Economic systems are often subject to structural variability. For the achievement of correct structural specification in econometric modelling it is then important to allow for parameters that are time-varying, and to apply estimation techniques suitably designed for inference in such models. One realistic model assumption for such parameter variability is the Markovian model, and Kaiman filtering is then assumed to be a convenient estimator. In the thesis several aspects of using Kaiman filtering approaches to estimation in that framework are considered. The application of the Kaiman filter to estimation in econometric models is straightforward if a set of basic assumptions are satisfied, and if necessary initial specifications can be accurately made. Typically, however, these requirements can generally not be perfectly met. It is therefore of great importance to know the consequences of deviations from the basic assumptions and correct initial specifications for inference, in particular for the small sample situations typical in econometrics. If the consequences are severe it is essential to develop techniques to cope with such aspects.For estimation in interdependent systems a two stage Kaiman filter is proposed and evaluated, theoretically, as well as by a small sample Monte Carlo study, and empirically. The estimator is approximative, but with promising small sample properties. Only if the transition matrix of the parameter model and an initial parameter vector are misspecified, the performance deteriorates. Furthermore, the approach provides useful information about structural properties, and forms a basis for good short term forecasting.In a reduced form fraaework most of the basic assumptions of the traditional Kaiman filter are relaxed, and the implications are studied. The case of stochastic regressors is, under reasonable additional assumptions, shown to result in an estimator structurally similar to that due to the basic assumptions. The robustness properties are such that in particular the transition matrix and the initial parameter vector should be carefully estimated. An estimator for the joint estimation of the transition matrix, the parameter vector and the model residual variance is suggested and utilized to study the consequences of a misspecified parameter model. By estimating th transitions the parameter estimates are seen to be robust in this respect. / <p>Härtill 4 delar</p> / digitalisering@umu

Informationssökningar av olika slag sker dagligen världen över och sökresultatet kan många gånger vara så stort att användarna har svårt att veta vilka sökresultat som är relevanta. I denna uppsats har syftet varit att undersöka hur sökresultat kan filtreras och sorteras med hjälp av platsbaserad sökning för att det ska bli mer relevant för användaren. Genom litteraturstudie och intervjuer har det blivit möjligt att ta reda på hur ett sökresultat skulle kunna filtreras och sorteras för att möta användarnas förväntningar. De teorier och slutsatser som framkom tillämpades vid utvecklingen av en prototyp. Prototypen testades och utvärderades sedan genom ett användartest där resultatet visar att filtreringen och sorteringen som används i studien kan göra sökresultatet mer relevant för användaren. / Information searches of various kinds take place daily around the world and search results can often be so large that users have difficulty knowing which results are relevant. In this paper the aim has been to examine how search results can be filtered and sorted by using location-based search to make the results more relevant to the user. Through a literature review and interviews it was possible to investigate how a search result can be filtered and sorted to meet user expectations. The theories and conclusions that emerged were applied in the development of a prototype. Usability tests were performed on the prototype and the results show that the filtering used in the study can provide a more relevant search result.

With the increasing availability of large datasets machine learning techniques are becoming an increasingly attractive alternative to expert-designed approaches to solving complex problems in domains where data is abundant. In this thesis we introduce several models for large sparse discrete datasets. Our approach, which is based on probabilistic models that use distributed representations to alleviate the effects of data sparsity, is applied to statistical language modelling and collaborative filtering. We introduce three probabilistic language models that represent words using learned real-valued vectors. Two of the models are based on the Restricted Boltzmann Machine (RBM) architecture while the third one is a simple deterministic model. We show that the deterministic model outperforms the widely used n-gram models and learns sensible word representations. To reduce the time complexity of training and making predictions with the deterministic model, we introduce a hierarchical version of the model, that can be exponentially faster. The speedup is achieved by structuring the vocabulary as a tree over words and taking advantage of this structure. We propose a simple feature-based algorithm for automatic construction of trees over words from data and show that the resulting models can outperform non-hierarchical neural models as well as the best n-gram models. We then turn our attention to collaborative filtering and show how RBM models can be used to model the distribution of sparse high-dimensional user rating vectors efficiently, presenting inference and learning algorithms that scale linearly in the number of observed ratings. We also introduce the Probabilistic Matrix Factorization model which is based on the probabilistic formulation of the low-rank matrix approximation problem for partially observed matrices. The two models are then extended to allow conditioning on the identities of the rated items whether or not the actual rating values are known. Our results on the Netflix Prize dataset show that both RBM and PMF models outperform online SVD models.

A North American man has approximately a one in six chance of being diagnosed with prostate cancer in his lifetime. In most cases, there is no clearly optimal treatment, so he may be invited to participate in a treatment decision between several medically reasonable options, each with potential short- and long-term side effects. Information needs are high at diagnosis and can continue to be elevated for years or decades. Many men and their families seek information online, where, due partly to the array of websites available and high variation in information preferences, it can be difficult to find personally relevant and useful websites. This research sought to address this issue by developing methods to categorize prostate cancer websites and exploring quantitative and qualitative relationships between websites, information-seekers, and individuals’ assessments of websites. The research involved a series of three studies. In the first study, 29 men with prostate cancer participated in a needs assessment involving questionnaires, an interview, and interaction with a prototype website. In the second study, a detailed classification system was developed and applied to a set of forty websites selected to be representative of the variety of prostate cancer websites available. The third (online) study collected clinical, cognitive, and psychosocial details from 65 participants along with their ratings of websites from study two. A number of hypotheses were tested. One finding was that, compared to men with greater trust, men with lower trust in their physician tended to judge commercial websites as less relevant and useful, and found websites with descriptions of personal experiences more relevant and useful. Analyses also addressed a number of exploratory questions, including whether website and individual attributes might predict preferences for websites. Using discriminant analysis on 80% of the data, two functions were identified that predicted ratings significantly better than chance. These relationships were then validated with 20% of the data held back for testing. The results are discussed in terms of their implications for information tailoring and recommender systems for prostate cancer patients searching for information online. Limitations of the current research and recommendations for future research are also presented.

In frequency-domain photothermal radiometry (FD-PTR) a low-power intensity-modulated optical excitation generates thermal-wave field inside the sample and the subsequent infrared radiation from the sample is analyzed to detect material’s inhomogeneities. The non-contact nature of FD-PTR makes it very suitable for non-destructive evaluation of broad range of materials. Moreover, the methodology is based on intrinsic contrast of light absorption which can be used as a diagnostic tool for inspection of malignancy in biological tissues. Nevertheless, the bottom line is that the physics of heat diffusion allows for a highly damped and dispersive propagation of thermal-waves. As a result, the current FD-PTR modalities suffer from limited inspection depth and poor axial/depth resolution. The main objective of this thesis is to show that using alternative types of modulation schemes (such as linear frequency modulation and binary phase coding) and radar matched filter signal processing, one can obtain localized responses from inherently diffuse thermal wave fields. In this thesis, the photothermal responses of turbid, transparent, and opaque media to linear frequency modulated and binary phase coded excitations are analytically derived. Theoretical simulations suggest that matched-filtering in diffusion-wave field acts as constructive interferometry, localizing the energy of the long-duty excitation under a narrow peak and allowing one to construct depth resolved images. The developed technique is the diffusion equivalent of optical coherence tomography and is named thermal coherence tomography. It was found that the narrow-band binary phase coded matched filtering yields optimal depth resolution, while the broad-band linear frequency modulation can be used to quantify material properties through the multi-parameter fitting of the experimental data to the developed theory. Thermophotonic detection of early dental caries is discussed in detail as a potential diagnostic application of the proposed methodologies. The performance of the diagnostic system is verified through a controlled demineralization protocol as well as in teeth with natural caries.

Computational linguistics is a sub-field of artificial intelligence; it is an interdisciplinary field dealing with statistical and/or rule-based modeling of natural language from a computational perspective. Traditionally, fuzzy logic is used to deal with fuzziness among single linguistic terms in documents. However, linguistic terms may be related to other types of uncertainty. For instance, different users search ‘cheap hotel’ in a search engine, they may need distinct pieces of relevant hidden information such as shopping, transportation, weather, etc. Therefore, this research work focuses on studying granular words and developing new algorithms to process them to deal with uncertainty globally. To precisely describe the granular words, a new structure called Granular Information Hyper Tree (GIHT) is constructed. Furthermore, several technologies are developed to cooperate with computing with granular words in spam filtering and query recommendation. Based on simulation results, the GIHT-Bayesian algorithm can get more accurate spam filtering rate than conventional method Naive Bayesian and SVM; computing with granular word also generates better recommendation results based on users’ assessment when applied it to search engine.

The Internet represents a unique opportunity for people to interact with each other across time and space, and online communities have existed long before the Internet's solidification in everyday living. There are two inherent challenges that online communities continue to contend with: motivating participation and organizing information. An online community's success or failure rests on the content generated by its users. Specifically, users need to continually participate by contributing new content and organizing existing content for others to be attracted and retained. I propose both participation and organization can be enhanced if users have an explicit awareness of the implicit social network which results from their online interactions. My approach makes this normally ``hidden" social network visible and shows users that these intangible relations have an impact on satisfying their information needs and vice versa. That is, users can more readily situate their information needs within social processes, understanding that the value of information they receive and give is influenced and has influence on the mostly incidental relations they have formed with others. First, I describe how to model a social network within an online discussion forum and visualize the subsequent relationships in a way that motivates participation. Second, I show that social networks can also be modeled to generate recommendations of information items and that, through an interactive visualization, users can make direct adjustments to the model in order to improve their personal recommendations. I conclude that these modeling and visualization techniques are beneficial to online communities as their social capital is enhanced by "weaving" users more tightly together.

Networked control systems (NCSs) are a kind of distributed control systems in which the data between control components are exchanged via communication networks. Because of the attractive advantages of NCSs such as reduced system wiring, low weight, and ease of system diagnosis and maintenance, the research on NCSs has received much attention in recent years. The first part (Chapter 2 - Chapter 4) of the thesis is devoted to designing new controllers for NCSs by incorporating the network-induced delays. The thesis also conducts research on filtering of multirate systems and identification of Hammerstein systems in the second part (Chapter 5 - Chapter 6).<br /><br /> Network-induced delays exist in both sensor-to-controller (S-C) and controller-to-actuator (C-A) links. A novel two-mode-dependent control scheme is proposed, in which the to-be-designed controller depends on both S-C and C-A delays. The resulting closed-loop system is a special jump linear system. Then, the conditions for stochastic stability are obtained in terms of a set of linear matrix inequalities (LMIs) with nonconvex constraints, which can be efficiently solved by a sequential LMI optimization algorithm. Further, the control synthesis problem for the NCSs is considered. The definitions of <em>H₂</em> and <em>H_∞</em> norms for the special system are first proposed. Also, the plant uncertainties are considered in the design. Finally, the robust mixed <em>H₂/H_&infin;</em> control problem is solved under the framework of LMIs. <br /><br /> To compensate for both S-C and C-A delays modeled by Markov chains, the generalized predictive control method is modified to choose certain predicted future control signal as the current control effort on the actuator node, whenever the control signal is delayed. Further, stability criteria in terms of LMIs are provided to check the system stability. The proposed method is also tested on an experimental hydraulic position control system. <br /><br /> Multirate systems exist in many practical applications where different sampling rates co-exist in the same system. The <em>l₂-l_&infin;</em> filtering problem for multirate systems is considered in the thesis. By using the lifting technique, the system is first transformed to a linear time-invariant one, and then the filter design is formulated as an optimization problem which can be solved by using LMI techniques. <br /><br /> Hammerstein model consists of a static nonlinear block followed in series by a linear dynamic system, which can find many applications in different areas. New switching sequences to handle the two-segment nonlinearities are proposed in this thesis. This leads to less parameters to be estimated and thus reduces the computational cost. Further, a stochastic gradient algorithm based on the idea of replacing the unmeasurable terms with their estimates is developed to identify the Hammerstein model with two-segment nonlinearities. <br /><br /> Finally, several open problems are listed as the future research directions.

In frequency-domain photothermal radiometry (FD-PTR) a low-power intensity-modulated optical excitation generates thermal-wave field inside the sample and the subsequent infrared radiation from the sample is analyzed to detect material’s inhomogeneities. The non-contact nature of FD-PTR makes it very suitable for non-destructive evaluation of broad range of materials. Moreover, the methodology is based on intrinsic contrast of light absorption which can be used as a diagnostic tool for inspection of malignancy in biological tissues. Nevertheless, the bottom line is that the physics of heat diffusion allows for a highly damped and dispersive propagation of thermal-waves. As a result, the current FD-PTR modalities suffer from limited inspection depth and poor axial/depth resolution. The main objective of this thesis is to show that using alternative types of modulation schemes (such as linear frequency modulation and binary phase coding) and radar matched filter signal processing, one can obtain localized responses from inherently diffuse thermal wave fields. In this thesis, the photothermal responses of turbid, transparent, and opaque media to linear frequency modulated and binary phase coded excitations are analytically derived. Theoretical simulations suggest that matched-filtering in diffusion-wave field acts as constructive interferometry, localizing the energy of the long-duty excitation under a narrow peak and allowing one to construct depth resolved images. The developed technique is the diffusion equivalent of optical coherence tomography and is named thermal coherence tomography. It was found that the narrow-band binary phase coded matched filtering yields optimal depth resolution, while the broad-band linear frequency modulation can be used to quantify material properties through the multi-parameter fitting of the experimental data to the developed theory. Thermophotonic detection of early dental caries is discussed in detail as a potential diagnostic application of the proposed methodologies. The performance of the diagnostic system is verified through a controlled demineralization protocol as well as in teeth with natural caries.

A Cyber-Physical System integrates computations and dynamics of physical processes. It is an engineering discipline focused on technology with a strong foundation in mathematical abstractions. It shares many of these abstractions with engineering and computer science, but still requires adaptation to suit the dynamics of the physical world. In such a dynamic system, mobility management is one of the key issues against developing a new service. For example, in the study of a new mobile network, it is necessary to simulate and evaluate a protocol before deployment in the system. Mobility models characterize mobile agent movement patterns. On the other hand, they describe the conditions of the mobile services. The focus of this thesis is on mobility modeling in cyber-physical systems. A macroscopic model that captures the mobility of individuals (people and vehicles) can facilitate an unlimited number of applications. One fundamental and obvious example is traffic profiling. Mobility in most systems is a dynamic process and small non-linearities can lead to substantial errors in the model. Extensive research activities on statistical inference and filtering methods for data modeling in cyber-physical systems exist. In this thesis, several methods are employed for multimodal data fusion, localization and traffic modeling. A novel energy-aware sparse signal processing method is presented to process massive sensory data. At baseline, this research examines the application of statistical filters for mobility modeling and assessing the difficulties faced in fusing massive multi-modal sensory data. A statistical framework is developed to apply proposed methods on available measurements in cyber-physical systems. The proposed methods have employed various statistical filtering schemes (i.e., compressive sensing, particle filtering and kernel-based optimization) and applied them to multimodal data sets, acquired from intelligent transportation systems, wireless local area networks, cellular networks and air quality monitoring systems. Experimental results show the capability of these proposed methods in processing multimodal sensory data. It provides a macroscopic mobility model of mobile agents in an energy efficient way using inconsistent measurements.