How do we see the motion of objects as well as their shapes? The Gaussian Derivative (GD) spatial model is extended to time to help answer this question. The GD spatio-temporal model requires only two numbers to describe the complete three-dimensional space-time shapes of individual receptive fields in primate visual cortex. These two numbers are the derivative numbers along the respective spatial and temporal principal axes of a given receptive field. Nine transformation parameters allow for a standard geometric association of these intrinsic axes with the extrinsic environment. The GD spatio-temporal model describes in one framework the following properties of primate simple cell fields: motion properties, number of lobes in space-time, spatial orientation. location, and size. A discrete difference-of-offset-Gaussians (DOOG) model provides a plausible physiological mechanism to form GD-like model fields in both space and time. The GD model hypothesizes that receptive fields at the first stage of processing in the visual cortex approximate 'derivative analyzers' that estimate local spatial and temporal derivatives of the intensity profile in the visual environment. The receptive fields as modeled provide operators that can allow later stages of processing in either a biological or machine vision system to estimate the motion as well as the shapes of objects in the environment.
Grumman Data SystemsP a t h p l a n n e r s f o r manipulator arms i n e v i t a b l y u s e some form of s e a r c h , such as A*, o f t e n c a r r i e d o u t i n c o n f i g u r a t i o n space. These s e a r c h methods are u s u a l l y very g e n e r a l , i n t h a t t h e y would work reg a r d l e s s of t h e s i z e s and shapes of t h e f o r b i d d e n r e g i o n s . But f o r b i d d e n r e g i o n s i n c o n f i g u r a t i o n s p a c e f o r a p a r t i c u l a r arm are n o t p e r f e c t l y a r b it r a r y .
This paper presents the methodology of Digital Panel Assembly (DPA), a computer aided approach in automotive body panel assembly. Core to the methodology is special-purpose finite element software, EAVS, that can simulate the panel assembly processes and predict assembly dimensions by taking into considerations of the compliant nature of panels and sub-assemblies. To validate the methodology, a non-contact EOIS optical scanning procedure for panel measurement is established. The validation study shows that the reported methodology can accurately predict the 1st level panel sub-assemblies. Finally, the resource requirements, functional capabilities, and scalability of the digital panel assembly methodology towards a complete Body-in-White implementation are discussed.
The glass phase in the bodies studied was more sensitive to variations in heat treatment than were the physical properties. Increased rates of heating caused less glass and mullite formation, less quartz corrosion, more pores and blebs, and increased heterogeneity of the glassy matrix. Microstructure and physical properties indicated an optimum heating rate between 50" and 90" per hour. Soaking increased the amount of glass, more so after slow than after rapid heating. Soaking after rapid heating did not reduce the number of blebs. Soaking too long and at too high temperatures made blebs more numerous.The optimum heating rate and minimum soaking time governed the shortest heating schedule productive of the best microstructure and physical properties.A minimum soaking time of one hour seemed desirable.
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