The performance of the maximum ratio combining method in correlated Rician-fading channels for antenna-diversity signal combining

The performance of the maximum ratio combining method for the combining of antenna-diversity signals in correlated Rician-fading channels is rigorously studied. The distribution function of the normalized signal-to-noise ratio (SNR) is expanded in terms of a power series and calculated numerically. This power series can easily take into account the signal correlations and antenna gains and can be applied to any number of receiving antennas. An application of the method to dual-antenna diversity systems produces useful distribution curves for the normalized SNR which can be used to find the diversity gain. It is revealed that signal correlation in Rician-fading channels helps to increase the diversity gain rather than to decrease it as in the Rayleigh fading channels. It is also shown that with a relative strong direct signal component, the diversity gain can be much higher than that without a direct signal component.

Quantifying the complexity of relative clause and cleft sentences

Explanations of the difficulty of relative-clause sentences implicate complexity but the measurement of complexity remains controversial. Four experiments investigated how far relational complexity (RC) theory, that has been found valid for cognitive development and human reasoning, accounts for the difficulty of 16 types of English, object- and subject-extracted relative-clause constructions. RC corresponds to the number of nouns assigned to thematic roles in the same decision. Complexity estimates based on RC and those based on maximal integration cost (MIC) were strongly correlated and accounted for similar variance in sentence difficulty (subjective ratings, comprehension accuracy, reading times). Consistent with RC theory, sentences that required more than 4 role assignments in the same decision were extremely difficult for many participants. Performance on nonlinguistic relational tasks predicted comprehension of object-extracted sentences, before and after controlling for subject-extractions. Working memory tasks predicted comprehension of object-extractions before controlling for subjectextractions. The studies extend the RC approach to a linguistic domain.

Constructing internationally comparable real income aggregates by combining sparse benchmark data with annual national accounts data: A state-space approach

The importance of availability of comparable real income aggregates and their components to applied economic research is highlighted by the popularity of the Penn World Tables. Any methodology designed to achieve such a task requires the combination of data from several sources. The first is purchasing power parities (PPP) data available from the International Comparisons Project roughly every five years since the 1970s. The second is national level data on a range of variables that explain the behaviour of the ratio of PPP to market exchange rates. The final source of data is the national accounts publications of different countries which include estimates of gross domestic product and various price deflators. In this paper we present a method to construct a consistent panel of comparable real incomes by specifying the problem in state-space form. We present our completed work as well as briefly indicate our work in progress.

Combining influence maps and cellular automata for reactive game agents

Agents make up an important part of game worlds, ranging from the characters and monsters that live in the world to the armies that the player controls. Despite their importance, agents in current games rarely display an awareness of their environment or react appropriately, which severely detracts from the believability of the game. Some games have included agents with a basic awareness of other agents, but they are still unaware of important game events or environmental conditions. This paper presents an agent design we have developed, which combines cellular automata for environmental modeling with influence maps for agent decision-making. The agents were implemented into a 3D game environment we have developed, the EmerGEnT system, and tuned through three experiments. The result is simple, flexible game agents that are able to respond to natural phenomena (e.g. rain or fire), while pursuing a goal.