A Typing Discipline for Hardware Interfaces

Modern Systems-on-a-Chip (SoC) are constructed by composition of IP (Intellectual Property) Cores with the communication between these IP Cores being governed by well described interaction protocols. However, there is a disconnect between the machine readable specification of these protocols and the verification of their implementation in known hardware description languages. Although tools can be written to address such separation of concerns, the tooling is often hand written and used to check hardware designs a posteriori. We have developed a dependent type-system and proof-of-concept modelling language to reason about the physical structure of hardware interfaces using user provided descriptions. Our type-system provides correct-by-construction guarantees that the interfaces on an IP Core will be well-typed if they adhere to a specified standard

Towards verifying correctness of wireless sensor network applications using Insense and Spin

The design and implementation of wireless sensor network applications often require domain experts, who may lack expertise in software engineering, to produce resource-constrained, concurrent, real-time software without the support of high-level software engineering facilities. The Insense language aims to address this mismatch by allowing the complexities of synchronisation, memory management and event-driven programming to be borne by the language implementation rather than by the programmer. The main contribution of this paper is all initial step towards verifying the correctness of WSN applications with a focus on concurrency. We model part of the synchronisation mechanism of the Insense language implementation using Promela constructs and verify its correctness using SPIN. We demonstrate how a previously published version of the mechanism is shown to be incorrect by SPIN, and give complete verification results for the revised mechanism.Preprin

Deep Learning for Audio Signal Processing

Given the recent surge in developments of deep learning, this article provides a review of the state-of-the-art deep learning techniques for audio signal processing. Speech, music, and environmental sound processing are considered side-by-side, in order to point out similarities and differences between the domains, highlighting general methods, problems, key references, and potential for cross-fertilization between areas. The dominant feature representations (in particular, log-mel spectra and raw waveform) and deep learning models are reviewed, including convolutional neural networks, variants of the long short-term memory architecture, as well as more audio-specific neural network models. Subsequently, prominent deep learning application areas are covered, i.e. audio recognition (automatic speech recognition, music information retrieval, environmental sound detection, localization and tracking) and synthesis and transformation (source separation, audio enhancement, generative models for speech, sound, and music synthesis). Finally, key issues and future questions regarding deep learning applied to audio signal processing are identified.Comment: 15 pages, 2 pdf figure

Diacritic Restoration and the Development of a Part-of-Speech Tagset for the Māori Language

This thesis investigates two fundamental problems in natural language processing: diacritic restoration and part-of-speech tagging. Over the past three decades, statistical approaches to diacritic restoration and part-of-speech tagging have grown in interest as a consequence of the increasing availability of manually annotated training data in major languages such as English and French. However, these approaches are not practical for most minority languages, where appropriate training data is either non-existent or not publically available. Furthermore, before developing a part-of-speech tagging system, a suitable tagset is required for that language. In this thesis, we make the following contributions to bridge this gap: Firstly, we propose a method for diacritic restoration based on naive Bayes classifiers that act at word-level. Classifications are based on a rich set of features, extracted automatically from training data in the form of diacritically marked text. This method requires no additional resources, which makes it language independent. The algorithm was evaluated on one language, namely Māori, and an accuracy exceeding 99% was observed. Secondly, we present our work on creating one of the necessary resources for the development of a part-of-speech tagging system in Māori, that of a suitable tagset. The tagset described was developed in accordance with the EAGLES guidelines for morphosyntactic annotation of corpora, and was the result of in-depth analysis of the Māori grammar

Translating Between Programming Languages Using A Canonical Representation And Attribute Grammar Inversion

Automatic translation between programming languages is an important tool for increasing program reusability. Often the need arises to transport a large software system from one source language environment to another. Performing such a translation by hand is a large undertaking, costly in manpower and very error-prone. For this reason, several researchers have built automated tools to aid them in particular such projects [3, 1]. In this paper we present a new methodology for building source-to-source translators. This methodology involves designing a canonical form to represent programs of all source languages involved, and using attribute grammars (AGs) and automatic AG-inversion to build bidirectional translators between the various source languages and the canonical form. To test the feasibility of these ideas, we have created a system to translate between the C and Pascal programming languages