Underwater Depth and Temperature Sensing Based on Fiber Optic Technology for Marine and Fresh Water Applications

Duraibabu, Dinesh Babu; Leen, Gabriel; Toal, Daniel; Newe, Thomas; Lewis, Elfed; Dooly, Gerard

doi:10.3390/s17061228

Cited by 58 publications

(17 citation statements)

References 25 publications

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“…We believe if the fish in the cage can be monitored through IoT technology, e.g., by using various sensors such as underwater camera [ 20 ], velocimeter [ 21 ], temperature sensor, etc. [ 22 ], substantial economic benefits can be obtained by collecting huge amount of long-term breeding and environment data and subjecting them to Big data analysis. Recently, our research team has set forth an AI initiative, which is funded by MOST under the title: Applying artificial intelligence (AI) techniques to implement a Practical Smart Cage Aquaculture Management System , the system architecture of which is shown in Figure 7 .…”

Section: Resultsmentioning

confidence: 99%

Fast Visual Tracking Based on Convolutional Networks

Huang

Tsao

Kuo³

et al. 2018

Sensors

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Recently, an upsurge of deep learning has provided a new direction for the field of computer vision and visual tracking. However, expensive offline training time and the large number of images required by deep learning have greatly hindered progress. This paper aims to further improve the computational performance of CNT which is reported to deliver 5 fps performance in visual tracking, we propose a method called Fast-CNT which differs from CNT in three aspects: firstly, an adaptive k value (rather than a constant 100) is determined for an input video; secondly, background filters used in CNT are omitted in this work to save computation time without affecting performance; thirdly, SURF feature points are used in conjunction with the particle filter to address the drift problem in CNT. Extensive experimental results on land and undersea video sequences show that Fast-CNT outperforms CNT by 2~10 times in terms of computational efficiency.

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Section: Resultsmentioning

confidence: 99%

Fast Visual Tracking Based on Convolutional Networks

Huang

Tsao

Kuo³

et al. 2018

Sensors

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“…Measuring errors also exist in fiber in-line Mach-Zehnder Interferometer (MZI) designs, which have a common characteristic of RI sensitivity to the liquid analyte: thus the following features 1) the sensor is used with a bare fiber core [6]; and 2) a thin clad fiber structure, which has a large evanescent field exposed to the surrounding [7]; 3) the sensor is based on cladding mode and core mode interference, which usually is formed by employing two heterogenic fibers (e.g., multimode fiber [8] or PCF [9]) or abnormal fiber fusion splicing (e.g., core offset [10], Sshaped [11] or peanut-like [12] arrangements), used to excite the high order/cladding modes propagating in the sensing fiber, which lead to a relatively large energy spread out of the doped core -as a result, the silica cladding acts as the "core" of the sensing structure. In light of this, noting previous work where similar cases were seen and where extrinsic FP cavity structures were proposed [13] - [17], particular attention needs to be paid to the use of specialized materials and the process of packaging shell design, essential for marine applications. All of this significantly increases the cost and difficulty of fabrication.…”

Section: Introductionmentioning

confidence: 82%

Underwater Pressure and Temperature Sensor Based on a Special Dual-Mode Optical Fiber

Lei

Dong

et al. 2020

IEEE Access

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In this paper, an all fiber optic sensor based on a Mach-Zehnder interferometer (MZI) has been proposed for the simultaneous measurement of underwater pressure and temperature, utilizing a dualmode fiber (DMF) which has been specially designed, and supporting only the LP01 and LP02 modes propagating in the fiber. In this design, an in-line MZI sensor that was constructed by splicing a DMF between two pieces of single mode fibers, shows a critical wavelength (CWL) which exists in the transmission spectrum of the LP01-LP02 mode interference. Since the two peaks, located closest to the CWL (and from both lower and higher wavelengths), shift in opposite directions and show different sensitivities under temperature and water pressure variations, the DMF-MZI sensor is capable of measuring both the water pressure and the temperature simultaneously. The CWL-based interference spectrum is stable with the variation of underwater salinity or impurities seen around the fiber surface and independent of the polarization states of the transmission light. As a result, in the operation of the DMF-MZI sensor, underwater pressure and temperature sensitivities increase significantly, when the peak wavelengths are close to that of the CWL. A theoretical analysis has been developed and used to predict that the sensitivities of this specific DMF-MZI structure which can be further improved by increasing the physical length of the DMF and by adjusting the position of the first left/right peak to be closer to the critical wavelength. This co-located, multi-parameter all-fiber sensor developed in this way and showing relatively high sensitivity is easy to implement in the underwater environment. It does not require a complex shell design and the peaks nearest to a CWL are convenient, allowing easy identification and detection, thereby providing a large measurement range to satisfy the requirements of practical marine and fresh water measurements. INDEX TERMS fiber sensor, temperature, pressure, dual mode fiber, Mach-Zehnder interferometer.

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“…Another development that uses multiple sensors is that of Duraibabu et al [12], which describes in detail the creation of an optical fibre pressure and temperature sensor (OFTPS) to measure these two variables from the same point in either fresh or ocean water. The OFTPS is made of glass and combines a Fabry-Perot interferometer (FPI) to measure pressures and a fibre Bragg grating (FBG) to detect temperatures simultaneously.…”

Section: Introductionmentioning

confidence: 99%

RiverCore: IoT Device for River Water Level Monitoring over Cellular Communications

Moreno

Aquino

Ibarreche

et al. 2019

Sensors

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Flooding is one of the most frequent and costly natural disasters affecting mankind. However, implementing Internet of Things (IoT) technology to monitor river behavior may help mitigate or prevent future disasters. This article outlines the hardware development of an IoT system (RiverCore) and defines an application scenario in a specific hydrological region of the state of Colima (Mexico), highlighting the characteristics of data acquisition and data processing used. Both fixed position and moving drifter node systems are described along with web-based data acquisition platform developments integrated with IoT techniques to retrieve data through 3G cellular networks. The developed architecture uses the Message Queuing Telemetry Transport (MQTT) protocol, along with encryption and security mechanisms, to send real-time data packages from fixed nodes to a server that stores retrieved data in a non-relational database. From this, data can be accessed and displayed through different customizable queries and graphical representations, allowing future use in flood analysis and prediction systems. All of these features are presented along with graphical evidence of the deployment of the different devices and of several cellular communication and on-site data acquisition tests.

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Underwater Depth and Temperature Sensing Based on Fiber Optic Technology for Marine and Fresh Water Applications

Cited by 58 publications

References 25 publications

Fast Visual Tracking Based on Convolutional Networks

Fast Visual Tracking Based on Convolutional Networks

Underwater Pressure and Temperature Sensor Based on a Special Dual-Mode Optical Fiber

RiverCore: IoT Device for River Water Level Monitoring over Cellular Communications

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