The aeronautic and aerospace industries are exploring new approached to reduce the mass of cables, bulky electronic systems. This rationally leads onto aircraft weight reduction as well as the amount of CO2 and greenhouse gas emitted by aircrafts. To reduce the mass of cables, merging/embedding different electronic systems in a single chip is an alternative. In this approach, massive electronic modules are miniaturized in a so-called SoC. Different SoCs can be embedded in a single package called SiP.
The aeronautic and aerospace industries are exploring new approached to reduce the mass of cables, bulky electronic systems. This rationally leads onto aircraft weight reduction as well as the amount of CO2 and greenhouse gas emitted by aircrafts. To reduce the mass of cables, power harvesting technique could be utilized. In this approach, the energy needed to power on electronic systems can be harvested from available and reliable sources such as vibration, passenger’s seat heat, data line idle states etc.
The development of test means for aircraft flight control systems for is a complex, multidisciplinary and time consuming task. In this project a Master student will develop a reusable model-based development framework, based on an open source tool and methodology. This model-based systems engineering approach will allow the formalization of the generic aspects of the flight control system test means as well as the variability and specific aspects.
This project targets the design of a highly accurate proximity sensing system that is capable of operating in a wide distance range under wide variations in temperature and for different sensor characteristics. The system is based on passive inductive proximity sensors that can withstand harsh environments, and, therefore, are widely used in avionic applications. Our design methodology consists of implementing a sensor excitation logic and a low-complexity response processing logic in FPGA.
The need for better measurement of emergency response teams has been recognized as one of the key challenges that characterize the field of team work studies. Ideally, when assessing team performance, one should combine information about the nature of the team (e.g., what is the team structure?) and about its members (e.g., is one of the member exhausted?). This challenge is particularly hard to address in the context of emergency response, due to the inherent complexity and dynamism of the domain. For instance, how do you assess team member’s level of fatigue when deployed in the field?
Teams in crisis management operate in uncertainty and time pressure conditions, which severely constrain team performance. Systems capable of detecting critical levels of cognitive functioning could help teams to adapt better to the situation they face by allowing an intelligent re-allocation of tasks across agents. Traditionally, adaptive systems are based on the operator behavioral response (such as performance).
SYnRGY is a computational tool designed to support command and control operations in the context of crisis management. Although SYnRGY has been designed from a user-centered perspective, some degree of training is required to bring novice users up to a level of competence required to use the system. The objective of the current proposal is to capture the expert model of crisis management and design a prototype intelligent tutoring system based on that model. The objective will be achieved in three phases. The purpose of the first phase is to develop a realistic crisis management scenario.