Identity verification systems are an important part of our every day life. A typical example is the Automatic Teller Machine (ATM) which employs a simple identity verification scheme: the user is asked to enter their secret password after inserting their ATM card; if the password matches the one prescribed to the card, the user is allowed access to their bank account. This scheme suffers from a major drawback: only the validity of the combination of a certain possession (the ATM card) and certain knowledge (the password) is verified. The ATM card can be lost or stolen, and the password can be compromised. Thus new verification methods have emerged, where the password has either been replaced by, or used in addition to, biometrics such as the person’s speech, face image or fingerprints. Apart from the ATM example described above, biometrics can be applied to other areas, such as telephone & internet based banking, airline reservations & check-in, as well as forensic work and law enforcement applications. Biometric systems based on face images and/or speech signals have been shown to be quite effective. However, their performance easily degrades in the presence of a mismatch between training and testing conditions. For speech based systems this is usually in the form of channel distortion and/or ambient noise; for face based systems it can be in the form of a change in the illumination direction. A system which uses more than one biometric at the same time is known as a multi-modal verification system; it is often comprised of several modality experts and a decision stage. Since a multi-modal system uses complimentary discriminative information, lower error rates can be achieved; moreover, such a system can also be more robust, since the contribution of the modality affected by environmental conditions can be decreased. This thesis makes several contributions aimed at increasing the robustness of single- and multi-modal verification systems. Some of the major contributions are listed below. The robustness of a speech based system to ambient noise is increased by using Maximum Auto-Correlation Value (MACV) features, which utilize information from the source part of the speech signal. A new facial feature extraction technique is proposed (termed DCT-mod2), which utilizes polynomial coefficients derived from 2D Discrete Cosine Transform (DCT) coefficients of spatially neighbouring blocks. The DCT-mod2 features are shown to be robust to an illumination direction change as well as being over 80 times quicker to compute than 2D Gabor wavelet derived features. The fragility of Principal Component Analysis (PCA) derived features to an illumination direction change is solved by introducing a pre-processing step utilizing the DCT-mod2 feature extraction. We show that the enhanced PCA technique retains all the positive aspects of traditional PCA (that is, robustness to compression artefacts and white Gaussian noise) while also being robust to the illumination direction change. Several new methods, for use in fusion of speech and face information under noisy conditions, are proposed; these include a weight adjustment procedure, which explicitly measures the quality of the speech signal, and a decision stage comprised of a structurally noise resistant piece-wise linear classifier, which attempts to minimize the effects of noisy conditions via structural constraints on the decision boundary.

Conventional pattern recognition systems have two components: feature analysis and pattern classification. Feature analysis is achieved in two steps: parameter extraction step and feature extraction step. In the parameter extraction step, information relevant for pattern classification is extracted from the input data in the form of parameter vector. In the feature extraction step, the parameter vector is transformed to a feature vector. Feature extraction can be conducted independently or jointly with either parameter extraction or classification. Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) are the two popular independent feature extraction algorithms. Both of them extract features by projecting the parameter vectors into a new feature space through a linear transformation matrix. But they optimize the transformation matrix with different intentions. PCA optimizes the transformation matrix by finding the largest variations in the original feature space. LDA pursues the largest ratio of between-class variation and within-class variation when projecting the original feature space to a subspace. The drawback of independent feature extraction algorithms is that their optimization criteria are different from the classifier’s minimum classification error criterion, which may cause inconsistency between feature extraction and the classification stages of a pattern recognizer and consequently, degrade the performance of classifiers. A direct way to overcome this problem is to conduct feature extraction and classification jointly with a consistent criterion. Minimum classification Error (MCE) training algorithm provides such an integrated framework. MCE algorithm was first proposed for optimizing classifiers. It is a type of discriminative learning algorithm but achieves minimum classification error directly. The flexibility of the framework of MCE algorithm makes it convenient to conduct feature extraction and classification jointly. Conventional feature extraction and pattern classification algorithms, LDA, PCA, MCE training algorithm, minimum distance classifier, likelihood classifier and Bayesian classifier, are linear algorithms. The advantage of linear algorithms is their simplicity and ability to reduce feature dimensionalities. However, they have the limitation that the decision boundaries generated are linear and have little computational flexibility. SVM is a recently developed integrated pattern classification algorithm with non-linear formulation. It is based on the idea that the classification that a.ords dot-products can be computed efficiently in higher dimensional feature spaces. The classes which are not linearly separable in the original parametric space can be linearly separated in the higher dimensional feature space. Because of this, SVM has the advantage that it can handle the classes with complex nonlinear decision boundaries. However, SVM is a highly integrated and closed pattern classification system. It is very difficult to adopt feature extraction into SVM’s framework. Thus SVM is unable to conduct feature extraction tasks. This thesis investigates LDA and PCA for feature extraction and dimensionality reduction and proposes the application of MCE training algorithms for joint feature extraction and classification tasks. A generalized MCE (GMCE) training algorithm is proposed to mend the shortcomings of the MCE training algorithms in joint feature and classification tasks. SVM, as a non-linear pattern classification system is also investigated in this thesis. A reduced-dimensional SVM (RDSVM) is proposed to enable SVM to conduct feature extraction and classification jointly. All of the investigated and proposed algorithms are tested and compared firstly on a number of small databases, such as Deterding Vowels Database, Fisher’s IRIS database and German’s GLASS database. Then they are tested in a large-scale speech recognition experiment based on TIMIT database.

Important global hydrodynamic relationships for shallow spouted beds of high-density particles were characterized in terms of three features: minimum spouting velocity, overall bed pressure drop at minimum spouting velocity; and fountain height. Spouted bed literature is sparse for shallow beds (static particle depth to bed diameter ≤ 1) and beds with heavy particles (density > 3000 kg/m3). Correlations for such beds were developed here by varying column diameter, static bed height, particle diameter, particle density, gas density and gas flow in an ambient temperature and pressure bed. The degree of correlation between each of the observed hydrodynamic features and a set of selected dimensionless groups from the literature was evaluated with principal components analysis. The minimum spouting velocity correlated strongly with the ratios of particle to bed diameter, of particle to gas density, and of static bed height to particle diameter, and weakly with Archimedes number. Overall bed pressure drop at minimum spouting correlated strongly with Archimedes number, the ratio of static bed height to particle diameter and Froude number. Fountain height correlated strongly with the ratios of the superficial gas velocity to minimum spouting velocity, of static bed height to particle diameter and of the particle to the bed diameter. Principal component regression models were developed for minimum spouting velocity, bed pressure drop, and fountain height with respect to a selected set of dimensionless parameters. All models have regression coefficient values exceeding 85%. Predictions using models developed in this study were compared with correlations in the literature and found to give better results for the experimental conditions studied. Most likely the literature models were less accurate because they were extrapolated. Distinct bed pressure drop relationships with gas flow were observed for certain ranges of particle diameter and static bed height. In addition three dynamical spouting modes were observed, and named as regular, erratic and bimodal. A spouting regime map is proposed based on the spouting regimes defined in this investigation. The correspondence between bed pressure drop relationships and spouting regimes is still unclear.

Introduction Gender as a social and cultural construction has an impact on physiotherapist and patient beliefs, understanding, and behaviour and could affect physiotherapy encounters. Gender studies in early rehabilitation are scarce. The aim of this thesis was to study gender during different parts of the rehabilitation process for primary health care patients with neck and back pain. Method The analyses are based on data from three different samples. One sample is composed of physiotherapists and two samples consist of patients consulting primary health care providers because of neck and back pain. All data were gathered from primary health care provided in Västerbotten County. Baseline data on 73 physiotherapists and 586 of their patients with neck and back pain were collected by questionnaire during three consecutive days in 2006. Patient data included affected pain site and treatment procedures used by the physiotherapist (Study I). Differences in treatment procedures used by female and male physiotherapists and differences in use for female or male patients were analysed using Chi square-test, Fisher’s exact tests, Mann-Whitney U tests and logistic regressions with cluster analysis. Thematised interviews with 12 patients were made before the patient’s first appointment with a physiotherapist or doctor and repeated after three months. Data were analysed according to grounded theory (Study II) and qualitative content analysis (Study III). A comprehensive questionnaire was answered at the first appointment when patients sought a physiotherapist in primary health care. The questionnaires included questions about pain intensity, self-rated health, function, psychological stress reactions, domestic work, work environment, self-efficacy and kinesiophobia. Response patterns were linked to the International Classification of Functioning Disability and Health (ICF) and analysed using principal component analysis (PCA) and partial least squares projections to latent structures (PLS). Result Patients were given the same treatment procedures irrespective of gender. The treatment procedures most often used were training of joint motion (48%), training of muscle functions and strength training (31%), massage (31%), physical treatment (28%), information about health/ill health (24%), and acupuncture (18%). Female and male physiotherapists used the same treatment procedures with a few exceptions. Female physiotherapists used treatment for mental functions and acupuncture more often than male physiotherapists. The women gave their patients a unique mixture of treatment procedures more frequently (43%) compared to their male colleagues (25%). Male physiotherapists used more training of joint motion. "To be confirmed" emerged as the core category when analysing interviews that considered expectations or experiences. Five categories were extracted: "To be taken seriously", "To get an explanation", "To be individually assessed and treated", "To be invited to participate", and "To be taken care of in a trustworthy environment". These were factors leading to confirmation. Two ideal types were identified: "confident" and "ambiguous". The "confident" did not doubt their right to health care and blamed their work for causing the pain. They related to a positive identity of strong or hard working. The "ambiguous" were afraid of being regarded as old, whining women and not being taken seriously. They were ashamed of having neck or back pain and blamed themselves; they thought they were not fit enough. The ideal types were not completely defined by gender, but more men were among the "confident" ideal type and more women were among the "ambiguous" type. Patients reacted differently to feelings of being confirmed or not, and this depended on whether they were the "confident" or "ambiguous" ideal type. The "confident" were satisfied and reacted with reorientation when they felt confirmed, even if they were not totally cured. When not confirmed, the "confident" reacted with anger, frustration, and feelings of shame or remained proud and blamed the health care personnel for being incompetent. The "ambiguous" also were satisfied and felt reoriented when they were confirmed. They then moved from being an "ambiguous" type to a more "confident" type. When the "ambiguous" were not confirmed in healthcare, they became dissatisfied and unhappy. They doubted the assessment, felt forlorn, and felt increased shame. Not being confirmed was experienced more negatively by women than by men irrespective of ideal type. Interesting information was found about how patients view their body in relation to pain during analysis of expectations and experiences in study II interviews. This led to Study III. In study III, "Fear of hurting the fragile body" emerged as an interview theme. Five categories supported or undermined beliefs about pain and physical activity: "The mechanical body", "Messages about activity", "Earlier experiences of pain and activity", "To be a good citizen", and "Support to be active". Patients thought their pain was due to tissue damage and viewed their bodies in a mechanical way. Clear messages from health care personnel about activity led to less fear of physical activity. Vague and contradictory messages led to more fear. Gender-stereotyped messages were given to patients. "The take it carefully" was such a message, and was more often to women when women were thought to be weak and in need of training. Another message was "Pain goes with heavy work". This message was more often given to men when men were thought to be strong and not in need of training. Earlier experiences of pain and activity could have been positive or negative. If positive, the experiences led to less fear of engaging in physical activity. A wish to be a good citizen, such as being a good parent, led to patients being more engaged in child care and playing more than they thought was good for their pain. Women, more than men, expressed avoidance of sick leave because they did not want to be a burden to society or to their work colleagues. Patients were anxious about how to do the "correct" exercises to avoid further injury. Practical support and a follow up to adjust the training program were important to reduce the fear of engaging in physical activity and to maintain motivation. One hundred and eighteen patients (84 women and 34 men) completed the questionnaire. PCA of all questions identified five significant components. The model explained 37% of the variance. The predictive power was 17%. PC1 explained 17% of the variance and the predictive power was 0.13%. PC1 was mainly explained by questions classified in ICF as Activity and Participation. These included questions about physical function and self-efficacy (classified as Content of Thought). Questions about support (classified as Environmental Factors) and stress reactions (classified as Body Function (Emotional Functions)) mainly explained PC2. PC3 was mainly explained by reported pain and symptoms from muscles (classified as Body Functions) and domestic work and leisure time activities (classified as Activity and Participation). There were differences in t-scores between women and men in PC2 (p=0.045) and PC3 (p=0.003). Variables that discriminated between women and men were questions about stress reactions and support at work in PC2, and questions about pain intensity and domestic work in PC3. Conclusion As a physiotherapist working with neck and back pain rehabilitation patients, it is important to be aware of both one’s own and the patient’s preconceptions about women and men. It is also important to be aware of the impact of gender on the professional role when choosing treatment procedures in order to ensure that choices will be based on evidence of effectiveness and not from stereotypes. Awareness of the patient’s individual needs and subsequent adaptation of treatments is also important. Some patients display a negative self-assessment and shame. They need more support to be able to reorient. Unless these patients are confirmed, they are at risk of prolonged disability. Gender stereotypes can hinder rehabilitation of neck and back pain if women are seen as weak and in need of protection and men are seen as strong and not in need of preventive muscle training. When assessing neck and back pain patients with questionnaires, gender has less significance than when asking questions about physical function and self-efficacy. Questions about emotions of stress reactions, support at work, and pain intensity contribute to gender differences for women. Questions on the level of domestic work contribute to gender differences for men.

<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.

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.