The sensors and microprocessor radios of the Internet of Things require power and one possibility is a hybrid source consisting of energy harvesting coupled with a rechargeable battery. This article concerns a system with a low, but near constant, supply of harvested energy that is sufficient to slowly charge a battery, which buffers that energy and can then supply the higher power level necessary at data transmission. The cycle life of the battery is then significant and this article describes the cycling of a commercial lithium coin cell, Panasonic ML2020, for a small depth of discharge. Initial work was on a Bio-Logic battery tester and cells were successfully tested, without failure, for a few million cycles at a very shallow depth of discharge. Thereafter a battery tester was developed, based on the Particle Photon, that was much less expensive than a channel of a commercial battery tester. Four cells have been cycled, using this tester, for more than 50 million times with no deterioration in performance evident. Capacitors and an NiMH cell are being similarly tested.

EMG-based gesture recognition shows promise for human-machine interaction. Systems are often afflicted by signal and electrode variability which degrades performance over time. We present an end-to-end system combating this variability using a large-area, high-density sensor array and a robust classification algorithm. EMG electrodes are fabricated on a flexible substrate and interfaced to a custom wireless device for 64-channel signal acquisition and streaming. We use brain-inspired high-dimensional (HD) computing for processing EMG features in one-shot learning. The HD algorithm is tolerant to noise and electrode misplacement and can quickly learn from few gestures without gradient descent or back-propagation. We achieve an average classification accuracy of 96.64% for five gestures, with only 7% degradation when training and testing across different days. Our system maintains this accuracy when trained with only three trials of gestures; it also demonstrates comparable accuracy with the state-of-the-art when trained with one trial.

Two digital printing methods for the fabrication of active matrix thin-film transistor (AM-TFT) backplanes for displays are described. A process using printed resists layers, referred to as digital lithography, was used to fabricate arrays of hydrogenated amorphous silicon TFTs. TFTs were also fabricated using a combination of digital lithography to pattern metals and inkjet printing to pattern and deposit a polymeric semiconducting layer. The relative performance of amorphous silicon and polymer TFTs were evaluated. The utility of digital lithographic processing was demonstrated by the fabrication of prototype reflective displays using electrophoretic media.