Philips

About: Philips is a company organization based out in Vantaa, Finland. It is known for research contribution in the topics: Signal & Layer (electronics). The organization has 68260 authors who have published 99663 publications receiving 1882329 citations. The organization is also known as: Koninklijke Philips Electronics N.V. & Royal Philips Electronics.

Chiral nematic order in liquid crystals imposed by an engineered inorganic nanostructure

Abstract: Control over the orientational order of liquid crystals (LCs) is critical to optical switching and display applications. Porous polymer networks have been used to influence the orientation of embedded chiral liquid crystals1, yielding for example reflective displays. Here we show that inorganic films with a porous structure engineered on the submicrometre scale by glancing-angle deposition2,3 can be used to control the orientation of LCs impregnated into the voids. The inorganic material contains helical columns that orient rod-like nematic LCs into a phase similar to a chiral nematic1,4 but with direct control of the local molecular arrangement (for example, the helical pitch) imposed by the inorganic microstructure. We also show that reactive LC molecules in this composite material can be crosslinked by photopolymerization while retaining the imposed structure.

Three-dimensional sound creation assisted by visual information

Abstract: A sound imaging system and method for generating multi-channel audio data from an audio/video signal having an audio component and a video component. The system comprises: a system for associating sound sources within the audio component to video objects within the video component of the audio/video signal; a system for determining position information of each sound source based on a position of the associated video object in the video component; and a system for assigning sound sources to audio channels based on the position information of each sound source.

Global Convergence of Genetic Algorithms: A Markov Chain Analysis

Abstract: In this paper we are trying to make a step towards a concise theory of genetic algorithms (GAs) and simulated annealing (SA). First, we set up an abstract stochastic algorithm for treating combinatorial optimization problems. This algorithm generalizes and unifies genetic algorithms and simulated annealing, such that any GA or SA algorithm at hand is an instance of our abstract algorithm. Secondly, we define the evolution belonging to the abstract algorithm as a Markov chain and find conditions implying that the evolution finds an optimum with probability 1. The results obtained can be applied when designing the components of a genetic algorithm.