Nearly every decision must be made with a degree of uncertainty regarding the outcome. Decision making based on modeling and simulation predictions needs to incorporate and aggregate uncertain evidence. To validate multiscale simulation models, it may be necessary to consider evidence collected at a length scale that is different from the one at which a model predicts. In addition, traditional methods of uncertainty analysis do not distinguish between two types of uncertainty: uncertainty due to inherently random inputs, and uncertainty due to lack of information about the inputs. This thesis examines and applies a Bayesian approach for model parameter validation that uses generalized interval probability to separate these two types of uncertainty. A generalized interval Bayes’ rule (GIBR) is used to combine the evidence and update belief in the validity of parameters. The sensitivity of completeness and soundness for interval range estimation in GIBR is investigated. Several approaches to represent complete ignorance of probabilities’ values are tested. The result from the GIBR method is verified using Monte Carlo simulations. The method is first applied to validate the parameter set for a molecular dynamics simulation of defect formation due to radiation. Evidence is supplied by the comparison with physical experiments. Because the simulation includes variables whose effects are not directly observable, an expanded form of GIBR is implemented to incorporate the uncertainty associated with measurement in belief update. In a second example, the proposed method is applied to combining the evidence from two models of crystal plasticity at different length scales.

Recognition of speech at the receiver end generally gets degraded in distant talking atmospheres of laptops, teleconfereing, video conferences and in hands free telephony, where the quality of speech gets contaminated and severely disturbed because of the additive noises. To make useful and effective, the exact speech signals has to be extracted from the noise signals and the user has to be given the clean speech. In such conditions the convenience of microphone array has been preferred as a means of civilizing the quality of arrested signals. A consequential growth in laptop technology and microphone array processing have made possible to improve intelligibility of speech while communication. So this contention target on reducing the additive noises from the original speech, beside design and use of different algorithms. In this thesis a multi-channel microphone array with its speech enhancement of signals to Wiener Beamformar and Generalized side lobe canceller (GSC) are used for Laptops in a noisy environment. Systems prescribed above were implemented, processed and evaluated on a computer using Mat lab considering SNR, SNRI as the main objective of quality measures. Systems were tested with two speech signals, among which one is Main speech signal and other is considered as Noise along with another random noise, sampling them at 16 KHz .Three Different source originations were taken into consideration with different input SNR’s of 0dB, 5dB, 10dB, 15dB, 20dB, 25dB. Simulation Results showed that Noise is been attenuated to a great extent. But Variations in SNR and SNRI has been observed, because of the different point origination of signals in the respective feilds.Variation in SNR and SNRI is been observed when the distance between the main speech originating point and microphone is too long compared to the noise signals. This states that origination of signals plays a huge role in maintaining the speech quality at the receiver end. / D.No 4-22, Gandla street, papanaidupeta-517526 chittoor district,Andhra pradesh India naveenkumarthupalli@gmail.com

Multilevel measurement models (MMM), an application of hierarchical generalized linear models (HGLM), model the relationship between ability levels estimates and item difficulty parameters, based on examinee responses to items. A benefit of using MMM is the ability to include additional levels in the model to represent a nested data structure, which is common in educational contexts, by using the multilevel framework. Previous research has demonstrated the ability of the one-parameter MMM to accurately recover both item difficulty parameters and examinee ability levels, when using both 2- and 3-level models, under various sample size and test length conditions (Kamata, 1999; Brune, 2011). Parameter invariance of measurement models, that parameter estimates are equivalent regardless of the distribution of the ability levels, is important when the typical assumption of a normal distribution of ability levels in the population may not be correct. An assumption of MMM is that the distribution of examinee abilities, which is represented by the level-2 residuals in the HGLM, is normal. If the distribution of abilities in the population are not normal, as suggested by Micceri (1989), this assumption of MMM is violated, which has been shown to affect the estimation of the level-2 residuals. The current study investigated the parameter invariance of the 2-level 1P-MMM, by examining the accuracy of item difficulty parameter estimates and examinee ability level estimates. Study conditions included the standard normal distribution, as a baseline, and three non-normal distributions having various degrees of skew, in addition to various test lengths and sample sizes, to simulate various testing conditions. The study's results provide evidence for overall parameter invariance of the 2-level 1P-MMM, when accounting for scale indeterminacy from the estimation process, for the study conditions included. Although, the error in the item difficulty parameter and examinee ability level estimates in the study were not of practical importance, there was some evidence that ability distributions may affect the accuracy of parameter estimates for items with difficulties greater than represented in this study. Also, the accuracy of abilities estimates for non-normal distributions seemed less for conditions with greater test lengths and sample sizes, indicating possible increased difficulty in estimating abilities from non-normal distributions. / text

A wealth of research on social life has examined the causes and consequences of social identity. I build on this literature by expanding the study of the concept beyond its current focus on how social identity manifests in the individual to a collective-level understanding of social identity as it manifests in groups. This is achieved by bridging the study of social identity with the study of social networks. In this dissertation, I argue that sharing a social identity that meets certain criteria serves as a type of connection which binds group members together into a collective unit. I refer to these connections as symbolic social network ties. Symbolic social network ties exist in social entities characterized by entitativity, which is the property of a social group that defines it as a coherent social unit—a social object in and of itself. Three criteria are necessary for a set of individuals to possess entitativity: boundedness, membership-based interaction, and the capacity to act and be acted upon as a manifest corporate actor in relation to other (individual and corporate) actors. Entitativity varies by degree across entities due to differences in the extent to which the entity exceeds minimal levels of the criteria defining entitativity. The effects of symbolic social network ties are a consequence of the combined effects of entitativity and social identity. To provide an initial assessment of the effects of symbolic social network ties on social life, in this dissertation I use a two-study approach to examine their impact on cooperative collective action. In Study 1, I use the experimental method to test the effects of symbolic social network ties, and social identity more broadly, on cooperation in generalized exchange. Generalized exchange is a form of collective action that is risky but has a number of benefits for collectivities and their members. I compare effects across three levels of social identity: no social identity, category-based social identity, and entity-based symbolic social network ties. Results strongly support my theoretical argument; entity-based symbolic social network ties have a stronger impact on cooperation than category-based social identity. Indeed, the level of cooperation in the category-based social identity condition is not significantly different from the level of cooperation found in the no social identity control condition. The second study uses survey data to assess whether the causal findings from Study 1 hold in the context of real world entities. In Study 2, I examine the relationship between symbolic social network ties and community involvement in small towns. Community involvement is a contextually specific form of collective action that can be vital to the success of a community. Specifically, I examine how variations in each of the three criteria of entitativity—boundedness, interaction, and corporate actor capacity—relate to residents’ propensity to participate in two forms of community involvement: voluntary participation in community improvement activities and active membership in local organizations. As predicted, I find that boundedness and interaction are positively related to both forms of community involvement; corporate actor capacity, however, was not found to be significantly related to either form of community involvement. Implications of these results and potential directions for future research are discussed.

In this work a block parameter estimation method for linear periodically time-varying systems is discussed. The whole work consists of two parts: theoretical and practical. The theoretical part is based on the description of the model, its creation and structure. Furthermore, Markov estimation or an estimation of the least squares generalized method and the description of the generalized model are described in this work. The practical part is devoted to carrying out of the experiments and their description. The experiments of modeling have been performed using MATLAB program. In addition, the functions matrica, period were created and used to do the estimations. The results of the experiments are illustrated in charts and diagrams. Finally, the conclusions about the efficiency of the block parameter estimation method are done.

No description available.

No description available.

In health related studies, non-inferiority tests are used to demonstrate that a new treatment is not worse than a currently existing treatment by more than a pre-specified margin. In this thesis, we discuss three approaches; a Z-score approach, a generalized p-value approach and a Bayesian approach, to test the non-inferiority hypotheses in two-arm trials for ratio of log-normal means. The log-normal distribution is widely used to describe the positive random variables with positive skewness which is appealing for data arising from studies with small sample sizes. We demonstrate the approaches using data arising from an experimental aging study on cognitive penetrability of posture control. We also examine the suitability of three methods under various sample sizes via simulations. The results from the simulation studies indicate that the generalized p-value and the Bayesian approaches reach an agreement approximately and the degree of the agreement increases when the sample sizes increase. However, the Z-score approach can produce unsatisfactory results even under large sample sizes.

As organizations continue to invest heavily in Information Systems (IS) to support business processes, the underutilization of such systems is a key concern that challenges efforts to exploit their benefits. What is most desirable is for users to engage in forms of deep use that effectively leverage the features of the IS for work tasks. But, too often users engage in surface-level use, minimizing their interactions with the IS. Yet for many users how they use an IS changes over time to become progressively deeper as the IS is embedded more in the performance of various tasks. To date there has been limited research on post-adoption IS use, particularly on how individuals choose to or are influenced to learn about, selectively adopt and apply, and then extend IS use. This research therefore seeks to bridge a gap in the literature by responding to calls for greater attention to changes in IS post-adoption use. This study draws on evolutionary theory, that is, Generalized Darwinism and its key principles of variation, selection and retention, to understand and explain how individuals’ IS use change over time, as they enact routines supported by the IS. Using a multi-method research design, this study includes an exploratory phase (qualitative) followed by a confirmatory phase (quantitative). For the qualitative phase, case studies were used to explore change in IS use; a cross-section of 39 users (i.e. basic, intermediate and advanced) of large-scale IS (e.g. CRM) from across three (3) organizations were interviewed. The findings from the qualitative phase coupled Generalized Darwinism principles of variation, selection and retention, supporting theories (e.g. motivation theory) and prior research in IS, were used to develop a conceptual model that framed changes in post-adoption use for further analysis. The model was then tested using data collected from a field survey (86 users) and analyzed using the Partial Least Squares (PLS) approach to structural equation modeling. The study showed that variations occur as individuals used formerly unused (available) features, modified use of currently used sets of features, substituted or replaced one (already-used) feature with another feature and found novel or innovative uses of IS features. There were also a number of similarities in the findings from the case study and the survey regarding the triggers and enablers of variations and the impact of variations on retention, and in turn the impact of retention on deeper use via emergent use, integrative use and extended use. Both the case studies and the survey confirmed the importance of feedback valence, intrinsic motivation, and domain-related knowledge and of key sub-dimensions such as intrinsic motivation to learn, knowledge of IS features and work process understanding as triggers of variations. Satisfaction, in addition to variations was also instrumental in determining which variants in use were selected and incorporated into one’s work routine (retention). Furthermore, the results suggest that as changes occurred over time, such changes resulted in more deeply ingrained use behaviours, by way of infusion. At the same time, some differences were observed among the case studies and between the case study outcomes and the survey findings, with some of the factors identified as important in the case findings, such as peer learning, extrinsic motivation, and perceived (IS) resources, not being significant as predictors of variations in the survey context. Overall, the findings on changes in IS use and factors involved provided insights into how change occurs via variation, selection and retention and the outcome of the change (i.e. deeper use). It is anticipated that the findings of this research will contribute to the post-adoption IS use literature and provide useful insights for managers as they tackle the problem of IS underutilization.

Currently technologies such as EEG, EMG and EOG recordings are the established methods used in the analysis of sleep. But if these methods are to be employed to study sleep in rodents, extensive surgery and recovery is involved which can be both time consuming and costly. This thesis presents and analyzes a cost effective, non-invasive, high throughput system for detecting the sleep and wake patterns in mice using a piezoelectric sensor. This sensor was placed at the bottom of the mice cages to monitor the movements of the mice. The thesis work included the development of the instrumentation and signal acquisition system for recording the signals critical to sleep and wake classification. Classification of the mouse sleep and wake states were studied for a linear classifier and a Neural Network classifier based on 23 features extracted from the Power Spectrum (PS), Generalized Spectrum (GS), and Autocorrelation (AC) functions of short data intervals. The testing of the classifiers was done on two data sets collected from two mice, with each data set having around 5 hours of data. A scoring of the sleep and wake states was also done via human observation to aid in the training of the classifiers. The performances of these two classifiers were analyzed by looking at the misclassification error of a set of test features when run through a classifier trained by a set of training features. The best performing features were selected by first testing each of the 23 features individually in a linear classifier and ranking them according to their misclassification rate. A test was then done on the 10 best individually performing features where they were grouped in all possible combinations of 5 features to determine the feature combinations leading to the lowest error rates in a multi feature classifier. From this test 5 features were eventually chosen to do the classification. It was found that the features related to the signal energy and the spectral peaks in the 3Hz range gave the lowest errors. Error rates as low as 4% and 9% were achieved from a 5-feature linear classifier for the two data sets. The error rates from a 5-feature Neural Network classifier were found to be 6% and 12% respectively for these two data sets.