Community-Aware Graph Signal Processing

The emerging field of graph signal processing (GSP) allows to transpose classical signal processing operations (e.g., filtering) to signals on graphs. The GSP framework is generally built upon the graph Laplacian, which plays a crucial role to study graph properties and measure graph signal smoothness. Here instead, we propose the graph modularity matrix as the centerpiece of GSP, in order to incorporate knowledge about graph community structure when processing signals on the graph, but without the need for community detection. We study this approach in several generic settings such as filtering, optimal sampling and reconstruction, surrogate data generation, and denoising. Feasibility is illustrated by a small-scale example and a transportation network dataset, as well as one application in human neuroimaging where community-aware GSP reveals relationships between behavior and brain features that are not shown by Laplacian-based GSP. This work demonstrates how concepts from network science can lead to new meaningful operations on graph signals.Comment: 21 pages, 4 figures, Accepted to Signal Processing Magazine: Special Issue on Graph Signal Processing: Foundations and Emerging Direction

Innovation design engineering: non-linear progressive education for diverse intakes

This paper discusses the non-linear progressive educational techniques developed and adopted by the Innovation Design Engineering (IDE) masters degree at the Royal College of Art and Imperial College, London. In particular a focus is applied to the development of creative processes for diverse intakes without recourse to overt systems presentation. Innovation design engineering is viewed as a cutting-edge product design, experimentation and enterprise discipline with applicants drawn from three areas including engineering, industrial design and other art, design and business disciplines. The co-education of such a diverse intake requires careful balancing of an academic programme to ensure that all parties are stimulated and enabled to expand their knowledge and skills base while also contributing to a communal environment via team-based activities. Designers work at the centre of complex, demanding projects, juggling creatively in teams, to generate great ideas, designs and successful products. In order to achieve such goals it is critical for students to attain high levels of self reflection, social networking, work-collaboration and interdisciplinarity. This is achieved by surrounding the students with experts and leaders in their fields to support them in their design ventures. Through reflection and theorising, a conceptual base for educating innovative design engineers is explored. One of the techniques described provided evidence to suggest running a design enterprise strand in the programme, a proposal that has now been implemented. Students elect from three learning strands: experimental design; design for manufacture; and design enterprise. The design enterprise strand addresses product, idea and service launching, finance, marketing, commercialisation, designing service support infrastructures and establishing production and supplier relationships. Design for manufacture is the traditional core industrial design activity associated with advanced manufacturing, new markets, user centred design, aesthetics and technology innovation. Experimental design is a research driven rigorous approach to developing fundamental new industrial concepts, paradigms, technologies, designs and insights. The strands reflect the expanding scope of industrial design and hint at the generation of new sub-disciplines