W STRONĘ INNEGO ŚWIATA – CZYLI O POTENCJALE INTERENTU WSZECHRZECZY

Internet-based technologies are moving faster and faster into many spheres of our lives and at the same time are a key component of the ongoing technological revolution, which is why there are many ongoing scientific projects aimed at their development. The article presents a discussion on the development of Internet-based technologies known as the Internet of Everything (IoE). The paper presents the areas in which these technologies are most often used. A multi-layered reference model and a procedure for subsequent actions in designing innovative solutions in this area are presented.Technologie internetowe wkraczają coraz szybciej w liczne sfery naszego życia i jednocześnie stanowią kluczowy komponent trwającej dziś rewolucji technologicznej, dlatego też prowadzonych jest obecnie wiele projektów naukowych ukierunkowanych na ich rozwój. Artykuł przedstawia dyskusję dotyczącą rozwoju technologii internetowych znanych pod nazwą Internetu wszechrzeczy (IoE). W pracy pokazano obszary, w których technologie te znajdują najczęściej zastosowania. Przytoczono wielowarstwowy model referencyjny oraz procedurę kolejnych działań przy projektowaniu nowatorskich rozwiązań w tym zakresie

Energy-Efficient Circuit Designs for Miniaturized Internet of Things and Wireless Neural Recording

Internet of Things (IoT) have become omnipresent over various territories including healthcare, smart building, agriculture, and environmental and industrial monitoring. Today, IoT are getting miniaturized, but at the same time, they are becoming more intelligent along with the explosive growth of machine learning. Not only do IoT sense and collect data and communicate, but they also edge-compute and extract useful information within the small form factor. A main challenge of such miniaturized and intelligent IoT is to operate continuously for long lifetime within its low battery capacity. Energy efficiency of circuits and systems is key to addressing this challenge. This dissertation presents two different energy-efficient circuit designs: a 224pW 260ppm/°C gate-leakage-based timer for wireless sensor nodes (WSNs) for the IoT and an energy-efficient all analog machine learning accelerator with 1.2 µJ/inference of energy consumption for the CIFAR-10 and SVHN datasets. Wireless neural interface is another area that demands miniaturized and energy-efficient circuits and systems for safe long-term monitoring of brain activity. Historically, implantable systems have used wires for data communication and power, increasing risks of tissue damage. Therefore, it has been a long-standing goal to distribute sub-mm-scale true floating and wireless implants throughout the brain and to record single-neuron-level activities. This dissertation presents a 0.19×0.17mm2 0.74µW wireless neural recording IC with near-infrared (NIR) power and data telemetry and a 0.19×0.28mm2 0.57µW light tolerant wireless neural recording IC.PHDElectrical and Computer EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169712/1/jongyup_1.pd