skip to main content

Attention:

The NSF Public Access Repository (NSF-PAR) system and access will be unavailable from 11:00 PM ET on Friday, April 12 until 2:00 AM ET on Saturday, April 13 due to maintenance. We apologize for the inconvenience.


Search for: All records

Creators/Authors contains: "Chen, Si"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Free, publicly-accessible full text available February 1, 2025
  2. Silent speech interfaces have been pursued to restore spoken communication for individuals with voice disorders and to facilitate intuitive communications when acoustic-based speech communication is unreliable, inappropriate, or undesired. However, the current methodology for silent speech faces several challenges, including bulkiness, obtrusiveness, low accuracy, limited portability, and susceptibility to interferences. In this work, we present a wireless, unobtrusive, and robust silent speech interface for tracking and decoding speech-relevant movements of the temporomandibular joint. Our solution employs a single soft magnetic skin placed behind the ear for wireless and socially acceptable silent speech recognition. The developed system alleviates several concerns associated with existing interfaces based on face-worn sensors, including a large number of sensors, highly visible interfaces on the face, and obtrusive interconnections between sensors and data acquisition components. With machine learning-based signal processing techniques, good speech recognition accuracy is achieved (93.2% accuracy for phonemes, and 87.3% for a list of words from the same viseme groups). Moreover, the reported silent speech interface demonstrates robustness against noises from both ambient environments and users’ daily motions. Finally, its potential in assistive technology and human–machine interactions is illustrated through two demonstrations – silent speech enabled smartphone assistants and silent speech enabled drone control. 
    more » « less
    Free, publicly-accessible full text available November 27, 2024
  3. Abstract

    The oceanic bottom mixed layer (BML) is a well mixed, weakly stratified, turbulent boundary layer. Adjacent to the seabed, the BML is of intrinsic importance for studying ocean mixing, energy dissipation, particle cycling and sediment-water interactions. While deep-seabed mining of polymetallic nodules is anticipated to commence in the Clarion-Clipperton Zone (CCZ) of the northeastern tropical Pacific Ocean, knowledge gaps regarding the form of the BML and its potentially key influence on the dispersal of sediment plumes generated by deep-seabed mining activities are yet to be addressed. Here, we report recent field observations from the German mining licence area in the CCZ that characterise the structure and variability of the BML locally. Quasi-uniform profiles of potential temperature extending from the seafloor reveal the presence of a spatially and temporally variable BML with an average local thickness of approximately 250 m. Deep horizontal currents in the region have a mean speed of 3.5 cm s$$^{-1}$$-1and a maximum speed of 12 cm s$$^{-1}$$-1at 18.63 ms above bottom over an 11 month record. The near-bottom currents initially have a net southeastward flow, followed by westward and southward flows with the development of complex, anticyclonic flow patterns. Theoretical predictions and historical data show broad consistency with mean BML thickness but cannot explain the observed heterogeneity of local BML thickness. We postulate that deep pressure anomalies induced by passing surface mesoscale eddies and abyssal thermal fronts could affect BML thickness, in addition to local topographic effects. A simplified transport model is then used to study the influence of the BML on the interplay between turbulent diffusion and sediment settling in the transport of deep-seabed mining induced sediment plumes. Over a range of realistic parameter values, the effects of BML on plume evolution can vary significantly, highlighting that resolving the BML will be a crucial step for accurate numerical modelling of plume dispersal.

     
    more » « less
    Free, publicly-accessible full text available June 1, 2024
  4. Free, publicly-accessible full text available May 16, 2024
  5. Abstract

    Skin-integrated haptic interfaces that can relay a wealth of information from the machine to the human are of great interest. However, existing haptic devices are not yet able to produce haptic cues that are compatible with the skin. In this work, we present the stretchable soft actuators for haptic feedback, which can match the perception range, spatial resolution, and stretchability of the skin. Pressure-amplification structures are fabricated using a scalable self-assembly process to ensure an output pressure beyond the skin perception threshold. Due to the minimized device size, the actuator array can be fabricated with a sufficiently high spatial resolution, which makes the haptic device applicable for skin locations with the highest spatial acuity. A haptic feedback system is demonstrated by employing the developed soft actuators and highly sensitive pressure sensors. Two proof-of-concept applications are developed to illustrate the capability of transferring information related to surface textures and object shapes acquired at the robot side to the user side.

     
    more » « less
  6. Captions play a major role in making educational videos accessible to all and are known to benefit a wide range of learners. However, many educational videos either do not have captions or have inaccurate captions. Prior work has shown the benefits of using crowdsourcing to obtain accurate captions in a cost-efficient way, though there is a lack of understanding of how learners edit captions of educational videos either individually or collaboratively. In this work, we conducted a user study where 58 learners (in a course of 387 learners) participated in the editing of captions in 89 lecture videos that were generated by Automatic Speech Recognition (ASR) technologies. For each video, different learners conducted two rounds of editing. Based on editing logs, we created a taxonomy of errors in educational video captions (e.g., Discipline-Specific, General, Equations). From the interviews, we identified individual and collaborative error editing strategies. We then further demonstrated the feasibility of applying machine learning models to assist learners in editing. Our work provides practical implications for advancing video-based learning and for educational video caption editing. 
    more » « less