This paper addresses anomaly detection and monitoring for swarm drone flights. While the current practice of swarm flight typically relies on the operator's naked eyes to monitor health of the multiple vehicles, this work proposes a machine learning-based framework to enable detection of abnormal behavior of a large number of flying drones on the fly. The method works in two steps: a sequence of two unsupervised learning procedures reduces the dimensionality of the real flight test data and labels them as normal and abnormal cases; then, a deep neural network classifier with one-dimensional convolution layers followed by fully connected multi-layer perceptron extracts the associated features and distinguishes the anomaly from normal conditions. The proposed anomaly detection scheme is validated on the real flight test data, highlighting its capability of online implementation.

Epitaxy of single-crystalline materials laid the foundation for numerous electronics as a core technology. Nevertheless, because the single-crystalline epilayers are covalently bonded to substrates, only limited applications were explored. The recent development of layer transfer techniques has suggested another way involving the production of freestanding membranes that are free from substrates. In this review, we comprehensively catalog recent advances for freestanding-membrane-based electronics from transistors and memory storage to sensors and energy harvesters. We highlight their unique advantages, including flexibility, unique physical coupling, heterointegrability, and costeffectiveness, and summarize their challenges and perspectives.