Investigation of the Scattering Noise in Underwater Optical Wireless Communications

Majlesein, Behnaz; Ghassemlooy, Zabih

doi:10.3390/sci3020027

Cited by 8 publications

(4 citation statements)

References 31 publications

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“…The quality of color image data is often degraded by additive noise, impulse noise, or mixed additive and impulse noise [1]. Additive noise, as a temporal type of noise source, includes dark noise, shot noise, and noise from mechanical vibrations, which, generally, can be approximated by a Gaussian function with a signal-dependent mean [2]. Impulse noise is normally produced by noisy sensors and/or transmission errors [3][4][5][6] in the data transmission or communication channels, as well as during the data capturing process from digital cameras [7].…”

Section: Introductionmentioning

confidence: 99%

Digital Filtering Techniques Using Fuzzy-Rules Based Logic Control

Yin,

Hadjiloucas

2023

J. Imaging

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This paper discusses current formulations based on fuzzy-logic control concepts as applied to the removal of impulsive noise from digital images. We also discuss the various principles related to fuzzy-ruled based logic control techniques, aiming at preserving edges and digital image details efficiently. Detailed descriptions of a number of formulations for recently developed fuzzy-rule logic controlled filters are provided, highlighting the merit of each filter. Fuzzy-rule based filtering algorithms may be designed assuming the tailoring of specific functional sub-modules: (a) logical controlled variable selection, (b) the consideration of different methods for the generation of fuzzy rules and membership functions, (c) the integration of the logical rules for detecting and filtering impulse noise from digital images. More specifically, we discuss impulse noise models and window-based filtering using fuzzy inference based on vector directional filters as associated with the filtering of RGB color images and then explain how fuzzy vector fields can be generated using standard operations on fuzzy sets taking into consideration fixed or random valued impulse noise and fuzzy vector partitioning. We also discuss how fuzzy cellular automata may be used for noise removal by adopting a Moore neighbourhood architecture. We also explain the potential merits of adopting a fuzzy rule based deep learning ensemble classifier which is composed of a convolutional neural network (CNN), a recurrent neural networks (RNN), a long short term memory neural network (LSTM) and a gated recurrent unit (GRU) approaches, all within a fuzzy min-max (FMM) ensemble. Fuzzy non-local mean filter approaches are also considered. A comparison of various performance metrics for conventional and fuzzy logic based filters as well as deep learning filters is provided. The algorhitms discussed have the following advantageous properties: high quality of edge preservation, high quality of spatial noise suppression capability especially for complex images, sound properties of noise removal (in cases when both mixed additive and impulse noise are present), and very fast computational implementation.

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Section: Introductionmentioning

confidence: 99%

Digital Filtering Techniques Using Fuzzy-Rules Based Logic Control

Yin,

Hadjiloucas

2023

J. Imaging

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“…Research on various underwater optical channel characteristics and models has progressed in recent years, but researchers have mostly completed only theoretical analysis and simulation studies or experimental studies at shorter distances (several meters) 10 – 16 Nevertheless, the implementation of the UWOC system is more complicated in the unique environment of the ocean, so relatively few experimental studies on the channel characteristics over medium-and-long distances have been conducted. Therefore, it is possible to simulate open ocean conditions on an experimental setup of limited length.…”

Section: Introductionmentioning

confidence: 99%

Experiment-based channel characteristics for underwater wireless optical communication over medium-and-long distance

Guo

et al. 2023

Opt. Eng.

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We provide an experimental study of the channel characteristics in an underwater wireless optical communication (UWOC) system given by a 35-m transmission distance in various water types. A UWOC system using a low-power 488-nm laser diode is established to comprehensively evaluate the influence of the link distance, water turbidity, receiver parameters, and link misalignment on the communication link power. The results show that the link misalignment-induced power loss is significant and the effect of receiver parameters on power is limited. However, a higher turbidity or longer signal transmission distance can help to reduce the link misalignment effect and manifest the receiver parameters effect on the received power. In particular, in turbid waters with an attenuation coefficient of 0.471 m −1 , the change of the signal reception power curve is small over a certain degree of the link misalignment in the 35-m physical distance, and the reasonable configuration of receiver parameters effectively improves the signal reception quality in the UWOC system. These results can provide theoretical guidance for optimizing the UWOC system.

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“…Nevertheless, UOWC is very challenging since optical signals are affected by misalignment [ 4 , 5 ], ocean turbulence [ 6 , 7 ] and the absorption existing in the underwater channel, aside from scattering due to the presence of suspended particles, especially in turbid waters [ 8 , 9 ]. Even though UOWC is the most suitable model for the scenarios depicted in Figure 1 , practical systems still need to be improved with increasingly innovative tools and techniques.…”

Section: Introductionmentioning

confidence: 99%

Misalignment-Resilient Propagation Model for Underwater Optical Wireless Links

Araujo

Tavares

Marques

et al. 2022

Sensors

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This paper proposes a multiple-lens receiver scheme to increase the misalignment tolerance of an underwater optical wireless communications link between an autonomous underwater vehicle (AUV) and a sensor plane. An accurate model of photon propagation based on the Monte Carlo simulation is presented which accounts for the lens(es) photon refraction at the sensor interface and angular misalignment between the emitter and receiver. The results show that the ideal divergence of the beam of the emitter is around 15° for a 1 m transmission length, increasing to 22° for a shorter distance of 0.5 m but being independent of the water turbidity. In addition, it is concluded that a seven-lense scheme is approximately three times more tolerant to offset than a single lens. A random forest machine learning algorithm is also assessed for its suitability to estimate the offset and angle of the AUV in relation to the fixed sensor, based on the power distribution of each lens, in real time. The algorithm is able to estimate the offset and angular misalignment with a mean square error of 5 mm (6 mm) and 0.157 rad (0.174 rad) for a distance between the transmitter and receiver of 1 m and 0.5 m, respectively.

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Investigation of the Scattering Noise in Underwater Optical Wireless Communications

Cited by 8 publications

References 31 publications

Digital Filtering Techniques Using Fuzzy-Rules Based Logic Control

Digital Filtering Techniques Using Fuzzy-Rules Based Logic Control

Experiment-based channel characteristics for underwater wireless optical communication over medium-and-long distance

Misalignment-Resilient Propagation Model for Underwater Optical Wireless Links

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