scholarly journals Biomimetic and flexible piezoelectric mobile acoustic sensors with multiresonant ultrathin structures for machine learning biometrics

2021 ◽  
Vol 7 (7) ◽  
pp. eabe5683
Author(s):  
Hee Seung Wang ◽  
Seong Kwang Hong ◽  
Jae Hyun Han ◽  
Young Hoon Jung ◽  
Hyun Kyu Jeong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lead Zirconate Titanate ◽  
Basilar Membrane ◽  
Speaker Identification ◽  
Lead Zirconate ◽  
Training Data ◽  
Acoustic Sensor ◽  
Acoustic Sensors ◽  
Human Machine Interaction ◽  
Sensor Module

Flexible resonant acoustic sensors have attracted substantial attention as an essential component for intuitive human-machine interaction (HMI) in the future voice user interface (VUI). Several researches have been reported by mimicking the basilar membrane but still have dimensional drawback due to limitation of controlling a multifrequency band and broadening resonant spectrum for full-cover phonetic frequencies. Here, highly sensitive piezoelectric mobile acoustic sensor (PMAS) is demonstrated by exploiting an ultrathin membrane for biomimetic frequency band control. Simulation results prove that resonant bandwidth of a piezoelectric film can be broadened by adopting a lead-zirconate-titanate (PZT) membrane on the ultrathin polymer to cover the entire voice spectrum. Machine learning–based biometric authentication is demonstrated by the integrated acoustic sensor module with an algorithm processor and customized Android app. Last, exceptional error rate reduction in speaker identification is achieved by a PMAS module with a small amount of training data, compared to a conventional microelectromechanical system microphone.

Mechanomyogram measurement by lead zirconate titanate-based acoustic sensor

2018 ◽  
Vol 57 (11S) ◽  
pp. 11UD09 ◽  
Author(s):  
Yusuke Takei ◽  
Ryo Aoki ◽  
Takeshi Kobayashi ◽  
Tomoyuki Takahata ◽  
Isao Shimoyama
Keyword(s):  
Lead Zirconate Titanate ◽  
Lead Zirconate ◽  
Acoustic Sensor ◽  
Zirconate Titanate

scholarly journals Machine learning and deep learning

2021 ◽  
Author(s):  
Christian Janiesch ◽  
Patrick Zschech ◽  
Kai Heinrich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Analytical Model ◽  
Intelligent Systems ◽  
Model Building ◽  
Training Data ◽  
Learning Models ◽  
Human Machine Interaction ◽  
Learning Machine

AbstractToday, intelligent systems that offer artificial intelligence capabilities often rely on machine learning. Machine learning describes the capacity of systems to learn from problem-specific training data to automate the process of analytical model building and solve associated tasks. Deep learning is a machine learning concept based on artificial neural networks. For many applications, deep learning models outperform shallow machine learning models and traditional data analysis approaches. In this article, we summarize the fundamentals of machine learning and deep learning to generate a broader understanding of the methodical underpinning of current intelligent systems. In particular, we provide a conceptual distinction between relevant terms and concepts, explain the process of automated analytical model building through machine learning and deep learning, and discuss the challenges that arise when implementing such intelligent systems in the field of electronic markets and networked business. These naturally go beyond technological aspects and highlight issues in human-machine interaction and artificial intelligence servitization.

Design and Fabrication of a Lead Zirconate Titanate (PZT) Thin Film Acoustic Sensor

2003 ◽  
Vol 54 (1) ◽  
pp. 595-606 ◽  
Author(s):  
R. G. Polcawich ◽  
M. Scanlon ◽  
J. Pulskamp ◽  
J. Clarkson ◽  
J. Conrad ◽  
...  
Keyword(s):  
Thin Film ◽  
Lead Zirconate Titanate ◽  
Lead Zirconate ◽  
Acoustic Sensor ◽  
Zirconate Titanate ◽  
Pzt Thin Film

Convergent-bceam diffraction studies on lead zirconate titanate Ceramics

1986 ◽  
Vol 44 ◽  
pp. 482-483
Author(s):  
M.L.A. Dass ◽  
T.A. Bielicki ◽  
G. Thomas ◽  
T. Yamamoto ◽  
K. Okazaki
Keyword(s):  
Lead Zirconate Titanate ◽  
Direct Proof ◽  
Lead Zirconate ◽  
Subsolidus Phase ◽  
Pzt Ceramics ◽  
Subsolidus Phase Diagram ◽  
Zirconate Titanate ◽  
Two Phases ◽  
Cbd Method ◽  
Titanate Ceramics

Lead zirconate titanate, Pb(Zr,Ti)O3 (PZT), ceramics are ferroelectrics formed as solid solutions between ferroelectric PbTiO3 and ant iferroelectric PbZrO3. The subsolidus phase diagram is shown in figure 1. PZT transforms between the Ti-rich tetragonal (T) and the Zr-rich rhombohedral (R) phases at a composition which is nearly independent of temperature. This phenomenon is called morphotropism, and the boundary between the two phases is known as the morphotropic phase boundary (MPB). The excellent piezoelectric and dielectric properties occurring at this composition are believed to.be due to the coexistence of T and R phases, which results in easy poling (i.e. orientation of individual grain polarizations in the direction of an applied electric field). However, there is little direct proof of the coexistence of the two phases at the MPB, possibly because of the difficulty of distinguishing between them. In this investigation a CBD method was found which would successfully differentiate between the phases, and this was applied to confirm the coexistence of the two phases.

Ion Beam Sputter Deposition Of Ferroelectric Oxide Thin Films

1991 ◽  
Vol 223 ◽  
Author(s):  
Thomas M. Graettinger ◽  
O. Auciello ◽  
M. S. Ameen ◽  
H. N. Al-Shareef ◽  
K. Gifford ◽  
...  
Keyword(s):  
Thin Films ◽  
Lead Zirconate Titanate ◽  
Growth Parameters ◽  
Ion Beam ◽  
Lead Zirconate ◽  
Electro Optic ◽  
Integrated Optical ◽  
Pzt Films ◽  
Ion Beam Sputter Deposition ◽  
Ferroelectric Oxide

ABSTRACTFerroelectric oxide films have been studied for their potential application as integrated optical materials and nonvolatile memories. Electro-optic properties of potassium niobate (KNbO3) thin films have been measured and the results correlated to the microstructures observed. The growth parameters necessary to obtain single phase perovskite lead zirconate titanate (PZT) thin films are discussed. Hysteresis and fatigue measurements of the PZT films were performed to determine their characteristics for potential memory devices.

Scalable Approach to High Coverages on Oxides via Iterative Training of a Machine-Learning Algorithm

2019 ◽  
Author(s):  
Andrew Medford ◽  
Shengchun Yang ◽  
Fuzhu Liu
Keyword(s):  
Machine Learning ◽  
Chemical Potential ◽  
Learning Algorithm ◽  
Absolute Error ◽  
Low Energy ◽  
Training Data ◽  
High Coverage ◽  
Metal Compounds ◽  
Adsorption Energies ◽  
The Stability

Understanding the interaction of multiple types of adsorbate molecules on solid surfaces is crucial to establishing the stability of catalysts under various chemical environments. Computational studies on the high coverage and mixed coverages of reaction intermediates are still challenging, especially for transition-metal compounds. In this work, we present a framework to predict differential adsorption energies and identify low-energy structures under high- and mixed-adsorbate coverages on oxide materials. The approach uses Gaussian process machine-learning models with quantified uncertainty in conjunction with an iterative training algorithm to actively identify the training set. The framework is demonstrated for the mixed adsorption of CH<sub>x</sub>, NH<sub>x</sub> and OH<sub>x</sub> species on the oxygen vacancy and pristine rutile TiO<sub>2</sub>(110) surface sites. The results indicate that the proposed algorithm is highly efficient at identifying the most valuable training data, and is able to predict differential adsorption energies with a mean absolute error of ~0.3 eV based on <25% of the total DFT data. The algorithm is also used to identify 76% of the low-energy structures based on <30% of the total DFT data, enabling construction of surface phase diagrams that account for high and mixed coverage as a function of the chemical potential of C, H, O, and N. Furthermore, the computational scaling indicates the algorithm scales nearly linearly (N<sup>1.12</sup>) as the number of adsorbates increases. This framework can be directly extended to metals, metal oxides, and other materials, providing a practical route toward the investigation of the behavior of catalysts under high-coverage conditions.

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