Self-Sustaining Acoustic Sensor With Programmable Pattern Recognition for Underwater Monitoring

Mayer, Philipp; Magno, Michele; Benini, Luca

doi:10.1109/tim.2018.2890187

Cited by 35 publications

(22 citation statements)

References 33 publications

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“…The stereo-audio sensing application presented in [44] utilizes on-chip feature extraction for high accuracy and reports a power consumption of 55 µW, which is comparable to the 43 µW power consumption of the RAMP in this paper. The underwater acoustic monitoring system presented in [45] focuses on a different application space but performs a spectral analysis like many other sensing systems highlighted in this section. The underwater system has a reported power consumption of 62 µW for its MSP and was constructed primarily from commercially available components.…”

Section: Resultsmentioning

confidence: 99%

“…The systems described in [40][41][42][43][44][45] were all fabricated in 65 nm, 90 nm, or 180 nm processes. Smaller process sizes naturally bring lower supply voltages and power consumption, particularly for digital systems.…”

Section: Resultsmentioning

confidence: 99%

“…While the acoustic monitoring systems in [6,[40][41][42][43][44][45] perform their respective tasks at low power levels, their application scope is constrained. In contrast, the AVDC implementation proposed in this paper was built on a highly reconfigurable platform, which opens up many more signal-processing possibilities; AVDC represents only a single application of the RAMP system, and many more low-power applications can be developed on the RAMP.…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

An Acoustic Vehicle Detector and Classifier Using a Reconfigurable Analog/Mixed-Signal Platform

Bhattacharyya

Andryzcik

Graham

2020

JLPEA

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The wireless sensor nodes used in a growing number of remote sensing applications are deployed in inaccessible locations or are subjected to severe energy constraints. Audio-based sensing offers flexibility in node placement and is popular in low-power schemes. Thus, in this paper, a node architecture with low power consumption and in-the-field reconfigurability is evaluated in the context of an acoustic vehicle detection and classification (hereafter “AVDC”) scenario. The proposed architecture utilizes an always-on field-programmable analog array (FPAA) as a low-power event detector to selectively wake a microcontroller unit (MCU) when a significant event is detected. When awoken, the MCU verifies the vehicle class asserted by the FPAA and transmits the relevant information. The AVDC system is trained by solving a classification problem using a lexicographic, nonlinear programming algorithm. On a testing dataset comprising of data from ten cars, ten trucks, and 40 s of wind noise, the AVDC system has a detection accuracy of 100%, a classification accuracy of 95%, and no false alarms. The mean power draw of the FPAA is 43 μ W and the mean power consumption of the MCU and radio during its validation and wireless transmission process is 40.9 mW. Overall, this paper demonstrates that the utilization of an FPAA-based signal preprocessor can greatly improve the flexibility and power consumption of wireless sensor nodes.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

An Acoustic Vehicle Detector and Classifier Using a Reconfigurable Analog/Mixed-Signal Platform

Bhattacharyya

Andryzcik

Graham

2020

JLPEA

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“…The recognition of infrequent events is of interest in many fields (environmental monitoring [ 1 , 2 , 3 , 4 , 5 ], safety and security [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 ], communication [ 14 , 15 , 16 ], agriculture, health monitoring [ 17 ]). It requires continuous operation of an electronic system comprising sensing, detection and recognition functions, which are power-hungry tasks [ 7 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Passive Extraction of Signal Feature Using a Rectifier with a Mechanically Switched Inductor for Low Power Acoustic Event Detection

Gazivoda

Oletić

Trigona

et al. 2020

Sensors

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Analog hardware used for signal envelope extraction in low-power interfaces for acoustic event detection, owing to its low complexity and power consumption, suffers from low sensitivity and performs poorly under low signal to noise ratios (SNR) found in undersea environments. To overcome those problems, in this paper, we propose a novel passive electromechanical solution for the signal feature extraction in low frequency acoustic range (200–1000 Hz), in the form of a piezoelectric vibration transducer, and a rectifier with a mechanically switched inductor. A simulation study of the novel solution is presented, and a proof-of-concept device is developed and experimentally characterized. We demonstrate its applicability and show the advantages of the passive electromechanical device in comparison to the active electrical solution in terms of operation with lower input signals (<20 mV compared to 40 mV), and higher robustness in low SNR conditions (output voltage loss for −10 dB ≤ SNR < 40 dB of 1 mV, compared to 10 mV). In addition to the signal processing performance improvements, compared to our previous work, the utilization of the presented novel passive feature extractor would also decrease power consumption of a detector’s channel by over 76%, enabling life-time extension and/or increased quality of detection with larger number of channels. To the best of our knowledge, this is the first solution presented in the literature that demonstrates the possibility of using a passive electromechanical feature extractor in a low-power analog wake-up event detector interface.

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“…Efforts related to positioning a system based on GPS have been made; the characterization of the whole system in this case was done by means of a statistical study, considering different numbers of beacons [27]. Additionally, a novel programmable event-driven acoustic detector featuring audio pattern recognition for underwater deployment has been developed in [28]. Even a robust algorithm related to an autonomous underwater vehicle (AUV) was proposed to improve the supervised missions [29].…”

Section: Introductionmentioning

confidence: 99%

Design and Construction of an ROV for Underwater Exploration

Aguirre-Castro

Inzunza-González

García-Guerrero

et al. 2019

Sensors

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The design of a remotely operated vehicle (ROV) with a size of 18.41 cm × 29.50 cm × 33.50 cm, and a weight of 15.64 kg, is introduced herein. The main goal is to capture underwater video by remote control communication in real time via Ethernet protocol. The ROV moves under the six brushless motors governed through a smart PID controller (Proportional + Integral + Derivative) and by using pulse-wide modulation with short pulses of 1 μs to improve the stability of the position in relation to the translational, ascent or descent, and rotational movements on three axes to capture images of 800 × 640 pixels on a video graphic array standard. The motion control, 3D position, temperature sensing, and video capture are performed at the same time, exploiting the four cores of the Raspberry Pi 3, using the threading library for parallel computing. In such a way, experimental results show that the video capture stage can process up to 42 frames per second on a Raspberry Pi 3. The remote control of the ROV is executed under a graphical user interface developed in Python, which is suitable for different operating systems, such as GNU/Linux, Windows, Android, and OS X. The proposed ROV can reach up to 100 m underwater, thus solving the issue of divers who can only reach 30 m depth. In addition, the proposed ROV can be useful in underwater applications such as surveillance, operations, maintenance, and measurement.

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Self-Sustaining Acoustic Sensor With Programmable Pattern Recognition for Underwater Monitoring

Cited by 35 publications

References 33 publications

An Acoustic Vehicle Detector and Classifier Using a Reconfigurable Analog/Mixed-Signal Platform

An Acoustic Vehicle Detector and Classifier Using a Reconfigurable Analog/Mixed-Signal Platform

Passive Extraction of Signal Feature Using a Rectifier with a Mechanically Switched Inductor for Low Power Acoustic Event Detection

Design and Construction of an ROV for Underwater Exploration

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