Fog Weather Research Articles

Remote sensing of ship polarization information through sea fog based on retinal visual information processing mechanisms

ABSTRACT It is challenging to observe the polarization information of ships in sea fog weather due to the depolarization phenomenon. Depolarization refers to the phenomenon that the polarization information of incident light is distorted after passing through the scattering medium such as sea fog, which poses a great challenge to polarization remote sensing of ships. Therefore, ship polarization information remote sensing through sea fog is equivalent to recovering the original polarization information of ships. In this paper, a new method is proposed for recovering the original polarization information of ships in sea fog environment. The method is inspired by the visual information processing mechanisms of the retina. In addition, a new type of visual receptive field, called deformable receptive field, is proposed to improve the performance of the polarization information restoration method. The proposed method does not depend on the hazy image formation model, the depth-dependent transmission, and the training on hazy and fog-free image pairs. As a by-product, fog-free images can be recovered using the proposed method. The effectiveness of the proposed method has been verified by an experiment on ship polarization information remote sensing in sea fog weather.

Sea Fog Recognition near Coastline Using Millimeter-Wave Radar Based on Machine Learning

Sea fog is a hazardous natural phenomenon that reduces visibility, posing a threat to ports and nearshore navigation, making the identification of nearshore sea fog crucial. Millimeter-wave radar has significant advantages over satellites in capturing sudden and localized sea fog weather. The use of millimeter-wave radar for sea fog identification is still in the exploratory stage in operational fields. Therefore, this paper proposes a nearshore sea fog identification algorithm that combines millimeter-wave radar with multiple machine learning methods. Firstly, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to partition radar echoes, followed by the K-means clustering algorithm (KMEANS) to divide the partitions into recognition units. Then, Sea-Fog-Recognition-Convolutional Neural Network (SFRCNN) is used to classify whether the recognition units are sea fog areas, and finally, the partition coverage algorithm is employed to improve identification accuracy. The experiments conducted using millimeter-wave radar observation data from the Pingtan Meteorological Observation Base in Fujian, China, achieved an identification accuracy of 96.94%. The results indicate that the proposed algorithm performs well and expands the application prospects of such equipment in meteorological operations.

Legibility of variable message signs on foggy highway: Effect of text color and spacing

Variable message signs are frequently utilized as highway communication facilities to inform vehicles about the state of the roads and the weather. However, standards and design specifications for variable message signs are unclear. This study examines the effect of the own factors and environmental factors on the legibility of variable message signs in order to optimize the design of variable message signs on highways. Three font colors (green, red, and yellow) and three levels of word spacing (0.1H, 0.3H, and 0.5H) were chosen for the variable message signs. For the environmental factors, three different weather types were represented according to visibility, namely sunny (visibility 10,000 m), light fog (visibility = 1000), and heavy fog (visibility = 200). The legibility time and distance were recorded for 23 participants, and then the legibility of the variable message signs was analyzed. The results showed that own factors and environmental factors influenced the legibility of the variable message signs. Experiment 1 results showed that the legibility of the variable message signs was the best when the text was yellow in light fog weather conditions. Experiment 2 results showed that the legibility of the variable message signs was better than other experimental scenarios in light fog conditions with a 0.3H word spacing. The findings of this study will be used by relevant agencies to establish design guidelines for variable message signs.

Harnessing the power of ML for robust SISO and MIMO FSO communication systems in fog weather

This study examines Free Space Optical (FSO) communication’s performance in different fog conditions, focusing on Single-Input Single-Output (SISO) and Multiple-Input Multiple-Output (MIMO) setups. In low fog, SISO handles signal degradation well. MIMO improves link robustness in moderate fog. High fog challenges traditional FSO, leading to ML integration to optimize communication parameters. For weather channel classification, a preprocessing scheme reduces features from 12 to 2 given that the 12 features are Bit Error Rate (BER), Quality Factor (Q-factor) and Received Optical Power (ROP) for different 4 users. A Gaussian Process Classifier (GPC) achieves an accuracy greater than 99%, surpassing SVM, Decision Tree, and Random Forest. GPC’s fit and predict functions execute in 0.15 s, outperforming NuSVM (0.2 s). This study highlights FSO, SISO, MIMO, and Machine Learning (ML) practicality in enhancing communication resilience in adverse weather, especially in fog-prone areas.

Disaster resilience hybrid ring-mesh topology basics integrated PON/FSO system incorporating 2D-MFRS code

Abstract This work proposes a hybrid ring-mesh topology basics bidirectional 4 × 40/40 Gbps integrated passive optical network/free space optics (PON/FSO) system. The system incorporates two-dimensional modified fixed right shifting (2D-MFRS) code with enhanced disaster resilience and fault protection capability especially in remote places where the primary fibre may be destroyed. The obtained results depict that proposed system supporting 250 subscribers can provides faithful hybrid 100 km fiber and 1 km FSO transmission. Also, maximum FSO range of 21 km with fixed 50 km fiber distance can be obtained concerning weak-to-strong turbulent and rain with fog weather conditions. In addition, it also offers −39 dB m receiver sensitivity and the proposed design is superb over other work.

Classification guided thick fog removal network for drone imaging: ClassifyCycle.

The foggy images captured by drones are nonuniform due to inhomogeneous distribution of fog in higher altitude, leading to the obvious fog thickness differences in the images. This paper proposes a classification guided thick fog removal network for drone imaging, termed ClassifyCycle. The drone images are input into the proposed classification module (ICLFn) to enhance the reliability of follow-up learning network. The style migration module (ISMn) is introduced to reduce the image distortion, such as hue artifact and texture distort. The proposed network ClassifyCycle does not require paired foggy and corresponding fog-free datasets, avoiding the phenomena of overexposure, distortion, color deviation and fog residue after defogging. Extensive experimental results show that the proposed ClassifyCycle network surpasses the state-of-the-art algorithms on synthetic and realistic drone images captured in thick fog weather.

An improved YOLO v4 used for grape detection in unstructured environment.

Visual recognition is the most critical function of a harvesting robot, and the accuracy of the harvesting action is based on the performance of visual recognition. However, unstructured environment, such as severe occlusion, fruits overlap, illumination changes, complex backgrounds, and even heavy fog weather, pose series of serious challenges to the detection accuracy of the recognition algorithm. Hence, this paper proposes an improved YOLO v4 model, called YOLO v4+, to cope with the challenges brought by unstructured environment. The output of each Resblock_body in the backbone is processed using a simple, parameterless attention mechanism for full dimensional refinement of extracted features. Further, in order to alleviate the problem of feature information loss, a multi scale feature fusion module with fusion weight and jump connection structure was pro-posed. In addition, the focal loss function is adopted and the hyperparameters α, γ are adjusted to 0.75 and 2. The experimental results show that the average precision of the YOLO v4+ model is 94.25% and the F1 score is 93%, which is 3.35% and 3% higher than the original YOLO v4 respectively. Compared with several state-of-the-art detection models, YOLO v4+ not only has the highest comprehensive ability, but also has better generalization ability. Selecting the corresponding augmentation method for specific working condition can greatly improve the model detection accuracy. Applying the proposed method to harvesting robots may enhance the applicability and robustness of the robotic system.

Machine learning based performance estimation of terrestrial Ro-FSO Link

In this work, a radio over free-space optical communication (Ro-FSO) link has been examined considering quadrature amplitude modulation (64-QAM) based orthogonal frequency division multiplexing (OFDM) technique for a turbulence channel. The performance of the system has been investigated considering log normal and gamma-gamma atmospheric scintillation models under clear air, rain and fog weather conditions. Artificial neural network (ANN), k-nearest neighbour (KNN), and decision tree (DT) machine learning (ML) techniques have been applied for estimation of quality of received signal in terms of bit error rate BER. ANN model exhibits the highest value of R-squared (R2) of 0.9967 and lowest value of root mean square error (RMSE) of 0.0134 as compared to other ML techniques resulting in the best fit model.

Image transmission performance analysis through free space optical communication link using coherent QPSK modulation under various environmental conditions

AbstractFree space optics (FSO) technology has been used recently in many applications due to its various advantages such as high bandwidth, easy deployment, high security, free installation license and immunity to electromagnetic interference. However, this type of communication is vulnerable to meteorological conditions such as rain, haze and fog which significantly degrade the system's efficiency. To enhance the effectiveness of the FSO systems, Quadrature Phase Shift Keying (QPSK) modulation based on coherent detection constitutes a promising technique for achieving high spectral efficiency and long‐distance FSO links with the characteristics of low‐cost implementation and simple design. This work aims to investigate the performance of FSO communication links on image transmission under the presence of atmospheric attenuations. A comparative study has been made using QPSK modulation scheme and on–off‐keying (OOK) modulation scheme to estimate the maximum range that was used to recover an RGB transmitted image over an FSO link under different meteorological weathers. The obtained results clearly show that the quality of the received image has degraded significantly during transmission through free space yielding to limit the propagation range. Moreover, the proposed FSO communication system performs well when using QPSK modulation rather than OOK modulation in different environmental conditions. Thus, to improve the quality of the received image under adverse fog weather condition, high power and small beam divergence angle have been suggested to increase system's performance. The proposed solution increases the communication range of the FSO link by 230 m and 100 m for moderate and heavy fog, respectively.

Towards Extreme Weather Detection: Dark Channel Priori Enhanced Detection with New Labelled Data

With more and more cars entering our lives, the number of car accidents is also increasing. To solve this problem, driverless technology has entered our field of vision. An effective way to realize unmanned driving technology is the application of deep learning. At present, the practice of various large companies in this field is mainly to realize the integrated deep learning of image recognition, judgment, and response on automobiles. The model performance is satisfactory in normal condition. However, if the vehicles encounter some extreme weather, e.g., foggy, it is difficult to ensure the safety of deep learning model due to the insufficient accuracy of image recognition. To solve this severe problem, we advance three strategies. Firstly, considering high-capacity backbone will mitigate this situation, Yolov5 with stronger performance was used as the detector in the experiment. Secondly, image pre-process would be benefit to this foggy situation. Thus, we introduce dark channel priori defogging to our detection framework. Thirdly, since deep learning is data hungry, we labelled image collected in heavy fog weather condition and utilize them to train the network. Abundant experiment demonstrates the effectiveness of the proposed strategies. We gain a precision of 0.89734 after training the model 299 times and the safety can be guaranteed with the high accuracy of image recognition.

Analysis of hybrid RF/FSO wireless communications links under foggy weather conditions

Abstract Terrestrial free-space optical (FSO) links are based on the fundamental idea of a light beam transporting information, enabling extremely high data rates. Fog still presents a significant obstacle to enhancing the availability and dependability of terrestrial FSO lines because the fog particles scatter the light as it travels and only significantly attenuate the signal. Using a hybrid network with an FSO link and a backup link will solve the problem. GHz offers outstanding performance in this frequency band. In this research, the attenuation caused by fog is predicted using an empirical model based on measurements of visibility and their impacts on FSO/RF links. Multiple functioning FSO and RF links operating at 1 GHz, 10 GHz, and 25 GHz, as well as visibility, were simultaneously monitored for changes in signal intensity. The bit error rate (BER) for the optical modulation formats NRZ-OOK, L-PPM, and RZ-OOK is investigated under various fog scenarios. According to the results of the simulation, RF link performance at 25 GHz is better suited for hybrid system fog weather circumstances. On the other hand, we discuss the appropriateness of fog models within these various modulation formats.

Analysis of Unmanned Four-Wheeled Bot with AI Evaluation Feedback Linearization Method

In this research paperwork, thereis the design and implementation of aBot with the ability to work in four directions of movement forward, backward, left, and right using aself-governingstability system. The bot's resultingbe in command of objective is to follow a path at the required speed, while its primary control purpose is to maintain equilibrium whenever the balance position is unstable owing to a change in the center of gravity. We report our surveys into the concertevaluation of a highly linear four-wheeledmatchingmachine using a PID regulator and a PI-PD regulator. Here I have added advantages with the AI evaluation feedback linearization technique to detect and process with auto error time solutions. The key benefits include cogency in the actual application; switchdevice, enhanced performance, and capacity to overcome uncertainties. Simulated and experimental findings are used to compare and support a performance evaluation of the system. Numerous automatic systems for detecting traffic accidents have been developed by researchers. These techniques frequently make use of many applications such as smartphones, infrared sensors, and mobile applications.All of these techniques fall short when it comes to the instinctiverecognition of traffic accidents. The sifters used in smartphones may make it difficult to detect low-speed collisions. The suggested system does not specify the threshold distances at which an IR sensor will react. It is suggested to use a revolutionary method based on ultrasonic sensors.Using an ultrasonic sensor to identify accidents allows for the ability to do so not only in different street contexts but also in industrial settings, busy intersections, and weather circumstances like clouds, fog weather, rain, and heavy traffic.

Validation of a hybrid RF/FSO communication system under foggy weather conditions

Abstract Future high-capacity data links are best suited for free space optical (FSO) communications since they have a larger bandwidth and secure transmission compared to RF links. FSO has several uses including indoor and outdoor settings. The impacts of weather are extensive, with fog being the main mitigating factor reducing link availability. The solution to the problem is to employ a hybrid network with RF/FSO and backup link. Different liquid water densities were used to analyze the performance of the hybrid RF/FSO system in foggy weather. In terms of BER, Q factor, and data rate, a comparison between RF and FSO lines is made. The findings demonstrated that in lower-medium of liquids with water density, the influence of fog weather on RF link is minimal. Under the same situations, an FSO link is useful as a backup link in higher liquid water density concentrations.

Influence of key parameters of ice accretion model under coexisting rain and fog weather

Based on 30 complete wire icing processes lasted longer than 24 h observed from the Enshi, Jinsha, Dacaoping and Shennongding of Shennongjia in mountainous areas of Hubei province during the winter of 2008–2016, the macroscopic effects of rain–fog weather on the ice accretion process were analyzed. Furthermore, the distribution characteristics of key simulation parameters in supercooled fog (SF) and freezing rain (FR) were discussed according to the physical model of icing process. Finally, the evolution characteristics of the simulated ice thickness in rain–fog weather were proposed. Results showed that the duration of ice accretion in mountainous areas is the key factor affecting the maximum ice thickness; the freezing rain is most frequent during the glaze icing process, which leads to the substantial growth of ice thickness. The average growth rates of ice thickness with and without freezing rain are 1.26 mm h−1 and -0.11 mm h−1, respectively. Collision rate is the main parameter for inhibiting ice accretion of SF, with an average value of ∼ 0.1, while freezing rate is the main parameter for inhibiting ice accretion of FR, with an average value of ∼ 0.6. The ice accretion of SF shows the characteristics of periodic growth, while the ice accretion of FR shows the explosive growth of ice thickness, which makes the simulated values of icing closer to the observations. The ice formation efficiency of FR was more than twice that of SF, with a negative feedback mechanism to the ice accumulation of SF.

OCDMA Performance on FSO Turbulent Weather Channel on Li-Fi Systems

 Because of advances in Light-Emitting Diodes LEDs and visible-light communication (VLC), future technology is light fidelity. The interference problems with radio frequency (RF) communication was overcome by using visible light communication (VLC) technology, which has higher bandwidth and greater security. On the other hand, the FSO link is affected by complex weather, Where Different atmospheric conditions affect the optical power of the FSO system very significantly, decreasing significantly. The effects of turbulent weather on OCDMA technology are examined in this paper and compared with and without using this technology at different distances in L-Bands in light of the increased number of users. There are various challenges that Li-Fi technology faces in maintaining the system performance with the increase in the number of users. These issues will cause a decrease in the system’s performance parameters, such as BER. Further, the challenges that exist are the total bandwidth usage and the number of users without affecting the system. Finally, security should be maintained at the highest level at the lowest cost. With the best results, the research shows that Optical Code division multiple access OCDMA technologies enhance system performance and stability, especially over long distances. On the contrary, the system will fail to function correctly over the same distance if the technology mentioned above is not utilized. For example, the heavy dust, the system achieved approximately from 1.02 * 10-12 to 9.5 * 10-11 with our proposed technology, whereas it achieved approximately from 2.8 * 10-7 to 4 * 10-4 without the proposed technology at a distance ranging from 60m to 160m.  the system was tested during heavy fog weather, considered the most challenging situation, with an n attenuation of 340dB/M. The results revealed that the system achieved roughly 1.11*10-12 to 9.25*10-11, but without OCDMA technology, it achieved between 3.8*10-8 and 1.4*10-4 at 20 m to 120 m. These include heavy dust, fog, and moderate attenuation of 340, 242, and 85 dB/m, which have been used.

Analysis of the Microphysical Structure and Evolution Characteristics of a Typical Sea Fog Weather Event in the Eastern Sea of China

This study is the first to use the observation data of a fog monitor, a visibility meter, and an automatic weather station to carry out a comprehensive observation experiment from the perspective of microphysics on a severe sea fog process in Beilun District, China, from 14 to 15 June 2021. The results show the following: (1) Temperature is closely related to nucleation, condensation growth, and other processes. The decrease (increase) in temperature is the main reason for the enhancement (weakening) of nucleation and the growth of condensation (evaporation of droplets), which leads to an increase (or decrease) in microphysical quantities, such as droplet number concentration and liquid water content. (2) The average droplet number spectral distribution roughly conforms to the Gamma distribution, and the spectral distribution of the fog process presents a ”multi-peak” structure, with peak diameters of 6 μm, 12 μm, 16 μm, 24 μm, and 44 μm. Droplets with a diameter of less than 16 μm account for 75% of the droplet size distribution. (3) During this sea fog process, three microphysical parameters, namely, number concentration, liquid water content, and average diameter, are all positively correlated in pairs, but the positive correlation between the number concentration and the average diameter is weak. This shows that the condensation nucleation and the condensation growth of droplets are the main processes in this sea fog process and that the collision process occurs but is not the dominant process. The sea fog comprehensive observation experiment provides an important demonstration of the microphysics research of sea fog in the eastern coastal areas of China and provides more reference information for sea fog research and equipment comparisons between different regions. At the same time, it also provides an essential scientific basis for the short-term forecast of sea fog in the future and for the optimization of the microphysical parameters of related models.

Performance analysis of 400 Gbit/s hybrid space division multiplexing‐polarization division multiplexing‐coherent detection‐orthogonal frequency division multiplexing‐based free‐space optics transmission system

SummaryA high‐capacity free‐space optics‐based terrestrial communication link using spectral‐efficient space division multiplexing and polarization division multiplexing techniques has been proposed in this research work. Orthogonal frequency division multiplexing with the use of coherent detection has been proposed for the transmission of quadrature amplitude modulated high‐speed signals. Eight independent 50‐Gbit/s data streams are transported by two orthogonal polarization states (X and Y) of four distinct Hermite–Gaussian spatial modes to increase the transmission rate of the link to 400 Gbit/s over a single channel. Through numerical simulations, we report feasible transportation of 400‐Gbit/s information at 10 km under clear weather with faithful performance. Further, the impact of atmospheric scintillation and fog weather has been evaluated using simulations. The results report that the maximum range reduces to 6 km under clear weather in the presence of strong atmospheric scintillation. Moreover, the maximum range of 2000, 1700, and 1300 m is reported for light, moderate, and heavy fog conditions respectively.

A Real-Time Ship Detector via a Common Camera

Advanced radars and satellites, suitable for remote monitoring, inappropriately reach the economical requirements of short-range detection. Compared with far-sightedness skills, common visible-light sensors offer more ample features conducive to distinguishing the classes. Therefore, ship detection based on visible-light cameras should cooperate with remote detection technologies. However, compared with detectors applied in inland transportation, the lack of fast ship detectors, detecting multiple ship classes, is non-negligible. To fill this gap, we propose a real-time ship detector based on fast U-Net and remapping attention (FRSD) via a common camera. The fast U-Net offered compresses features in the channel dimension to decrease the number of training parameters. The remapping attention introduced boosts the performance in various rain–fog weather conditions while maintaining the real-time speed. The ship dataset proposed contains more than 20,000 samples, alleviating the lack of ship datasets containing various classes. Data augmentation of the cross-background is especially proposed to further promote the diversity of the detecting background. In addition, the rain–fog dataset proposed, containing more than 500 rain–fog images, simulates various marine rain–fog scenarios and soaks the testing image to validate the robustness of ship detectors. Experiments demonstrate that FRSD performs relatively robustly and detects 9 classes with an mAP of more than 83%, reaching a state-of-the-art level.

A Comprehensive Modeling of Vehicle-to-Vehicle Based VLC System Under Practical Considerations, an Investigation of Performance, and Diversity Property

In this work, a vehicle-to-vehicle (V2V) visible light communications (VLC) model for two <b>practical scenarios</b>, is proposed. In <xref ref-type="other" rid="other1">scenario 1</xref>, the random lateral shift of vehicles and the deterministic longitudinal separation between two communicating vehicles are considered, whereas in <xref ref-type="other" rid="other2">scenario 2</xref>, longitudinal separation between two vehicles is considered to be random, and lateral shift of vehicles is considered to be deterministic. To this end, we emphasize <b>comprehensive modeling</b> of the <b>practical characteristics</b> of the considered V2V-VLC system, such as <b>random path loss</b> due to the <b>random mobility</b> of the vehicle, <b>random lateral shift</b> and <b>random longitudinal separation</b> of the vehicle. Moreover, we analyze the performance of the proposed V2V-VLC model in terms of different metrics under the consideration of a <b>novel channel model</b>. Considering our <b>findings</b>, it is observed that the random lateral shift of the vehicle and the random longitudinal separation between two vehicles have a significant impact on the V2V-VLC system performance. Further, at a distance of 40 m, for example, the path loss <b>penalties</b> for moderate and dense fog weather scenarios are 2 and 3 dB, respectively, compared with the clear weather. Furthermore, the <b>combined impact</b> of path loss and atmospheric turbulence affects the V2V-VLC performance significantly.

Effect of Fiber-Telescope Coupling Losses on Wideband Wavelength Division Multiplexing in Free Space Optical Communications

The effect of telescope losses (TL) on wideband wavelength division multiplexing (Wideband-WDM) based on free-space optical communication (FSOC) is investigated via OptiSystem software. In this work, both single and dual channel FSOC systems are investigated under different communication weathers: clear, haze, rain, and fog with attenuation losses of 0.2 dB/km, 2.3 dB/km, 4.3 dB/km, and 8.0 dB/km, respectively. The TL measures the coupling efficiency between the fiber and telescope in the transceiver assembly; therefore, is considered an important design parameter for the FSOC and hybrid optical communication system. According to the results, the maximum TL is 23 dB and 29 dB for single and dual FSOC, respectively, at the clear weather condition and for 1 km communication distance. This means that the dual-channel system could afford higher losses than the single system by approximately 26.1 %. Furthermore, this ratio is approximately the same for the other weather conditions, 28.6 %, 31.6 %, and 40 % for the hazy, rain, and fog weather, respectively.