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Increasingly functional microscopic machines are poised to have massive technical influence in areas including targeted drug delivery, precise surgical interventions, and environmental remediation. Such functionalities would increase markedly if collections of these microscopic machines were able to coordinate their function to achieve cooperative emergent behaviors. Implementing such coordination, however, requires a scalable strategy for synchronization—a key stumbling block for achieving collective behaviors of multiple autonomous microscopic units. Here, we show that pulse-coupled complementary metal-oxide semiconductor oscillators offer a tangible solution for such scalable synchronization. Specifically, we designed low-power oscillating modules with attached mechanical elements that exchange electronic pulses to advance their neighbor’s phase until the entire system is synchronized with the fastest oscillator or “leader.” We showed that this strategy is amenable to different oscillator connection topologies. The cooperative behaviors were robust to disturbances that scrambled the synchronization. In addition, when connections between oscillators were severed, the resulting subgroups synchronized on their own. This advance opens the door to functionalities in microscopic robot swarms that were once considered out of reach, ranging from autonomously induced fluidic transport to drive chemical reactions to cooperative building of physical structures at the microscale. 

Cognitive radio aims at identifying unused radio-frequency (RF) bands with the goal of re-using them opportunistically for other services. While compressive sensing (CS) has been used to identify strong signals (or interferers) in the RF spectrum from sub-Nyquist measurements, identifying unused frequencies from CS measurements appears to be uncharted territory. In this paper, we propose a novel method for identifying unused RF bands using an algorithm we call least matching pursuit (LMP). We present a sufficient condition for which LMP is guaranteed to identify unused frequency bands and develop an improved algorithm that is inspired by our theoretical result. We perform simulations for a CS-based RF whitespace detection task in order to demonstrate that LMP is able to outperform black-box approaches that build on deep neural networks.