconsin--Madison, 1950. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves [205]-209).

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In mobile communications, channel side information at transmitters can increasecapacity. For moving relays nodes, local nodes placed on buses and trams in urbanareas, the channel state information is outdated for control delays of severalmilliseconds, as in the LTE system. Prediction of the channel based on statistic is notadequate for vehicular velocities. In this thesis, prediction made with an additionalantenna, a ``predictor antenna", placed in front of the main antenna is evaluated. Thepredictor utilises that the channel of the predictor antenna is highly correlated to thechannel experienced by the main antenna somewhat later, when the main antenna hasmoved to the position previous occupied by the predictor antenna. A normalisedcorrelation of up to 0.98 could be measured between the channels of the antennasfor an antenna separation of several wavelengths, but it was found that the closeenvironment and the antenna pattern have a big impact on the correlation. Thepredictions made with the antenna are also combined with predictions based onstatistics of past measurements from the main antenna to see if a better result can beachieved. For a prediction range of 0.5 carrier wavelengths, a prediction as good as anormalised mean square error (NMSE) of -13.9 dB could be seen. This is sufficient togive a gain in the performance when using link adaptation and opportunistic multi-userscheduling, based on channel state information at transmitter. The evaluations isbased on measurements on a 20 MHz downlink channel at 2.68 GHz.

Along with the advancement of chip architecture and density of processor design, there are more functional units that can execute in parallel on a chip. In order to make good use of them, it is important to obtain enough and accurate instructions ahead of time. Branch prediction provides a way to know the instruction stream ahead of time. Its prediction accuracy is thus one of the key factors of system performance. In our research, we designed a branch prediction method based upon the loop-relevance classifications of conditional jump instructions. It divides conditional jump instructions into two classifications: loop-exit and non-loop-exit conditional jump instructions. We utilized various prediction methods to perform the branch prediction tasks for these two classes of conditional branch instructions, separately. Inside these methods, dynamic learning from actual branch results is carried out to switch to suitable prediction models such that more prediction accuracy can be obtained. In this thesis research, in order to validate the accuracy of this prediction method vs. other prediction methods, we designed a software performance evaluation environment to do trace-driven simulation of types of branch prediction methods. It consists of an instruction trace extractor and a set of trace-driven simulators. Experiment results shows that our prediction methods performs near the same as other prediction methods on the scalar processing programs that have little or no amount of regularly behaved loops. However, on the scientific or engineering programs that exhibit certain percentage of regularly behaved loops, experiment results shows that our prediction method recognizes their loop behavior patterns and achieves better prediction accuracy.

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Thesis (M.A.)--University of Wisconsin--Madison, 1972. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.

M.A. (Philosophy) / The purpose of my project is to provide necessary and sufficient conditions for a prediction to be considered good. Alex Broadbent (2013) claims that a good prediction is a stable prediction, thereby providing an internalist account of judging predictions. In contrast, this project demonstrates that an externalist approach to identifying good predictions is not only possible but, on the proposed view, more reliable too. Robert Nozick’s notion of sensitivity provides a means of understanding what makes a good prediction. It is argued that a good prediction is a sensitive prediction; one where a prediction activity tracks the truth of the claims and assumptions used to produce prediction claims. To gauge whether a prediction activity tracks the truth it is suggested that we ask the following question of the prediction: if the claims and assumptions appealed to in the prediction activity were false, would the same prediction claims have been made if at all? If the prediction claims would have been made in spite of this, then the prediction is not sensitive. Otherwise, the prediction satisfies the following tracking condition for good prediction: in the closest possible world to our own where one or more of the claims and assumptions appealed to in the prediction activity are false the prediction claims would be different or not made at all.

With the abundance of genomic data after the Human Genome Project, the need for analysis, and annotation of these data arise. Annotation of genomes helps us understand the functionality of different parts of the genomes of various species. In this thesis, we propose a simple, and fast homology-based gene prediction method called Exon Hunter (EH) that achieves a performance comparable with state-of-the-art methods in mitochondrial genomes. Mitochondria are crucial for a eukaryotic cell, and mutation in its DNA has connections with disorders such as Alzheimer and cancer. We used Hidden Markov Model (HMM) Protein Profile of a number of genes to search for protein-coding genes in different genomes. Our method forms every subset of the hit set, and calculates a score for each subset according to an objective function. Then it chooses the subset with the\ highest score. Finally, we analyze the codon usage bias of our dataset, and we discuss how it can help us improve this prediction. ExonHunter is written in Python and is publicly available on github.com/amirh-hajianpour/ExonHunter.

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