Passive Extraction of Signal Feature Using a Rectifier with a Mechanically Switched Inductor for Low Power Acoustic Event Detection
Abstract
:1. Introduction
2. Event Detector Architecture
2.1. Time-Frequency Signal Pattern and Event Detection System
2.2. Passive Electromechanical Feature Extractor
3. Passive Electromechanical Feature Extractor Simulation Model and Simulation Results
3.1. Simulation Model
3.2. Simulation Results
4. Experimental Characterization of Passive Electromechanical Feature Extractor
4.1. Measurement Setup
4.2. Measurement Procedure
4.2.1. Transfer Characteristics
4.2.2. Frequency Selectivity
4.2.3. Design Parameters
4.3. Measurement Results
5. Comparison of Active Electrical and Passive Electromechanical Feature Extractor
5.1. Measurement Setup
5.2. Measurement Procedure
5.2.1. Synthetic Signal
5.2.2. Prerecorded Signal
5.3. Results
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Peckens, C.; Porter, C.; Rink, T. Wireless Sensor Networks for Long-Term Monitoring of Urban Noise. Sensors 2018, 18, 3161. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Luo, L.; Qin, H.; Song, X.; Wang, M.; Qiu, H.; Zhou, Z. Wireless Sensor Networks for Noise Measurement and Acoustic Event Recognitions in Urban Environments. Sensors 2020, 20, 2093. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Mayer, P.; Magno, M.; Benini, L. Self-Sustaining Acoustic Sensor with Programmable Pattern Recognition for Underwater Monitoring. IEEE Trans. Instrum. Meas. 2019, 68, 2346–2355. [Google Scholar] [CrossRef]
- Mois, G.; Folea, S.; Sanislav, T. Analysis of Three IoT-Based Wireless Sensors for Environmental Monitoring. IEEE Trans. Instrum. Meas. 2017, 66, 2056–2064. [Google Scholar] [CrossRef]
- Fourniol, M.; Gies, V.; Barchasz, V.; Kussener, E. Low-Power Wake-Up System based on Frequency Analysis for Environmental Internet of Things. In Proceedings of the 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, Oulu, Finland, 2–4 July 2018; pp. 1–6. [Google Scholar] [CrossRef]
- Wang, Y.; Zhou, R.; Liu, Z.; Yan, B. A Low-Power CMOS Wireless Acoustic Sensing Platform for Remote Surveillance Applications. Sensors 2020, 20, 178. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Thoen, B.; Ottoy, G.; Rosas, F.; Lauwereins, S.; Rajendran, S.; De Strycker, L.; Pollin, S.; Verhelst, M. Saving energy in WSNs for acoustic surveillance applications while maintaining QoS. In Proceedings of the SAS 2017—2017 IEEE Sensors Applications Symposium, Glassboro, NJ, USA, 13–15 March 2017; pp. 1–6. [Google Scholar] [CrossRef]
- Kucukbay, S.E.; Sert, M.; Yazici, A. Use of Acoustic and Vibration Sensor Data to Detect Objects in Surveillance Wireless Sensor Networks. In Proceedings of the 21st International Conference on Control Systems and Computer Science (CSCS), Bucharest, Romania, 29–31 May 2017; pp. 207–212. [Google Scholar] [CrossRef]
- Salazar-García, C.; Castro-González, R.; Chacón-Rodríguez, A. RISC-V based sound classifier intended for acoustic surveillance in protected natural environments. In Proceedings of the 2017 IEEE 8th Latin American Symposium on Circuits & Systems (LASCAS), Bariloche, Argentina, 20–23 February 2017; pp. 1–4. [Google Scholar] [CrossRef]
- Prieto, M.D.; Millan, D.Z.; Wang, W.; Ortiz, A.M.; Redondo, J.A.O.; Martinez, L.R. Self-powered wireless sensor applied to gear diagnosis based on acoustic emission. IEEE Trans. Instrum. Meas. 2016, 65, 15–24. [Google Scholar] [CrossRef] [Green Version]
- Fu, H.; Sharif-Khodaei, Z.; Aliabadi, M.H.F. An energy-efficient cyber-physical system for wireless on-board aircraft structural health monitoring. Mech. Syst. Signal Process. 2019, 128, 352–368. [Google Scholar] [CrossRef]
- Solimine, J.; Niezrecki, C.; Inalpolat, M. An experimental investigation into passive acoustic damage detection for structural health monitoring of wind turbine blades. Struct. Health Monit. 2020, 1–15. [Google Scholar] [CrossRef]
- Gong, L.; Zhao, Y.; Xiang, C.; Li, Z.; Qian, C.; Yang, P. Robust Light-Weight Magnetic-Based Door Event Detection with Smartphones. IEEE Trans. Mob. Comput. 2019, 18, 2631–2646. [Google Scholar] [CrossRef]
- Bello, H.; Zeng, X.; Nordin, R.; Jian, X. Advances and opportunities in passive wake-up radios with wireless energy harvesting for the internet of things applications. Sensors 2019, 19, 3078. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Sánchez, A.; Blanc, S.; Yuste, P.; Perles, A.; Serrano, J.J. An ultra-low power and flexible acoustic modem design to develop energy-efficient underwater sensor networks. Sensors 2012, 12, 6837–6856. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Fuketa, H.; O’Uchi, S.; Matsukawa, T. A 0.3-V 1-μw Super-Regenerative Ultrasound Wake-Up Receiver with Power Scalability. IEEE Trans. Circuits Syst. II Express Briefs 2017, 64, 1027–1031. [Google Scholar] [CrossRef]
- Oletic, D.; Bilas, V. Energy-efficient respiratory sounds sensing for personal mobile asthma monitoring. IEEE Sens. J. 2016, 16, 8295–8303. [Google Scholar] [CrossRef]
- Rovere, G.; Fateh, S.; Benini, L. A 2.2-µW Cognitive Always-On Wake-Up Circuit for Event-Driven Duty-Cycling of IoT Sensor Nodes. IEEE J. Emerg. Sel. Top. Circuits Syst. 2018, 8, 543–554. [Google Scholar] [CrossRef]
- Goux, N.; Badets, F. Review on event-driven wake-up sensors for ultra-low power time-domain design. In Proceedings of the 2018 IEEE 61st International Midwest Symposium on Circuits and Systems (MWSCAS), Windsor, ON, Canada, 5–8 August 2018; pp. 554–557. [Google Scholar] [CrossRef]
- Olsson, R.H.; Bogoslovov, R.B.; Gordon, C. Event driven persistent sensing: Overcoming the energy and lifetime limitations in unattended wireless sensors. In Proceedings of the 2016 IEEE Sensors, Orlando, FL, USA, 30 October–3 November 2016; pp. 1–3. [Google Scholar] [CrossRef] [Green Version]
- Oletic, D.; Korman, L.; Magno, M.; Bilas, V. Time-frequency pattern wake-up detector for low-power always-on sensing of acoustic events. In Proceedings of the 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Houston, TX, USA, 14–17 May 2018; pp. 1–6. [Google Scholar] [CrossRef]
- Alías, F.; Socoró, J.C.; Sevillano, X. A review of physical and perceptual feature extraction techniques for speech, music and environmental sounds. Appl. Sci. 2016, 6, 143. [Google Scholar] [CrossRef] [Green Version]
- Carmel, D.; Yeshurun, A. Detection of alarm sounds in noisy environments. In Proceedings of the 25th European Signal Processing Conference (EUSIPCO), Kos, Greece, 28 August–2 September 2017; pp. 1839–1843. [Google Scholar] [CrossRef] [Green Version]
- Alvarez-Rosario, A.; Padovese, L.R. Real-time performance of energy based underwater acoustic event detectors embedded in a single-board computer. In Proceedings of the 2015 IEEE/OES Acoustics in Underwater Geosciences Symposium (RIO Acoustics), Rio de Janeiro, Brazil, 29–31 July 2015; pp. 1–5. [Google Scholar] [CrossRef]
- Oletic, D.; Gazivoda, M.; Bilas, V. A programmable 3-channel acoustic wake-up interface enabling always-on detection of underwater events within 20 µA. Proc. Eurosensors 2018, 2, 768. [Google Scholar] [CrossRef] [Green Version]
- Moctezuma, F.P.; Prieto, M.D.; Martinez, L.R. Performance Analysis of Acoustic Emission Hit Detection Methods Using Time Features. IEEE Access 2019, 7, 71119–71130. [Google Scholar] [CrossRef]
- Giusa, F.; Giuffrida, A.; Trigona, C.; Andò, B.; Bulsara, A.R.; Baglio, S. “Random mechanical switching harvesting on inductor”: A novel approach to collect and store energy from weak random vibrations with zero voltage threshold. Sens. Actuators A Phys. 2013, 198, 35–45. [Google Scholar] [CrossRef]
- Trigona, C.; Giuffrida, S.; Andò, B.; Baglio, S.; Elettrica, I.; Doria, V.A. Micromachined “Random Mechanical Harvester on Inductor” to recovery energy from very low-amplitude vibrations with zero-voltage threshold. In Proceedings of the 2016 IEEE Sensors, Orlando, FL, USA, 30 October–3 November 2016; pp. 2–4. [Google Scholar] [CrossRef]
- Bradai, S.; Naifar, S.; Trigona, C.; Baglio, S.; Kanoun, O. Electromagnetic transducer with bistable-RMSHI for energy harvesting from very weak kinetic sources. In Proceedings of the 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Houston, TX, USA, 14–17 May 2018; pp. 1–5. [Google Scholar] [CrossRef]
- Gazivoda, M.; Oletic, D.; Trigona, C.; Bilas, V. Measurement of Weak Signal Energy at Acoustic Frequencies by using RMSHI as a Passive Conditioning Circuit. In Proceedings of the 2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Auckland, New Zealand, 20–23 May 2019; pp. 1735–1739. [Google Scholar] [CrossRef]
- Gazivoda, M.; Oletic, D.; Bilas, V. Characterization and comparison of envelope detectors for wake-up sensor interfaces at audio frequencies. In Proceedings of the 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), Dubrovnik, Croatia, 25–28 May 2020; pp. 1–6. [Google Scholar] [CrossRef]
- Trigona, C.; Andò, B.; Baglio, S. Performance Measurement Methodologies and Metrics for Vibration Energy Scavengers. IEEE Trans. Instrum. Meas. 2017, 66, 3327–3339. [Google Scholar] [CrossRef]
- Ott, H.W. Noise Reduction Techniques in Electronic Systems; Wiley-Interscience: Livingston, NJ, USA, 1988; ISBN 0-471-85068-3. [Google Scholar]
- Vinolo, C.; Toma, D.; Manuel, A.; Del Rio, J. Sea motion electrical energy generator for low-power applications. In Proceedings of the 2013 MTS/IEEE OCEANS-Bergen, Bergen, Norway, 10–14 June 2013. [Google Scholar] [CrossRef]
- Giuffrida, A.; Giusa, F.; Trigona, C.; Andò, B.; Baglio, S. Optimal Parameters Selection for Novel Low-Voltage Vibration Energy Harvesters. In Sensors Microsystems, Proceedings of the 17th National Conference, Brescia, Italy, 5–7 February 2013; Springer: Cham, Switzerland, 2014; Volume 268, pp. 313–316. [Google Scholar] [CrossRef]
- Underwater Video of Twin Engine Boat Props High Speed. Available online: https://www.youtube.com/watch?v=6uQ7IDqbmAE (accessed on 7 June 2018).
Parameter | Unit | Value | Method of Estimation |
---|---|---|---|
m | kg | 0.00082 | Model |
d | kg/s | 0.001 | Experiment |
δ | kg m−2 s−2 | 300,000 | Model |
∏ | V/m | 1.13 | Experiment |
τ | s | 162 | Literature [27] |
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Gazivoda, M.; Oletić, D.; Trigona, C.; Bilas, V. Passive Extraction of Signal Feature Using a Rectifier with a Mechanically Switched Inductor for Low Power Acoustic Event Detection. Sensors 2020, 20, 5445. https://doi.org/10.3390/s20185445
Gazivoda M, Oletić D, Trigona C, Bilas V. Passive Extraction of Signal Feature Using a Rectifier with a Mechanically Switched Inductor for Low Power Acoustic Event Detection. Sensors. 2020; 20(18):5445. https://doi.org/10.3390/s20185445
Chicago/Turabian StyleGazivoda, Marko, Dinko Oletić, Carlo Trigona, and Vedran Bilas. 2020. "Passive Extraction of Signal Feature Using a Rectifier with a Mechanically Switched Inductor for Low Power Acoustic Event Detection" Sensors 20, no. 18: 5445. https://doi.org/10.3390/s20185445
APA StyleGazivoda, M., Oletić, D., Trigona, C., & Bilas, V. (2020). Passive Extraction of Signal Feature Using a Rectifier with a Mechanically Switched Inductor for Low Power Acoustic Event Detection. Sensors, 20(18), 5445. https://doi.org/10.3390/s20185445