Wireless charging holds significant promise for IoT devices and transportation networks by facilitating convenient and autonomous power supply. Traditional wireless charging technologies have typically adhered to a singular approach, choosing between near-field coupling or far-field radiation. However, our investigations uncover that each method outperforms the other at specific distances. This insight leads us to integrating the advantages of both to enable rapid wireless charging across any distance within the charging range. For this vision, we poses an intriguing question: "Can we develop a system that supports both near-field and far-field charging simultaneously?"

Global Navigation Satellite System (GNSS) is extensively utilized for outdoor positioning and navigation. However, achieving high-precision indoor positioning is challenging due to the significant attenuation of GNSS signals indoors. To address this issue, we propose an innovative indoor GNSS positioning system called GPMS, which uses passive metasurface technology to redirect GNSS signals from outdoors into indoor spaces. These passive metasurfaces are strategically optimized for indoor coverage by steering and scattering the GNSS signals across a wide range of incident angles. We further develop a novel localization algorithm that can determine which metasurface the signal goes through and localize the user using the set of metasurfaces as anchor points. A distinct advantage of our localization algorithm is that it can be implemented on existing mobile devices without any hardware modifications. We implement the prototype of GPMS, and deploy six metasurfaces in two indoor environments, a 10×50 m2 office floor and a 15×20 m2 lecture room, to evaluate system performance. In terms of coverage, our GPMS increases the C/N0 from 9.1 dB-Hz to 23.2 dB-Hz and increases the number of visible satellites from 3.6 to 21.5 in the office floor. In terms of indoor positioning accuracy, our proposed system decreases the absolute positioning error from 30.6 m to 3.2 m in the office floor, and from 11.2 m to 2.7 m in the lecture room, demonstrating the feasibility and benefits of metasurface-assisted GNSS for indoor positioning.

Silent Speech Interfaces (SSI) have been developed to convert silent articulatory gestures into speech, aiding communication in public spaces and assisting individuals with aphasia. Previous SSIs, which rely on wearable devices or cameras, often pose issues like prolonged contact or privacy risks. Recent advancements in acoustic sensing present new opportunities for gesture sensing, but they typically focus on content classification rather than reconstructing audible speech. This results in the loss of crucial speech characteristics such as rate, intonation, and emotion.In this paper, we propose UltraSR, a novel sensing system designed for accurate audible speech reconstruction by analyzing the disturbance of tiny articulatory gestures on reflected ultrasound signals. UltraSR employs a multi-scale feature extraction scheme to aggregate information from multiple views and introduces a new model that maps ultrasound to speech signals, enabling the reconstruction of audible speech from silent gestures.Instead of the laborious collection of massive training data, UltraSR constructs an inverse task to generate virtual gestures from widely available audio (e.g., phone calls) for efficient model training. Additionally, it incorporates a finetuning mechanism using unlabeled data for user adaptation.We implemented UltraSR on a portable smartphone and evaluated it in various environments. Results show that UltraSR can achieve a Character Error Rate (CER) as low as 5.22% and reduce the CER from 80.13% to 6.31% for new users with only 1 hour of ultrasound data, outperforming state-of-the-art acoustic-based approaches while preserving rich speech information.

Emotion-cause pair extraction is an emergent natural language processing task; the target is to extract all pairs of emotion clauses and corresponding cause clauses from unannotated emotion text. Previous studies have employed two-step approaches. However, this research may lead to error propagation across stages. In addition, previous studies did not correctly handle the situation where emotion clauses and cause clauses are the same clauses. To overcome these issues, the authors first use a multitask learning model that is based on graph from the perspective of sorting, which can simultaneously extract emotion clauses, cause clauses and emotion-cause pairs via an end-to-end strategy. Then the authors propose to convert text into graph structured data, and process this scenario through a unique graph convolutional neural network. Finally, the authors design a semantic decision mechanism to address the scenario in which there are multiple emotion-cause pairs in a text.

Silent Speech Interface (SSI) has been proposed as a means of reconstructing audible speech from silent articulatory gestures for covert voice communication in public and voice assistance for the aphasic. Prior arts of SSI, either relying on wearable devices or cameras, may lead to extended contact requirements or privacy leakage risks. The recent advances in acoustic sensing have brought new opportunities for sensing gestures, but their original intention is to infer speech content for classification instead of audible speech reconstruction, resulting in the loss of some important speech information (e.g., speech rate, intonation, and emotion). In this paper, we propose, the first system that supports accurate audible speech reconstruction by analyzing the disturbance of tiny articulatory gestures on the reflected ultrasound signal. The design of introduces a new model that provides the unique mapping relationship between ultrasound and speech signals, so that the audible speech can be successfully reconstructed from the silent speech. However, establishing the mapping relationship depends on plenty of training data. Instead of the time-consuming collection of massive amounts of data for training, we construct an inverse task that constitutes a dual form with the original task to generate virtual gestures from widely available audio (e.g., phone calls) for facilitating model training. Furthermore, we introduce a fine-tuning mechanism using unlabeled data for user adaptation. We implement using a portable smartphone and evaluate it in various environments. The evaluation results show that can reconstruct speech with a (Character Error Rate) CER as low as 7.62%, and decrease the CER from 82.77% to 9.42% on new users with only 1 hour of ultrasound signals provided, which outperforms state-of-the-art acoustic-based approaches while preserving rich speech information.