Multi Relational Data Mining has become popular due to the limitations of propositional problem definition in structured domains and the tendency of storing data in relational databases. However, as patterns involve multiple relations, the search space of possible hypothesis becomes intractably complex. In order to cope with this problem, several relational knowledge discovery systems have been developed employing various search strategies, heuristics and language pattern limitations. In this thesis, Inductive Logic Programming (ILP) based concept discovery is studied and two systems based on a hybrid methodology employing ILP and APRIORI, namely Confidence-based Concept Discovery and Concept Rule Induction System, are proposed. In Confidence-based Concept Discovery and Concept Rule Induction System, the main aim is to relax the strong declarative biases and user-defined specifications. Moreover, this new method directly works on relational databases. In addition to this, the traditional definition of confidence from relational database perspective is modified to express Closed World Assumption in first-order logic. A new confidence-based pruning method based on the improved definition is applied in the APRIORI lattice. Moreover, a new hypothesis evaluation criterion is used for expressing the quality of patterns in the search space. In addition to this, in Concept Rule Induction System, the constructed rule quality is further improved by using an improved generalization metod. Finally, a set of experiments are conducted on real-world problems to evaluate the performance of the proposed method with similar systems in terms of support and confidence.

The objective of the proposed research is to advance the power efficiency of wireless neural stimulating systems in inductively powered implantable medical devices (IMD). Several innovative system- and circuit-level techniques are proposed towards the development of power-management circuits and wireless neural stimulating systems with inductive power transmission to improve the overall stimulation power efficiency. Neural stimulating IMDs have been proven as effective therapies to alleviate neurological diseases, while requiring high power and performance for more efficacious treatments. Therefore, power-management circuits and neural stimulators in IMDs should have high power efficiencies to operate with smaller received power from a larger distance. Neural stimulating systems are also required to have high stimulation efficacy for activating the target tissue with a minimum amount of energy, while ensuring charge-balanced stimulation. These features provide several advantages such as a long battery life in an external power transmitter, extended-range inductive power transfer, efficacious and safe stimulation, and less tissue damage from overheating. The proposed research presents several approaches to design and implement the power-efficient wireless neural stimulating IMDs: 1) optimized power-management circuits for inductively powered biomedical microsystems, 2) a power-efficient neural stimulating system with adaptive supply control, and 3) a wireless switched-capacitor stimulation (SCS) system, which is a combination structure of the power-management circuits and neural stimulator, to maximize both stimulator efficiency (before electrodes) and stimulus efficacy (after electrodes).

Traditional methods of teaching magnetic induction with lab investigations using a battery, wire and compass are best reserved for demonstration purposes to introduce this particular topic. The modern student who sits in a physics course also lives in a world filled with an increasing number of small portable devices that will eventually be charged wirelessly using some form of magnetic induction. The topic of magnetic induction needs to be placed in the modern context it deserves since the future of transmitting power will eventually be through wireless means. The wireless power transfer kit described in this report is designed to improve student understanding and the application of magnetic induction in an engaging, relevant manner. / text

Ο Επαγωγικός Λογικός Προγραμματισμός (Inductive Logic Programming ή, σε συντομογραφία ILP) είναι ο ερευνητικός τομέας της Τεχνητής Νοημοσύνης (Artificial Intelligence) που δραστηριοποιείται στη τομή των γνωστικών περιοχών της Μάθησης Μηχανής (Machine Learning) και του Λογικού Προγραμματισμού (Logic Programming).Ο όρος επαγωγικός εκφράζει την ιδέα του συλλογισμού από το επί μέρους στο γενικό. Μέσω της επαγωγικής μάθησης μηχανής ο ILP επιτυγχάνει το στόχο του που είναι η δημιουργία εργαλείων και η ανάπτυξη τεχνικών για την εξαγωγή υποθέσεων από παρατηρήσεις (παραδείγματα) και η σύνθεση-απόκτηση νέας γνώσης από εμπειρικές παρατηρήσεις. Σε αντίθεση με της περισσότερες άλλες προσεγγίσεις της επαγωγικής μάθησης ο ILP ενδιαφέρεται για της ιδιότητες του συμπερασμού με κανόνες για την σύγκλιση αλγορίθμων και για την υπολογιστική πολυπλοκότητα των διαδικασιών. Ο ILP ασχολείται με την ανάπτυξη τεχνικών και εργαλείων για την σχεσιακή ανάλυση δεδομένων. Εφαρμόζεται απευθείας σε δεδομένα πολλαπλών συσχετισμών για την ανακάλυψη προτύπων. Τα πρότυπα που ανακαλύπτονται από τα συστήματα στον ILP εκφράζονται ως λογικά προγράμματα. Τα λογικά προγράμματα αποτελούνται από ειδικούς κανόνες, οι οποίοι χωρίζονται στις προϋποθέσεις και στα συμπεράσματα. Ο ILP έχει χρησιμοποιηθεί εκτεταμένα σε προβλήματα που αφορούν τη μοριακή βιολογία, την βιοχημεία και την χημεία. Τα παραδείγματα, οι κανόνες εκφράζουν την γνώση υποβάθρου εκφράζονται σε μια γλώσσα λογικού προγραμματισμού όπως η Prolog. Ο Επαγωγικός Λογικός Προγραμματισμός διαφοροποιείται από τις άλλες μορφές Μάθησης Μηχανής, αφ’ ενός μεν λόγω της χρήσης μιας εκφραστικής γλώσσας αναπαράστασης και αφ’ ετέρου από τη δυνατότητά του να χρησιμοποιεί τη γνώση υποβάθρου. Διάφορες εφαρμογές έχουν αναπτυχθεί, εκ των οποίων η πιο πρόσφατη είναι η Progol, που αποτελείται από ένα διερμηνέα της Prolog ο οποίος συνοδεύεται από έναν αλγόριθμο Αντίστροφης Συνεπαγωγής (Inverse Entailment) που κατασκευάζει νέες προτάσεις με τη γενίκευση των παραδειγμάτων που περιέχονται στη βάση δεδομένων της Prolog. Η θεωρία του Επαγωγικού Λογικού Προγραμματισμού εγγυάται ότι η Progol θα διεξάγει μια αποδεκτή αναζήτηση στο διάστημα των γενικεύσεων, βρίσκοντας το ελάχιστο σύνολο προτάσεων, από το οποίο όλα τα παραδείγματα μπορούν να προκύψουν. Στην συγκεκριμένη εργασία η Progol είναι το εργαλείο που χρησιμοποιείται για την ανάπτυξη των παραδειγμάτων εφαρμογής του ILP. / The Inductive Reasonable Planning (Inductive Logic Programming or, in abbreviation ILP) is the inquiring sector Artificial Intelligence that is activated in the section of cognitive regions of Learning of Machine (Machine Learning) and Reasonable Planning (Logic Programming). The term inductive expresses the idea of reasoning from on part in general. Via the inductive learning of machine the ILP achieves his objective that is the creation of tools and the growth of techniques for the export of affairs from observations (examples) and composition of new knowledge from empiric observations. Contrary to more other approaches of inductive learning the ILP is interested for its inference attributes with rules on the convergence of algorithms and on the calculating complexity of processes. The ILP deals with the growth of techniques and tools for the relational analysis of data. It is applied directly in data of multiple correlations on the discovery of models. The models that are discovered by the systems in the ILP are expressed as reasonable programs. The reasonable programs are constituted by special rules, which are separated in the conditions and in the conclusions. The ILP has been used extensive in problems that concern the molecular biology, the biochemistry and the chemistry. The examples, the rules express the knowledge of background are expressed in a language of reasonable planning as the Prolog. The Inductive Reasonable Planning is differentiated by the other forms of Learning of Machine, on the one hand men because the use of expressive language of representation and on the other hand by his possibility of using the knowledge of background. Various applications have been developed, from which most recent is Progol, that is constituted from interpreter of Prolog which is accompanied by a algorithm of Inverse Entailment that manufactures new proposals with the generalisation of examples that is contained in the base of data of Prolog. theory of Inductive Reasonable Planning guarantees that the Progol will carry out a acceptable search in the interval of generalisations, finding the minimal total of proposals, from which all the examples can result. In the particular work the Progol is the tool that is used for the growth of examples of application of ILP.

La première étude systématique de la théorie des modèles positive était faite par Ben Yaacov qui a proposé une approche uniforme aux travaux précurseurs accomplis indépendamment par Robinson, Shelah, Hrushovski et Pillay avec un souci croissant d'incorporer les techniques modernes de la théorie des modèles dans le contexte des logiques réduites. Ben Yaacov et Poizat dans leur travail intitulé fondements de la logique positive ont défini un nouveau cadre pour la théorie des modèles positive, qui détermine le contexte de cette thèse. Dans le premier chapitre nous rappelons les outils de la théorie des modèle positive et nous développons des notions et des outils qui nous seront utiles dans le reste des chapitres. Parmi ceux-ci, il convient de souligner les extensions universelles. Elles caractérisent les bases d'amalgamation dans le deuxième chapitre, et sont cruciales dans la construction des domaines universels positifs. Dans le deuxième chapitre nous étudions la notion d'amalgamation qui s'avère centrale dans la théorie des modèles positive. Elle nous permettra d'étudier la conservation de la séparation topologique entre les extensions élémentaires positives, et de caractériser les théories de Robinson et l'élimination des quanteurs dans certaines classes des structures. Dans le troisième chapitre, nous continuons l'étude de la stabilité positive déjà entamée par Ben Yaacov, et nous en proposons une nouvelle caractérisation par une notion d'ordre propre à la théorie des modèles positive.

This thesis investigates stress and coping in high performance squash coaching from the perspectives of both the coach and the player. An introduction to the thesis (chapter 1), a discussion of key concepts and theories from the general stress and coping literature (chapter 2), and a systematic review of the stress and coping in sport literature (chapter 3) are presented. The empirical work is reported in two phases. Phase one (chapters 4, 5 & 6) documents an in-depth study of 18 high performance squash coaches. Retrospective interviews were used to collect both qualitative and quantitative data. Inductive content analysis (Patton, 1980) revealed 223 raw data themes for stress, which collapsed into 12 general dimensions, and 415 raw data themes for coping, from which 13 general dimensions emerged. Descriptive statistics on stress source characteristics revealed a number of interesting trends that required further investigation. Further, general ('multi-purpose') and specific coping strategies were identified. Coping effectiveness and frequency data detailed the analysis. Phase two (chapters 7, 8 & 9) investigates stress and coping experiences of elite squash players during coaching activities via two studies. In study one, data were collected through telephone interviews, and analysed using inductive content analysis (QSR NUDIST), revealing 227 raw data themes for stress and coping from which 9 stress and 8 coping general dimensions emerged. These results were used to develop a postal questionnaire (study two) administered to 84 elite squash players on the England Squash World Class Performance Programme (Jan. 1999). A response rate of 60% was obtained, data was input into SPSS, and various statistical tests revealed significant contributions to 5 major investigative themes detennined at the outset. An overall picture of the landscape in stress and coping in high performance squash coaching is offered, and conclusions and future directions are presented (chapter 10).

No description available.

This dissertation investigates the validity of the hypothesis that biblical images [imagery] in the narrative model of preaching enhance relevance and recall possibilities of the sermon, filling the open spaces for the listener in a meaningful way. "Imagery" is researched in its application in various genres of the narrative sermon, e.g. the inductive, the narrative as such, metaphor, parable and transformational preaching. In the final analysis, the Midrash hermeneutical model as theoretical exposition and fresh proposition for homiletical possibilities for today was suggested and instruments proposed to aid in the preparaUon of Midrashic narrative sermons. / Philosophy, Practical and Systematic Theology / D.Th. (Practical theology)

abstract: Three-dimensional (3D) inductors with square, hexagonal and octagonal geometries have been designed and simulated in ANSYS HFSS. The inductors have been designed on Silicon substrate with through-hole via with different width, spacing and thickness. Spice modeling has been done in Agilent ADS and comparison has been made with results of custom excel based calculator and HFSS simulation results. Single ended quality factor was measured as 12.97 and differential ended quality factor was measured as 15.96 at a maximum operational frequency of 3.65GHz. The single ended and differential inductance was measured as 2.98nH and 2.88nH respectively at this frequency. Based on results a symmetric octagonal inductor design has been recommended to be used for application in RF biosensing. A system design has been proposed based on use of this inductor and principle of inductive sensing using magnetic labeling. / Dissertation/Thesis / M.S. Electrical Engineering 2012

The master thesis describes in its introduction the deductive and inductive approach to teaching English grammar, compares them, and also lists the pros and cons of each approach. The main content of the theoretical part is the presentation of key concepts and principles of inductive teaching of English grammar. The practical part converts theoretical knowledge to real grammar teaching. Pupils learned how to express past events within four lessons. These sample lessons are examples of good practice of teaching grammar inductively. The reflection of these lessons is provided at the end of this part.