Recently, wearable devices have become increasingly popular in our lives because of their neat features and stylish appearance. However, their tiny sizes bring about new challenges to human-device interaction such as texts input. Although some novel methods have been put forward, they possess different defects and are not applicable to deal with the problem. As a result, we propose an acoustic-based texts-entry system, i.e., EchoWrite, by which texts can be entered with a finger writing in the air without wearing any additional device. More importantly, different from many previous works, EchoWrite runs in a training-free style which reduces the training overhead and improves system scalability. We implement EchoWrite with commercial devices and conduct comprehensive experiments to evaluate its texts-entry performance. Experimental results show that EchoWrite enables users to enter texts at a speed of 7.5 WPM without practice, and 16.6 WPM after about 30-minute practice. This speed is better than touch screen-based method on smartwatches, and comparable with previous related works. Moreover, EchoWrite provides favorable user experience of entering texts.

This paper presents the design and implementation of mmRipple, which empowers commodity mmWave radars with the communication capability through smartphone vibrations. In mmRipple, a smartphone (transmitter) sends messages by modulating smartphone vibrations, while a mmWave radar (receiver) receives the messages by detecting and decoding the smartphone vibrations with mmWave signals. By doing so, a smartphone user can not only be passively sensed by a mmWave radar, but also actively send messages to the radar using her smartphone without any hardware modifications to either the smartphone or the mmWave radar. mmRipple addresses a series of unique technical challenges, including vibration signal generation, tiny vibration sensing, multiple object separation, and movement interference mitigation. We implement and evaluate mmRipple using commodity mmWave radars and smartphones in different practical conditions. Experimental results show that mmRipple achieves an average vibration pattern recognition accuracy of 98.60% within a 2m communication range, and 97.74% within 3m on 11 different types of smartphones. The communication range can be further extended up to 5m with an accuracy of 91.67% with line-of-sight path. To our best knowledge, mmRipple is the first work that allows smartphones to send data to COTS mmWave radars via smartphone vibrations and will enable many new applications such as vibration-based near field communication and pedestrian-to-sensing-infrastructure communication.

Federated Learning (FL) facilitates collaborative training of a shared global model without exposing clients' private data. In practical FL systems, clients (e.g., edge servers, smartphones, and wearables) typically have disparate system resources. Conventional FL, however, adopts a one-size-fits-all solution, where a homogeneous large global model is transmitted to and trained on each client, resulting in an overwhelming workload for less capable clients and starvation for other clients. To address this issue, we propose FedConv, a client-friendly FL framework, which minimizes the computation and memory burden on resource-constrained clients by providing heterogeneous customized sub-models. FedConv features a novel learning-on-model paradigm that learns the parameters of the heterogeneous sub-models via convolutional compression. Unlike traditional compression methods, the compressed models in FedConv can be directly trained on clients without decompression. To aggregate the heterogeneous sub-models, we propose transposed convolutional dilation to convert them back to large models with a unified size while retaining personalized information from clients. The compression and dilation processes, transparent to clients, are optimized on the server leveraging a small public dataset. Extensive experiments on six datasets demonstrate that FedConv outperforms state-of-the-art FL systems in terms of model accuracy (by more than 35% on average), computation and communication overhead (with 33% and 25% reduction, respectively).

Recently, wearable devices have become increasingly popular in our lives because of their neat features and stylish appearance. However, due to the tiny size, it is inconvenient for users to interact with a device using conventional methods, especially for text entry. Although some methods have been proposed to handle this problem, they have different limitations and are not applicable to many existing mobile devices. As a result, we take the first step to propose a digits-entry system, i.e., AcouDigits, in which digits can be entered in the air using a finger without taking help from any additional hardware. We implement AcouDigits on two commercial devices and conduct experiments to evaluate its performance in recognizing ten basic digits. Experimental results show that AcouDigits can achieve average accuracies of 91.7% and 87.4% in recognizing basic digits and 26 English alphabets, respectively.