FedKNOW: Federated Continual Learning with Signature Task Knowledge Integration at Edge

Deep Neural Networks (DNNs) have been ubiquitously adopted in internet of things and are becoming an integral of our daily life. When tackling the evolving learning tasks in real world, such as classifying different types of objects, DNNs face the challenge to continually retrain themselves according to the tasks on different edge devices....

EdgeVisionBench: A Benchmark of Evolving Input Domains for Vision Applications at Edge

Vision applications powered by deep neural networks (DNNs) are widely deployed on edge devices and solve the learning tasks of incoming data streams whose class label and input feature continuously evolve, known as domain shift. Despite its prominent presence in real-world edge scenarios, existing benchmarks used by domain adaptation methods...

Review of Laser Sintering of Nanosilver Pastes for Die Attachment: Technologies and Trends

Nanosilver pastes have been regarded as the most promising die-attach materials for high-temperature and high-power applications due to their advantages such as excellent thermal conductivity, electrical conductivity, high temperature resistance, and good shear strength. However, the common hot pressing sintering process for nanosilver pastes...

Relaxing the control-gain assumptions of DSC design for nonlinear MIMO systems

This work focuses on adaptive neural dynamic surface control (DSC) for an extended class of nonlinear MIMO strict-feedback systems whose control gain functions are continuous and possibly unbounded. The method is based on introducing a compact set which is eventually proved to be an invariant set: thanks to this set, the restrictive...