Postmortem microbial communities are being extensively studied for their utility in forensic science investigations. Microbial communities associated with decomposition, necrobiome, have been shown to react in a predictable manner to the postmortem interval or time since death. These communities are affected by environmental factors such as temperature and humidity which can cause variabilities in the community structural and functional turnover. However, the transmigration patterns across organs and functional activity as decomposition progresses is still relatively unknown in a highly controlled system. This study aims to describe the community structural changes that take place during a highly controlled decomposition in mice along with one of the first representations of visualizing transmigration and detecting functional pathway differences between postmortem times. Although, postmortem microbial communities have been viewed under the scope of forensics, there are also extended uses of early postmortem microbiome communities that represent the antemortem microbiome for health research. Additionally, in this study we aim to provide evidence for the use of the early postmortem microbiome as a public health surveillance tool by detecting antibiotic resistance determinants with their corresponding bacterial genera in human autopsies. These results have provided important baseline microbial community structure and function data for forensic research in murine models and have identified antibiotic resistance determinants of high public health concern in human autopsy samples.

In forensic anthropology, laboratory-based (LBM) and field-based (FM) models can be used to develop new methods and to research the stability and rate at which bio-molecules degrade. In this study, both these methods were used to investigate the effects that temperature, time after death (TAD) and other environmental factors had on the concentration of and change in molecular structure (increase in free pyrrole content, ninhydrin reactive nitrogen (NRN) and iron). of collagen, haemoglobin (Hb) and DNA in porcine teeth For the LBM, porcine teeth were heated at 900C for 0-4 hours, 1 hour intervals, (total number of teeth n=35). A porcine FM was established at the Miertjie Le Roux Experimental Farm of the University of Pretoria. From the decomposing pigs, teeth were collected at TAD intervals of 20 days (n=35). The morphology of the teeth were evaluated and recorded. Methods for collagen and DNA isolation, quantification of protein, collagen, haemoglobin (Hb), free pyrrole content (FPC), ninhydrin reactive nitrogen (NRN), total iron, Fe2+ and Fe3+ as well as a real-time PCR method for the detection of mitochondrial cytb gene in porcine teeth were established. These methods were used to determine the concentration and structural integrity of these molecules in the LBM and FM teeth. The morphology of the LBM teeth was regular with only minor changes in colour with time heated. The collagen and Hb concentration did not change with time. A decrease in total iron (not statistically significant) and Fe3+ (p=0.014; R2=0.74) was found and was associated with an increase in Fe2+ (p=0.014; R2=0.965). No change in free pyrolle content was found. The total protein concentration determined using the Biuret method showed a decrease with time (p=0.009; R2=0.99). For DNA, a linear decrease in concentration (p=0.00; R2=0.93) was found. This DNA could still be used for the successful amplification of the cytb gene. As for DNA a similar decrease in NRN (p=0.00; R2=0.99) was also found whether this is related to protein or DNA degradation is unknown. From this data the total protein, DNA and NRN showed a definite time related change in concentration. For the field model the teeth were brown, cracked, weathered and corroded. As for LBM, there were no time related changes in mass and collagen content. A significant decrease in total protein concentration (p=0.00; R2=0.52) and FPC (p=0.01; R2=0.98) was observed. Hb, FPC and iron levels (total iron, Fe3+ and Fe 2) did not change with time but concentrations FPC and iron were higher than those found in the LBM. Also total protein concentration although it decreases with time was also increased when compared to the LBM (sentence is awkward, re-word). This could be due to increase bacteria activity that results in an increase in protein biomass, iron accumulation and pyrolle synthesis. In contrast Hb levels were the same as LBM and are species specific and not related to increased bacterial activity. NRN showed a time related decrease in concentration (p=0.09; R2=0.99) and was also twice that found in the LBM. This is related to decomposition of porcine protein and DNA as well as that derived from bacteria. A decrease in DNA concentration with time was found (p=0.00; R2=0.88). DNA from all samples and could be used for the amplification of cytb. In conclusion the LBM allows for rapid method development and the investigation of the effect of single factors on the integrity of bio-molecules such as protein and DNA. The FM can then be used to further investigate the effect of many additional environmental factors on the concentration and structure of the same bio-molecules. Using both models, it was found that total protein, DNA and NRN showed a time related change in concentration while the concentration of collagen and Hb remained constant. Copyright / Dissertation (MSc)--University of Pretoria, 2011. / Anatomy / unrestricted

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本研究以新台幣兌美元之匯率日資料作為主要研究標的，同時加入台灣加權股價指數及金融業隔夜拆借利率之日資料作為股價與利率之代理變數，利用Beveridge-Nelson分解趨勢的方法將變數資料拆解成趨勢項與循環項之時間序列資料，藉此捕捉匯率資料具有景氣循環的特性。在循環項的序列資料，以向量自我迴歸模型來分析並予以估計，趨勢項的部分，利用共整合檢定來探討趨勢項變數間長期的均衡關係，再以向量誤差修正模型予以估計，得到未來30天期之匯率走勢。接著，再以RMSE與MAE指標來衡量不同模型之匯率預測績效，以期能找出最適之匯率預測模型。 實證研究結果發現，將匯率資料先透過Beveridge-Nelson分解趨勢的方法予以拆解後，再利用時間序列模型進行分析及預測，時間序列模型的預測能力都比原始匯率利用時間序列模型進行預測或透過ARIMA模型進行預測還要來的好。因此，根據實證研究的結果，若企業與政府在進行匯率預測的分析時，能夠考慮先將匯率資料透過Beveridge-Nelson分解方法予以處理，便能更有效提升模型的預測能力，除了企業能夠降低避險成本來提高公司整體績效，對於國家而言，有效的掌握匯率的趨勢便能夠迅速且正確的制定政策，提升國家的經濟發展。

<p>The Multiple-input and Multiple-output (MIMO) channel model is very useful for the presentation of a wide range of wireless communication systems. This thesis addresses the joint design of a precoder and a receiver for a MIMO channel model, in a scenario in which perfect channel state information (CSI) is available at both ends. We develop a novel framework for the transmitting-receiving procedure. Under the proposed framework, the receiver decomposes the channel matrix by using a block QR decomposition, where Q is a unitary matrix and R is a block upper triangular matrix. The optimal maximum likelihood (ML) detection process is employed within each diagonal block of R. Then, the detected block of symbols is substituted and subtracted sequentially according to the block QR decomposition based successive cancellation. On the transmitting end, the expression of probability of error based on ML detection is chosen as the design criterion to formulate the precoder design problem. This thesis presents a design of MIMO transceivers in the particular case of having 4 transmitting and 4 receiving antennas with full CSI knowledge on both sides. In addition, a closed-form expression for the optimal precoder matrix is obtained for channels satisfying certain conditions. For other channels not satisfying the specific condition, a numerical method is applied to obtain the optimal precoder matrix.</p> / Master of Applied Science (MASc)

Video is a sequence of 2D images of the 3D world generated by a camera. As the camera moves relative to the real scene and elements of that scene themselves move, correlated frame-to-frame changes in the video images are induced. Humans easily identify such changes as scene motion and can readily assess attempts to quantify it. For a machine, the identification of the 2D frame-to-frame motion is difficult. This problem is addressed by the computer vision process of tracking. Tracking underpins the solution to the problem of augmenting general video sequences with artificial imagery, a staple task in the visual effects industry. The problem is difficult because tracking in general video sequences is complicated by the presence of non-rigid motion, repeated texture and arbitrary occlusions. Existing methods provide solutions that rely on imposing limitations on the scenes that can be processed or that rely on human artistry and hard work. I introduce new paradigms, frameworks and algorithms for overcoming the challenges of processing general video and thus provide solutions that fill the gap between the `automated' and `manual' approaches. The work is easily sectioned into three parts, which can be considered separately or taken together for dealing with video without limitations. The initial focus is on directly addressing practical issues of human interaction in the tracking process: a new solution is developed by explicitly incorporating the user into an interactive algorithm. It is a novel tracking system based on fast full-frame patch searching and high-speed optimal track determination. This approach makes only minimal assumptions about motion and appearance, making it suitable for the widest variety of input video. I detail an implementation of the new system using k-d trees and dynamic programming. The second distinct contribution is an important extension to tracking algorithms in general. It can be noted that existing tracking algorithms occupy a spectrum in their use of global motion information. Local methods are easily confused by occlusions, repeated texture and image noise. Global motion models offer strong predictions to see through these difficulties and have been used in restricted circumstances, but are defeated by scenes containing independently moving objects or modest levels of non-rigid motion. I present a well principled way of combining local and global models to improve tracking, especially in these highly problematic cases. By viewing rank-constrained tracking as a probabilistic model of 2D tracks instead of 3D motion, I show how one can obtain a robust motion prior that can be easily incorporated in any existing tracking algorithm. The development of the global motion prior is based on rank-constrained factorization of measurement matrices. A common difficulty comes from the frequent occurrence of occlusions in video, which means that the relevant matrices are often not complete due to missing data. This defeats standard factorization algorithms. To fully explain and understand the algorithmic complexities of factorization in this practical context, I present a common notation for the direct comparison of existing algorithms and propose a new family of hybrid approaches that combine the superb initial performance of alternation methods with the convergence power of the Newton algorithm. Together, these investigations provide a wide-ranging, yet coherent exploration of tracking non-rigid objects in video.

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