The decrease in the cost of sensors during the last years, and the arrival of the 5th generation of mobile technology will greatly benefit Internet of Things (IoT) innovation. Accordingly, the use of IoT in new agronomic practices might be a vital part for improving soil quality, optimising water usage, or improving the environment. Nonetheless, the implementation of IoT systems to foster environmental awareness in educational settings is still unexplored. This work addresses the educational need to train students on how to design complex sensor-based IoT ecosystems. Hence, a Project-Based-Learning approach is followed to explore multidisciplinary learning processes implementing IoT systems that varied in the sensors, actuators, microcontrollers, plants, soils and irrigation system they used. Three different types of planters were implemented, namely, hydroponic system, vertical garden, and rectangular planters. This work presents three key contributions that might help to improve teaching and learning processes. First, a holistic architecture describing how IoT ecosystems can be implemented in higher education settings is presented. Second, the results of an evaluation exploring teamwork performance in multidisciplinary groups is reported. Third, alternative initiatives to promote environmental awareness in educational contexts (based on the lessons learned) are suggested. The results of the evaluation show that multidisciplinary work including students from different expertise areas is highly beneficial for learning as well as on the perception of quality of the work obtained by the whole group. These conclusions rekindle the need to encourage work in multidisciplinary teams to train engineers for Industry 4.0 in Higher Education.
In this paper the data precision behavior of Adaptive Filters is studied when implemented in fixed point. The performance evaluation has been tested under two parameters: the number of bits used for the integer and fractional parts in fixed point format and two's complement arithmetic and the preprocessing order of data speech. To validate our results we have evaluated the Relative Mean Square Error between the results obtained using the standard 32 bits floating point and different fixed point formats. The input data being used for testing have been speech sounds in Spanish for Speech Recognition purposes [6]. The obtained results allow us to optimally dimension an arithmetic unit in fixed-point formats than can be quickly implemented by high-level synthesis tools. The results from this work can also be used for implementing the pre-processing stage for begin-end word detection.
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