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Micromachines
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Advances in Piezoelectric Sensors
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Advances in Piezoelectric Sensors

A special issue of Micromachines (ISSN 2072-666X). This special issue belongs to the section "A：Physics".

Deadline for manuscript submissions: 30 September 2025 | Viewed by 578

Special Issue Editor

Dr. Dinko Oletić

E-Mail Website
Guest Editor
University of Zagreb Faculty of Electrical Engineering and Computing, Unska 3, 10000 Zagreb, Croatia
Interests: low-power design of MEMS-based piezoelectric sensor systems; modelling, design, characterization of piezoelectric resonant MEMS devices; design of accompanying low-power sensor signal condition circuits, wake-up interfaces, and embedded systems for near-sensor processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Piezoelectric sensors play a key role in many domains of industrial, medical, consumer and research sensing applications, including industrial non-destructive testing, preventive maintenance, structural health monitoring, vibration and shock monitoring, flow sensing, medical ultrasound, audible-frequency consumer acoustic microphones, underwater hydrophones, etc. This interdisciplinary topic covers the latest advances in piezoelectric sensors including, but not limited to, the following areas:

- Novel piezoelectric materials. Bulk and thin-film piezoelectric material deposition method. Material property modelling and novel experimental piezoelectric response characterization methods. Non-toxic and biocompatible materials and flexible piezoelectric materials. Optimization of piezoelectric coefficient, electromechanical coupling and polarization direction. High temperature operation.

- Advances in piezoelectric transducer design. Bulk-piezoelectric transducers and arrays. Contact transducers for and ultrasonic acoustic emission (AE) testing. Hydrophone designs. Air-coupled piezoelectric devices. Piezoelectric microelectromechanical (MEMS) devices. Resonant piezoelectric MEMS sensors. Thin-film bulk acoustic resonators. Piezoelectric micromachined ultrasonic transducers (PMUT) and ultrasonic MEMS arrays. Surface-acoustic wave (SAW) devices. Gravimetric piezoelectric resonant MEMS sensors. Novel audible acoustic-frequency piezoelectric MEMS microphone designs. Piezoelectric MEMS voice-activity detectors. Bandwidth and sensitivity enhancement. Analytical and numerical finite element modelling, experimental prototypes and device response characterization.

- Electronic sensor interfaces for piezoelectric sensors. Charge amplifiers and charge conditioning circuits for readout of high-impedance or high-frequency piezoelectric devices. Ultra-low power acoustic or ultrasonic event detection and wake-up circuits. Self-powered piezo-electric sensor devices and conditioning circuits for harnessing the charge generated by the piezoelectric sensors.

- Embedded networked electronic systems for piezoelectric sensing. Autonomous, field-deployable systems for real-time acquisition, near-sensor processing, logging, and communication of piezoelectric signals. Novel system architectures and components for low-power design. Ultrasound, photoacoustics, and acoustic emission sensors on chip. System-on-chip, system-in-package, and heterogenous integration of piezoelectric transducers and electronics. Functional characterization of prototype implementations. Implementation of signal processing and machine learning algorithms on resource-constrained embedded hardware.

- Emerging applications of piezoelectric sensors. Photoacoustic spectroscopy in gas sensing. Ultrasonic and photoacoustic imaging for biomedical diagnostics. Industrial NDT applications in preventive maintenance, cavitation, cracking, leakage detection, material delamination, corrosion monitoring etc. Novel application of acoustic emission sensors in biology, precision agriculture, forestry, biomedicine, etc. Laboratory experiments and field deployments. Propagation path modelling between ultrasonic signal source and piezoelectric transduvers, acoustic waveguides. Application of novel feature-extraction methods, and clustering or classification algorithms for event detection, source identification or localization. Combining physical modelling with machine learning methods.

Dr. Dinko Oletić
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Micromachines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2100 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • piezoelectric sensors
  • MEMS
  • PMUT
  • FBAR
  • materials
  • modelling
  • sensor interfaces
  • low-power design
  • embedded sensors
  • signal processing
  • acoustic event detection
  • ultrasonic imaging
  • photoacoustics
  • NDT
  • acoustic emisssion testing

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Published Papers (1 paper)

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Research

15 pages, 8737 KiB  
Article
A Piezoelectric Micromachined Ultrasonic Transducer-Based Bone Conduction Microphone System for Enhancing Speech Recognition Accuracy
by Chongbin Liu, Xiangyang Wang, Jianbiao Xiao, Jun Zhou and Guoqiang Wu
Micromachines 2025, 16(6), 613; https://doi.org/10.3390/mi16060613 - 23 May 2025
Viewed by 414
Abstract
Speech recognition in noisy environments has long posed a challenge. Air conduction microphone (ACM), the devices typically used, are susceptible to environmental noise. In this work, a customized bone conduction microphone (BCM) system based on a piezoelectric micromachined ultrasonic transducer is developed to [...] Read more.
Speech recognition in noisy environments has long posed a challenge. Air conduction microphone (ACM), the devices typically used, are susceptible to environmental noise. In this work, a customized bone conduction microphone (BCM) system based on a piezoelectric micromachined ultrasonic transducer is developed to capture speech through real-time bone conduction (BC), while a commercial ACM is integrated for simultaneous capture of speech through air conduction (AC). The system enables simpler and more robust BC speech capture. The BC speech capture achieves a signal-to-noise amplitude ratio over five times greater than that of AC speech capture in an environment with a noise level of 68 dB. Instead of using only AC-captured speech, both BC- and AC-captured speech are input into a speech enhancement module. The noise-insensitive BC-captured speech serves as a speech reference to adapt the SE backbone of AC-captured speech. The two types of speech are fused, and noise suppression is applied to generate enhanced speech. Compared with the original noisy speech, the enhanced speech achieves a character error rate reduction of over 20%, approaching the speech recognition accuracy of clean speech. The results indicate that this speech enhancement method based on the fusion of BC- and AC-captured speech efficiently integrates the features of both types of speech, thereby improving speech recognition accuracy in noisy environments. This work presents an innovative system designed to efficiently capture BC speech and enhance speech recognition in noisy environments. Full article
(This article belongs to the Special Issue Advances in Piezoelectric Sensors)
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