Yihao Zhou

Sep 23, 2024 | nature.com | Xun Zhao |Yihao Zhou |Aaron Li |Jing Xu |Shreesh Karjagi |Justin Li | +5 more

Wearable acoustic sensors can be used for voice recognition. However, the capabilities of such devices, which are typically based on solid materials, are often restricted by ambient noise, motion artefacts and low conformability to the skin. Here we report a liquid acoustic sensor for voice recognition. The approach is based on a three-dimensional oriented and ramified magnetic network structure of neodymium–iron–boron magnetic nanoparticles suspended in a carrier fluid, which behaves like a permanent magnet. The sensor can discriminate small pressures (0.9 Pa), has a high signal-to-noise ratio (69.1 dB) and provides self-filtering capabilities that can remove low-frequency biomechanical motion artefact (less than 30 Hz). We use the liquid acoustic sensor—together with a machine learning algorithm—to create a wearable voice recognition system that offers a recognition accuracy of 99% in a noisy environment. An acoustic sensor that is based on a network of magnetic nanoparticles suspended in a carrier fluid can be used—together with a machine learning algorithm—to create a wearable voice recognition system with an accuracy of 99% in a noisy environment.

Apr 26, 2024 | nature.com | Xun Zhao |Yihao Zhou

AbstractBrownian motion allows microscopically dispersed nanoparticles to be stable in ferrofluids, as well as causes magnetization relaxation and prohibits permanent magnetism. Here we decoupled the particle Brownian motion from colloidal stability to achieve a permanent fluidic magnet with high magnetization, flowability and reconfigurability.

Jan 5, 2024 | science.org | Scott H. Oppler |Tianlong Wang |Yihao Zhou |Xun Zhao

Research Article APPLIED SCIENCES AND ENGINEERING Yihao Zhou https://orcid.org/0000-0002-7299-5911, Xun Zhao https://orcid.org/0000-0001-5543-0081, [...] , Jing Xu https://orcid.org/0000-0003-3157-6902, Guorui Chen https://orcid.org/0000-0001-8387-5723, [...] , Trinny Tat https://orcid.org/0000-0002-9068-8325, Justin Li, and Jun Chen https://orcid.org/0000-0002-3439-0495 [email protected]+4 authors +2 authors fewerAuthors Info & Affiliations Abstract Future exploitation of marine resources in...

Jul 13, 2023 | journals.plos.org | Yihao Zhou |Xu Dong |Ruoyu Wang |Yating Xing

Study design and risk of biasAll studies had various aspects of bias risk because of methodological deficiencies (Fig 2). Four studies used randomized digital tables [13–15,25], five studies randomized in order of visited [12,16,19,21,28], two studies randomized according to lots [18,24], and seven studies were evaluated as unclear risk due to absence details of random sequence generation. All studies were considered unclear risk as lack of allocation concealment process.