Self-Powered Long-Life Microsystem for Vibration Sensing and Target Recognition

Yang, Deng; Duan, Wenrui; Xuan, Guozhe; Hou, Lulu; Zhang, Zhen; Song, Mingxue; Zhao, Jing

doi:10.3390/s22249594

Cited by 4 publications

(5 citation statements)

References 29 publications

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“…The application processor CPU1 is an ARM Cortex-M4 core for overall control, and the network processor CPU2 is an ARM Cortex-M0 with the Bluetooth protocol stack. In addition, the MCU has a floating-point unit (FPU) and embedded memory (1 MB Flash, 256 KB SRAM) to meet the computing and processing requirements of this microsystem [ 14 ]. The ICS-43434 MEMS omnidirectional microphone has an impressive ultra-high 120 dB SPL AOP, as well as a high SNR of 64 dB, and a broadband frequency response of 60 Hz to 20 kHz.…”

Section: Hardware Designmentioning

confidence: 99%

“…The size of the microsystem is presented in Table 1 . In sleep mode, the microsystem consumes 0.18 mW, and this increases to 36 mW in active mode [ 14 ].…”

Section: Hardware Designmentioning

confidence: 99%

“…However, they had poor integration, large size, high power consumption, and high cost. Several reports have also focused on intelligent microsystems, including a 15 cm 3 and 35 g microsystem for vibration sensing and target recognition [ 14 ]. Although the microsystem was well-integrated and could realize vibration wake-up and self-energy supply, its recognition accuracy was not optimal, and it did not implement wireless networking.…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Microsystem for Sound Event Recognition in Edge Computing Using End-to-End Mesh Networking

Hou

Duan

Xuan

et al. 2023

Sensors

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Wireless acoustic sensor networks (WASNs) and intelligent microsystems are crucial components of the Internet of Things (IoT) ecosystem. In various IoT applications, small, lightweight, and low-power microsystems are essential to enable autonomous edge computing and networked cooperative work. This study presents an innovative intelligent microsystem with wireless networking capabilities, sound sensing, and sound event recognition. The microsystem is designed with optimized sensing, energy supply, processing, and transceiver modules to achieve small size and low power consumption. Additionally, a low-computational sound event recognition algorithm based on a Convolutional Neural Network has been designed and integrated into the microsystem. Multiple microsystems are connected using low-power Bluetooth Mesh wireless networking technology to form a meshed WASN, which is easily accessible, flexible to expand, and straightforward to manage with smartphones. The microsystem is 7.36 cm3 in size and weighs 8 g without housing. The microsystem can accurately recognize sound events in both trained and untrained data tests, achieving an average accuracy of over 92.50% for alarm sounds above 70 dB and water flow sounds above 55 dB. The microsystems can communicate wirelessly with a direct range of 5 m. It can be applied in the field of home IoT and border security.

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Section: Hardware Designmentioning

confidence: 99%

“…The size of the microsystem is presented in Table 1 . In sleep mode, the microsystem consumes 0.18 mW, and this increases to 36 mW in active mode [ 14 ].…”

Section: Hardware Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Microsystem for Sound Event Recognition in Edge Computing Using End-to-End Mesh Networking

Hou

Duan

Xuan

et al. 2023

Sensors

Self Cite

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“…Yang et al proposed a self-powered microsystem for vibration detection and target recognition that harvested solar energy from the environment and stored it in a lithium battery to power the microcontroller unit and sensors. The microsystem used five-sided PV cells to maximize the harvested solar energy for the system's size [19]. This method of harvesting energy by placing PV cells on each of the system's five surfaces is also highly suitable for indoor environments, but currently, there is a lack of relevant research.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Self-Powered IoT Sensor Nodes for Harvesting Hybrid Indoor Ambient Light and Heat Energy

Xiao

Yin

et al. 2023

Sensors

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Sensor nodes are critical components of the Internet of Things (IoT). Traditional IoT sensor nodes are typically powered by disposable batteries, making it difficult to meet the requirements for long lifetime, miniaturization, and zero maintenance. Hybrid energy systems that integrate energy harvesting, storage, and management are expected to provide a new power source for IoT sensor nodes. This research describes an integrated cube-shaped photovoltaic (PV) and thermal hybrid energy-harvesting system that can be utilized to power IoT sensor nodes with active RFID tags. The indoor light energy was harvested using 5-sided PV cells, which could generate 3 times more energy than most current studies using single-sided PV cells. In addition, two vertically stacked thermoelectrical generators (TEG) with a heat sink were utilized to harvest thermal energy. Compared to one TEG, the harvested power was improved by more than 219.48%. In addition, an energy management module with a semi-active configuration was designed to manage the energy stored by the Li-ion battery and supercapacitor (SC). Finally, the system was integrated into a 44 mm × 44 mm × 40 mm cube. The experimental results showed that the system was able to generate a power output of 192.48 µW using indoor ambient light and the heat from a computer adapter. Furthermore, the system was capable of providing stable and continuous power for an IoT sensor node used for monitoring indoor temperature over a prolonged period.

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“…By adopting the wake-up strategy, most of the wasted power is conserved, and the power efficiency is significantly improved, which greatly extends the battery life of the microsystem [ 4 ]. Different types of signals are used for event detection in wake-up microsystems, such as acoustic, mechanical, magnetic, optical, infrared, RF, et al [ 5 , 6 , 7 , 8 , 9 , 10 ]. Among them, the acoustic signal has the advantages of strong universality, long monitoring distance, rich data information, and abundant acoustic sensors.…”

Section: Introductionmentioning

confidence: 99%

Acoustic Wake-Up Technology for Microsystems: A Review

Yang

Zhao

2023

Micromachines

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Microsystems with capabilities of acoustic signal perception and recognition are widely used in unattended monitoring applications. In order to realize long-term and large-scale monitoring, microsystems with ultra-low power consumption are always required. Acoustic wake-up is one of the solutions to effectively reduce the power consumption of microsystems, especially for monitoring sparse events. This paper presents a review of acoustic wake-up technologies for microsystems. Acoustic sensing, acoustic recognition, and system working mode switching are the basis for constructing acoustic wake-up microsystems. First, state-of-the-art MEMS acoustic transducers suitable for acoustic wake-up microsystems are investigated, including MEMS microphones, MEMS hydrophones, and MEMS acoustic switches. Acoustic transducers with low power consumption, high sensitivity, low noise, and small size are attributes needed by the acoustic wake-up microsystem. Next, acoustic features and acoustic classification algorithms for target and event recognition are studied and summarized. More acoustic features and more computation are generally required to achieve better recognition performance while consuming more power. After that, four different system wake-up architectures are summarized. Acoustic wake-up microsystems with absolutely zero power consumption in sleep mode can be realized in the architecture of zero-power recognition and zero-power sleep. Applications of acoustic wake-up microsystems are then elaborated, which are closely related to scientific research and our daily life. Finally, challenges and future research directions of acoustic wake-up microsystems are elaborated. With breakthroughs in software and hardware technologies, acoustic wake-up microsystems can be deployed for ultra-long-term and ultra-large-scale use in various fields, and play important roles in the Internet of Things.

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Self-Powered Long-Life Microsystem for Vibration Sensing and Target Recognition

Cited by 4 publications

References 29 publications

Intelligent Microsystem for Sound Event Recognition in Edge Computing Using End-to-End Mesh Networking

Intelligent Microsystem for Sound Event Recognition in Edge Computing Using End-to-End Mesh Networking

Investigation of Self-Powered IoT Sensor Nodes for Harvesting Hybrid Indoor Ambient Light and Heat Energy

Acoustic Wake-Up Technology for Microsystems: A Review

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