Radio propagation modeling for capacity optimization in wireless relay MIMO systems with partial CSI

The enormous growth of wireless communication systems makes it important to evaluate the capacity of such channels. Multiple Input Multiple Output (MIMO) wireless communication systems are shown to yield significant performance improvement to data rates when compared to the traditional Single Input Single Output (SISO) wireless systems. The benefits of multiple antenna elements at the transmitter and receiver have become necessary to the research and the development of the next generation of mobile communication systems. In this paper we propose the use of Relaying MIMO wireless communication systems for use over long throughput. We investigate how Relays can be used in a "demodulate-and-forward" operation when the transmitter is equipped with spatially correlated multiple antenna elements and the receiver has only partial knowledge of the statistics of the channel. We show that Relays between the source and destination nodes of a wireless communication system in MIMO configuration improve the throughput of the system when compared to the typical MIMO systems, or achieve the desired channel capacity with significantly lower power resources needed.

Deriving explanations from partial temporal information

The representation and manipulation of natural human understanding of temporal phenomena is a fundamental field of study in Computer Science, which aims both to emulate human thinking, and to use the methods of human intelligence to underpin engineering solutions. In particular, in the domain of Artificial Intelligence, temporal knowledge may be uncertain and incomplete due to the unavailability of complete and absolute temporal information. This paper introduces an inferential framework for deriving logical explanations from partial temporal information. Based on a graphical representation which allows expression of both absolute and relative temporal knowledge in incomplete forms, the system can deliver a verdict to the question if a given set of statements is temporally consistent or not, and provide understandable logical explanation of analysis by simplified contradiction and rule based reasoning.

Globalising knowledge

The technology enablers of Friedman’s Flat World have made enormous differences to knowledge creation and sharing. The disaggregation of supply chains has been followed by the partial disaggregation of knowledge supply chains as some knowledge producers set up innovation centres in various locations around the world. But there is considerable evidence that instead of a flat world distribution of knowledge production there are hubs of innovation and knowledge creation developing in a relatively limited number of locations around the world. This paper discusses this clustering effect and looks at some of the possible explanations. In particular it looks at the human and social aspects of knowledge creation and sharing that resist distance and are starting to be taken into account in the design of technological approaches to knowledge management.