RAM Air and Wind Energy harvesting survey for Electrical Vehicles and transportation
This paper presents a survey on how the implementation of RAM Air/Wind Energy has the potential to become a feasible energy source by extending the range traveled for electric cars, Unmanned Aerial Vehicles (UAVs), trucks, and rail transports. Through the concepts of the Venturi Effect, the Bernoulli Principle, and the application of RAT in the aviation system, the main goal will be achieved. In due course, RAM Air/Wind Energy will bring about the future of electrical transportation.
UAV-Deployed Impulsive Source Localization with Sensor Network
The unmanned aerial vehicle (UAV) is a light weight flight system that can carry sensors and cameras for data collection. Tremendous excitement surrounds the use of UAVs because they can be deployed easily and rapidly for data collection; they also can be programmed to execute missions with high degrees of autonomy. For these reasons, UAVs hold promise in accelerating the collection of data in geophysical explorations. In this study, a UAV platform is explored for the collection of data from geophones deployed to measure the vibrations of a concrete slab foundation. The UAV is designed to drop a weight as a controlled energy source. Both the energy and location of the impulsive load are adjustable by the flight parameters of the UAV. The study adopts a time-domain analysis for source localization using the dense array of geophones
Fully Integrated Bidirectional CMOS-MEMS Flow Sensor With Low Power Pulse Operation
Leveraging More-than-Moore technology, we demonstrate an integrated CMOS MEMS flow sensor via a very compact system on chip (SoC) that can sense the bidirectional N2 gas flow. Our SoC features a very low-noise instrumentation amplifier that is implemented on a CMOS wafer and serves as a readout integrated circuit for a thermoresistive micro-calorimetric flow sensor which is fabricated on a MEMS wafer. A compact heterogeneous integration is achieved by combining the two wafers via the proprietary InvenSense AlN process. The measured sensitivity of our CMOS MEMS flow sensor is 98 mV/sccm which is three times better than the state-of-the-art counterpart. The flow range measured by our SoC is from −26 to 26 m/s (from −50 to 50 sccm). Moreover, the pulsed heater operation makes this proposed SoC flow sensor a very low power (<9 mW) and, thus, a promising candidate for the Internet-of-Things applications in smart home/green buildings.