Alexander White

Codrin Tugui, Tirth Thakar, Anatol Gogoj et al.

Machines designed for operation in Space, as well as other extreme environments, need to be both resilient and adaptable when mission parameters change. Soft robots offer advantages in adaptability, but most lack resilience to the pressure and temperature extremes found as close as the Stratosphere. Dielectric elastomer actuators overcome some of those limitations when built as solid state compliant capacitors capable of converting electrical energy into mechanical work, but the elastomer resilience limits the device's operating window. Here we present a crosslinking mechanism for silicone elastomers under ultraviolet light using trimethyl(methylcyclopentadienyl)platinum(IV) as a catalyst to react hydrosilane to vinyl groups. The formation of carbon-carbon bonds enables fast processing under UV light and exceptional electro-mechanical performance in dielectric elastomer actuators. The material resilience advantage is demonstrated in controlled experiments at -40° and 120° C, as well as near vacuum, in comparison with state-of-the-art acrylic and silicone chemistries. Fully autonomous systems controlling grippers made with the novel silicone were integrated into payloads for high altitude balloon testing. Two stratospheric balloon missions were carried out and demonstrated DEAs as a viable soft robotic technology under space-like conditions (as high as 23.6 km elevation, at <0.05 atm and -55° C). The combinations of chemical building blocks and catalyst can be further expanded to address other challenges for silicones, including adhesion and additive manufacturing.

Zuo-Wei Yeh, Chia-Hua Hsu, Alexander White et al.

The inner operations of the human brain as a biological processing system remain largely a mystery. Inspired by the function of the human brain and based on the analysis of simple neural network systems in other species, such as Drosophila, neuromorphic computing systems have attracted considerable interest. In cellular-level connectomics research, we can identify the characteristics of biological neural network, called population, which constitute not only recurrent fullyconnection in network, also an external-stimulus and selfconnection in each neuron. Relying on low data bandwidth of spike transmission in network and input data, Spiking Neural Networks exhibit low-latency and low-power design. In this study, we proposed a configurable population-based digital spiking neuromorphic processor in 180nm process technology with two configurable hierarchy populations. Also, these neurons in the processor can be configured as novel models, integer quadratic integrate-and-fire neuron models, which contain an unsigned 8-bit membrane potential value. The processor can implement intelligent decision making for avoidance in real-time. Moreover, the proposed approach enables the developments of biomimetic neuromorphic system and various low-power, and low-latency inference processing applications.