Background
Image fusion, the combination of two or more images with complementary information, is a new and rapidly developing area of research. Ways in which to assess the quality of the output of the image fusion process are therefore becoming more of a necessity (Pohl & van Genderen, 1998). One approach is to use computational metrics, often based on models of the human visual system, to predict how an individual will perceive an image; a framework for such approaches is laid out in the ARSIS (Amélioration de la Résolution Spatiale par Injection de Structures or spatial resolution enhancement by the insertion of structures) concept (Ranchin et al., 2003), and metrics include the Xydeas and Petrovic (2000) edge-detector, and Piella’s (2004) Image Quality Index. However, more simple computational metrics have been found to correlate poorly with subjective ratings of image quality (Eskiciouglu, 2000), whilst more advanced systems come at the cost of greater computational complexity, and might not yield far better results anyway (Ahumada, 1996). It must also be remembered that the end-use of the image will alter how the quality needs to be assessed, as an image that will not be seen by a human, but will be immediately reprocessed, might entail a different theoretical approach to those that will be seen by humans.

Issues
An alternative approach is to apply tasks to the image fusion process that go beyond simply asking ‘how good/nice/pretty does this image look?’ We are applying various tasks to different fused images, and looking to find closer correlations with the metrics, or to try and create new metrics that would be appropriate under these task-dependent circumstances.

Application
Modern military (surveillance) and civilian (medical imaging) applications involve various forms of image fusion, and knowing which fusion scheme would yield faster or more accurate responses in a given scenario could save lives.

References
Ahumada, A. J. (1996). Simplified vision models for image quality assessment. In J. Morreale (Ed.) SID International Symposium Digest of Technical Papers, 27, (pp.397-400), Santa Ana, CA: SID.

Eskiciouglu, A. M. (2000). Quality measurement for monochrome images in the past 25 years. Proceedings of the IEEE International Conference on Acoustics, Speech & Signal Processing (ICASSP), 4, 1907-1910.

Piella, G. (2004). A new quality measure for image fusion. Proceedings of the International Conference on Information Fusion, Stockhom, Sweden, June 28-July 1, 2004.

Pohl, C. & van Genderen, J. L. (1998). Multisensor image fusion in remote sensing: Concepts, methods and applications. International Journal of Remote Sensing, 19(5), 823-854.

Ranchin, T., Aiazzi, B., Alparone, L., Baronti, S., & Wald, L. (2003). Image fusion – The ARSIS concept and some successful implementation schemes. ISPRS Journal of Photogrammetry & Remote Sensing, 58, 4-18.

Xydeas, C. S., & Petrovic, V. S. (2000). Objective image fusion performance measure. Electronic Letters, 36(4), 308-309.

Background
The concept of cognitive style has been widely used in much research. For example, Kolb's (1976) learning style model (Kolb, 1976), Riding’s (1991) two-dimensional model and Witkin's (1967) field dependence/independence model are all well known cognitive style models. However, there are inconsistencies and confusion between the definitions of cognitive style. Thus, it is important to examine the relationship between the commonly used measures of cognitive style in order to determine if they are measuring the same dimensions of cognitive style.

Issues
The hypotheses of our study are: 1) individuals who are field independent will be more likely to be analytics as defined by Riding(1991) and individuals who are field dependent will be more likely to be wholists; 2) individuals who are categorized as imagers by Riding will score high on the concrete experience (CE) dimension of LSI and individuals who are categoried as verbalisers will score high on the abstract conceptualisation (AC) dimension; 3) the active- reflective (AERO) dimension of LSI (Learning Style Inventory; Kolb, 1976) is independent from the other two cognitive style models.

Application
Participants are asked to complete three cognitive style tests in counterbalanced order: 1) Riding’s Cognitive Style Analysis, 2) the Group Embedded Figures Test, and 3) Kolb’s Learning Style Inventory. Relationships between the three tests will be investigated.

References
Kolb, D. A. (1976). Management and learning processes. California Management Review, 18, 21-31.

Riding, R. J. (1991). Cognitive Styles Analysis. Birmingham: Learning and Training Technology.

Witkin, H. A. (1967). A cognitive style approach to cross culture research. International Journal of Psychology, 2, 233-250.

Background
Design is a multidisciplinary activity that affects every aspect of daily life. The earliest stage of design is known as conceptual design during which ideas and concepts are generated and transformed into a “product” model (Zheng, Chan, & Gibson, 2001) that provides a means for evaluating design alternatives (Bernot, Doherty, & Malone, 1990) and allows insights into the design problem (Espinosa, Salomone, & Iribarren, 2004). It is estimated that 80%–90% of design costs are determined during the first 10-20% of the design process (Wood & Agogino, 1996). Thus, conceptual design is a crucial stage, as “a poorly conceived design concept can never be compensated for by a good detailed design” (Xu, Li, & Tang, 2005, p. 2397).

During conceptual design the designer(s) spends considerable time searching through design space for potential solutions (see Stauffer & Ullman, 1991). However, this search is often bounded as a result of the inherent limitations in the designers’ cognitive capabilities, a reliance on the designers’ existing knowledge-base (Williams & Noyes, in press) and the high degree of uncertainty and poor problem definition (Parmee, 2005). Thus, one way to improve the effectiveness and efficiency of the design process and the quality of the end product is to provide support for the designer(s) during the conceptual stage of design. One possible approach to supporting the designer during conceptual design is through the provision of computational tools. Conventional computer-aided design tools focus largely on the later stages of design, at which point the product is physical, tangible, and comprehensible (Parmee, Hall, Miles, Noyes, & Simons, 2006). Because of a number of associated shortcomings, such tools are not suitable for conceptual design (see Williams & Noyes, in press). Thus, it is necessary that any computational tool to support conceptual design must take into consideration the requirements of the designer and the peculiarities of this stage of design.

Issues
The implementation of computational intelligence techniques (i.e. evolutionary and adaptive computing, fuzzy agents, etc.) may support and extend the search and exploration process thereby overcoming the bottleneck resulting from the designer-centred conceptual design process. However, in order to provide a fully interactive and supportive computational environment a number of design issues must be considered concerning the interface (e.g. use of adaptive interface, creativity support software, natural input and ecological output devices) and the impact of such an environment on the design team (i.e. performance, communication, collaboration, etc.).

Thus, working as part of the recently established multi-disciplinary, Institute of People-Centred Computation (IP-CC) the initial aim is to identify and specifically define key issues and requirements for the envisaged computational system, and present potential avenues for further investigation.

Application
Through an understanding of generic issues in a variety of design domains (e.g. drug design, wearable technology, software design, architecture, product design, etc.) it should be possible to develop a generic, user-centred, computational environment that supports designer’s during conceptual design.

References
Bernot, C., Doherty, M. F., & Malone, M. F. (1990). Patterns of composition change in multicomponent batch distillation. Chemical Engineering Science, 45(5), 1207–1221.

Espinosa, J., Salomone, E., & Iribarren, O. (2004). Computer-aided conceptual design of batch distillation systems. Industrial Engineering and Chemical Research, 43, 1723–1733.

Parmee, I. C. (2005). Human-centric computational intelligence strategies for concept exploration and knowledge discover. The Analyst: Journal of the Royal Society of Chemistry, 130(1), 21–34.

Parmee, I. C., Hall, A. E., Miles, J. C., Noyes, J., & Simons, C. (2006). Discover in Design: Developing a people-centred computational approach. In D. Marjanovic (Ed.), Proceedings of the 9th International Design Conference DESIGN 2006 (pp. 595–602). Glasgow: The Design Society.

Stauffer, L. A., & Ullman, D. G. (1991). Fundamental processes of mechanical designers based on empirical data. Journal of Engineering Design, 2(2), 113–125.

Williams, D. J., & Noyes, J. M. (in press). The future of conceptual design: Development of a user-centred computational environment. Ergonomics in Design

Wood, W. H. III, & Agogino, A. M. (1996). Case-based conceptual design information server for concurrent engineering. Computer-Aided Design, 28(5), 361–369.

Xu, L., Li, Z., and Tang, F. (2005). A polychromatic sets approach to the conceptual design of machine tools. International Journal of Production Research, 43(12), 2397-2421.

Zheng, J. M., Chan, K. W., & Gibson, I. (2001). Desktop virtual reality interface for computer-aided conceptual design using geometric techniques. Journal of Engineering Design, 12, 309–329.

Background
The actual performance of humans engaged in search and detection of information is usually embedded in the execution of tasks. Consequently, humans consult the information to determine whether there is relevant information available in order to assist them in realising the task objectives. It is suggested that searchers orient on relevant information embedded in a collection of information with the assistance of relevancy markers. A relevancy marker indicates that the part of the information which follows the marker is relevant according to the originator of the information.

Issues
A first step in this work is to determine which relevancy markers people use to orient on relevant information. We examine this in a variety of presentation formats in which information is often presented. These formats are text, spoken dialogue, images, moving images (films) and sound.

Application
• The cognitive and computational modelling of human search mechanisms in data sets of varying size.
• The development of computational tools to support humans in performing these tasks.
• To create opportunities for improved design of interfaces on data sets.


References
Clark, H. H. (1996). Using Language. New York: Cambridge University Press.

Fox Tree, J. E., & Schrock, J. C. (1999). Discourse markers in spontaneous speech: Oh what a difference an oh makes. Journal of Memory and Language, 40, 280-295.

Garrod, S., & Doherty, G. (1994). Conversation, co-ordination and convention: an empirical investigation of how groups establish linguistic conventions. Cognition, 53, 181-215.

Krauss, R. M., & Fussell, S. R. (1996). Social psychological models of interpersonal communication. In E. T. Higgins & A. W. Kruglanski (Eds.), Social Psychology. Handbook of Basic Principles. New York: Guilford.

Pickering, M. J., & Garrod, S. (2004). Towards a mechanistic psychology of dialogue. Behavioral and Brain Sciences, 27(2), 169-225.

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Background
The arrival of the communication age has brought immediacy to non-verbal communication. Email and chat applications do not have the capability to transmit the full richness of Face to Face (FtF) communication, as many of the verbal and para-verbal cues (such as prosody, facial expressions, gestures and turn taking markers) are not encodable. Most people and companies routinely use emails as a method of communication which range from the highly trivial to the highly important. The ubiquity and the use of these technologies in important decision processes necessitates the study of human behaviour in using computer-mediated communication (CMC).

Issues
The main point of inquiry is establishing what is lost and what is gained in CMC. I am using Stiles' (1978) Verbal Response Modes taxonomy to code linguistic interaction, in order to establish a typology of verbal behaviour in FtF and CMC settings. Simultaneous collection of co-variates such as attitudes towards CMC, age, sex, anonymity, group numbers, person construal and task will enable a multivariate analysis using Structural Equation Modeling to determine the way in which these variables moderate CMC.

Application
A thorough understanding of the strengths and weaknesses of CMC would have a profound benefit on their use in business, governmental and military applications. Organizations will profit from this knowledge, as communication efficiency is increased.

References
Baltes, B.B., Dickson, M.W., Sherman, M.P., Bauer, C.C., & LaGanke, J.S. (2002) Computer-Mediated Communication and Group Decision Making: A Meta Analysis. Organizational Behavior and Human Decision Processes, 87(1), 156-179

Walther, J.B., Boos, M., & Jonas, K.J. (2002) Misattribution and Attributional Redirection in Distributed Virtual Groups. Proceedings of the 35th Hawaii International Conference on System Sciences, 1-10.

Background
A primary role of flight deck auditory alerts is to direct the pilot's attention to a particular system event. In a stage process the pilot must detect and recognise the alert, analyse its meaning in relation to other information presented on the flight deck and aim to resolve any abnormal or unsafe operating conditions (Pritchett, 2001). Consequently, auditory alerts represent a starting cue to tasks that involve both problem solving and decision making.

Issues
Recent research suggests that sound, presented in the background, can have a disruptive effect on certain working memory processes (e.g. delayed serial recall, see Jones, 1999 for a full review). This has strong implications for the flight deck. Once a pilot has detected an alert, and it continues to sound, is it having a detrimental effect on how the alert event is handled? Following a user-centred survey, a series of complex tasks were designed assessing the effects of continuous high intensity (95dBA) alerts on complex verbal and visuo-spatial processing. After the experimental stage, Patterson's (1982) alert detection principles were applied to a dynamic, real-time alert presentation system aimed at alleviating many of the documented concerns (Peryer, Noyes, Pleydell-Pearce, Lieven, 2005).

Application
The project is funded by the Civil Aviation Authority (UK).


References
Jones, D.M. (1999). The cognitive psychology of auditory distraction: The 1997 BPS Broadbent lecture. British Journal of Psychology, 90, 167-187.

Patterson, R.D. (1982). Guidelines for auditory warnings on civil aircraft. CAA Paper 82017. London: Civil Aviation Authority.

Peryer, G., Noyes, J., Pleydell-Pearce, C.W., & Lieven, N. (2005). Auditory alert characteristics: A survey of pilot views. The International Journal of Aviation Psychology, 15(3), 233-250.

Pritchett, A.R. (2001). Reviewing the role of cockpit alerting systems. Human Factors and Aerospace Safety 1(1): 5-39.

Background
My PhD studies, supervised by Dr Jan Noyes and Professor Alan Baddeley, form an investigation into a possible interaction between movement and verbal memory for prose, particularly in relation to expert verbatim learners such as actors. My work looks at the effect of general non-interpretative movement such as walking on participants immediate and delayed verbatim verbal recall of acoustic material. Future work will be in the direction of what specific movements enhance verbal memory most, and how to apply the findings to possible learning situations, particularly for learning disabled groups.

Issues
To be added

Application
To be added


References
To be added

Background
While human error continues to be a contentious issue within the aerospace domain, the reduction of human error incidents on the aircraft flight deck continues to be a key driver in aviation human factors research and has led to the development of numerous cognitive support aids for the aircraft pilot.

Issues
My research is concerned with investigating how predictive information can be developed as a cognitive support tool for use on the aircraft flight deck. The basic premise of this research investigates the viability of presenting predictive information towards an alert, thus relieving the pilot (by reducing working memory strain and cognitive workload) of having to undertake complex mental calculations in order to predict the future state of systems on the flight deck. A prediction of this nature specifically involves extrapolating the current position of an aircraft system to some future point and indicating where the operator will be in the future assuming no action is taken. These extrapolations are based on mathematically derived, computerised generated calculations and thus the predictive information presented to the operator is based on these metrics.

Application
While predictive information has significant benefits on the aircraft flight deck, the applicability of predictive information in domains other than aerospace is also possible. For instance, a predictive aid could be applied to the dashboard on automobiles giving drivers an indication that they will potentially be at a critical fuel level in 'x' amount of time, thus enabling them to take the appropriate action.


References
Rogers, W.H. (1998). Thinking ahead: Using strategic behaviour to avoid errors on the Commercial flight deck. In Proceedings of the 2nd International Workshop On Human Error, Safety, and System Development. Seattle, WA, USA.

Trujillo, A.C. (1996). Airline Transport Preferences for predictive information. Technical Memorandum NASA TM-4702. Hampton, VA: NASA Langley Research Center.

Trujiilo, A.C. (1997). Pilot performance with predictive system status information. In IEEE International Conference on Systems, Man, and Cybernetics: Computational cybernetics and simulation.(pp.73-80). Orlando: USA.

Trujiilo, A.C. (1998). Pilot mental workload with predictive system status Information. Paper presented at the Fourth Symposium on Human Interaction with Complex Systems, Ohio, USA.

Wickens, C.D., & Morphew, E.(1997). Predictive features of a cockpit traffic display: A workload assessment. Technical Report ARL-97-6/NASA-97-3. Savoy, IL: University of Illinois, Institute of Aviation, Aviation Research Lab.