Scalable Approach to Uncertainty Quantification and Robust Design of Interconnected Dynamical Systems

Development of robust dynamical systems and networks such as autonomous aircraft systems capable of accomplishing complex missions faces challenges due to the dynamically evolving uncertainties coming from model uncertainties, necessity to operate in a hostile cluttered urban environment, and the distributed and dynamic nature of the communication and computation resources. Model-based robust design is difficult because of the complexity of the hybrid dynamic models including continuous vehicle dynamics, the discrete models of computations and communications, and the size of the problem. We will overview recent advances in methodology and tools to model, analyze, and design robust autonomous aerospace systems operating in uncertain environment, with stress on efficient uncertainty quantification and robust design using the case studies of the mission including model-based target tracking and search, and trajectory planning in uncertain urban environment. To show that the methodology is generally applicable to uncertain dynamical systems, we will also show examples of application of the new methods to efficient uncertainty quantification of energy usage in buildings, and stability assessment of interconnected power networks

Deep Learning Techniques for Music Generation -- A Survey

This paper is a survey and an analysis of different ways of using deep learning (deep artificial neural networks) to generate musical content. We propose a methodology based on five dimensions for our analysis: Objective - What musical content is to be generated? Examples are: melody, polyphony, accompaniment or counterpoint. - For what destination and for what use? To be performed by a human(s) (in the case of a musical score), or by a machine (in the case of an audio file). Representation - What are the concepts to be manipulated? Examples are: waveform, spectrogram, note, chord, meter and beat. - What format is to be used? Examples are: MIDI, piano roll or text. - How will the representation be encoded? Examples are: scalar, one-hot or many-hot. Architecture - What type(s) of deep neural network is (are) to be used? Examples are: feedforward network, recurrent network, autoencoder or generative adversarial networks. Challenge - What are the limitations and open challenges? Examples are: variability, interactivity and creativity. Strategy - How do we model and control the process of generation? Examples are: single-step feedforward, iterative feedforward, sampling or input manipulation. For each dimension, we conduct a comparative analysis of various models and techniques and we propose some tentative multidimensional typology. This typology is bottom-up, based on the analysis of many existing deep-learning based systems for music generation selected from the relevant literature. These systems are described and are used to exemplify the various choices of objective, representation, architecture, challenge and strategy. The last section includes some discussion and some prospects.Comment: 209 pages. This paper is a simplified version of the book: J.-P. Briot, G. Hadjeres and F.-D. Pachet, Deep Learning Techniques for Music Generation, Computational Synthesis and Creative Systems, Springer, 201

Metodologia Per la Caratterizzazione di amplificatori a basso rumore per UMTS

In questo lavoro si presenta una metodologia di progettazione elettronica a livello di sistema, affrontando il problema della caratterizzazione dello spazio di progetto dell' amplificatore a basso rumore costituente il primo stadio di un front end a conversione diretta per UMTS realizzato in tecnologia CMOS con lunghezza di canale .18u. La metodologia è sviluppata al fine di valutare in modo quantititativo le specifiche ottime di sistema per il front-end stesso e si basa sul concetto di Piattaforma Analogica, che prevede la costruzione di un modello di prestazioni per il blocco analogico basato su campionamento statistico di indici di prestazioni del blocco stesso, misurati tramite simulazione di dimensionamenti dei componenti attivi e passivi soddisfacenti un set di equazioni specifico della topologia circuitale. Gli indici di prestazioni vengono successivamente ulizzati per parametrizzare modelli comportamentali utilizzati nelle fasi di ottimizzazione a livello di sistema. Modelli comportamentali atti a rappresentare i sistemi RF sono stati pertanto studiati per ottimizzare la scelta delle metriche di prestazioni. L'ottimizzazione dei set di equazioni atti a selezionare le configurazione di interesse per il campionamento ha al tempo stesso richiesto l'approfondimento dei modelli di dispositivi attivi validi in tutte le regioni di funzionamento, e lo studio dettagliato della progettazione degli amplificatori a basso rumore basati su degenerazione induttiva. Inoltre, il problema della modellizzazione a livello di sistema degli effetti della comunicazione tra LNA e Mixer è stato affrontato proponendo e analizzando diverse soluzioni. Il lavoro ha permesso di condurre un'ottimizzazione del front-end UMTS, giungendo a specifiche ottime a livello di sistema per l'amplificatore stesso

Custom Integrated Circuits

Contains reports on four research projects.U.S. Air Force - Office of Scientific Research (Contract F49620-81-C-0054)U.S. Air Force - Office of Scientific Research (Contract F49620-84-C-0004)National Science Foundation (Grant ECS81-18160)National Science Foundation (Grant ECS83-10941

Working Notes from the 1992 AAAI Workshop on Automating Software Design. Theme: Domain Specific Software Design

The goal of this workshop is to identify different architectural approaches to building domain-specific software design systems and to explore issues unique to domain-specific (vs. general-purpose) software design. Some general issues that cut across the particular software design domain include: (1) knowledge representation, acquisition, and maintenance; (2) specialized software design techniques; and (3) user interaction and user interface

Measurements, Models, Systems and Design

531 s.