<p>Many hydrocarbon reservoirs are situated in barren sequences that display poor stratigraphic control. Correlation between the wells can become extremely difficult and traditional correlation techniques can prove to be inadequate. Past studies have shown that trace and major element concentrations can be used as a correlation tool. This practice of using geochemical fingerprints to characterize between wells is called Chemostratigraphic analysis. (Pearce et al, 1999) Chemostratigraphy has been recognized as a very important correlation technique as it can be used for rocks of any age, in any geological setting as well as sequences that are traditionally defined as barren. Chemostratigraphic analyses can be used as a means of getting rid of ambiguities within data produced by traditional correlation methods such as Biostratigraphy, Lithostratigraphy and Geophysical Logging. In areas where stratigraphic data is not available it can be used to construct correlation frameworks for the sequences found in the area. The motivation behind this study is that the research is not only worthy of academic investigation, but can also provide the industry with new insights into areas that were previously misunderstood because traditional correlation methods were not adequate. The study area, the Orange basin, is located offshore South Africa and is largely underexplored. The basin, that hosts two gas field namely the Ibhubesi and the Kudu gas fields, has large potential but in the past has not been given due attention with only 34 wells being drilled in the area. The Orange basin has recently been the topic of investigation because of the belief that it may be hosts to more hydrocarbons. This study will utilise Chemostratigraphy to attempt to provide geological information on this relatively under-explored basin. The aim of this research study is to produce a chemostratigraphic framework -scheme for the Orange Basin in order to facilitate reservoir scale interwell correlation. The Objectives of this research study will be to identify chemostratigraphic units or indices, to prove the adequate use of chemostratigraphy as an independent correlation technique and to integrate the chemostratigraphy and petrophysical characteristics of the four wells to facilitate lithological identification.</p>

A large amount of the information in conversations come from non-verbal cues such as facial expressions and body gesture. These cues are lost when we don't communicate face-to-face. But face-to-face communication doesn't have to happen in person. With video communication we can at least deliver information about the facial mimic and some gestures. This thesis is about video communication over distances; communication that can be available over networks with low capacity since the bitrate needed for video communication is low. A visual image needs to have high quality and resolution to be semantically meaningful for communication. To deliver such video over networks require that the video is compressed. The standard way to compress video images, used by H.264 and MPEG-4, is to divide the image into blocks and represent each block with mathematical waveforms; usually frequency features. These mathematical waveforms are quite good at representing any kind of video since they do not resemble anything; they are just frequency features. But since they are completely arbitrary they cannot compress video enough to enable use over networks with limited capacity, such as GSM and GPRS. Another issue is that such codecs have a high complexity because of the redundancy removal with positional shift of the blocks. High complexity and bitrate means that a device has to consume a large amount of energy for encoding, decoding and transmission of such video; with energy being a very important factor for battery-driven devices. Drawbacks of standard video coding mean that it isn't possible to deliver video anywhere and anytime when it is compressed with such codecs. To resolve these issues we have developed a totally new type of video coding. Instead of using mathematical waveforms for representation we use faces to represent faces. This makes the compression much more efficient than if waveforms are used even though the faces are person-dependent. By building a model of the changes in the face, the facial mimic, this model can be used to encode the images. The model consists of representative facial images and we use a powerful mathematical tool to extract this model; namely principal component analysis (PCA). This coding has very low complexity since encoding and decoding only consist of multiplication operations. The faces are treated as single encoding entities and all operations are performed on full images; no block processing is needed. These features mean that PCA coding can deliver high quality video at very low bitrates with low complexity for encoding and decoding. With the use of asymmetrical PCA (aPCA) it is possible to use only semantically important areas for encoding while decoding full frames or a different part of the frames. We show that a codec based on PCA can compress facial video to a bitrate below 5 kbps and still provide high quality. This bitrate can be delivered on a GSM network. We also show the possibility of extending PCA coding to encoding of high definition video.

Fluidized bed dryers are used in the pharmaceutical industry for the batch drying of pharmaceutical granulate. Maintaining optimal hydrodynamic conditions throughout the drying process is essential to product quality. Due to the complex interactions inherent in the fluidized bed drying process, mechanistic models capable of identifying these optimal modes of operation are either unavailable or limited in their capabilities. Therefore, empirical models based on experimentally generated data are relied upon to study these systems.<p> Principal Component Analysis (PCA) and Partial Least Squares (PLS) are multivariate statistical techniques that project data onto linear subspaces that are the most descriptive of variance in a dataset. By modeling data in terms of these subspaces, a more parsimonious representation of the system is possible. In this study, PCA and PLS are applied to data collected from a fluidized bed dryer containing pharmaceutical granulate. <p>System hydrodynamics were quantified in the models using high frequency pressure fluctuation measurements. These pressure fluctuations have previously been identified as a characteristic variable of hydrodynamics in fluidized bed systems. As such, contributions from the macroscale, mesoscale, and microscales of motion are encoded into the signals. A multiresolutional decomposition using a discrete wavelet transformation was used to resolve these signals into components more representative of these individual scales before modeling the data. <p>The combination of multiresolutional analysis with PCA and PLS was shown to be an effective approach for modeling the conditions in the fluidized bed dryer. In this study, datasets from both steady state and transient operation of the dryer were analyzed. The steady state dataset contained measurements made on a bed of dry granulate and the transient dataset consisted of measurements taken during the batch drying of granulate from approximately 33 wt.% moisture to 5 wt.%. Correlations involving several scales of motion were identified in both studies.<p> In the steady state study, deterministic behavior related to superficial velocity, pressure sensor position, and granulate particle size distribution was observed in PCA model parameters. It was determined that these properties could be characterized solely with the use of the high frequency pressure fluctuation data. Macroscopic hydrodynamic characteristics such as bubbling frequency and fluidization regime were identified in the low frequency components of the pressure signals and the particle scale interactions of the microscale were shown to be correlated to the highest frequency signal components. PLS models were able to characterize the effects of superficial velocity, pressure sensor position, and granulate particle size distribution in terms of the pressure signal components. Additionally, it was determined that statistical process control charts capable of monitoring the fluid bed hydrodynamics could be constructed using PCA<p>In the transient drying experiments, deterministic behaviors related to inlet air temperature, pressure sensor position, and initial bed mass were observed in PCA and PLS model parameters. The lowest frequency component of the pressure signal was found to be correlated to the overall temperature effects during the drying cycle. As in the steady state study, bubbling behavior was also observed in the low frequency components of the pressure signal. PLS was used to construct an inferential model of granulate moisture content. The model was found to be capable of predicting the moisture throughout the drying cycle. Preliminary statistical process control models were constructed to monitor the fluid bed hydrodynamics throughout the drying process. These models show promise but will require further investigation to better determine sensitivity to process upsets.<p> In addition to PCA and PLS analyses, Multiway Principal Component Analysis (MPCA) was used to model the drying process. Several key states related to the mass transfer of moisture and changes in temperature throughout the drying cycle were identified in the MPCA model parameters. It was determined that the mass transfer of moisture throughout the drying process affects all scales of motion and overshadows other hydrodynamic behaviors found in the pressure signals.

In the present study / ten autosomal human-specific Alu insertion polymorphisms / ACE, APO, A25, B65, D1, FXIIIB, HS4.32, HS4.69, PV92 and TPA25 were analyzed in approximately 100 unrelated individuals from Anatolia. Alu insertion polymorphisms offer several advantages over other nuclear DNA polymorphisms for human evolution studies. The frequencies of the ten biallelic Alu insertions in Anatolians were calculated and all systems were found to be in Hardy-Weinberg equilibrium (p&gt / 0.05). By combining the results of this study with results of previous studies done on worldwide populations, the genetic distance (Nei&rsquo / s DA) between each pair of populations was calculated and neighbor joining trees were constructed. In general, geographically closer populations were found to be also genetically similar. Principal component analysis (PCA) was performed and Anatolia was found to be in the European cluster. As a result of PCA / it was concluded that FXIIIB, PV92 and ACE were the variables contributing the most to the explanation of the variation between the populations. Additionally / canonical variates analysis (CVA) concluded that the most discriminative markers for the groups of populations were PV92, D1, ACE and HS4.32. Pair-wise Fst values were also calculated between Anatolians and some of the populations for which the data was available. It was concluded that, Anatolians have non-significant pair-wise Fst values with Swiss and French Acadian populations. Lastly, heterozygosity vs. distance from centroid graph was constructed and it was found that Anatolians and India-Hindu had exactly the expected heterozygosity value predicted by the model of Harpending and Ward (1982).

Gulf oysters are harvested and consumed year-round, with more than 90% consumed in a raw, unprocessed state. A chief concern of policymakers in recent years is the incidence of Vibrio vulnificus infection following raw seafood consumption. V.vulnificus refers to a halophilic bacterium naturally occurring in brackish coastal waters, which concentrates in filter-feeding oysters. Proposed FDA legislation requiring processing of all raw Gulf oysters sold during warmer summer months threatens the Gulf oyster industry, as little to no research regarding demand for post-harvest processing (PHP) has preceded the potential mandate. This research endeavors to examine the relationship between oyster consumers' fears of V.vulnificus infection and their willingness-to-pay (WTP) for processing of an oyster meal. The psychological model of Protection Motivation Theory (PMT) is employed alongside the economic framework of contingent valuation (CV) to result in an analysis of oyster processing demand with respect to threats and efficacy. A survey administered to 2,172 oyster consumers in six oyster producing states elicits projected consumption and PMT data. Principal Component Analysis is used to reduce the number of PMT variables to a smaller size, resulting in five individual principal components representing the PMT elements of source information, threat appraisal, coping appraisal, maladaptive coping, and protection motivation. Using survey data, the marginal willingness-to-pay (MWTP) for PHP per oyster meal is also calculated, and the five created PMT variables are regressed on this calculation using four separate OLS models. Results indicate significant correlation for four of the five created PMT variables. In addition, a mean MWTP for PHP of $0.31 per oyster meal is determined, contributing to the demand analysis for processing of Gulf oysters. The findings suggest a strong relationship between the fear elements and the demand for processing, and support arguments in favor of further research on specific PHP treatments and the necessity for a valid PMT survey instrument.

We live in a dynamic world full of noises and interferences. The conventional acoustic echo cancellation (AEC) framework based on the least mean square (LMS) algorithm by itself lacks the ability to handle many secondary signals that interfere with the adaptive filtering process, e.g., local speech and background noise. In this dissertation, we build a foundation for what we refer to as the system approach to signal enhancement as we focus on the AEC problem. We first propose the residual echo enhancement (REE) technique that utilizes the error recovery nonlinearity (ERN) to "enhances" the filter estimation error prior to the filter adaptation. The single-channel AEC problem can be viewed as a special case of semi-blind source separation (SBSS) where one of the source signals is partially known, i.e., the far-end microphone signal that generates the near-end acoustic echo. SBSS optimized via independent component analysis (ICA) leads to the system combination of the LMS algorithm with the ERN that allows for continuous and stable adaptation even during double talk. Second, we extend the system perspective to the decorrelation problem for AEC, where we show that the REE procedure can be applied effectively in a multi-channel AEC (MCAEC) setting to indirectly assist the recovery of lost AEC performance due to inter-channel correlation, known generally as the "non-uniqueness" problem. We develop a novel, computationally efficient technique of frequency-domain resampling (FDR) that effectively alleviates the non-uniqueness problem directly while introducing minimal distortion to signal quality and statistics. We also apply the system approach to the multi-delay filter (MDF) that suffers from the inter-block correlation problem. Finally, we generalize the MCAEC problem in the SBSS framework and discuss many issues related to the implementation of an SBSS system. We propose a constrained batch-online implementation of SBSS that stabilizes the convergence behavior even in the worst case scenario of a single far-end talker along with the non-uniqueness condition on the far-end mixing system. The proposed techniques are developed from a pragmatic standpoint, motivated by real-world problems in acoustic and audio signal processing. Generalization of the orthogonality principle to the system level of an AEC problem allows us to relate AEC to source separation that seeks to maximize the independence, hence implicitly the orthogonality, not only between the error signal and the far-end signal, but rather, among all signals involved. The system approach, for which the REE paradigm is just one realization, enables the encompassing of many traditional signal enhancement techniques in analytically consistent yet practically effective manner for solving the enhancement problem in a very noisy and disruptive acoustic mixing environment.

Fluidized bed dryers are used in the pharmaceutical industry for the batch drying of pharmaceutical granulate. Maintaining optimal hydrodynamic conditions throughout the drying process is essential to product quality. Due to the complex interactions inherent in the fluidized bed drying process, mechanistic models capable of identifying these optimal modes of operation are either unavailable or limited in their capabilities. Therefore, empirical models based on experimentally generated data are relied upon to study these systems.<p> Principal Component Analysis (PCA) and Partial Least Squares (PLS) are multivariate statistical techniques that project data onto linear subspaces that are the most descriptive of variance in a dataset. By modeling data in terms of these subspaces, a more parsimonious representation of the system is possible. In this study, PCA and PLS are applied to data collected from a fluidized bed dryer containing pharmaceutical granulate. <p>System hydrodynamics were quantified in the models using high frequency pressure fluctuation measurements. These pressure fluctuations have previously been identified as a characteristic variable of hydrodynamics in fluidized bed systems. As such, contributions from the macroscale, mesoscale, and microscales of motion are encoded into the signals. A multiresolutional decomposition using a discrete wavelet transformation was used to resolve these signals into components more representative of these individual scales before modeling the data. <p>The combination of multiresolutional analysis with PCA and PLS was shown to be an effective approach for modeling the conditions in the fluidized bed dryer. In this study, datasets from both steady state and transient operation of the dryer were analyzed. The steady state dataset contained measurements made on a bed of dry granulate and the transient dataset consisted of measurements taken during the batch drying of granulate from approximately 33 wt.% moisture to 5 wt.%. Correlations involving several scales of motion were identified in both studies.<p> In the steady state study, deterministic behavior related to superficial velocity, pressure sensor position, and granulate particle size distribution was observed in PCA model parameters. It was determined that these properties could be characterized solely with the use of the high frequency pressure fluctuation data. Macroscopic hydrodynamic characteristics such as bubbling frequency and fluidization regime were identified in the low frequency components of the pressure signals and the particle scale interactions of the microscale were shown to be correlated to the highest frequency signal components. PLS models were able to characterize the effects of superficial velocity, pressure sensor position, and granulate particle size distribution in terms of the pressure signal components. Additionally, it was determined that statistical process control charts capable of monitoring the fluid bed hydrodynamics could be constructed using PCA<p>In the transient drying experiments, deterministic behaviors related to inlet air temperature, pressure sensor position, and initial bed mass were observed in PCA and PLS model parameters. The lowest frequency component of the pressure signal was found to be correlated to the overall temperature effects during the drying cycle. As in the steady state study, bubbling behavior was also observed in the low frequency components of the pressure signal. PLS was used to construct an inferential model of granulate moisture content. The model was found to be capable of predicting the moisture throughout the drying cycle. Preliminary statistical process control models were constructed to monitor the fluid bed hydrodynamics throughout the drying process. These models show promise but will require further investigation to better determine sensitivity to process upsets.<p> In addition to PCA and PLS analyses, Multiway Principal Component Analysis (MPCA) was used to model the drying process. Several key states related to the mass transfer of moisture and changes in temperature throughout the drying cycle were identified in the MPCA model parameters. It was determined that the mass transfer of moisture throughout the drying process affects all scales of motion and overshadows other hydrodynamic behaviors found in the pressure signals.

The incidence of breast cancer became the most common female cancer, and the fourth cause of female cancer death. In this study, matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF/MS) have been combined with multivariate statistics to investigate breast cancer tissues and cell lines. Core needle biopsy and fine needle aspiration (FNA) are techniques largely applied in the diagnosis of breast cancer. In this study, we have established an efficient protocol for detecting breast tissue and FNA samples with MALDI-TOF/MS. With the help of statistical analysis software, we can find the lipid-derived ion signals which can be use to distinguish breast cancer tumor tissues from non-tumor parts. This strategy can differentiate normal and tumor tissue, which is potential to apply in clinical diagnoses. The analysis of breast cancer tissue is challenging as the complexity of the tissue sample. Direct tissue analyses by matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) allows us to investigate the molecular structure and their distribution while maintaining the integrity of the tissue and avoiding the loss of signals from extraction steps. Combined MALDI-IMS with statistic software, tissues can be analyzed and classified based on their molecular content which is helpful to distinguish tumor regions from non-tumor regions of breast cancer tissue. Our result shows the differences in the distribution and content of lipids between tumor and non-tumor tissue which can be supplements of current pathological analysis in tumor margins. In this study, MALDI-TOF/MS combined with multivariate statistics were used to rapidly differentiate breast cancer cell lines with different estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2) status. The protocol for efficiently detecting peptides and proteins in breast cancer cells with MALDI-TOF/MS was established, two multivariate statistics including principle component analysis (PCA) and hierarchical clustering analysis were used to process the obtaining MALDI mass spectra of six different breast cancer cell lines and one normal breast cell lines. Based on the difference of the peptide and protein profiles, breast cancer cell lines with same ER and HER-2 status were grouped in nearby region on the PCA score plot. The results of hierarchical cluster analysis also revealed high conformity between breast cancer cell protein profiles and respective hormone receptor types.

Neighboring northern Kaohsiung with a dense population of petrochemical and petroleum industrial complexes included China Petroleum Company (CPC) refinery plant, Renwu and Dazher petrochemical industrial plants. In recent years, although many scholars have conducted regional studies, but are still limited by the lack of relevant information evidences (such as odorous matters identification and VOCs fingerprint database), while unable to clearly identify the causes of poor ambient air quality. By sampling and analyzing VOCs, we will be able to understand the major sources of VOCs in northern Kaohsiung and their contribution, and to provide the air quality management and control countermeasures for local environmental protection administration. In this study, we sampled and analyzed the speciation of VOCs and sulfur-containing odorous matters (SOMs) in the CPC refinery plants, Renwu and Dazher petrochemical complexes simultaneously with stack sampling. The sampling of VOCs and SOMs were conducted on January 7th, 14th, and 19th, 2011 (dry season) and May 6th, 13rd, and 23rd, 2011 (wet season). We established the emission source database, investigated the characteristics of VOC fingerprints, and estimate the emission factor of each stack. It helps us understand the temporal and spatial distribution of VOCs and ascertain major sources and their contribution of VOCs. Major VOCs emitted from the stacks of the CPC refinery plant were toluene and acetone. It showed that petroleum refinery processes had similar VOCs characteristics and fingerprints. The fingerprints of stack emissions at Renwu and Dashe industrial complexes varied with their processes. Hydrogen sulfide was the major sulfur-containing odorous matter in all petrochemical plants. Compared to other petrochemical complexes, Renwu industrial complex emitted a variety of SOMs species as well as relatively high concentrations of sulfur-containing odorous matters. The petrochemical industrial complexes in the industrial ambient of VOCs analysis results showed that isobutane, butane, isopentane, pentane, propane of alkanes, propene of alkenes, toluene, ethylbenzene, xylene, styrene of aromatics, 2-Butanone (MEK), acetone, of carbonyls are major species of VOCs. In addition, ethene+acetylene+ethane (C2), 1,2-dichloroethane, chloromethane, dichloromethane, MTBE were also occasionally found. Sulfur-containing odorous matter (SOMs) analytical results showed that major odorous matters included hydrogen sulfide, methanethiol, dimethyl sulfide, and carbon disulfide. The highest hydrogen sulfide concentration went up to 5.5 ppbv. In this study, the species of VOCs were divided into alkanes, alkenes, aromatics, carbonyls, and others. The temporal and spatial distribution of various types of VOCs strongly correlated with near-surface wind direction. The most obvious contaminants were alkanes, aromatics, and carbonyls of the dispersion to the downwind. Generally, the ambient air surrounding the petrochemical industrial complexes was influenced by various pollutants in the case of high wind speeds. It showed that stack emission and fugitive sources had an important contribution to ambient air quality. TSOMs and hydrogen sulfide emitting mainly from local sources resulted in high concentration of TSOMs and hydrogen sulfide surrounding the petrochemical industrial complex. Principal component analysis (PCA) results showed that the surrounding areas of petrochemical industrial complexes, regardless of dry or wet seasons, were mainly influenced by the process emissions and solvent evaporation. The impact of traffic emission sources ranked the second. Chemical mass balance receptor modeling showed that stack emissions from the CPC refinery plants contributed about 48 %, while fugitive emission sources and mobile sources contributed about 30 % and 11%, respectively. The stack emissions from Renwu industrial complex contributed about 75 %, while fugitive emission sources and mobile sources contributed about 17 % and 5 %, respectively. The stack emissions from Dazher industrial complex contributed about 68 %, while fugitive emission sources and mobile sources contributed about 21 % and 2 %, respectively.

In this study a fully automatic and scale invariant feature extractor which does not require manual initialization or special equipment is proposed. Face location and size is extracted using skin segmentation and ellipse fitting. Extracted face region is scaled to a predefined size, later upper and lower facial templates are used for feature extraction. Template localization and template parameter calculations are carried out using Principal Component Analysis. Changes in facial feature coordinates between analyzed image and neutral expression image are used for expression classification. Performances of different classifiers are evaluated. Performance of proposed feature extractor is also tested on sample video sequences. Facial features are extracted in the first frame and KLT tracker is used for tracking the extracted features. Lost features are detected using face geometry rules and they are relocated using feature extractor. As an alternative to feature based technique an available holistic method which analyses face without partitioning is implemented. Face images are filtered using Gabor filters tuned to different scales and orientations. Filtered images are combined to form Gabor jets. Dimensionality of Gabor jets is decreased using Principal Component Analysis. Performances of different classifiers on low dimensional Gabor jets are compared. Feature based and holistic classifier performances are compared using JAFFE and AF facial expression databases.