Fuzzy Soft Shadow in Augmented Reality Systems

Realistic soft shadows in Augmented Reality (AR) is a fascinating topic in computer graphics. Many researchers are involved to have a significant improvement on this demand. In this paper, we have presented a new technique to produce soft shadows using one of the well-known methods in mathematics called Fuzzy Logic. Fuzzy logic is taken into account to generate the realistic soft shadows in AR. The wide light source is split into some parts that each of them plays the rule of a single light source. The desired soft shadow is generated by splitting the wide light source into multiple parts and considering each part as a single light source. The method which we called Fuzzy Soft Shadow is employed in AR to enhance the quality of semi-soft shadows and soft shadows

SciTech News Volume 71, No. 1 (2017)

Columns and Reports From the Editor 3 Division News Science-Technology Division 5 Chemistry Division 8 Engineering Division Aerospace Section of the Engineering Division 9 Architecture, Building Engineering, Construction and Design Section of the Engineering Division 11 Reviews Sci-Tech Book News Reviews 12 Advertisements IEEE

An Introduction to Ontology

Analytical philosophy of the last one hundred years has been heavily influenced by a doctrine to the effect that one can arrive at a correct ontology by paying attention to certain superficial (syntactic) features of first-order predicate logic as conceived by Frege and Russell. More specifically, it is a doctrine to the effect that the key to the ontological structure of reality is captured syntactically in the ‘Fa’ (or, in more sophisticated versions, in the ‘Rab’) of first-order logic, where ‘F’ stands for what is general in reality and ‘a’ for what is individual. Hence “f(a)ntology”. Because predicate logic has exactly two syntactically different kinds of referring expressions—‘F’, ‘G’, ‘R’, etc., and ‘a’, ‘b’, ‘c’, etc.—so reality must consist of exactly two correspondingly different kinds of entity: the general (properties, concepts) and the particular (things, objects), the relation between these two kinds of entity being revealed in the predicate-argument structure of atomic formulas in first-order logic

Demonstration of PIV in a Transonic Compressor

Particle Imaging Velocimetry (PIV) is a powerful measurement technique which can be used as an alternative or complementary approach to Laser Doppler Velocimetry (LDV) in a wide range of research applications. PIV data are measured simultaneously at multiple points in space, which enables the investigation of the non-stationary spatial structures typically encountered in turbomachinery. Many of the same issues encountered in the application of LDV techniques to rotating machinery apply in the application of PIV. Preliminary results from the successful application of the standard 2-D PIV technique to a transonic axial compressor are presented. The lessons learned from the application of the 2-D PIV technique will serve as the basis for applying 3-component PIV techniques to turbomachinery

Decision-making and problem-solving methods in automation technology

The state of the art in the automation of decision making and problem solving is reviewed. The information upon which the report is based was derived from literature searches, visits to university and government laboratories performing basic research in the area, and a 1980 Langley Research Center sponsored conferences on the subject. It is the contention of the authors that the technology in this area is being generated by research primarily in the three disciplines of Artificial Intelligence, Control Theory, and Operations Research. Under the assumption that the state of the art in decision making and problem solving is reflected in the problems being solved, specific problems and methods of their solution are often discussed to elucidate particular aspects of the subject. Synopses of the following major topic areas comprise most of the report: (1) detection and recognition; (2) planning; and scheduling; (3) learning; (4) theorem proving; (5) distributed systems; (6) knowledge bases; (7) search; (8) heuristics; and (9) evolutionary programming