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Lighting, and its emergence as a digital and networked medium, represents an ideal platform for conducting research on each sensor and human-derived strategies of management. Notably, strong-state lighting makes doable the management of the intensity, spatial, and coloration attributes of lighting in real-time. This technology offers a superb opportunity to conduct new experiments designed to review how we perceive,
choose, and subsequently management illumination. For instance, given the close to-infinite variation of attainable lighting attributes, how may one design an intuitive control system? Moreover, how can one reconcile the objective nature of sensor-primarily based controls with the subjective impressions of humans? How would possibly this method guide the design of lighting controls and ultimately guide the design of lighting itself? These questions are asked with the benefit of hindsight.
Simple management schemes utilizing sliders, knobs, dials, and motion sensors currently in use fail to anticipate human understanding of the controls and the potential results that changes in illumination can have upon us. On this work, the issue of how humans work together with this new lighting medium is solid as a human-laptop interplay. I describe the design and validation of a natural interface for lighting by abstracting the manifold lighting parameters right into a simpler set of controls. Conceptually, this "simpler set" is predicated on the idea that we're able to discerning the similarities and variations between lighting preparations (scenes). I hypothesize that this natural ordering (a metric space in a latent multidimensional foundation) will be quantitatively extracted and analyzed. First, in a series of controlled experiments, I present how one can derive this mapping and i show, using empirical proof, how future sensor networks will ultimately emulate our subjective impressions of lighting. Second, using data obtained in a consumer-research, I quantitatively derive performance estimates of my proposed lighting consumer interface, and statistically distinction these performance results with those obtained using a traditional interface comprised of sliders and buttons. I reveal that my strategy allows the consumer to achieve their illumination targets while substantially lowering process-time and fatigue.
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