This thesis presents and justifies a framework for programming real-time signal processing systems. The framework extends the existing "block-diagram" programming model; it has three components: a very high-level textual language, a visual language, and the dataflow process network model of computation. The dataflow process network model, although widely-used, lacks a formal description, and I provide a semantics for it. The formal work leads into a new form of actor. Having established the semantics of dataflow processes, the functional language Haskell is layered above this model, providing powerful features---notably polymorphism, higher-order functions, and algebraic program transformation---absent in block-diagram systems. A visual equivalent notation for Haskell, Visual Haskell, ensures that this power does not exclude the "intuitive" appeal of visual interfaces; with some intelligent layout and suggestive icons, a Visual Haskell program can be made to look very like a block dia...

This thesis presents and justi es a framework for programming real-time signal process-ing systems. The framework extends the existing \block-diagram " programming model� it has three components: a very high-level textual language, a visual language, and the data ow process network model of computation. The data ow process network model, although widely-used, lacks a formal description, and I provide a semantics for it. The formal work leads into a new form of actor. Having established the semantics of data ow processes, the functional language Haskell is layered above this model, providing powerful features|notably polymorphism, higher-order func-tions, and algebraic program transformation|absent in block-diagram systems. A visual equivalent notation for Haskell, Visual Haskell, ensures that this power does not exclude the \intuitive " appeal of visual interfaces � with some intelligent layout and suggestive icons, a Visual Haskell program can be made to look very like ablock diagram program. Finally, the functional language is used to further extend data ow process networks, by simulating timed and dynamically-varying networks.