Adaptive Markov chain Monte Carlo and Bayesian filtering for state space models

This thesis is concerned with the state and parameter estimation in state space models. The estimation of states and parameters is an important task when mathematical modeling is applied to many different application areas such as the global positioning systems, target tracking, navigation, brain imaging, spread of infectious diseases, biological processes, telecommunications, audio signal processing, stochastic optimal control, machine learning, and physical systems. In Bayesian settings, the estimation of states or parameters amounts to computation of the posterior probability density function. Except for a very restricted number of models, it is impossible to compute this density function in a closed form. Hence, we need approximation methods. A state estimation problem involves estimating the states (latent variables) that are not directly observed in the output of the system. In this thesis, we use the Kalman filter, extended Kalman filter, Gauss–Hermite filters, and particle filters to estimate the states based on available measurements. Among these filters, particle filters are numerical methods for approximating the filtering distributions of non-linear non-Gaussian state space models via Monte Carlo. The performance of a particle filter heavily depends on the chosen importance distribution. For instance, inappropriate choice of the importance distribution can lead to the failure of convergence of the particle filter algorithm. In this thesis, we analyze the theoretical Lᵖ particle filter convergence with general importance distributions, where p ≥2 is an integer. A parameter estimation problem is considered with inferring the model parameters from measurements. For high-dimensional complex models, estimation of parameters can be done by Markov chain Monte Carlo (MCMC) methods. In its operation, the MCMC method requires the unnormalized posterior distribution of the parameters and a proposal distribution. In this thesis, we show how the posterior density function of the parameters of a state space model can be computed by filtering based methods, where the states are integrated out. This type of computation is then applied to estimate parameters of stochastic differential equations. Furthermore, we compute the partial derivatives of the log-posterior density function and use the hybrid Monte Carlo and scaled conjugate gradient methods to infer the parameters of stochastic differential equations. The computational efficiency of MCMC methods is highly depend on the chosen proposal distribution. A commonly used proposal distribution is Gaussian. In this kind of proposal, the covariance matrix must be well tuned. To tune it, adaptive MCMC methods can be used. In this thesis, we propose a new way of updating the covariance matrix using the variational Bayesian adaptive Kalman filter algorithm.

Power and Performance Management Strategies on NoC-Based Multicore Platforms, Using DVFS, Adaptive Algorithm and Task Parallelism

This work presents synopsis of efficient strategies used in power managements for achieving the most economical power and energy consumption in multicore systems, FPGA and NoC Platforms. In this work, a practical approach was taken, in an effort to validate the significance of the proposed Adaptive Power Management Algorithm (APMA), proposed for system developed, for this thesis project. This system comprise arithmetic and logic unit, up and down counters, adder, state machine and multiplexer. The essence of carrying this project firstly, is to develop a system that will be used for this power management project. Secondly, to perform area and power synopsis of the system on these various scalable technology platforms, UMC 90nm nanotechnology 1.2v, UMC 90nm nanotechnology 1.32v and UMC 0.18 μmNanotechnology 1.80v, in order to examine the difference in area and power consumption of the system on the platforms. Thirdly, to explore various strategies that can be used to reducing system’s power consumption and to propose an adaptive power management algorithm that can be used to reduce the power consumption of the system. The strategies introduced in this work comprise Dynamic Voltage Frequency Scaling (DVFS) and task parallelism. After the system development, it was run on FPGA board, basically NoC Platforms and on these various technology platforms UMC 90nm nanotechnology1.2v, UMC 90nm nanotechnology 1.32v and UMC180 nm nanotechnology 1.80v, the system synthesis was successfully accomplished, the simulated result analysis shows that the system meets all functional requirements, the power consumption and the area utilization were recorded and analyzed in chapter 7 of this work. This work extensively reviewed various strategies for managing power consumption which were quantitative research works by many researchers and companies, it's a mixture of study analysis and experimented lab works, it condensed and presents the whole basic concepts of power management strategy from quality technical papers.

Moral reasoning and ethics in educational leadership /

School leaders face difficult decisions regarding discipline matters. Often, such decisions play an important role in determining the moral tone of the school and the health of the school community. Many stakeholders are affected by the outcome of such decisions. Codes of conduct, board and school policies, and discipline meetings are often shrouded under secrecy, making the discipline process mysterious. .; In this study I examined the process of moral reasoning. I sought to determine the extent to which school leaders were aware that they were involved in a process of moral reasoning, and ftirthermore, what kind of moral reasoning they practiced. As well, I investigated the ethical grounds and foundations underlying moral reasoning. Thus, in this study I probed the awareness of the process of moral reasoning and sought to find the ethical grounding of decision making. This qualitative study featured short field research. The process involved individual interviews with three different participants: school leaders of a public. Catholic, and an independent school. It found that each school leader practiced moral reasoning to varying degrees through the discipline process. It also explored the possible democratization of moral reasoning by linking to concepts such as fairness, due process, public accountability, and greater participation in the administering of discipline. This study has implications for practice, theory, and future research. The examination of school leaders as the primary focus for discipline matters opens the door to future research that could explore differences between the school systems and possibly other parties affected by moral reasoning in discipline cases.

Finding the Open Spaces in Closed Systems: An Exploration of Critical Pedagogy with a Social Justice Directive in an Ontario Secondary School Classroom

This study examines the first experience of students, teachers, and an administrator in implementing a teacher-designed Leadership in Social Justice Program at a large urban Ontario secondary school. The program aimed to infuse a Freirean concept of critical pedagogical praxis (Freire, 1970/1993) in a grade 12 integrated educational experience with a social justice directive. Data were collected through two questionnaires and eight in-depth interviews. The data identified three areas of awareness that described ways in which student participants were impacted most profoundly (a) developing self-awareness, (b) understanding a new educational paradigm, and (c) finding a place in the world. The study found that the program was successful in highlighting the possibility for more meaningful education and engaged many students deeply; however, its success was limited by the lead teacher’s failure to fully grasp and implement tenets of Freirean critical pedagogy that involved the role of the teacher in pedagogical processes.

The development of a Flanders Interaction Analysis style instrument for use in educational computer mediated communication research

Please consult the paper edition of this thesis to read. It is available on the 5th Floor of the Library at Call Number: Z 9999 E38 D56 1992

Can Ontario Be the Next Finland? Assessing Whether the Finnish Model Can Meet the Educational Needs of Ontario, Canada

This study examines the adaptability of the Finland model to meet the educational disparities currently observed in the education system in Ontario, Canada. A literature review and a database highlight key characteristics of the Finland model. From this information, Finland and Ontario’s systems are found to be similar in the areas of systemic structure and educational philosophies, and international testing and performance standards. The systems are found to be different in the areas of geography and demographics, social perceptions and attitudes towards education, school system structure, teaching philosophies, teacher education and professional status, and standardized and high-stakes testing. Discussion regarding use of Finnish philosophies to meet Ontario’s needs in the following areas takes place: social perceptions and attitudes towards education, our teaching philosophies, our teacher education and professional status, and our use of high-stakes and standardized testing. Opportunities for future research are also discussed and the major research paper includes a workshop and survey.

From cognition to action : a psychological investigation of action regulation in complex and uncertain social systems

Cette thèse présente une revue des réflexions récentes et plus traditionnelles provenant de la théorie des systèmes, de la créativité en emploi, des théories d’organisation du travail et de la motivation afin de proposer une perspective psychologique de la régulation des actions des individus au sein d’environnements de travail complexes et incertains. Des composantes de la Théorie de la Régulation de l’Action (Frese & Zapf, 1994) ainsi que de la Théorie de l’Auto-Détermination (Deci & Ryan, 2000) sont mises en relation afin d’évaluer un modèle définissant certains schémas cognitifs clés associés aux tâches individuelles et collectives en emploi. Nous proposons que ces schémas cognitifs, organisés de manière hiérarchique, jouent un rôle central dans la régulation d’une action efficace au sein d’un système social adaptatif. Nos mesures de ces schémas cognitifs sont basées sur des échelles de mesure proposées dans le cadre des recherches sur l’ambiguïté de rôle (eg. Sawyer, 1992; Breaugh & Colihan, 1994) et sont mis en relation avec des mesures de satisfaction des besoins psychologiques (Van den Broeck, Vansteenkiste, De Witte, Soenens & Lens, 2009) et du bien-être psychologique (Goldberg, 1972). Des données provenant de 153 employés à temps plein d’une compagnie de jeu vidéo ont été récoltées à travers deux temps de mesure. Les résultats révèlent que différents types de schémas cognitifs associés aux tâches individuelles et collectives sont liés à la satisfaction de différents types de besoin psychologiques et que ces derniers sont eux-mêmes liés au bien-être psychologique. Les résultats supportent également l’hypothèse d’une organisation hiérarchique des schémas cognitifs sur la base de leur niveau d’abstraction et de leur proximité avec l’exécution concrète de l’action. Ces résultats permettent de fournir une explication initiale au processus par lequel les différents types de schémas cognitifs développés en emplois et influencé par l’environnement de travail sont associés à l’attitude des employés et à leur bien-être psychologique. Les implications pratiques et théoriques pour la motivation, l’apprentissage, l’habilitation, le bien-être psychologique et l’organisation du travail dans les environnements de travail complexes et incertains sont discutés.

Studies on function projective synchronization in chaotic and hyperchaotic systems

FPS is a more general form of synchronization. Hyperchaotic systems possessing more than one positive Lypaunov exponent exhibit highly complex behaviour and are more suitable for some applications like secure communications. In this thesis we report studies of FPS and MFPS of a few chaotic and hyperchaotic systems. When all the parameters of the system are known we show that active nonlinear control method can be efectively used to obtain FPS. Adaptive nonlinear control and OPCL control method are employed for obtaining FPS and MFPS when some or all parameters of the system are uncertain. A secure communication scheme based on MFPS is also proposed in theory. All our theoretical calculations are verified by numerical simulations.

Energy Management in Wireless Sensor Networks: A Crosslayer,Channel Adaptive Approach Towards Performance Optimization

The proliferation of wireless sensor networks in a large spectrum of applications had been spurered by the rapid advances in MEMS(micro-electro mechanical systems )based sensor technology coupled with low power,Low cost digital signal processors and radio frequency circuits.A sensor network is composed of thousands of low cost and portable devices bearing large sensing computing and wireless communication capabilities. This large collection of tiny sensors can form a robust data computing and communication distributed system for automated information gathering and distributed sensing.The main attractive feature is that such a sensor network can be deployed in remote areas.Since the sensor node is battery powered,all the sensor nodes should collaborate together to form a fault tolerant network so as toprovide an efficient utilization of precious network resources like wireless channel,memory and battery capacity.The most crucial constraint is the energy consumption which has become the prime challenge for the design of long lived sensor nodes.

Short-Term Load Forecast Of A Low Loadfactor Power System For Optimization Of Merit Order Dispatch Using Adaptive Learning Algorithm

Short term load forecasting is one of the key inputs to optimize the management of power system. Almost 60-65% of revenue expenditure of a distribution company is against power purchase. Cost of power depends on source of power. Hence any optimization strategy involves optimization in scheduling power from various sources. As the scheduling involves many technical and commercial considerations and constraints, the efficiency in scheduling depends on the accuracy of load forecast. Load forecasting is a topic much visited in research world and a number of papers using different techniques are already presented. The accuracy of forecast for the purpose of merit order dispatch decisions depends on the extent of the permissible variation in generation limits. For a system with low load factor, the peak and the off peak trough are prominent and the forecast should be able to identify these points to more accuracy rather than minimizing the error in the energy content. In this paper an attempt is made to apply Artificial Neural Network (ANN) with supervised learning based approach to make short term load forecasting for a power system with comparatively low load factor. Such power systems are usual in tropical areas with concentrated rainy season for a considerable period of the year

Adaptive Real-time Anomaly-based Intrusion Detection using Data Mining and Machine Learning Techniques

Die zunehmende Vernetzung der Informations- und Kommunikationssysteme führt zu einer weiteren Erhöhung der Komplexität und damit auch zu einer weiteren Zunahme von Sicherheitslücken. Klassische Schutzmechanismen wie Firewall-Systeme und Anti-Malware-Lösungen bieten schon lange keinen Schutz mehr vor Eindringversuchen in IT-Infrastrukturen. Als ein sehr wirkungsvolles Instrument zum Schutz gegenüber Cyber-Attacken haben sich hierbei die Intrusion Detection Systeme (IDS) etabliert. Solche Systeme sammeln und analysieren Informationen von Netzwerkkomponenten und Rechnern, um ungewöhnliches Verhalten und Sicherheitsverletzungen automatisiert festzustellen. Während signatur-basierte Ansätze nur bereits bekannte Angriffsmuster detektieren können, sind anomalie-basierte IDS auch in der Lage, neue bisher unbekannte Angriffe (Zero-Day-Attacks) frühzeitig zu erkennen. Das Kernproblem von Intrusion Detection Systeme besteht jedoch in der optimalen Verarbeitung der gewaltigen Netzdaten und der Entwicklung eines in Echtzeit arbeitenden adaptiven Erkennungsmodells. Um diese Herausforderungen lösen zu können, stellt diese Dissertation ein Framework bereit, das aus zwei Hauptteilen besteht. Der erste Teil, OptiFilter genannt, verwendet ein dynamisches "Queuing Concept", um die zahlreich anfallenden Netzdaten weiter zu verarbeiten, baut fortlaufend Netzverbindungen auf, und exportiert strukturierte Input-Daten für das IDS. Den zweiten Teil stellt ein adaptiver Klassifikator dar, der ein Klassifikator-Modell basierend auf "Enhanced Growing Hierarchical Self Organizing Map" (EGHSOM), ein Modell für Netzwerk Normalzustand (NNB) und ein "Update Model" umfasst. In dem OptiFilter werden Tcpdump und SNMP traps benutzt, um die Netzwerkpakete und Hostereignisse fortlaufend zu aggregieren. Diese aggregierten Netzwerkpackete und Hostereignisse werden weiter analysiert und in Verbindungsvektoren umgewandelt. Zur Verbesserung der Erkennungsrate des adaptiven Klassifikators wird das künstliche neuronale Netz GHSOM intensiv untersucht und wesentlich weiterentwickelt. In dieser Dissertation werden unterschiedliche Ansätze vorgeschlagen und diskutiert. So wird eine classification-confidence margin threshold definiert, um die unbekannten bösartigen Verbindungen aufzudecken, die Stabilität der Wachstumstopologie durch neuartige Ansätze für die Initialisierung der Gewichtvektoren und durch die Stärkung der Winner Neuronen erhöht, und ein selbst-adaptives Verfahren eingeführt, um das Modell ständig aktualisieren zu können. Darüber hinaus besteht die Hauptaufgabe des NNB-Modells in der weiteren Untersuchung der erkannten unbekannten Verbindungen von der EGHSOM und der Überprüfung, ob sie normal sind. Jedoch, ändern sich die Netzverkehrsdaten wegen des Concept drif Phänomens ständig, was in Echtzeit zur Erzeugung nicht stationärer Netzdaten führt. Dieses Phänomen wird von dem Update-Modell besser kontrolliert. Das EGHSOM-Modell kann die neuen Anomalien effektiv erkennen und das NNB-Model passt die Änderungen in Netzdaten optimal an. Bei den experimentellen Untersuchungen hat das Framework erfolgversprechende Ergebnisse gezeigt. Im ersten Experiment wurde das Framework in Offline-Betriebsmodus evaluiert. Der OptiFilter wurde mit offline-, synthetischen- und realistischen Daten ausgewertet. Der adaptive Klassifikator wurde mit dem 10-Fold Cross Validation Verfahren evaluiert, um dessen Genauigkeit abzuschätzen. Im zweiten Experiment wurde das Framework auf einer 1 bis 10 GB Netzwerkstrecke installiert und im Online-Betriebsmodus in Echtzeit ausgewertet. Der OptiFilter hat erfolgreich die gewaltige Menge von Netzdaten in die strukturierten Verbindungsvektoren umgewandelt und der adaptive Klassifikator hat sie präzise klassifiziert. Die Vergleichsstudie zwischen dem entwickelten Framework und anderen bekannten IDS-Ansätzen zeigt, dass der vorgeschlagene IDSFramework alle anderen Ansätze übertrifft. Dies lässt sich auf folgende Kernpunkte zurückführen: Bearbeitung der gesammelten Netzdaten, Erreichung der besten Performanz (wie die Gesamtgenauigkeit), Detektieren unbekannter Verbindungen und Entwicklung des in Echtzeit arbeitenden Erkennungsmodells von Eindringversuchen.

Opportunities and constraints of peri-urban buffalo and dairy cattle systems in Faisalabad, Punjab, Pakistan

In the big cities of Pakistan, peri-urban dairy production plays an important role for household income generation and the supply of milk and meat to the urban population. On the other hand, milk production in general, and peri-urban dairy production in particular, faces numerous problems that have been well known for decades. Peri-urban dairy producers have been especially neglected by politicians as well as non-government-organizations (NGOs). Against this background, a study in Pakistan’s third largest city, Faisalabad (Punjab Province), was carried out with the aims of gathering basic information, determining major constraints and identifying options for improvements of the peri-urban milk production systems. For data collection, 145 peri-urban households (HH) engaged in dairy production were interviewed face to face using a structured and pretested questionnaire with an interpreter. For analyses, HH were classified into three wealth groups according to their own perception. Thus, 38 HH were poor, 95 HH well off and 12 HH rich (26.2%, 65.5% and 8.3%, respectively). The richer the respondents perceived their HH, the more frequently they were actually in possession of high value HH assets like phones, bank accounts, motorbikes, tractors and cars. Although there was no difference between the wealth groups with respect to the number of HH members (about 10, range: 1 to 23), the educational level of the HH heads differed significantly: on average, heads of poor HH had followed education for 3 years, compared to 6 years for well off HH and 8 years for rich HH. About 40% of the poor and well off HH also had off-farm incomes, while the percentage was much higher - two thirds (67%) - for the rich HH. The majority of the HH were landless (62%); the rest (55 HH) possessed agricultural land from 0.1 to 10.1 ha (average 2.8 ha), where they were growing green fodder: maize, sorghum and pearl millet in summer; berseem, sugar cane and wheat were grown in winter. Dairy animals accounted for about 60% of the herds; the number of dairy animals per HH ranged from 2 to 50 buffaloes (Nili-Ravi breed) and from 0 to 20 cows (mostly crossbred, also Sahiwal). About 37% (n=54) of the HH did not keep cattle. About three quarters of the dairy animals were lactating. The majority of the people taking care of the animals were family workers; 17.3% were hired labourers (exclusively male), employed by 11 rich and 32 well off HH; none of the poor HH employed workers, but the percentages were 33.7% for the well off and 91.7% for the rich HH. The total number of workers increased significantly with increasing wealth (poor: 2.0; well off:2.5; rich: 3.4). Overall, 69 female labourers were recorded, making up 16.8% of employed workers and one fourth of the HH’s own labourers. Apparently, their only duty was to clean the animals´ living areas; only one of them was also watering and showering the animals. Poor HH relied more on female workers than the other two groups: 27.1% of the workers of poor HH were women, but only 14.8% and 6.8% of the labour force of well off and rich HH were female. Two thirds (70%) of the HH sold milk to dhodis (middlemen) and one third (35%) to neighbours; three HH (2%) did doorstep delivery and one HH (1%) had its own shop. The 91 HH keeping both species usually sold mixed milk (97%). Clients for mixed and pure buffalo milk were dhodis (78%, respectively 59%) and neighbours (28%, respectively 47%). The highest milk prices per liter (Pakistani Rupees, 100 PKR @ 0.8 Euro) were paid by alternative clients (44 PKR; 4 HH), followed by neighbours (40 PKR, 50 HH); dhodis paid lower prices (36 PKR, 99 HH). Prices for pure buffalo and mixed milk did not differ significantly. However, HH obtaining the maximum price from the respective clients for the respective type of milk got between 20% (mixed milk, alternative clients) and 68% (mixed milk, dhodi) more than HH fetching the minimum price. Some HH (19%) reported 7% higher prices for the current summer than the preceding winter. Amount of milk sold and distance from the HH to the city center did not influence milk prices. Respondents usually named problems that directly affected their income and that were directly and constantly visible to them, such as high costs, little space and fodder shortages. Other constraints that are only influencing their income indirectly, e.g. the relatively low genetic potential of their animals due to neglected breeding as well as the short- and long-term health problems correlated with imbalanced feeding and insufficient health care, were rarely named. The same accounts for problems accompanying improper dung management (storage, disposal, burning instead of recycling) for the environment and human health. Most of the named problems are linked to each other and should be addressed within the context of the entire system. Therefore, further research should focus on systematic investigations and improvement options, taking a holistic and interdisciplinary approach instead of only working in single fields. Concerted efforts of dairy farmers, researchers, NGOs and political decision makers are necessary to create an economic, ecological and social framework that allows dairy production to serve the entire society. For this, different improvement options should be tested in terms of their impact on environment and income of the farmers, as well as feasibility and sustainability in the peri-urban zones of Faisalabad.

Influence of farmers educational level on comprehending, acting-upon and sharing of agro advisories

Although short messaging service (SMS) through mobile phones has quickly gained popularity among most other sectors in India, its potential is not fully realised in the agriculture sector as a cost effective service to reach farmers and elicit desirable action. Despite the cost effectiveness, mobile messaging has remained a challenge in the farming sector in terms of its end use and action as far as extension systems are concerned. While there could be many influencing factors affecting the utility of mobile messages, this study assumed that educational level of farmers could be a major factor. A telephone survey was conducted to ascertain the influence of farmers’ education on the level of utilisation of mobile-based advisories. Farmers with higher education level showed better comprehension of advisories, actedupon the advisories more promptly and shared the information with fellow farmers more often than those with lower education level. There was a significant association between comprehending, sharing and acting upon advisories. This has implications to achieve enhanced extension reach with higher efficiency in terms of cost and time.

Adaptive integral assessment package for the A2UN@ project

This paper presents a first approach of Evaluation Engine Architecture (EEA) as proposal to support adaptive integral assessment, in the context of a virtual learning environment. The goal of our research is design an evaluation engine tool to assist in the whole assessment process within the A2UN@ project, linking that tool with the other key elements of a learning design (learning task, learning resources and learning support). The teachers would define the relation between knowledge, competencies, activities, resources and type of assessment. Providing this relation is possible obtain more accurate estimations of student's knowledge for adaptive evaluations and future recommendations. The process is supported by usage of educational standards and specifications and for an integral user modelling

Analysis and regeneration of hypermedia contents through Java and XML tools

This paper presents a tool for the analysis and regeneration of Web contents, implemented through XML and Java. At the moment, the Web content delivery from server to clients is carried out without taking into account clients' characteristics. Heterogeneous and diverse characteristics, such as user's preferences, different capacities of the client's devices, different types of access, state of the network and current load on the server, directly affect the behavior of Web services. On the other hand, the growing use of multimedia objects in the design of Web contents is made without taking into account this diversity and heterogeneity. It affects, even more, the appropriate content delivery. Thus, the objective of the presented tool is the treatment of Web pages taking into account the mentioned heterogeneity and adapting contents in order to improve the performance on the Web