Communication system model for information rate evaluation of differential detection over time-varying channels

A communication system model for mutual information performance analysis of multiple-symbol differential M-phase shift keying over time-correlated, time-varying flat-fading communication channels is developed. This model is a finite-state Markov (FSM) equivalent channel representing the cascade of the differential encoder, FSM channel model and differential decoder. A state-space approach is used to model channel phase time correlations. The equivalent model falls in a class that facilitates the use of the forward backward algorithm, enabling the important information theoretic results to be evaluated. Using such a model, one is able to calculate mutual information for differential detection over time-varying fading channels with an essentially finite time set of correlations, including the Clarke fading channel. Using the equivalent channel, it is proved and corroborated by simulations that multiple-symbol differential detection preserves the channel information capacity when the observation interval approaches infinity.

The airEXODUS evacuation model and its application to evacuation certification, crew training and accident investigation.

Computer based mathematical models describing the aircraft evacuation process have a vital role to play in the design and development of safer aircraft, in the implementation of safer and more rigorous certification criteria, cabin crew training and in post mortuum accident investigation. As the risk of personal injury and costs involved in performing large-scale evacuation experiments for the next generation 'Ultra High Capacity Aircraft' (UHCA) are expected to be high, the development and use of these evacuation modelling tools may become essential if these aircraft are to prove a viable reality. In this paper the capabilities and limitations of the airEXODUS evacuation model are described. Its successful application to the prediction of a recent certification trial, prior to the actual trial taking place, is described. Also described is a newly defined parameter known as OPS which can be used as a measure of evacuation trial optimality. In addition, sample evacuation simulations in the presence of fire atmospheres are described. Finally, the data requiremnets of the airEXODUS evacuation model is discussed along with several projects currently underway at the the Univesity of Greenwich designed to obtain this data. Included in this discussion is a description of the AASK - Aircraft Accident Statistics and Knowledge - data base which contains detailed information from aircraft accident survivors.

Distributed belief revision as applied within a descriptive model of jury deliberations

Belief revision is a well-research topic within AI. We argue that the new model of distributed belief revision as discussed here is suitable for general modelling of judicial decision making, along with extant approach as known from jury research. The new approach to belief revision is of general interest, whenever attitudes to information are to be simulated within a multi-agent environment with agents holding local beliefs yet by interaction with, and influencing, other agents who are deliberating collectively. In the approach proposed, it's the entire group of agents, not an external supervisor, who integrate the different opinions. This is achieved through an election mechanism, The principle of "priority to the incoming information" as known from AI models of belief revision are problematic, when applied to factfinding by a jury. The present approach incorporates a computable model for local belief revision, such that a principle of recoverability is adopted. By this principle, any previously held belief must belong to the current cognitive state if consistent with it. For the purposes of jury simulation such a model calls for refinement. Yet we claim, it constitutes a valid basis for an open system where other AI functionalities (or outer stiumuli) could attempt to handle other aspects of the deliberation which are more specifi to legal narrative, to argumentation in court, and then to the debate among the jurors.

The Jama legal narrative part I: The JAMA Model and narrative interpretation patterns

For the purposes of starting to tackle, within artificial intelligence (AI), the narrative aspects of legal narratives in a criminal evidence perspective, traditional AI models of narrative understanding can arguably supplement extant models of legal narratives from the scholarly literature of law, jury studies, or the semiotics of law. Not only: the literary (or cinematic) models prominent in a given culture impinge, with their poetic conventions, on the way members of the culture make sense of the world. This shows glaringly in the sample narrative from the Continent-the Jama murder, the inquiry, and the public outcry-we analyse in this paper. Apparently in the same racist crime category as the case of Stephen Lawrence's murder (in Greenwich on 22 April 1993) with the ensuing still current controversy in the UK, the Jama case (some 20 years ago) stood apart because of a very unusual element: the eyewitnesses identifying the suspects were a group of football referees and linesmen eating together at a restaurant, and seeing the sleeping man as he was set ablaze in a public park nearby. Professional background as witnesses-cum-factfinders in a mass sport, and public perceptions of their required characteristics, couldn't but feature prominently in the public perception of the case, even more so as the suspects were released by the magistrate conducting the inquiry. There are sides to this case that involve different expected effects in an inquisitorial criminal procedure system from the Continent, where an investigating magistrate leads the inquiry and prepares the prosecution case, as opposed to trial by jury under the Anglo-American adversarial system. In the JAMA prototype, we tried to approach the given case from the coign of vantage of narrative models from AI.

Belief revision as applied within a descriptive model of jury deliberations

Belief revision is a well-researched topic within Artificial Intelligence (AI). We argue that the new model of belief revision as discussed here is suitable for general modelling of judicial decision making, along with the extant approach as known from jury research. The new approach to belief revision is of general interest, whenever attitudes to information are to be simulated within a multi-agent environment with agents holding local beliefs yet by interacting with, and influencing, other agents who are deliberating collectively. The principle of 'priority to the incoming information', as known from AI models of belief revision, is problematic when applied to factfinding by a jury. The present approach incorporates a computable model for local belief revision, such that a principle of recoverability is adopted. By this principle, any previously held belief must belong to the current cognitive state if consistent with it. For the purposes of jury simulation such a model calls for refinement. Yet, we claim, it constitutes a valid basis for an open system where other AI functionalities (or outer stimuli) could attempt to handle other aspects of the deliberation which are more specific to legal narratives, to argumentation in court, and then to the debate among the jurors.

A forest model archive?

The recent history and current trends in the collection and archiving of forest information and models is reviewed. The question is posed as to whether the community of forest modellers ought to take some action in setting up a Forest Model Archive (FMA) as a means of conserving and sharing the heritage of forest models that have been developed over several decades. The paper discusses the various alternatives of what an FMA could be, and should be. It then goes on to formulate a conceptual model as the basis for the construction of a FMA. Finally the question of software architecture is considered. Again there are a number of possible solutions. We discuss the alternatives, some in considerable detail, but leave the final decisions on these issues to the forest modelling community. This paper has spawned the “Greenwich Initiative” on the FMA. An internet discussion group on the topic will be started and launched by the “Trafalar Group”, which will span both IUFRO 4.1 and 4.11, and further discussion is planned to take place at the Forest Modelling Conference in Portugal, June 2002.

Model-based pilot and data power adaptation in psam with periodic delayed feedback

We consider the optimum design of pilot-symbol-assisted modulation (PSAM) schemes with feedback. The received signal is periodically fed back to the transmitter through a noiseless delayed link and the time-varying channel is modeled as a Gauss-Markov process. We optimize a lower bound on the channel capacity which incorporates the PSAM parameters and Kalman-based channel estimation and prediction. The parameters available for the capacity optimization are the data power adaptation strategy, pilot spacing and pilot power ratio, subject to an average power constraint. Compared to the optimized open-loop PSAM (i.e., the case where no feedback is provided from the receiver), our results show that even in the presence of feedback delay, the optimized power adaptation provides higher information rates at low signal-to-noise ratios (SNR) in medium-rate fading channels. However, in fast fading channels, even the presence of modest feedback delay dissipates the advantages of power adaptation.

Hazard and risk assessment from Hurricane Ivan (2004) in Grenada using Geographical Information Systems and remote sensing

Hurricanes are destructive storms with strong winds, intense storm surges, and heavy rainfall. The resulting impact from a hurricane can include structural damage to buildings and infrastructure, flooding, and ultimately loss of human life. This paper seeks to identify the impact of Hurricane Ivan on the aected population of Grenada, one of the Caribbean islands. Hurricane Ivan made landfall on 7th September 2004 and resulted in 80% of the population being adversely aected. The methods that were used to model these impacts involved performing hazard and risk assessments using GIS and remote sensing techniques. Spatial analyses were used to create a hazard and a risk map. Hazards were identied initially as those caused by storm surges, severe winds speeds, and flooding events related to Hurricane Ivan. These estimated hazards were then used to create a risk map. An innovative approach was adopted, including the use of hillshading to assess the damage caused by high wind speeds. This paper explains in detail the methodology used and the results produced.

Mutual information performance of differential MPSK detection over time-varying channels

This paper provides mutual information performance analysis of multiple-symbol differential WSK (M-phase shift keying) over time-correlated, time-varying flat-fading communication channels. A state space approach is used to model time correlation of time varying channel phase. This approach captures the dynamics of time correlated, time-varying channels and enables exploitation of the forward-backward algorithm for mutual information performance analysis. It is shown that the differential decoding implicitly uses a sequence of innovations of the channel process time correlation and this sequence is essentially uncorrelated. It enables utilization of multiple-symbol differential detection, as a form of block-by-block maximum likelihood sequence detection for capacity achieving mutual information performance. It is shown that multiple-symbol differential ML detection of BPSK and QPSK practically achieves the channel information capacity with observation times only on the order of a few symbol intervals

The missing link between information and action: hastenings and delays as universal reactions to performance feedback

This chapter focuses on what the key decision makers in organizations decide after having received information on the current state of the organizational performance. Because of strong attributions to success and failure, it is impossible to predict in advance which concrete actions will occur. We can however find out what kinds of actions are decided upon by means of an organizational learning model that focuses on the hastenings and delays after performance feedback. As an illustration, the responses to performance signals by trainers and club owners in Dutch soccer clubs are analyzed.