Thick Fog Research Articles

IRS-assisted vehicular visible light communications systems: channel modeling and performance analysis.

Visible light communications (VLC) is a promising solution as an alternative for the fully occupied radio frequency bands in the near future. The rear (tail) and front of vehicles have lamps that can be used for vehicular visible light communications (VVLC) systems. However, one of the main challenges of VLC systems is the line-of-sight (LoS) blockage issue. In this paper, we propose the installation of intelligent reflecting surfaces (IRSs) (i.e.,smart mirrors) on the back of vehicles to overcome the issue in VVLC systems. We assume three different patterns of angular distribution for the radiation intensity: a commercially available LED with an asymmetrical pattern (Philips Luxeon Rebel), a symmetrical Lambertian pattern, and an asymmetrical Gaussian pattern. In the first section of this paper, we obtain the channel model for the IRS-assisted VVLC systems, then we investigate the path loss results versus link distance under different conditions such as weather type (clear, rainy, moderate fog, and thick fog) and radiation patterns. Moreover, the impact of system parameters such as the aperture size of the photodetector (PD), side-to-side and front-to-front distances, the number of IRS elements, and the IRS area are studied. In the second part, we derive a closed-form expression for the maximum achievable link distance versus the probability of error for the IRS-assisted VVLC systems. In addition, in this section we analyze the impact of the parameters in a single-photon avalanche diode (SPAD), background noise, and the system parameters for the path loss.

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.

Evaluation of histogram image defogging methods based on histogram equalization, dark channel prior, and convolutional neural network

Air pollution condition is getting worse with the advancement of society development, environmental pollution has gradually intensified, and smog frequently occurs in more and more cities. On foggy day, the saturation and contrast of an image could be low, and colors tend to drift and distortion. As a result, seeking a simple and effective image de-fogging technique is important for the subsequent research. In this study, three existing classical de-fogging algorithms are reproduced: histogram equalization, dark channel prior method, and convolutional neural network. The three de-fogging algorithms were compared respectively under the conditions of thin fog, thick fog, high brightness, and low brightness, so as to analyze their advantages and disadvantages. It is concluded that there is no obvious difference among the three algorithms in the de-fogging effect under the conditions of thick fog and high brightness, but relatively speaking, the de-fogging image generated by the dark channel prior is more real. When the fog is thin, the dark channel prior and convolutional neural network work better. Under the condition of low brightness, the histogram equalization has a better de-fogging effect.

Departure Vessel Scheduling Optimization Considering Traffic Restrictions in Turning Basin: A Case Study for Xuwen Terminal

As the largest modern passenger Roll-on Roll-off (RoRo) terminal around the world, the berthing operation of Xuwen terminal is occasionally suspended due to bad weather, such as strong wind or thick fog. During the suspension, the number of stranded passengers and vehicles increasingly accumulates. As soon as the weather permits, the growth exerts great pressure, especially on large-scale vessels leaving the port, whose inefficiency may cause a loss of access to the terminal for inbound ships and chaos for port management. The focus of this study is to improve the efficiency of departure scheduling by optimizing traffic rules in the harbor basin. A mathematical optimization model is formulated for minimizing the total scheduling time, and then an adaptive simulated annealing (ASA) algorithm is proposed to solve the model. A specific decoding rule is introduced, referring to the characteristics of the mentioned model. After employing the operation data of the Xuwen terminal, a numerical experiment showed that the proposed scheduling method outperformed the first-come, first-served (FCFS) strategy and an improved ant colony algorithm (ACA). Moreover, the constructed simulation model of the terminal manifested the validity of the optimal solution.

GTMNet: a vision transformer with guided transmission map for single remote sensing image dehazing

Existing dehazing algorithms are not effective for remote sensing images (RSIs) with dense haze, and dehazed results are prone to over-enhancement, color distortion, and artifacts. To tackle these problems, we propose a model GTMNet based on convolutional neural networks (CNNs) and vision transformers (ViTs), combined with dark channel prior (DCP) to achieve good performance. Specifically, a spatial feature transform (SFT) layer is first used to smoothly introduce the guided transmission map (GTM) into the model, improving the ability of the network to estimate haze thickness. A strengthen-operate-subtract (SOS) boosted module is then added to refine the local features of the restored image. The framework of GTMNet is determined by adjusting the input of the SOS boosted module and the position of the SFT layer. On SateHaze1k dataset, we compare GTMNet with several classical dehazing algorithms. The results show that on sub-datasets of Moderate Fog and Thick Fog, the PSNR and SSIM of GTMNet-B are comparable to that of the state-of-the-art model Dehazeformer-L, with only 0.1 times of parameter quantity. In addition, our method is intuitively effective in improving the clarity and the details of dehazed images, which proves the usefulness and significance of using the prior GTM and the SOS boosted module in a single RSI dehazing.

WiFEX: Walk into the Warm Fog over Indo-Gangetic Plain Region

Abstract The presence of persistent heavy fog in northern India during winter creates hazardous situations for transportation systems and disrupts the lives of about 400 million people. The meteorological factors responsible for its genesis and predictability are not yet completely understood in this region. Given its high potential for socioeconomic impact, there is a pressing need for extensive research that understands the inherently complex nature of the phenomena through field observations and modeling exercises. WiFEX is a first-of-its-kind multi-institutional initiative dealing with intensive ground-based measurement campaigns for developing a suitable fog forecasting capability under the aegis of the smart cities mission of India. Measuring campaigns were conducted during the 2015–20 winters at the Indira Gandhi International Airport, New Delhi, covering more than 90 dense fog events. The field experiments involved extensive suites of in situ instruments and gathered simultaneous observations of micrometeorological conditions, radiative fluxes, turbulence, droplet/aerosol microphysics, aerosol optical properties, fog water chemistry, and vertical thermodynamical structure to describe the environmental stability in which fog develops. An operational modeling framework, the WRF Model, was set up to provide fog predictions during the measurement campaign. These field observations helped to interpret the strengths and deficiencies in the numerical modeling framework. Four scientific objectives were pursued: (i) the life cycle of optically thin and thick fog, (ii) microphysical properties in the polluted boundary layer, (iii) fog water chemistry, gas–aerosol partitioning during the fog life cycle, and (iv) numerical prediction of fog. This paper presents an overview of WiFEX and a synthesis of selected observational and modeling analyses/findings related to the abovementioned scientific topics.

Carbon fluxes associated with fog in an elfin cloud forest in Anaga (Tenerife, Canary Islands)

The role of fog in the relict laurel forests of the Macaronesia is still not fully understood and it is even more relevant in the Canary Islands, where precipitations are rather scarce and summers are dry. Eddy covariance CO2 exchange (FCO2) measurements in a “brezal de tejo” elfin cloud forest in Tenerife (Canary Islands), over a four-weeks period during the wet season, showed that the vegetation can sustain a carbon assimilation rate in an environment of fog, leading to higher median water use efficiency values (19.5 μmol mg−1 ≤ WUE ≤ 68.3 μmol mg−1) than during no-fog periods (WUE = 1.8 μmol mg−1), being highest under thick fog (WUE = 68.3 μmol mg−1). Median FCO2 values across the day were slightly greater under foggy versus no-fog conditions during the study period. These increments were more prominent under a low light environment, especially in the morning, thus highlighting the important difference between cloud immersion versus high clouds. A greater proportion of light diffusivity due to fog may enhance carbon assimilation, although additional concomitant factors may intervene during thick fog conditions. Also, net carbon assimilation was initiated earlier under foggy conditions. During cloud immersion and depleted light, the maximal CO2 exchange, FCO2_max, diurnal values were higher than those observed when fog was absent. Additionally, FCO2_max remained high (FCO2_max ≈ 13 μmol m−2 s−1) for large vapour pressure deficit conditions (VPD > 0.6 kPa). All this is indicative of a profligate water use strategy of the vegetation, presumably due to low stomatal control. Fog was present during 36.9 % of the time and, as a consequence, the estimated mean daily settling flux of water droplets was 1.186 mg m−2 s−1. For the entire period measured, the cumulative settling flux was 1.30 mm compared to the 97.6 mm of rainfall and the 36.6 mm of throughfall, and the canopy was wet at least 33.3 % of the time. The results suggest that such an anisohydric behaviour of the vegetation permits a continuous growth with maximal water use efficiency, while the frequent presence of fog and wetting of the canopy would minimize the risk of cavitation associated with such non-conservative water use response.

Design and Evaluation of ACFC—An Automatic Cloud/Fog Collector

Cloud and fog droplets are essential in atmospheric chemistry since they affect the distribution and chemical transformation of pollutants. Collecting sufficient volumes using cloud/fog samplers is the premise of cloud fog chemical studies. Accurate identification of fog events and high collection efficiency are the basic principles of sampler design. Therefore, an automatic cloud/fog collector (ACFC) has been designed, fabricated, and extensively tested for collecting samples of cloud/fog water. The control box and standard sensors for air temperature, relative humidity, and instantaneous rainfall were used to ensure sampling automation. Airflow measurement was used to guarantee the stability of the airspeed on the inlet section, and the airspeed is 7.5 m s−1. Moreover, the median collection rate of ACFC was 160–220 mL h−1, which was tested via a simulation experiment. To evaluate the actual performance of the device in the field, we obtained eight samples of cloud fog water from Shanghuang Observatory in eastern China from the summer through the fall of 2022. Collection rates varied from 62 to 169 mL h−1. For a cloud/fog sampler equipped with multiple sensors, the ACFC has excellent sampling efficiency in thick fog, sufficient cloud fog water samples can be collected in weak fog, and it can precisely identify fog mingled with rain.

Empirical Analysis of Autonomous Vehicle's LiDAR Detection Performance Degradation for Actual Road Driving in Rain and Fog.

Light detection and ranging (LiDAR) is widely used in autonomous vehicles to obtain precise 3D information about surrounding road environments. However, under bad weather conditions, such as rain, snow, and fog, LiDAR-detection performance is reduced. This effect has hardly been verified in actual road environments. In this study, tests were conducted with different precipitation levels (10, 20, 30, and 40 mm/h) and fog visibilities (50, 100, and 150 m) on actual roads. Square test objects (60 × 60 cm2) made of retroreflective film, aluminum, steel, black sheet, and plastic, commonly used in Korean road traffic signs, were investigated. Number of point clouds (NPC) and intensity (reflection value of points) were selected as LiDAR performance indicators. These indicators decreased with deteriorating weather in order of light rain (10-20 mm/h), weak fog (<150 m), intense rain (30-40 mm/h), and thick fog (≤50 m). Retroreflective film preserved at least 74% of the NPC under clear conditions with intense rain (30-40 mm/h) and thick fog (<50 m). Aluminum and steel showed non-observation for distances of 20-30 m under these conditions. ANOVA and post hoc tests suggested that these performance reductions were statistically significant. Such empirical tests should clarify the LiDAR performance degradation.

“Into the Widest Imagination”

“Into the Widest Imagination”

Light transmission polarization patterns and depolarization from water fog concentration variation

We present both experimental measurements and Monte-Carlo-based simulations of transmission polarization to analyze the concentration variation effects of the water fog particles. Simulations based on Monte Carlo program have been conducted for tracking photon transmission process with the radius and refractive index of water fog samples under different optical thicknesses. Correspondingly, experiments with an environment modulation system show that by controlling the water fog concentration, the Mueller matrix and the depolarization characteristic change in a regular way. Both the simulation and the experimental results show a good distinction of Mueller matrix among the light, moderate, and thick fog samples, and the depolarization property of thick fog environment is more prominent. Moreover, circular polarization persists better than linear polarization in more serious fog. These works can provide a tool to examine different media concentrations by the polarization patterns and broaden the application range of polarization.

Facing natural extremes

As a consequence of a large-scale volcanic outburst, the Laki eruption in 1783–1784, the focus of the world turned suddenly towards Iceland, a province of the Danish crown. The dynamic volcano in Iceland had far-reaching consequences for the outside world as the pollution was carried further by the wind, causing dramatic changes in weather conditions. The temperature in Europe fell by 1.5 °C over a two-year period. Icelanders endured extreme hardship as sulphuric haze swept the country during the summer of 1783 and temperature dropped dramatically for a time. The period which followed is termed ‘The Famine of the Mist’ in Icelandic history due to the thick fog caused by the eruption and the extreme cold weather. This article will discuss the experience the people of Iceland underwent at the time. Correspondence, which is the main source of the article, gives an intimate glimpse of people’s lives during this critical period caused by the Laki eruption. The letters reveal that, albeit exhausted and traumatized, people were striving to remain optimistic. The eyes of Europe and the enlightened world of scientists were cast on Iceland during these dramatic times. Icelandic contacts with the outside world, despite Iceland being located far away in the North Atlantic, were various and flowed in more than one direction. The administration of Iceland was centred in Copenhagen where a plan for free trade was already in preparation at the outbreak of the eruption.

Analysis of Thermal Imaging Performance under Extreme Foggy Conditions: Applications to Autonomous Driving.

Object detection is recognized as one of the most critical research areas for the perception of self-driving cars. Current vision systems combine visible imaging, LIDAR, and/or RADAR technology, allowing perception of the vehicle's surroundings. However, harsh weather conditions mitigate the performances of these systems. Under these circumstances, thermal imaging becomes the complementary solution to current systems not only because it makes it possible to detect and recognize the environment in the most extreme conditions, but also because thermal images are compatible with detection and recognition algorithms, such as those based on artificial neural networks. In this paper, an analysis of the resilience of thermal sensors in very unfavorable fog conditions is presented. The goal was to study the operational limits, i.e., the very degraded fog situation beyond which a thermal camera becomes unreliable. For the analysis, the mean pixel intensity and the contrast were used as indicators. Results showed that the angle of view (AOV) of a thermal camera is a determining parameter for object detection in foggy conditions. Additionally, results show that cameras with AOVs 18° and 30° are suitable for object detection, even under thick fog conditions (from 13 m meteorological optical range). These results were extended using object detection software, with which it is shown that, for the pedestrian, a detection rate ≥90% was achieved using the images from the 18° and 30° cameras.

Improving LiDAR Object Classification Based on PointNet in Noisy Environments

The recognition and detection of 3D point cloud data are important research tools in the field of computer vision, with important applications in many significant fields, such as in unmanned driving, high-precision mapping, and robot-assisted vision. At the same time, with the development of deep learning technology, research on the recognition and detection of 3D point cloud data combined with deep learning technology is receiving more and more attention. One of the main problems with the current self-driving cars is that the detection ability of the optical radar’s echo can be affected by bad weather such as heavy rain, snow, thick smoke, or thick fog. The real signal is attenuated and often very weak or submerged in a large amount of noise, which affects its judgment of the outside environment, meaning that the autonomous vehicle is unable to move. Therefore, it is urgent to solve this problem to improve the accuracy of post-stereoscopic images. This study uses LiDAR to collect point cloud data, and then applies PointNet for deep learning training. Random noise added to the original point cloud data is filtered out with a filter. The accuracy of the original signal state and the signal after filtering the noise is compared. There is an improvement of 60.8% in the method detailed in this study. This method can be widely developed and applied to improve the LiDAR technology in the future.

High Resolution-Based Coherent Photonic Radar Sensor for Multiple Target Detections

The last decade witnessed remarkable growth in the number of global road accidents. To minimize road accidents, transportation systems need to become more intelligent. Multiple detection of target vehicles under adverse weather conditions is one of the primary challenges of autonomous vehicles. Photonic radar sensors may become the promising technology to detect multiple targets to realize autonomous vehicles. In this work, high-speed photonic radar is designed to detect multiple targets by incorporating a cost-effective wavelength division multiplexing (WDM) scheme. Numerical simulations of the proposed WDM-based photonic radar is demonstrated in terms of received power and signal to noise (SNR) ratio. The performance of the proposed photonic radar is also investigated under diverse weather conditions, particularly low, medium, and thick fog. The proposed photonic radar demonstrated a significant range resolution of 7 cm when the target was placed at 80 m distance from the photonic radar sensor-equipped vehicle. In addition to this, traditional microwave radar is demonstrated to prove the effectiveness of the proposed photonic radar.

Noise-tolerant Bessel-beam single-photon imaging in fog.

Reliable laser imaging is crucial to the autonomous driving. In unfavorable weather condition, however, it always suffers from the acute background noise and signal attenuation due to the harmful strong scattering. We demonstrate a noise-tolerant LiDAR with the help of Bessel beam illumination and single-photon detection. After a 31.5-m propagation in thick fog, the Bessel beam employed by our noise-tolerant LiDAR still owns a central spot with the diameter of 1.86 mm, which supports a receiving field of view as small as 60 µrad and a great suppression of the background noise. This noise-tolerant LiDAR simultaneously performs well both in depth and intensity imaging in unfavorable weather, which can be functioned as a reliable imaging sensor in automatic driving.

Free-space optical channel performance under atmospheric losses using orthogonal frequency division multiplexing

&lt;span&gt;Free space optics (FSO) is a fast-growing technology that outperforms fiber optics in communication network infrastructure without spectrum licensing. The goal of this work is to evaluate the performance of the FSO communication system using direct detection 4-quadrature amplitude modulation (4-QAM) and 4-phase shift keying (4-PSK) with orthogonal frequency division multiplexing (OFDM) scheme. Simulating a direct detection OFDM-FSO system and comparing constellation diagrams, electrical power, and optical power have been used to conduct the analysis. The model is validated using a constellation diagram of received signals for various FSO channel ranges and weather conditions. According to simulation results, the OFDM-FSO architecture combined with the 4-QAM modulation technique produces the most efficient output with the least amount of power consumption for data rates up to 10 Gb/s and FSO channel ranges of up to &lt;br /&gt; 3 km for clear air, 1.7 km, and 1.3 km for thin and thick fog, and 0.7 km for heavy fog. This investigation can enhance the current fiber optic network's structure connected with the FSO system to provide last-mile connectivity in 5G modules.&lt;/span&gt;

Mitigation of Atmospheric Attenuations in Hybrid Fiber-optical and Free-space Optical Channel for Last-mile Communication System

The aim of this research is to investigate the performance of a hybrid fiber-optical (FO) and free-space optical (FSO) communication system to reach the last mile, as well as to propose a technique to overcome atmospheric attenuation. Installing transmission cables between buildings is costly and difficult, but FSO technology can be implemented where fiber-optic cables cannot be installed. This work considers attenuation effects in weather conditions such as haze, thin fog, thick fog, and heavy fog. A single FSO channel (1*1), dual FSO channels (2*2), and four FSO channels (4*4) are cascaded in a fiber optic channel to reach the last mile and to mitigate attenuation in signal transmission over free space. The Q-factor, bit error rate (BER) at the receiver, and eye diagrams were analyzed for all cases. This system is demonstrated up to 3.5km FSO channel range for haze and thin fog conditions and 2.2km and 1.7km range for thick and heavy fog conditions. The system results show improvement in BER as 4.93e-040 for 1*1 FSO channel, 1.07e-078 for 2*2 FSO channel, and 1.28e-154 for 4*4 FSO channel in case of 22dB/km atmospheric attenuation for 1.4km range with minimum power penalty. A data stream of 10 Gbps was transmitted under four attenuation conditions, and the results show that 4*4 FSO channels have a better Q-factor and BER than other systems.

The First Characterization of Fog Microphysics in the United Arab Emirates, an Arid Region on the Arabian Peninsula

AbstractFog microphysics characteristics play an important role in fog life cycle, radiation and visibility. Measurements of fog microphysics are important in understanding these processes and improving existing model parameterization schemes. From 28 January to 17 February 2021, we carried out the first measurements of fog microphysics in the arid region of the United Arab Emirates and present initial results in this paper. Five events occurred, the longest lasting about 4 hr 28 min and the remaining lasting 2 hr or less. In this study, the meteorological conditions at synoptic scale associated with the formation of fog, are presented as well as the microphysics characteristics of fog. Maximum cloud droplet count ranged from 80 to 700 cc, Liquid Water Content (LWC) from 0.06 to 0.58 g m−3 and the Median Volume Diameter reached 25 μm in all the cases. The estimated Liquid Water Path reached a maximum of 100 g m−2. The size distribution was bimodal for all visibility classes of mist, fog and thick fog with modes at around 4.5 μm and between 22 and 25 μm. The droplet size distribution broadened as the mature phase progressed, demonstrating a decrease in droplets in the 6–16 μm range and an increase in droplets above 19 μm. During the mature stage, droplet growth was observed with no associated decrease in LWC, suggesting growth by collision and coalescence was occurring. However, LWC did increase concurrently suggesting droplet growth by condensation was also taking place.

Performance analysis of free space optical system incorporating circular polarization shift keying and mode division multiplexing

Abstract In this work, a free-space optical (FSO) communication system with the integration of mode division multiplexing and circular polarization shift keying (CpolSK) is proposed at 2 × 40 Gbps using LG00 and LG01 modes. Effects of diverse weather conditions such as clear weather, light rain, moderate rain, heavy rain, thin fog, thick fog, and heavy fog are studied on system performance. Further, a detailed comparison of CpolSK and polarization shift keying (PolSK) is performed at different FSO lengths in terms of log bit error rate. For implementation, analysis, and comparison, Optiwave Optisystem software is used and results show that CpolSK covers 100 km link distance and PolSK limits to 90 km only. Also, LG00 mode performs better than LG01 mode under all weather instabilities in the proposed system.