Wearable technologies provide a non-invasive way to monitor user's activity, identity, and health in real-time, which have attracted tremendous interests from both academia and industry. Due to constraints in form...
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Wearable technologies provide a non-invasive way to monitor user's activity, identity, and health in real-time, which have attracted tremendous interests from both academia and industry. Due to constraints in form factor and power consumption, the sensing capabilities and functionalities of the wearables are usually limited by the available sensors. In the past decade, researchers have committed to realizing the sensing capability of multiple sensors via the signal from one sensor, which expanded the functionalities and sensing domains of traditional sensors. For the first time, we defined such sensing approach as "cross-sensing" and provided a comprehensive review on the cross-sensing towards wearable applications (i.e., human-machine interface, health services, and security). Specifically, this paper summarized the applied signal processing and machine learning algorithms, and discussed how cross-sensing would affect the development and innovation trends of wearable electronics. (C) 2021 Elsevier Inc. All rights reserved.
The widespread adoption of Federated Learning (FL), a privacy-preserving distributed learning methodology, has been impeded by the challenge of high communication overheads, typically arising from the transmission of ...
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Electronic skin with tactile perception enables intelligent robots and prostheses to perform dexterous manipulation and natural interaction with the human and surroundings. However, using single tactile sensing mechan...
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Electronic skin with tactile perception enables intelligent robots and prostheses to perform dexterous manipulation and natural interaction with the human and surroundings. However, using single tactile sensing mechanism to simultaneously percept geometry features and materials properties remains a challenge due to the bottleneck of signal decoupling. Herein, we report the MTSensing system - a wireless and fully-integrated tactile sensing system that can simultaneously recognize materials and textures based on a single flexible triboelectric sensor. The proposed triboelectric sensor converts touch into electrical signals and meanwhile, the signal processing pipeline decouples the signals into macro/micro features and feeds them into the corresponding deep learning models, which simultaneously predict the materials and textures of the contacted objects with the accuracies of 99.07% and 99.32%, respectively. The systematic integration of MTSensing hopes to pave the way for deploying low-cost and scalable electronic skin with multi-functional perceptions.
Federated learning (FL) has attracted tremendous attentions in recent years due to its privacy-preserving measures and great potential in some distributed but privacy-sensitive applications, such as finance and health...
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Federated learning (FL) has attracted tremendous attentions in recent years due to its privacy-preserving measures and great potential in some distributed but privacy-sensitive applications, such as finance and health. However, high communication overloads for transmitting high-dimensional networks and extra security masks remain a bottleneck of FL. This article proposes a communication-efficient FL framework with an Adaptive Quantized Gradient (AQG), which adaptively adjusts the quantization level based on a local gradient's update to fully utilize the heterogeneity of local data distribution for reducing unnecessary transmissions. In addition, client dropout issues are taken into account and an Augmented AQG is developed, which could limit the dropout noise with an appropriate amplification mechanism for transmitted gradients. Theoretical analysis and experiment results show that the proposed AQG leads to 18% to 50% of additional transmission reduction as compared with existing popular methods, including Quantized Gradient Descent (QGD) and Lazily Aggregated Quantized (LAQ) gradient-based methods without deteriorating convergence properties. Experiments with heterogenous data distributions corroborate a more significant transmission reduction compared with independent identical data distributions. The proposed AQG is robust to a client dropping rate up to 90% empirically, and the Augmented AQG manages to further improve the FL system's communication efficiency with the presence of moderate-scale client dropouts commonly seen in practical FL scenarios.
Advancement in human-robot interaction (HRI) is essential for the development of intelligent robots, but there lack paradigms to integrate remote control and tactile sensing for an ideal HRI. In this study, inspired b...
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