Machine learning-based self-powered acoustic sensor for speaker recognition

Han, Jae Hyun; Bae, Kangmin; Hong, Seong‐Kwan; Park, Hyunsin; Kwak, Jae-Man; Wang, Hee Seung; Joe, Daniel J.; Park, Jung Hwan; Jung, Young-Hoon; Hur, Shin; Yoo, Chang D.; Lee, Keon Jae

doi:10.1016/j.nanoen.2018.09.030

Cited by 143 publications

(102 citation statements)

References 42 publications

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“…When it comes to future sensors, artificial intelligence (AI) would unquestionably play a vital role in terms of "decoding" signals from the various sensor system and networks [179][180][181][182][183][184][185][186][187][188]. Furthermore, VR and AR will also rely much on novel sensors to provide better functionality such as immersive experience and feedback control.…”

Section: Mems/nems Vs Ai and Its Vr And Armentioning

confidence: 99%

“…As to the area of gas sensing prediction, Esposito et al introduced the dynamic neural network (DNN) method in a stochastic prediction of air pollutant concentrations to the chemical multisensory [182]. When it came to sound detection, Han et al reported a platform using a machine learning-assist method for speaker recognition as shown in Figure 12b [184]. They claimed that the Gaussian Mixture Model (GMM) method in machine learning can reach up to an outstanding speaker recognition rate of 97.5% compared to the reference MEMS microphone.…”

Section: Mems Vs Aimentioning

confidence: 99%

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Development Trends and Perspectives of Future Sensors and MEMS/NEMS

et al. 2019

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With the fast development of the fifth-generation cellular network technology (5G), the future sensors and microelectromechanical systems (MEMS)/nanoelectromechanical systems (NEMS) are presenting a more and more critical role to provide information in our daily life. This review paper introduces the development trends and perspectives of the future sensors and MEMS/NEMS. Starting from the issues of the MEMS fabrication, we introduced typical MEMS sensors for their applications in the Internet of Things (IoTs), such as MEMS physical sensor, MEMS acoustic sensor, and MEMS gas sensor. Toward the trends in intelligence and less power consumption, MEMS components including MEMS/NEMS switch, piezoelectric micromachined ultrasonic transducer (PMUT), and MEMS energy harvesting were investigated to assist the future sensors, such as event-based or almost zero-power. Furthermore, MEMS rigid substrate toward NEMS flexible-based for flexibility and interface was discussed as another important development trend for next-generation wearable or multi-functional sensors. Around the issues about the big data and human-machine realization for human beings’ manipulation, artificial intelligence (AI) and virtual reality (VR) technologies were finally realized using sensor nodes and its wave identification as future trends for various scenarios.

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Section: Mems/nems Vs Ai and Its Vr And Armentioning

confidence: 99%

Section: Mems Vs Aimentioning

confidence: 99%

Development Trends and Perspectives of Future Sensors and MEMS/NEMS

et al. 2019

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“…In particular, flexible memory is regarded as a vital component for high‐performance flexible electronics considering its fundamental functions in signal processing, data storage, and interdevice communication. Furthermore, a flexible artificial synapse can be employed in an implantable BMI, providing a valuable route to restore damaged neural functions such as vision, hearing, movement, and even complex cognitive behaviors …”

Section: Flexible Memristive Devicesmentioning

confidence: 99%

Unconventional Inorganic‐Based Memristive Devices for Advanced Intelligent Systems

Sung

Kim

et al. 2019

Adv Materials Technologies

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The latest progresses in software engineering such as cloud computing, big data analysis, and machine learning have accelerated the emergence of advanced intelligent systems (AIS). However, the current computing system has significant challenges in dealing with unstructured data (e.g., image, voice, physiological signals) because the von‐Neumann bottleneck induces latency and power consumption issues. Neuromorphic computing, which imitates the behaviors of neuron and synapse within the biological neural network, is considered a promising solution beyond von‐Neumann architecture, since its collocated structure of processor and memory enables parallel processing of unstructured data with remarkable efficiency. Memristors are considered as next‐generation nonvolatile memory devices due to fast speed, low power, and excellent scalability. However, a low reliability and leakage current issues remain as obstacle to the commercialization of memristors. Memristive devices have been widely investigated as a strong candidate for artificial synapses since their resistance modulation characteristics under electrical stimulus are analogous to the plasticity of the brain synapse. Although emulation of synaptic behavior by single memristor cells is demonstrated by many researchers, the development of fully functional memristive neural network requires further investigations. This paper introduces the recent advances and developments in the field of inorganic‐based unconventional memristive devices for future AIS applications.

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“…Recent progress in the field of thin‐film µLEDs is classified into five main categories: (i) research trends of µLED technology, (ii) device structure of µLEDs, (iii) µLED transfer methods, (iv) strategies for performance enhancement, and (v) potential research areas. µLED applications such as next‐generation displays, wearable optoelectronics, optogenetic/trichogenic biostimulators, and healthcare biomedical sensors will be discussed …”

Section: Introductionmentioning

confidence: 99%

Micro Light‐Emitting Diodes for Display and Flexible Biomedical Applications

Lee

Shin

Park

et al. 2019

Adv Funct Materials

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161

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Inorganic-based micro light-emitting diodes (µLEDs) have witnessed significant improvements in terms of display and biomedical applications, which can shift the paradigm of future optoelectronic systems. In particular, µLED displays are on the verge of becoming the next big interface platform for visual communications, expanding to various internet of things and wearable/bioapplications. Novel µLED concepts need to be upgraded to be able to satisfy their potential optoelectric applications, such as virtual reality, smart watches, and medical sensors for individual computing in this hyperconnected society. Here, representative progresses in the field of flexible µLEDs are reviewed with regard to device structures, massive µLED transfers, methods for performance enhancement, and applications.

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Machine learning-based self-powered acoustic sensor for speaker recognition

Cited by 143 publications

References 42 publications

Development Trends and Perspectives of Future Sensors and MEMS/NEMS

Development Trends and Perspectives of Future Sensors and MEMS/NEMS

Unconventional Inorganic‐Based Memristive Devices for Advanced Intelligent Systems

Micro Light‐Emitting Diodes for Display and Flexible Biomedical Applications

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