Ultrasonic Underwater Acoustic Modem Using Frequency Shift Keying (FSK) Modulation

Indriyanto, Slamet; Edward, Ian Joseph Matheus

doi:10.1109/icwt.2018.8527809

Cited by 24 publications

(13 citation statements)

References 8 publications

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“…An Arduino Uno, powered and controlled the transmitter/receiver components. Such a system was previously demonstrated in underwater, low power US data transmission over short distances [ 24 ]. The US transducer had a peak transmission at 40 kHz with 3 kHz bandwidth.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of a Machine Learning Algorithm to Classify Ultrasonic Transducer Misalignment and Deployment Using TinyML

Brennan,

Galvin

2024

Sensors

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The challenge for ultrasonic (US) power transfer systems, in implanted/wearable medical devices, is to determine when misalignment occurs (e.g., due to body motion) and apply directional correction accordingly. In this study, a number of machine learning algorithms were evaluated to classify US transducer misalignment, based on data signal transmissions between the transmitter and receiver. Over seven hundred US signals were acquired across a range of transducer misalignments. Signal envelopes and spectrograms were used to train and evaluate machine learning (ML) algorithms, classifying misalignment extent. The algorithms included an autoencoder, convolutional neural network (CNN) and neural network (NN). The best performing algorithm, was deployed onto a TinyML device for evaluation. Such systems exploit low power microcontrollers developed specifically around edge device applications, where algorithms were configured to run on low power, restricted memory systems. TensorFlow Lite and Edge Impulse, were used to deploy trained models onto the edge device, to classify signals according to transducer misalignment extent. TinyML deployment, demonstrated near real-time (<350 ms) signal classification achieving accuracies > 99%. This opens the possibility to apply such ML alignment algorithms to US arrays (capacitive micro-machined ultrasonic transducer (CMUT), piezoelectric micro-machined ultrasonic transducer (PMUT) devices) capable of beam-steering, significantly enhancing power delivery in implanted and body worn systems.

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Section: Methodsmentioning

confidence: 99%

Evaluation of a Machine Learning Algorithm to Classify Ultrasonic Transducer Misalignment and Deployment Using TinyML

Brennan,

Galvin

2024

Sensors

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“…Indriyanto and Edward 27 used frequency shift keying (FSK) Modulation to design an underwater acoustic modem with ultrasonic frequency. The modem's mark frequency is 40 kHz, and its spatial frequency is 43 kHz.…”

Section: Acoustic Communication In Underwatermentioning

confidence: 99%

Underwater acoustic sensor networks: Taxonomy on applications, architectures, localization methods, deployment techniques, routing techniques, and threats: A systematic review

Gola

Arya

2023

Concurrency and Computation

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SummaryWireless information routing beneath the ocean, or usually underwater, has supplied the most excellent technical methods of oceanic observations. Traditionally, ocean bottoms have been monitored by placing oceanographic sensors that collect data at specific and defined ocean zones. When the duties are performed, the oceanographic instruments are recovered. Because there is no collaborative exchange of collected data between the collecting point and the monitoring end, data cannot be observed remotely. Underwater sensor networks can be wirelessly connected with sensors and monitors without extensive cable. Underwater wireless sensor networks are what they are called (UWSNs). This article investigates the various aspects of UWSNs, such as their significance, applications, network design, demands, routing, deployments and challenges.

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“…These researchers investigated mobile communication systems with a frequency of 74 kHz and a data rate of 1kbps. Based on previous research, namely ultrasonic underwater acoustic modem using FSK modulation (Indriyanto & Edward, 2018), modems have been made with FSK modulation and use ultrasonic frequencies, with 1200 bps speed used.…”

Section: Previous Workmentioning

confidence: 99%

“…Underwater acoustic modem development using FSK modulation has an average bit rate of 200 bps to 400 bps (Wu et al, 2012) (Benson et al, 2010). A previous study on this matter used the modem in a speed of 1200 bps (Indriyanto & Edward, 2018). This study, however, will increase the speed to 2400bps using FSK modulation and analyze the modem's performance.…”

Section: Introductionmentioning

confidence: 99%

Underwater Data Transmission Using Frequency Shift Keying (FSK) Modulation with Bit Rate of 2400 bps

Indriyanto¹,

Isnawati²,

Hendry³

et al. 2020

bul. pos. dan telekomun.

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Underwater acoustic communication is a technology that uses sound or acoustic waves and water as its propagation medium. This technology has been used in various fields, such as underwater wireless sensor networks, underwater monitoring system, and surveillance systems. An acoustic modem is required to facilitate communication between nodes. In this paper, an underwater acoustic modem using Frequency Shift Keying (FSK) modulation has been designed. This modulation is widely used because of its reliability and simple design. FSK modem was designed using M=2 level or known as Binary FSK (BFSK) with 40 kHz mark frequency and 43 kHz space frequency. This study tested data packets sending and its error rate against the distance variation. Testing for 70-bit data resulted in 1% error at 100 cm distance and 37% error at 170 cm distance. When compared with the previous testing at 1200 bps which resulted in 0% and 35% error, it can be seen that error at 1200 bps is better than at 2400 bps, but the data transmission was better at 2400 bps. Addition to the number of bits sent and distance has an influence on the error value, i.e. the greater the distance and the amount of data sent, the greater the error value.

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Ultrasonic Underwater Acoustic Modem Using Frequency Shift Keying (FSK) Modulation

Cited by 24 publications

References 8 publications

Evaluation of a Machine Learning Algorithm to Classify Ultrasonic Transducer Misalignment and Deployment Using TinyML

Evaluation of a Machine Learning Algorithm to Classify Ultrasonic Transducer Misalignment and Deployment Using TinyML

Underwater acoustic sensor networks: Taxonomy on applications, architectures, localization methods, deployment techniques, routing techniques, and threats: A systematic review

Underwater Data Transmission Using Frequency Shift Keying (FSK) Modulation with Bit Rate of 2400 bps

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