This study provides insight into the reality of university-industry technology transfer through the assessment of some of the most influential factors for success or failure in research contracts. This widespread mechanism of technology transfer is examined in the light of exhaustive information and experience gathered from thirty interviews with qualified university researchers. The interviewees, who have been directly involved in collaborative projects with industry partners, have deeply described both sound and unsatisfactory cooperation cases, in order to explore which relevant circumstances have led to success or failure. The analysis drives to conclude that there are some features (beyond technological ones) related to the corporate partner's strategic and functional characteristics, which come to be decisive for success. For example, company's real interest and involvement during the technology transfer process, its capacity to assimilate new knowledge and a confident attitude towards the university research group are identified to be key elements for attaining an effective technology transfer. In this contribution, the importance of these aspects is contextualized and summarized in a model for successful technology transfer.

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The use of distributed artificial intelligence (DAI) techniques, particularly the multiagent systems theory, in a decentralized architecture, is proposed to manage cooperatively, all sensor tasks in a network of (air) surveillance radars with capabilities for autonomous operation. At the multisensor data fusion (DF) center, the fusion agent will periodically deliver to sensor agents a list with the system-level tasks that need to be fulfilled. For each system task, indications about its system-level priority are included (inferred global necessity of fulfilling the task) as well as the performance objectives that are required, expressed in different terms depending on the type of task (sector surveillance, target tracking, target identification, etc.). Periodically, the local manager at each sensor (the sensor agent) will decide on the list of sensor-level tasks to be executed by its sensor, providing also the sensor-level priority and performance objectives for each task. The problem of sensor(s)-to-task(s) assignment (including decomposition of systemlevel tasks into sensor-level tasks and translation of system-level performance requirements to sensor-level performance objectives) is the result of a negotiation process performed among sensor agents, initiated with the information sent to them by the fusion agent. With types of agents, a symbolic bottom-up fuzzy reasoning process is performed that considers the available fused or local target tracks, surveillance sectors data, and (external) intelligence information. As a result of these reasoning processes, performed at each agent planning level, the priorities of system-level and sensor-level tasks will be inferred and applied during the negation process. © 2003 Wiley Periodicals, Inc. THE MULTISENSOR MANAGEMENT PROBLEMThe increase in data-collecting capabilities of modern surveillance sensors, either military or civil, that are designed to cope with air tactical environments of continuously higher complexity has placed an enormous burden on system operator(s) (fighter pilot or air traffic/air defence controllers). Consequently, many modern sensors such as multifunction radars, 1 amenable to dynamic scheduling, * Author to whom all correspondence should be addressed: e-mail: molina@ia.uc3m.es. cannot be exploited efficiently without the development of intelligent automatic resource-allocation schemes. Automatic sensor management functionalities are required not only to reduce the human workload (alleviating the need for the operator to specify, based on the displayed information, each task to be performed by the sensor), but also to optimize the sensor-measurement process. Sensor operation parameters should be controlled to yield much faster adaptation to the changing environment and/or to maintain prespecified performance criteria. Thus, the main function of a sensor manager is the effective assignment of the limited sensor resources to sectors and/or existing tracks (targets), according to their individual needs, quality, and/or nature. To do so, instead of ...

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In this proposal, a real time bias estimation system for an airport surveillance data fusion system is presented. This bias estimation system is divided in two main parts. The first part estimates SMR bias terms, taking advantage of the knowledge of the airport map, which is useful because aircraft usually follow the axis of airport taxiways. The other part makes use of SMR corrected measures, which can be assumed to be unbiased. Using them, bias estimators for other important surface surveillance sensors are defined. These estimators are based on processing differences of measurement taken from each sensor and from the SMR. As simulation results show, if the sensor error models are precise enough, both estimations converge to the real bias values, and therefore unbiased measures may be obtained. These unbiased measurements should be provided to the fusion system, in order to enhance tracking performance. These estimation processes do not represent an important computer load increase for the data fusion system. The performance improvement in tracking is also presented.