Broadband Acoustical MEMS Transceivers for Simultaneous Range Finding and Microphone Applications

Anzinger, Sebastian; Bretthauer, Christian; Manz, Johannes; Krumbein, Ulrich; Dehe, A.

doi:10.1109/transducers.2019.8808264

Cited by 14 publications

(3 citation statements)

References 7 publications

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“…Those transducers need a low bias voltage and offer an audio performance of 68 dB(A) signal-to-noise ratio (SNR) and between 80 and 90 dB SNR in the ultrasonic frequency range. After the emission of the pulses, a free oscillation of the membrane (ringing) can override the incoming echo, producing a shadow zone that allows obstacle detection from 10 cm on [17].…”

Section: Figure 1: System Diagrammentioning

confidence: 99%

Gesture Recognition With Ultrasounds and Edge Computing

et al. 2021

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The aim of this work is to prove that it is possible to develop a system able to detect gestures based only on ultrasonic signals and Edge devices. A set of 7 gestures plus idle has been defined, being possible to combine them to increase the recognized gestures. In order to recognize them, Ultrasound transceivers will be used to detect the 2 dimensional gestures. The Edge device approach implies that the whole data is processed in the device at the network edge rather than depending on external devices or services such as Cloud Computing. The system presented in this paper has been proven to be able to measure Time of Flight (ToF) signals that can be used to recognize multiple gestures by the integration of two transceivers, with an accuracy between 84.18% and 98.4%. Due to the optimization of the preprocessing correlation technique to extract the ToF from the echo signals and our specific firmware design to enable the parallelization of concurrent processes, the system can be implemented as an Edge Device. INDEX TERMS Edge computing, Gesture recognition, Human System Interaction (HSI), Ultrasound.

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Section: Figure 1: System Diagrammentioning

confidence: 99%

Gesture Recognition With Ultrasounds and Edge Computing

et al. 2021

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“…For user detection, this work uses four dual-backplate MEMS microphone-based ultrasonic transceivers [ 21 ] in a square-shaped matrix, as shown in Figure 3 . The transceivers need low bias voltage and support the use of both audio microphones (with a 68 dB(A) signal-to-noise (SNR) performance) and an airborne ultrasonic transceiver (with between 80 and 90 dB SNR).…”

Section: System Descriptionmentioning

confidence: 99%

Air-Writing Character Recognition with Ultrasonic Transceivers

Saez-Mingorance

Mèndez

Mauro

et al. 2021

Sensors

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The interfaces between users and systems are evolving into a more natural communication, including user gestures as part of the interaction, where air-writing is an emerging application for this purpose. The aim of this work is to propose a new air-writing system based on only one array of ultrasonic transceivers. This track will be obtained based on the pairwise distance of the hand marker with each transceiver. After acquiring the track, different deep learning algorithms, such as long short-term memory (LSTM), convolutional neural networks (CNN), convolutional autoencoder (ConvAutoencoder), and convolutional LSTM have been evaluated for character recognition. It has been shown how these algorithms provide high accuracy, where the best result is extracted from the ConvLSTM, with 99.51% accuracy and 71.01 milliseconds of latency. Real data were used in this work to evaluate the proposed system in a real scenario to demonstrate its high performance regarding data acquisition and classification.

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“…MEMS acoustic transducers rely on the conversion of energy between mechanical/acoustic and electrical domains which can be achieved by different transduction mechanisms. The most commonly used are the electrostatic [15][16][17], piezoresistive [18] and piezoelectric [19][20][21][22][23] mechanisms. Specifically, the piezoelectric transduction mechanism compared to other principles has higher energy density and does not require polarization voltages [24].…”

Section: Introductionmentioning

confidence: 99%

Piezoelectric MEMS Acoustic Transducer with Electrically-Tunable Resonant Frequency

et al. 2022

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The paper presents a technique to obtain an electrically-tunable matching between the series and parallel resonant frequencies of a piezoelectric MEMS acoustic transducer to increase the effectiveness of acoustic emission/detection in voltage-mode driving and sensing. The piezoelectric MEMS transducer has been fabricated using the PiezoMUMPs technology, and it operates in a plate flexural mode exploiting a 6 × 6 mm doped silicon diaphragm with an aluminum nitride (AlN) piezoelectric layer deposited on top. The piezoelectric layer can be actuated by means of electrodes placed at the edges of the diaphragm above the AlN film. By applying an adjustable bias voltage Vb between two properly-connected electrodes and the doped silicon, the d31 mode in the AlN film has been exploited to electrically induce a planar static compressive or tensile stress in the diaphragm, depending on the sign of Vb, thus shifting its resonant frequency. The working principle has been first validated through an eigenfrequency analysis with an electrically induced prestress by means of 3D finite element modelling in COMSOL Multiphysics®. The first flexural mode of the unstressed diaphragm results at around 5.1 kHz. Then, the piezoelectric MEMS transducer has been experimentally tested in both receiver and transmitter modes. Experimental results have shown that the resonance can be electrically tuned in the range Vb = ±8 V with estimated tuning sensitivities of 8.7 ± 0.5 Hz/V and 7.8 ± 0.9 Hz/V in transmitter and receiver modes, respectively. A matching of the series and parallel resonant frequencies has been experimentally demonstrated in voltage-mode driving and sensing by applying Vb = 0 in transmission and Vb = −1.9 V in receiving, respectively, thereby obtaining the optimal acoustic emission and detection effectiveness at the same operating frequency.

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Broadband Acoustical MEMS Transceivers for Simultaneous Range Finding and Microphone Applications

Cited by 14 publications

References 7 publications

Gesture Recognition With Ultrasounds and Edge Computing

Gesture Recognition With Ultrasounds and Edge Computing

Air-Writing Character Recognition with Ultrasonic Transceivers

Piezoelectric MEMS Acoustic Transducer with Electrically-Tunable Resonant Frequency

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