Fog Weather Conditions Research Articles

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.

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.

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.

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.

Performance analysis of 1280 Gbps DWDM – FSO system employing advanced modulation schemes

In this work, we present a 1280 Gbps hybrid fiber + FSO system employing 32 channel dense wavelength division multiplexing (DWDM) and advanced modulation schemes. The system performance has been investigated by considering modified duobinary return-to-zero (MDRZ), duobinary return-to-zero (DRZ), and compressed spectrum return-to-zero (CSRZ) modulation formats under clear weather conditions. The effect of internal system parameters such as beam divergence, diameter of transmitter & receiver aperture has been analyzed in case of duobinary scheme as it outperforms the other two modulation formats. The performance of the proposed system has also been examined for different climatic turbulent conditions including rain, haze and fog weather conditions to predict the maximum range of transmission by employing duobinary modulation format. The received signal quality deteriorates below an unacceptable value in case of heavy fog, haze and rain weather conditions for the FSO range beyond 800 m, 1400 m and 1000 m respectively.

Link budget analysis of free space optical communication link for atmospheric conditions of India

The performance of FSO links gets degraded significantly due to fog weather conditions. This paper aims to perform the link budget analysis for different locations of India by analyzing the impact of foggy weather on the operation of FSO links. The meteorological data of daily visibilities of potential locations of India, have been analyzed carefully to calculate the probability of exceeding visibility, ranging from 1 to 5 km, for each of the locations. It has been observed that Ludhiana has recorded maximum fog events having visibility less than 1 km and Chennai has recorded maximum events having visibility more than 5 km. In addition to this, the average attenuation coefficients due to fog have been calculated for each of the locations, based on the meteorological data. Further, the maximum feasible link distances for various locations of India have been computed for three different practical configurations of FSO links by using the link margin equation. The coastal area of Chennai has delivered maximum feasible link ranges as compared to other considered locations of India.

ATSS Deep Learning-Based Approach to Detect Apple Fruits

In recent years, many agriculture-related problems have been evaluated with the integration of artificial intelligence techniques and remote sensing systems. Specifically, in fruit detection problems, several recent works were developed using Deep Learning (DL) methods applied in images acquired in different acquisition levels. However, the increasing use of anti-hail plastic net cover in commercial orchards highlights the importance of terrestrial remote sensing systems. Apples are one of the most highly-challenging fruits to be detected in images, mainly because of the target occlusion problem occurrence. Additionally, the introduction of high-density apple tree orchards makes the identification of single fruits a real challenge. To support farmers to detect apple fruits efficiently, this paper presents an approach based on the Adaptive Training Sample Selection (ATSS) deep learning method applied to close-range and low-cost terrestrial RGB images. The correct identification supports apple production forecasting and gives local producers a better idea of forthcoming management practices. The main advantage of the ATSS method is that only the center point of the objects is labeled, which is much more practicable and realistic than bounding-box annotations in heavily dense fruit orchards. Additionally, we evaluated other object detection methods such as RetinaNet, Libra Regions with Convolutional Neural Network (R-CNN), Cascade R-CNN, Faster R-CNN, Feature Selective Anchor-Free (FSAF), and High-Resolution Network (HRNet). The study area is a highly-dense apple orchard consisting of Fuji Suprema apple fruits (Malus domestica Borkh) located in a smallholder farm in the state of Santa Catarina (southern Brazil). A total of 398 terrestrial images were taken nearly perpendicularly in front of the trees by a professional camera, assuring both a good vertical coverage of the apple trees in terms of heights and overlapping between picture frames. After, the high-resolution RGB images were divided into several patches for helping the detection of small and/or occluded apples. A total of 3119, 840, and 2010 patches were used for training, validation, and testing, respectively. Moreover, the proposed method’s generalization capability was assessed by applying simulated image corruptions to the test set images with different severity levels, including noise, blurs, weather, and digital processing. Experiments were also conducted by varying the bounding box size (80, 100, 120, 140, 160, and 180 pixels) in the image original for the proposed approach. Our results showed that the ATSS-based method slightly outperformed all other deep learning methods, between 2.4% and 0.3%. Also, we verified that the best result was obtained with a bounding box size of 160 × 160 pixels. The proposed method was robust regarding most of the corruption, except for snow, frost, and fog weather conditions. Finally, a benchmark of the reported dataset is also generated and publicly available.

Raised cosine/NRZ line coding techniques for upgrading free space optical communication systems through various levels of fog

This study examines raised cosine/NRZ line coding techniques for upgrading free space optical (FSO) communication systems through various levels of fog.The max. Q factors are simulated and estimated for clear air, light fog, and moderate fog weather conditions at a data rate of 20 Gb/s. The optical signal to noise ratio is also measured for different weather conditions. The total power after both FSO channel and avalanche photodiode (APD) photo-detector receiver is estimated by an optical power meter for max. distances.The study shows that the max. propagation distance is extended to 50 kmthrough clear air, 2.25 km through light fog, and 1.6 km through moderate fog with acceptable max. Q factor

Fleet Management for HDVs and CAVs on Highway in Dense Fog Environment

Adverse weather conditions have a significant impairment on the safety, mobility, and efficiency of highway networks. Dense fog is considered the most dangerous within the adverse weather conditions. As to improve the traffic flow throughput and driving safety in dense fog weather condition on highway, this paper uses a mathematical modeling method to study and control the fleet mixed with human-driven vehicles (HDVs) and connected automatic vehicles (CAVs) in dense fog environment on highway based on distributed model predictive control algorithm (DMPC), along with considering the car-following behavior of HDVs driver based on cellular automatic (CA) model. It aims to provide a feasible solution for controlling the mixed flow of HDVs and CAVs more safely, accurately, and stably and then potentially to improve the mobility and efficiency of highway networks in adverse weather conditions, especially in dense fog environment. This paper explores the modeling framework of the fleet management for HDVs and CAVs, including the state space model of CAVs, the car-following model of HDVs, distributed model predictive control for the fleet, and the fleet stability analysis. The state space model is proposed to identify the status of the feet in the global state. The car-following model is proposed to simulate the driver behavior in the fleet in local. The DMPC-based model is proposed to optimize rolling of the fleet. Finally, this paper used the Lyapunov stability principle to analyze and prove the stability of the fleet in dense fog environment. Finally, numerical experiments were performed in MATLAB to verify the effectiveness of the proposed model. The results showed that the proposed fleet control model has the ability of local asymptotic stability and global nonstrict string stability.

A high-speed radio-over-free-space optics link using wavelength division multiplexing-mode division multiplexing-multibeam technique

Radio-over-Free-Space Optics (RoFSO) is a key technology for providing high-speed communication links in rural areas. In this work, an orthogonal frequency division multiplexing based RoFSO transmission link has been designed by adopting hybrid wavelength division multiplexing (WDM), mode division multiplexing (MDM) and multibeam technique. The proposed link is investigated under heavy fog weather conditions and its performance is compared for 1-beam and 4-beams transmission using numerical simulations. The numerical results show faithful transportation of high-speed data at 2000 m with acceptable performance of the link. Furthermore, the link performance is compared with previously reported works and the proposed RoFSO link with hybrid WDM-MDM-multibeam technique demonstrates an enhanced link range and transmission capacity performance.

Performance Analysis of 20 Gbit/s–40 GHz MDM-Ro-FSO Link Incorporating DPSK Modulation Scheme

Abstract The congestion in the radio frequency spectrum has posed a serious challenge for mobile service providers. Radio-over-free space optics (Ro-FSO) technology has been considered as a front runner to deal with the problem of spectrum congestion and to provide future generation high-capacity wireless networks. This work proposes designing and numerical analysis of differential phase shift keying (DPSK) modulation-based 20 Gbit/s–40 GHz hybrid mode division multiplexing-radio-over-free space optics (MDM-Ro-FSO) link up to 40 km transmission range under clear weather. Quality Factor (Q Factor), log of bit error rate (BER), signal to noise ratio (SNR), and received power are the key metrics used to probe the proposed system performance. The impact of increasing divergence angle and different fog weather conditions on the proposed system performance is also numerically evaluated. Further, the decomposition analysis of different spatial modes at the receiving unit is carried out in this work.

Multiple-vehicle collision influenced by misjudgment of space headway in traffic flow under fog weather condition

The multiple-vehicle collision easily happens under fog weather condition. To illustrate the effect of misjudgment of space headway on the multiple-vehicle collision in traffic flow, this paper derives collision criterion of the car-following model including risk illusions in fog weather (RIFM). Numerical simulations are carried out by varying driver’s sensitivity, vehicular density and initial velocity. This paper obtains the region map for the multiple-vehicle collision when a vehicle stops suddenly in traffic flow. The results indicate that although under low density conditions, the collision will happen just because of the misjudgment of space headway. In general, when drivers are affected by the misjudgment of space headway, the number of crumpled vehicles has positive correlation with initial velocity and vehicular density, and negative correlation with driver’s sensitivity. Therefore, in order to ensure traffic safety, the initial velocity and vehicular density should be reduced, and driver’s sensitivity should be increased under fog weather condition.

Comparative analysis of MIMO-based FSO and MIMO-based MGDM communications

The ever-growing need for high data rate, bandwidth efficiency, reliability, less complexity, and less power consumption in the communication systems is on the increase. Modern techniques have to be developed and put in place to meet these requirements. In the last few years, free space optical (FSO) communication systems have attracted considerable research efforts mainly due to their inherent potential transmission capacity, much higher than that offered by radio transmission technologies. However, despite of great potential of FSO communication, its performance is limited by the adverse effects (viz., absorption, scattering, and turbulence) of the atmospheric channel. Different studies on weather conditions and techniques employed to mitigate their effect are studied. On other hand, a MMF network may constitute the backbone network that feeds fixed-wired services (e.g. voice telephony) as well as wireless services (e.g. IEEE 802.x) throughout the building. The large bandwidth makes MMF a very attractive medium for low-cost broadband in building networks, in which several independent sets of services are integrated. In addition, research has shown that compared to conventional single-input single-output (SISO) systems, multiple-input multiple-output (MIMO) can actually increase the data rate of a communication system, without actually requiring more transmit power or bandwidth. In this paper, we develop a comparison between MIMO over multimode fiber (MMF) using MGDM and MIMO based FSO links. The achievable performance improvements, including received power levels, bit error rate (BER), and quality factor (Q-factor) are presented. Numerical results are obtained for single-input single-output (SISO) as well as for multiple-input multiple-output (MIMO) configurations. Simulation results show that MMF outperforms FSO links in terms of max Q-factor, minimum BER, and eye diagram. Seen that the dependence of the FSO system performance on the weather conditions is one of the most significant FSO limitations, we analyze the performance of FSO under clear, haze, and fog conditions using Q-factor, bit error rate (BER), etc., at varied distances. From the results, it can be concluded that as we move from clear weather conditions to haze and fog weather conditions, Q-factor of the received signal decreases.

Link Estimation of Different Indian Cities Under Fog Weather Conditions

The high data rate, unlicensed spectrum, and secure communication make FSO system attractive to be used in next-generation wireless networks. However, the atmospheric effects like rain, snow, haze and fog severely degrade the Free Space Optics (FSO) system performance. Out of all weather condition fog heavily attenuates the optical signal. Therefore, before establishing FSO link a complete metrological survey of the area should be done. In this paper, we present a link availability analysis of two Indian cities namely Amritsar and New Delhi under fog weather condition. In literature, various techniques are mentioned that are used to enhance the reliability of FSO system under harsh weather conditions. Orthogonal Frequency Division Multiplexing is widely used in FSO. To achieve high data rate and high link availability under fog, we have designed a coherent detection Orthogonal Frequency Division Multiplexing with spatial diversity FSO system and calculate the link availability for two Indian cities. In literature, only theoretical estimation of link availability is given but in this research, a practical estimation of link availability is presented. At first, the theoretical estimation of link availability is presented on the basis of visibility data. The visibility data is simulated by OPTISYSTEM software and link performance is calculated on the basis of Bit Error Rate and Signal to Noise Ratio. The practical estimation of link availability is calculated and it is concluded that link availability of Amritsar is higher than that of New Delhi. The designed system significantly improves the link availability even under harsh weather conditions like fog.

Analyzing the effect of fog weather conditions on driver lane-keeping performance using the SHRP2 naturalistic driving study data

Introduction: Driving in foggy weather conditions has been recognized as a major safety concern for many years. Driver behavior and performance can be negatively affected by foggy weather conditions due to the low visibility in fog. A number of previous studies focused on driver performance and behavior in simulated environments. However, very few studies have examined the impact of foggy weather conditions on specific driver behavior in naturalistic settings. Method: This study utilized the second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) dataset to evaluate driver lane-keeping behavior in clear and foggy weather conditions. Preliminary descriptive analysis was conducted and a lane-keeping model was developed using the ordered logistic regression approach to achieve the study goals. Results: This study found that individual variables such as visibility, traffic conditions, lane change, driver marital status, and geometric characteristics, as well as some interaction terms (i.e., weather and gender, surface condition and driving experience, speed limit and mileage last year) significantly affect lane-keeping ability. An important finding of this study illustrated that affected visibility caused by foggy weather conditions decreases lane-keeping ability significantly. More specifically, drivers in affected visibility conditions showed 1.37 times higher Standard Deviation of Lane Position (SDLP) in comparison with drivers who were driving in unaffected visibility conditions. Conclusions: These results provide a better understanding of driver lane-keeping behavior and driver perception of foggy weather conditions. Moreover, the results might be used to improve Lane Departure Warning (LDW) systems algorithm by allowing them to account for the effects of fog on visibility. Practical Applications: These results provide a better understanding of driver lane-keeping behavior and driver perception of foggy weather conditions. Moreover, the results might be used to improve Lane Departure Warning (LDW) systems algorithm by allowing them to account for the effects of fog on visibility.

Performance Limits of FSO Based SAC-OCDMA System Under Weather Conditions

AbstractIn this paper, the Spectral Amplitude Coding Optical Code Division Multiple Access (SAC-OCDMA) is investigated in Free Space Optics (FSO) using Zero Cross Correlation (ZCC) codes to evaluate its performance limits in terms of link range. The system is analyzed under clear, haze, moderate fog and dense fog weather conditions. This system has been evaluated numerically and by simulation analysis by maintaining the transmitted power at 10 dBm, for safety reason, according to International Telecommunication Union (ITU) and minimum acceptable BER of10{10}–9. The simulation results shown that the present system can transmit 622 Mbps/1 Gbps up to maximum link range of 1450 m/1100 m and 61 m/54 m under clear and dense fog conditions, respectively.

Performance of 120 Gbps single channel coherent DP-16-QAM in terrestrial FSO link under different weather conditions

The performance of a 120 Gbps single-carrier coherent transceiver system is evaluated in a terrestrial Free Space Optical Link (FSOL) under the haze, rain, and fog weather conditions. In-phase and Quadrature (IQ) based Dual Polarization 16 Quadrature Amplitude Modulation (DP-16-QAM) is implemented in OptiSystem v.13 simulation platform with homodyne detection and Digital Signal Processing (DSP) at the receiver. The IQ based optical modulator and the DSP unit can be fabricated in the form of Integrated Circuits (ICs) help to keep the system cost low. The system performance is evaluated in terms of Optical Signal to Noise Ratio (OSNR) requirements at an acceptable Bit Error Rate (BER ≤ 2 × 10−3, i.e., below the FEC limit) under low turbulence environment. Simulation results show that the system maintains good OSNR performance under severe weather conditions. This transceiver system shall be useful in short-reach, low-cost, high-capacity, robust communication link with dynamic environmental conditions.

Impacts of Weather and Environmental Conditions on Mobile Communication Signals

The impacts of weather and environmental conditions on mobile communication signals were determined in this study. A Glo mobile communication network operating in the 900MHz band was considered. The Glo fixed base transceiver station (BTS) location at Gloworld in Benin City was considered. A frequency-signal tracker software, version 2.5.1 was installed and configured into a notebook Intel palm top, relative parameters data were obtained from 200 meters from the Glo BTS from 28th of July to 31st of August 2016, with data obtained hourly. Morning, afternoon and evening, and dry weather, fog weather and raining conditions was based on the statistical central tendency parameters. The average refractivity gradient observed was -61.3 N/km. It was observed that dry weather, signal strength variation was within 32 dBm, fog, variation was within 34 dBm range, while the variation of rain was within 38 dBm range indicating higher variation. It was observed that the more the mobile station move away from the BTS the higher the signal loss and that temperature and refractivity gradient has 0.50 and 0.42 positive correlations. In addition, relative humidity and pressure possesses negative correlations of -0.50 and -0.44 respectively.