arXiv subjects

Zihuan Liu

Publications and source records attributed to Zihuan Liu.

5 recordsLinked to original sources

arXiv2026

MEMS Microphones as Ultrasonic Transducers: Nonlinear Electrostatic Actuation and a Parametric Array Prototype

This paper investigates commercial-style capacitive MEMS microphone dies as air-coupled ultrasonic transmitters under nonlinear pull-in and snap-back actuation and demonstrates a compact parametric-array prototype. A single die produces large diaphragm displacement and measurable ultrasonic pressure in air. A 28-die array driven at 83 and 93 kHz generates a directional component at the 10 kHz difference frequency. Measurements are compared with analytical radiation theory and finite-element modeling, and the effects of aperture, fill factor, device uniformity, and receiver nonlinearity are discussed.

eess.AS↗

arXiv2026

Bimorph Lithium Niobate Piezoelectric Micromachined Ultrasonic Transducers

Piezoelectric micromachined ultrasonic transducers (PMUTs) are widely utilized in applications that demand mechanical resilience, thermal stability, and compact form factors. Recent efforts have sought to demonstrate that single-crystal lithium niobate (LN) is a promising PMUT material platform, offering high electromechanical coupling (k2) and bidirectional performance. In addition, advances in LN film transfer technology have enabled high quality periodically poled piezoelectric films (P3F), facilitating a bimorph piezoelectric stack without intermediate electrodes. In this work, we showcase a bimorph PMUT incorporating a mechanically robust, 20 $μ$m thick P3F LN active layer. We establish the motivation for LN PMUTs through a material comparison, followed by extensive membrane geometry optimization and subsequent enhancement of the PMUT's k2. We demonstrate a 775 kHz flexural mode device with a quality factor (Q) of 200 and an extracted k2 of 6.4\%, yielding a high transmit efficiency of 65 nm/V with a mechanically robust active layer. We leverage the high performance to demonstrate extreme-temperature resilience, showcasing stable device operation up to 600 $^\circ$C and survival up to 900 $^\circ$C, highlighting LN's potential as a resilient PMUT platform.

cond-mat.mtrl-sci↗

arXiv2025

Bimorph Lithium Niobate Piezoelectric Micromachined Ultrasonic Transducer

This work demonstrates a prototype bi-layer piezoelectric micromachined ultrasonic transducer (PMUT) based on transferred periodically poled piezoelectric film (P3F) X-cut lithium niobate (LN). Opposite in-plane polarizations in the piezoelectric film stack are employed to enable efficient lateral field excitation of the flexural mode. Thanks to its high piezoelectric coefficient and low dielectric loss, the X-cut LN exhibits high figure of merits (FoMs) as both sensors and transducers. The fabricated PMUT demonstrates an out-of-plane mode near 1 MHz with an electromechanical coupling of 3.6\%. Laser Doppler vibrometry further validates the finite element analysis, showing a peak center displacement of 340 pm/V. These results establish bi-layer P3F LN PMUTs as a promising platform for compact and high-performance ultrasonic transducers. Future work will focus on theoretical analysis, modeling of the measured data, improving the design of the transducer topology, and mitigating feedthrough effects.

physics.app-ph↗

arXiv2020

A Role for Prior Knowledge in Statistical Classification of the Transition from MCI to Alzheimer's Disease

The transition from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is of great interest to clinical researchers. This phenomenon also serves as a valuable data source for quantitative methodological researchers developing new approaches for classification. However, the growth of machine learning (ML) approaches for classification may falsely lead many clinical researchers to underestimate the value of logistic regression (LR), yielding equivalent or superior classification accuracy over other ML methods. Further, in applications with many features that could be used for classifying the transition, clinical researchers are often unaware of the relative value of different selection procedures. In the present study, we sought to investigate the use of automated and theoretically-guided feature selection techniques, and as well as the L-1 norm when applying different classification techniques for predicting conversion from MCI to AD in a highly characterized and studied sample from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We propose an alternative pre-selection technique that utilizes an efficient feature selection based on clinical knowledge of brain regions involved in AD. The present findings demonstrate how similar performance can be achieved using user-guided pre-selection versus algorithmic feature selection techniques. Finally, we compare the performance of a support vector machine (SVM) with that of logistic regression on multi-modal data from ADNI. The present findings show that although SVM and other ML techniques are capable of relatively accurate classification, similar or higher accuracy can often be achieved by LR, mitigating SVM's necessity or value for many clinical researchers.

stat.AP↗

arXiv2020

Variational Bayes Neural Network: Posterior Consistency, Classification Accuracy and Computational Challenges

Bayesian neural network models (BNN) have re-surged in recent years due to the advancement of scalable computations and its utility in solving complex prediction problems in a wide variety of applications. Despite the popularity and usefulness of BNN, the conventional Markov Chain Monte Carlo based implementation suffers from high computational cost, limiting the use of this powerful technique in large scale studies. The variational Bayes inference has become a viable alternative to circumvent some of the computational issues. Although the approach is popular in machine learning, its application in statistics is somewhat limited. This paper develops a variational Bayesian neural network estimation methodology and related statistical theory. The numerical algorithms and their implementational are discussed in detail. The theory for posterior consistency, a desirable property in nonparametric Bayesian statistics, is also developed. This theory provides an assessment of prediction accuracy and guidelines for characterizing the prior distributions and variational family. The loss of using a variational posterior over the true posterior has also been quantified. The development is motivated by an important biomedical engineering application, namely building predictive tools for the transition from mild cognitive impairment to Alzheimer's disease. The predictors are multi-modal and may involve complex interactive relations.

stat.ML↗