Implantable medical devices (IMDs) such as cardiac pacemakers, cochlear implants, and neurostimulators improve millions of people’s lives every day. Over the years, IMDs have significantly improved in function, increased in lifetime, and decreased in size. However, further miniaturisation is limited by the batteries used to power these devices, and therefore methods to power them wirelessly are gaining in popularity. The technique most used to wirelessly power IMDs is electromagnetic energy transfer. However, at low frequencies, this method is limited to shallow implants due to its low directivity, while at high frequencies it is limited by attenuation in the human body. Therefore, ultrasound energy may be the future of wirelessly powering implants. Its high directivity and low attenuation in the human body opens up the possibility to wirelessly power deep implants without any harmful side effects. An ultrasound energy transfer set-up was established in this work to demonstrate that Philips’ capacitive micromachined ultrasound transducers (CMUTs) can be used as ultrasound energy harvesters. Normally, CMUTs need a bias voltage to function as an energy harvester. However, the CMUTs in this work are chargeable and therefore eliminate the need for this bias voltage. The results show that charged CMUTs can indeed be used as efficient energy harvesters of ultrasound and that information can be sent back to the ultrasound probe using echo modulation. The charged CMUTs showed to be able to harvest sufficient energy to stimulate a nerve and power a Bluetooth module, even when ultrasound sent through 90mm of biological (pork) tissue. The maximum power harvested during the ultrasound energy transfer experiments was 250mW over a transferring distance of 75mm.

In this thesis five different Direction Of Arrival algorithms will be developed for use with Microflown's Acoustic Vector Sensors, which will determine the direction an acoustic signal originates from. These algorithms will run on a main-station that will remotely receive data from an array of AVSs. The performance of these algorithms is examined using simulations and measurements obtained by a test setup. Finally, it is discussed under what circumstances which algorithm delivers the best performance.