Non-equilibrium thermodynamics theory of econometric source discovery for large data analysis

Bergem, Rutger van; Jenkins, Jeffrey L.; Benachenhou, Dalila; Szu, Harold

doi:10.1117/12.2054927

Cited by 1 publication

(2 citation statements)

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“…TMR-AR enhances imprecise data by transforming physical analog measurements into robust digital information useful for other disciplines [9] [14]. Education and training can certainly benefit from this approach as an interactive learning tool and exploration engine, just as long as the rendering pipeline incorporates models that can match the fidelity required to match the subject matter [10]. If this intuition is true and succeeds in improving realism, then difficult, dangerous, and costly controlled experiments can be investigated in a safe and effective way which promotes new methods of discovery and innovation.…”

Section: -Forming a Scale-invariant Virtual Representationmentioning

confidence: 98%

“…Another application is to see through fog, sand, dust, fire and smoke by mm wave active EOIR optics imaging like 3-D laser/Lidar. These could model a material surface as free electron monopoles accommodation having an optical frequency of 10 15 , much larger than the Debye ion-electron oscillation frequency of 10 10 . Alternatively, as electron-ion dipoles in the lower range of electromagnetic frequency, for a typical Smartphone, 1 GHz at 1 foot wavelength to 5 GHz (Blackberry) 0.2 foot, to collision avoidance radar at 100 GHz at 1% of a foot wavelength to enjoy line of the sight while avoiding rain drops, fog, mist, sand, and dust.…”

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confidence: 99%

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Telescopic multi-resolution augmented reality

2014

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To ensure a self-consistent scaling approximation, the underlying microscopic fluctuation components can naturally influence macroscopic means, which may give rise to emergent observable phenomena. In this paper, we describe a consistent macroscopic (cm-scale), mesoscopic (micron-scale), and microscopic (nano-scale) approach to introduce Telescopic Multi-Resolution (TMR) into current Augmented Reality (AR) visualization technology. We propose to couple TMR-AR by introducing an energy-matter interaction engine framework that is based on known Physics, Biology, Chemistry principles. An immediate payoff of TMR-AR is a self-consistent approximation of the interaction between microscopic observables and their direct effect on the macroscopic system that is driven by real-world measurements. Such an interdisciplinary approach enables us to achieve more than multiple scale, telescopic visualization of real and virtual information but also conducting thought experiments through AR. As a result of the consistency, this framework allows us to explore a large dimensionality parameter space of measured and unmeasured regions. Towards this direction, we explore how to build learnable libraries of biological, physical, and chemical mechanisms. Fusing analytical sensors with TMR-AR libraries provides a robust framework to optimize testing and evaluation through data-driven or virtual synthetic simulations. Visualizing mechanisms of interactions requires identification of observable image features that can indicate the presence of information in multiple spatial and temporal scales of analog data. The AR methodology was originally developed to enhance pilot-training as well as 'make believe' entertainment industries in a user-friendly digital environment We believe TMR-AR can someday help us conduct thought experiments scientifically, to be pedagogically visualized in a zoom-in-and-out, consistent, multi-scale approximations.

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Section: -Forming a Scale-invariant Virtual Representationmentioning

confidence: 98%

mentioning

confidence: 99%