The complexity of modern systems is continuously growing, thus demanding novel powerful development approaches.In this direction, model-driven and component-based software engineering have reached the status of promising paradigms for the development of complex systems. Moreover, in the embedded domain, their combination is believed to be helpful in handling the ever-increasing complexity of such systems.However, in order for them and their combination to definitively break through at industrial level, code generated from models through model transformations should preserve system properties modelled at design level. This research work focuses on aiding the preservation of system properties throughout the entire development process across different abstraction levels. Towards this goal, we provide the possibility of analysing and preserving system properties through a development chain constituted of three steps: (i) generation of code from system models, (ii) execution and analysis of generated code, and (iii) back-propagation of analysis results to system models.With the introduction of steps (ii) and (iii), properties that are hard to predict at modelling level are compared with runtime values and this consequently allows the developer to work exclusively at modelling level thus focusing on optimising system models with the help of those values. / Denna doktorsavhandling presenterar nya och förbättrade tekniker för modelldriven och komponentbaserad utveckling av programvara. Syftet är att bevara systemegenskaper, som specificerats i modeller, genom de olika stadierna av utvecklingen och när modeller översätts mellan olika abstraktionsnivåer och till kod. Vi introducerar möjligheter att studera och bevara systemets egenskaper genom att skapa en kedja i tre steg som: (i) genererar kod från systemmodellen, (ii) exekverar och analyserar den genererade koden och (iii) slutligen återkopplar analysvärden till systemmodellen. Introduktionen av steg (ii) och (iii) gör det möjligt att genomföra en detaljerad analys av egenskaper som är svåra, eller till och med omöjliga, att studera med hjälp av endast systemmodeller. Fördelen med det här tillvägagångssättet är att det förenklar för utvecklaren som slipper arbeta direkt med kod för att ändra systemegenskaper. Istället kan utvecklaren arbeta helt och hållet med modeller och fokusera på optimering av systemmodeller med hjälp av analysvärden från testkörningar av systemet. Vi är övertygade om att denna typ av teknik är nödvändig att utveckla för att stödja modelldriven utveckling av programvara eftersom dagens tekniker inte möjliggör för systemutvecklare att specificera, analysera och optimera systemegenskaper på modellnivå. / La continua crescita in complessitá dei sistemi software moderni porta alla necessitá di definire nuovi e piú efficaci approcci di sviluppo. In questa direzione, metodi basati su modelli (model-driven engineering) e componenti (component-based software engineering) sono stati riconosciuti come promettenti nuove alternative per lo sviluppo di sistemi complessi. Inoltre l'interazione tra loro é ritenuta particolarmente vantaggiosa nella gestione nello sviluppo di sistemi integrati. Affinché questi approcci, cosí come la loro interazione, possano definitivamente prendere piede in campo industriale, il codice generato dai modelli tramite apposite transformazioni deve essere in grado di preservare le proprietá di sistema, sia funzionali che extra-funzionali, definite nei modelli. Il lavoro di ricerca presentato in questa tesi di dottorato si focalizza sul preservamento delle proprietá di sistema nell'intero processo di sviluppo e attraverso i diversi livelli di astrazione. Il risultato principale é rappresentato da un approccio automatico di round-trip engineering in grado di sostenere il preservamento delle proprietá di sistema attraverso: 1) generazione automatica di codice, 2) monitoraggio e analisi dell'esecuzione del codice generate su piattaforme specifiche, e 3) offrendo la possibilitá di propagare verticalmente i risultati da runtime al livello di modellazione. In questo modo, quelle proprietá che possono essere stimate staticamente solo in maniera approssimativa, vengono valutate in rapporto ai valori ottenuti a runtime. Ció permette di ottimizzare il sistema a livello di design attraverso i modelli, piuttosto che manualmente a livello di codice, per assicurare il preservamento degli proprietá di sistema d'interesse.

The subject of this thesis is to develop a temporal neuro-fuzzy system for fore- casting the future behavior of a multivariate time series data. The system has two components combined by means of a system interface. First, a rule extraction method is designed which is named Fuzzy MAR (Multivari- ate Auto-regression). The method produces the temporal relationships between each of the variables and past values of all variables in the multivariate time series system in the form of fuzzy rules. These rules may constitute the rule-base in a fuzzy expert system. Second, a temporal neuro-fuzzy system which is named ANFIS unfolded in - time is designed in order to make the use of fuzzy rules, to provide an environment that keeps temporal relationships between the variables and to forecast the future behavior of data. The rule base of ANFIS unfolded in time contains temporal TSK(Takagi-Sugeno-Kang) fuzzy rules. In the training phase, Back-propagation learning algorithm is used. The system takes the multivariate data and the num- ber of lags needed which are the output of Fuzzy MAR in order to describe a variable and predicts the future behavior. Computer simulations are performed by using synthetic and real multivariate data and a benchmark problem (Gas Furnace Data) used in comparing neuro- fuzzy systems. The tests are performed in order to show how the system efficiently model and forecast the multivariate temporal data. Experimental results show that the proposed model achieves online learning and prediction on temporal data. The results are compared by other neuro-fuzzy systems, specifically ANFIS.

Thesis (Ph. D.)--West Virginia University, 1999. / Title from document title page. Document formatted into pages; contains x, 144 p. : ill. (some col.) Includes abstract. Includes bibliographical references (p. 81-84).

Thesis (Ph. D.)--University of Missouri-Columbia, 1996. / Typescript. Vita. Includes bibliographical references (leaves 187-202). Also available on the Internet.

Thesis (Ph. D.)--University of Missouri-Columbia, 1996. / Typescript. Vita. Includes bibliographical references (leaves 187-202). Also available on the Internet.

Thesis (M.S.)--Florida State University, 2005. / Advisor: Mark H. Weatherspoon, Florida State University, College of Engineering, Dept. of Electrical and Computer Engineering. Title and description from dissertation home page (viewed Jan. 26, 2006). Document formatted into pages; contains x, 107 pages. Includes bibliographical references.

Digital watermarking is a technique for digital multimedia copyright protection. The robustness and the imperceptibility are the main requirements of the watermark. This thesis deals with watermarking audio signals using artificial neural networks. There is described audio watermarking method in the DCT domain. Method is based on human psychoacoustic model and the techniques of neural networks.

University of Macau / Faculty of Science and Technology / Department of Computer and Information Science

No description available.

Digital watermarking is a technique for digital multimedia copyright protection. The robustness and the imperceptibility are the main requirements of the watermark. This thesis deals with watermarking audio signals using artificial neural networks. There is described audio watermarking method in the DCT domain. Method is based on human psychoacoustic model and the techniques of neural networks.