Detecting and correcting errors in parallel object oriented systems

Our research concerns the development of an operational formalism for the in-source specification of parallel, object oriented systems. These specifications are used to enunciate the behavioural semantics of objects, as a means of enhancing their reliability. A review of object oriented languages concludes that the advance in language sophistication heralded by the object oriented paradigm has, so far, failed to produce a commensurate increase in software reliability. The lack of support in modern object oriented languages for the notion of 'valid object behaviour', as distinct from state and operations, undermines the potential power of the abstraction. Furthermore, it weakens the ability of such languages to detect behavioural problems, manifest at run-time. As a result, in-language facilities for the signalling and handling of undesirable program behaviours or states (for example, assertions) are still in their infancy. This is especially true of parallel systems, where the scope for subtle error is greater. The first goal of this work was to construct an operational model of a general purpose, parallel, object oriented system in order to ascertain the fundamental set of event classes that constitute its observable behaviour. Our model is built on the CSP process calculus and uses a subset of the Z notation to express some aspects of state. This alphabet was then used to construct a formalism designed to augment each object type description with the operational specification of an object's behaviour: Event Pattern Specifications (EPS). EPSs are a labeled list of acceptable object behaviours which form part of the definition of every type. The thesis includes a description of the design and implementation of EPSs as part of an exception handling mechanism for the parallel, object oriented language Solve. Using this implementation, we have established that the run-time checking of EPS specifications is feasible, albeit it with considerable overhead. Issues arising from this implementation are discussed and we describe the visualization of EPSs and their use in semantic browsing

Data bases and data base systems related to NASA's aerospace program. A bibliography with indexes

This bibliography lists 1778 reports, articles, and other documents introduced into the NASA scientific and technical information system, 1975 through 1980

Management. A continuing bibliography with indexes

This bibliography cites 604 reports, articles, and other documents introduced into the NASA scientific and technical information system in 1979 covering the management of research and development, contracts, production, logistics, personnel, safety, reliability and quality control. Program, project, and systems management; management policy, philosophy, tools, and techniques; decision making processes for managers; technology assessment; management of urban problems; and information for managers on Federal resources, expenditures, financing, and budgeting are also covered. Abstracts are provided as well as subject, personal author, and corporate source indexes

Efficient and Robust Methods for Audio and Video Signal Analysis

This thesis presents my research concerning audio and video signal processing and machine learning. Specifically, the topics of my research include computationally efficient classifier compounds, automatic speech recognition (ASR), music dereverberation, video cut point detection and video classification.Computational efficacy of information retrieval based on multiple measurement modalities has been considered in this thesis. Specifically, a cascade processing framework, including a training algorithm to set its parameters has been developed for combining multiple detectors or binary classifiers in computationally efficient way. The developed cascade processing framework has been applied on video information retrieval tasks of video cut point detection and video classification. The results in video classification, compared to others found in the literature, indicate that the developed framework is capable of both accurate and computationally efficient classification. The idea of cascade processing has been additionally adapted for the ASR task. A procedure for combining multiple speech state likelihood estimation methods within an ASR framework in cascaded manner has been developed. The results obtained clearly show that without impairing the transcription accuracy the computational load of ASR can be reduced using the cascaded speech state likelihood estimation process.Additionally, this thesis presents my work on noise robustness of ASR using a nonnegative matrix factorization (NMF) -based approach. Specifically, methods for transformation of sparse NMF-features into speech state likelihoods has been explored. The results reveal that learned transformations from NMF activations to speech state likelihoods provide better ASR transcription accuracy than dictionary label -based transformations. The results, compared to others in a noisy speech recognition -challenge show that NMF-based processing is an efficient strategy for noise robustness in ASR.The thesis also presents my work on audio signal enhancement, specifically, on removing the detrimental effect of reverberation from music audio. In the work, a linear prediction -based dereverberation algorithm, which has originally been developed for speech signal enhancement, was applied for music. The results obtained show that the algorithm performs well in conjunction with music signals and indicate that dynamic compression of music does not impair the dereverberation performance