Abstract Featherweight Generic Go (FGG) is a minimal core calculus modeling the essential features of the programming language Go. It includes support for overloaded methods, interface types, structural subtyping, and generics. The most straightforward semantic description of the dynamic behavior of FGG programs is to resolve method calls based on runtime type information of the receiver. This article shows a different approach by defining a type-directed translation from ${\textrm{FGG}^{-}}$ to an untyped lambda-calculus. ${\textrm{FGG}^{-}}$ includes all features of FGG but type assertions. The translation of an ${\textrm{FGG}^{-}}$ program provides evidence for the availability of methods as additional dictionary parameters, similar to the dictionary-passing approach known from Haskell type classes. Then, method calls can be resolved by a simple lookup of the method definition in the dictionary. Every program in the image of the translation has the same dynamic semantics as its source ${\textrm{FGG}^{-}}$ program. The proof of this result is based on a syntactic, step-indexed logical relation. The step index ensures a well-founded definition of the relation in the presence of recursive interface types and recursive methods. Although being non-deterministic, the translation is coherent.

Contract monitoring for strict higher-order functional languages has an intuitive meaning, an established theoretical basis, and a standard implementation. For lazy functional languages, the situation is less clear-cut. There is no agreed-upon intended meaning or theory, and there are competing implementations with subtle semantic differences.

The language JavaGI extends Java 1.5 conservatively by a generalized interface mechanism. The generalization subsumes retroactive and type-conditional interface implementations, binary methods, symmetric multiple dispatch, interfaces over families of types, and static interface methods. These features make certain coding patterns redundant, increase the expressiveness of the type system, and permit solutions to extension and integration problems with components in binary form, for which previously several unrelated extensions had been suggested. This article explains JavaGI and motivates its design. Moreover, it formalizes a core calculus for JavaGI and proves type soundness, decidability of typechecking, and determinacy of evaluation. The article also presents the implementation of a JavaGI compiler and an accompanying run-time system. The compiler, based on the Eclipse Compiler for Java, offers mostly modular static typechecking and fully modular code generation. It defers certain well-formedness checks until load time to increase flexibility and to enable full support for dynamic loading. Benchmarks show that the code generated by the compiler offers good performance. Several case studies demonstrate the practical utility of the language and its implementation.

Generalized interfaces are an extension of the interface concept found in object-oriented languages such as Java or C#. The extension is inspired by Haskell's type classes. It supports retroactive and type-conditional interface implementations, binary methods, symmetric multimethods, interfaces over families of types, and static interface methods.

We propose a new paradigm for implementations of audio array processing algorithms on a network of distributed general-purpose computers. In contrast to currently existing DSP processor-based solutions, our approach offers new possibilities for advanced array signal processing by enabling the usage of general-purpose computing platforms with their superior computational and storage resources. We demonstrate that synchronization of sensors is essential for acoustic blind source separation (BSS) algorithms, and we propose a synchronization scheme that enables BSS on distributed, wirelessly networked computers and can easily be implemented on existing hardware.

We propose a universal synchronization scheme for distributed audio-video capture on heterogeneous computing devices such as laptops, tablets, PDAs, cellular phones, audio recorders, and camcorders. These devices typically possess sensors such as microphones and possibly cameras. In order to combine them wirelessly into a distributed sensing and computing system, it is necessary to provide relative time synchronization among the distributed sensors. In this work we propose a setup and an algorithm that provide synchronization between sampling times for a network of distributed multi-channel audio sensors connected to general purpose computing (GPC) platforms. Extensive experimental results on distributed acoustic Blind Source Separation (BSS) algorithms validate the performance of our synchronization scheme.