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Whereas research in MicroSystems Technology (MST) has grown rapidly over the last twenty years, the education of engineers at postgraduate level is still in its infancy. The number of dedicated MST research institutions, companies and university laboratories worldwide has grown at a dramatic rate and all require trained individuals who can be immediately operational in the workplace. With the possible exceptions of Germany and Switzerland, the authors of this article believe that the training requirements for such scientists, engineers and technicians have not been satisfactorily addressed as yet. Realising the need for adequate training in MST, the British funding agency for research in science and engineering, the EPSRC (Engineering and Physical Sciences Research Council) awarded a substantial grant to Heriot-Watt University in order to set up a Masters Training Package (MTP). The context of this grant, the syllabus developed for this Master of Science as well as its implementation are presented in this article. Advantages and drawbacks of block-based delivery of the course are presented from a pedagogical point of view for the different categories of students (full-time students, part-time students and company delegates). (Contains 5 figures.)

Agroforestry can be defined as an agroecosystem whereby soil is used holistically and synergistically by various stakeholders including farmers, livestock, and plants. As such, agroforestry offersnumerous benefits that include conservation of biodiversity, regulation of pests and diseases, increased quality of soil, air and water, efficient cycling of nutrients, and resilience to climate change. Review of published studies in agroforestry shows however that research in this area could benefit from increased real-time, spatial and temporal measurements. This situation is to be contrasted with that of precision agriculture in monocultures and precision livestock farming where progress made in sensor systems has attracted considerable research interest. It is advocated in this review article that wireless sensor networks could also significantly impact agroforestry through the monitoring of the local real-time interactions that occur between the various components constituting agroforestry systems. This review article proposes therefore the new field of data-driven agroforestry which lies at the intersection of precision agriculture, precision livestock farming, permaculture, and agroforestry. Data-driven agroforestry has the potential to not only help farmers harness the interactions between the different components of an agroforestry system to their advantage but also shine light on fundamental interactions between soil, plants, trees, and livestock while offering a sustainable agricultural method beneficial to all agroforestry stakeholders. [ABSTRACT FROM AUTHOR]

Most mobile phones today have capacitive micro-electro-mechanical systems (MEMSs) microphones that use either single or dual diaphragms. Methods to detect failures easily and non-invasively have become of critical importance for microphones mobile phone manufacturers as a basis for built-in self-test (BIST) and self-repair (BISR) strategies. In that regard, a four-layer framework is presented that includes lumped element modeling (LEM), failure mode simulation, failure mode discrimination, and recovery. The frequency response of the microphone is taken as the main output to analyze. To experimentally validate this framework, this article provides a failure mode induction method based on bias voltage sweeping and four new techniques, based solely on acoustic measurements to discriminate the states of electrostatic capture for single diaphragm capacitive MEMS microphones. These include 1) analysis of an acoustic signature that is unique to electrostatic capture based on cosine similarity analysis; 2) −3 dB point measurement; 3) +3 dB point measurement; and 4) cluster analysis. Measurement of pull-in voltage and snapback voltage ranges is further demonstrated based on sensitivity measurements in laboratory conditions and response magnitude and noise power measurements in non-laboratory conditions. Up to 100% success rate in detecting electrostatic capture of diaphragm is reported for this type of device. [ABSTRACT FROM AUTHOR]