Kolo and Nebesko: A Distributed Media Control Framework for the Arts

Mendelowitz, Eitan; Burke, Jeff

doi:10.1109/dfma.2005.33

Cited by 7 publications

(6 citation statements)

References 6 publications

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“…Kolo [56] seeks to address challenges faced by both novice authors and experienced developers in bridging devices for expressive purposes. The Kolo framework is built on a series of simple assumptions: (1) Network access to low latency and high sample rate sensor data (e.g., position information from a localization system) and corresponding control (e.g., of lighting) is baseline functionality for many new artworks; (2) control cannot only be hidden behind abstractions that do not provide access to aesthetically relevant parameters; (3) a useful control interface to most devices used in the expressive domain can be articulated as a collection of named data objects whose primitive data values (numbers, strings, lists, etc.)…”

Section: Kolomentioning

confidence: 99%

“…Nebesko is a high-level scripting language built on Kolo that complements Sylph and commercial multimedia authoring packages, including Max/MSP and Director (see Section 2.1), for which Kolo also has interfaces. Nebesko is an interpreted language based on an augmented finite state machine that allows runtime creation and manipulation of Kolo interconnections [56]. While Kolo already provides interfaces to existing tools used by artists, Director via an 'xtra' and Max/MSP via collection of 'objects', neither of these platforms was designed for authoring expressive works using Percom systems.…”

Section: Nebeskomentioning

confidence: 99%

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Embedding expression: Pervasive computing architecture for art and entertainment

Burke

Friedman

Mendelowitz

et al. 2006

Pervasive and Mobile Computing

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Section: Kolomentioning

confidence: 99%

Section: Nebeskomentioning

confidence: 99%

Embedding expression: Pervasive computing architecture for art and entertainment

Burke

Friedman

Mendelowitz

et al. 2006

Pervasive and Mobile Computing

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“…This project uses a laser rangefinder to identify obstacles in the road, and an on-board camera, which together with facial expression recognition technology, detects for example if the driver falls asleep (Cook et al, 2009). Another example is the UCLA's HyperMedia Studio project, which automatically adjusts the lighting and the sound of a stage performance according with the artists' positions and movements (Mendelowitz & Burke, 2005).…”

Section: Hcimentioning

confidence: 99%

Selection constructive based hyper-heuristic for dynamic scheduling

Gomes

Madureira

Cunha

2015

2015 10th Iberian Conference on Information Systems and Technologies (CISTI)

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ii iii ResumoA função de escalonamento desempenha um papel importante nos sistemas de produção. Os sistemas de escalonamento têm como objetivo gerar um plano de escalonamento que permite gerir de uma forma eficiente um conjunto de tarefas que necessitam de ser executadas no mesmo período de tempo pelos mesmos recursos. Contudo, adaptação dinâmica e otimização é uma necessidade crítica em sistemas de escalonamento, uma vez que as organizações de produção têm uma natureza dinâmica. Nestas organizações ocorrem distúrbios nas condições requisitos de trabalho regularmente e de forma inesperada. Alguns exemplos destes distúrbios são: surgimento de uma nova tarefa, cancelamento de uma tarefa, alteração na data de entrega, entre outros. Estes eventos dinâmicos devem ser tidos em conta, uma vez que podem influenciar o plano criado, tornando-o ineficiente. Portanto, ambientes de produção necessitam de resposta imediata para estes eventos, usando um método de reescalonamento em tempo real, para minimizar o efeito destes eventos dinâmicos no sistema de produção. Deste modo, os sistemas de escalonamento devem de uma forma automática e inteligente, ser capazes de adaptar o plano de escalonamento que a organização está a seguir aos eventos inesperados em tempo real.Esta dissertação aborda o problema de incorporar novas tarefas num plano de escalonamento já existente. Deste modo, é proposta uma abordagem de otimização -Hiper-heurística baseada em Seleção Construtiva para Escalonamento Dinâmico-para lidar com eventos dinâmicos que podem ocorrer num ambiente de produção, a fim de manter o plano de escalonamento, o mais robusto possível. Esta abordagem é inspirada em computação evolutiva e hiper-heurísticas. Do estudo computacional realizado foi possível concluir que o uso da hiper-heurística de seleção construtiva pode ser vantajoso na resolução de problemas de otimização de adaptação dinâmica. Palavras AbstractScheduling plays an important role in manufacturing systems. It produces a scheduling plan, in order to share resources to produce several different products in the same time period. However, dynamic adaptation and optimization is a critical need in real-world manufacturing scheduling systems, since contemporary manufacturing organizations have a dynamic nature, where disturbances on working conditions and requirements occur on a continuous basis. Disturbances often arise unexpectedly, and can be for example: urgent job arrival, job cancelation, due date change, delay in the arrival, among others. These dynamic events must be taken into account, since they may have a major impact on the scheduling plan, they can disorder the plan making it ineffective. Therefore, manufacturing environments require immediate response to these dynamic events, using a real-time rescheduling method, in order to minimize the effect of such unexpected events in the performance of the production' system. As result, scheduling systems should have the ability of automatically and intelligently maintain real-time adaptation and optimization to effi...

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“…UCLA's HyperMedia Studio project [107] adapts light and sound on a performance stage automatically in response to performers' positions and movements. The driver's intent project at MIT [126] recognizes driver's upcoming actions such as passing, turning, stopping, car following, and lane changing by monitoring hand and leg motions.…”

Section: Natural Interfacesmentioning

confidence: 99%