Precipitable water vapor (PWV) has caught the interest of researchers for extensive study on weather prediction in recent times. Nevertheless, the prediction of rainfall cannot be solely reliant on a single metric, as other atmospheric factors also play a significant role. Atmospheric gradient has gained little attention in the field of meteorology, especially for the prediction of significant weather events. Moreover, the existing research on rainfall forecasting is mostly focused on an individual station rather than a larger region. This article aims to predict rain events in a certain geographical region instead of a single station, employing a novel methodology for the tropical climatic zone. Different features of the atmospheric gradient, along with PWV, have been rigorously studied in relation to rainfall to determine the potential criteria for predicting rain events over an area. Both gradient convergence and flux shift toward greater positive values even 6 h before the rain event over the rainy region, when compared to the area with no significant rain events, which further increases with the advancement of time. PWV also manifests a similar trend to the atmospheric gradient. In this article, we have proposed a novel dual-parameter algorithm with PWV and either of the two gradient features, which gives approximately 90% true detection (TD) with a much lower false alarm (FA) rate of around 22% for a region of$8 {\mathrm {^{\circ}}} \times 8 {\mathrm {^{\circ}}}$. Furthermore, another two-layer forecasting algorithm has been established, which precisely predicts the location of rainfall for the next 6 h in the tropical region.

Ground-penetrating radar (GPR) is a vital tool in the domain of nondestructive testing; however, its capability to accurately discern subsurface targets faces challenges from substantial background clutter. Current methods aimed at clutter suppression often leave residual clutter or distort the hyperbolic tails of target-scattered signals, particularly in heterogeneous soil conditions. This study endeavors to tackle the complexities of clutter suppression in practical scenarios. To this end, we introduce a novel framework for GPR clutter suppression using stationary graph signal (SGS) processing techniques. In our proposed approach, GPR B-scan images are treated as graph signals and transformed into the graph frequency domain via graph Fourier transform (GFT). This framework incorporates B-scan images containing both targets and clutter alongside clutter-only B-scan images gathered within the same testing environment. B-scan images featuring both targets and clutter serve as reference data samples for constructing a graph shift operator (GSO), with clutter and targets’ scattering signals interpreted as SGS. Following the establishment of weak SGSs with respect to the GSO, a variant of the graph-based Wiener filter tailored for GPR applications is applied to effectuate clutter suppression. Through our proposed SGS processing-based filtering method, clutter can be effectively suppressed, thereby facilitating the restoration of target scattering signals. Extensive experiments conducted on both numerical simulation data and field test data underscore the efficacy of the proposed approach, which can be further applied to the general nondestructive testing realm.

Recent advancements in meteorology involve the use of ground-based sky cameras for cloud observation. Analyzing images from these cameras helps in calculating cloud coverage and understanding atmospheric phenomena. Traditionally, cloud image segmentation relied on conventional computer vision techniques. However, with the advent of deep learning, convolutional neural networks (CNNs) are increasingly applied for this purpose. Despite their effectiveness, CNNs often require many epochs to converge, posing challenges for real-time processing in sky camera systems. In this paper, we introduce a residual U-Net with deep supervision for cloud segmentation which provides better accuracy than previous approaches, and with less training consumption. By utilizing residual connection in encoders of UCloudNet, the feature extraction ability is further improved. In the spirit of reproducible research, the model code, dataset, and results of the experiments in this paper are available at: https://github.com/Att100/UCloudNet.

Precipitable water vapour (PWV) is a crucial atmospheric parameter for initiating rainfall, cloud formation, convection, etc. Nevertheless, PWV is not solely responsible for precipitation, as other atmospheric parameters also play a pivotal role in the occurrence of rain events. Consequently, research on weather forecasts necessitates the investigation of other atmospheric parameters that can improve the forecast of rain events with higher. Gradient is another critical parameter that contributes greatly to analysing the evolution of a weather system. In recent days, machine learning has gained immense importance in the field of meteorology and remote sensing. Various machine-learning models have been used in previous literature to establish an improved algorithm for rainfall prediction. In this paper, we have taken the initial step towards conducting our research for a region to accurately forecast the imminent rain events 6 hours ahead of time. U-net architecture performs better than CNNs, with approximately 80% forecast accuracy, making it a suitable deep-learning model for rainfall prediction in tropical regions. Furthermore, PWV exhibits the greatest impact on rainfall prediction over a region of the various input parameters studied in this article.

Tree root systems are crucial for providing structural support and stability to trees. However, in urban environments, they can pose challenges due to potential conflicts with the foundations of roads and infrastructure, leading to significant damage. Therefore, there is a pressing need to investigate the subsurface tree root system architecture (RSA). Ground-penetrating radar (GPR) has emerged as a powerful tool for this purpose, offering high-resolution and nondestructive testing (NDT) capabilities. One of the primary challenges in enhancing GPR’s ability to detect roots lies in accurately reconstructing the 3-D structure of complex RSAs. This challenge is exacerbated by subsurface heterogeneity and intricate interlacement of root branches, which can result in erroneous stacking of 2-D root points during 3-D reconstruction. This study introduces a novel approach using our developed wheel-based dual-polarized GPR system capable of capturing four polarimetric scattering parameters at each scan point through automated zigzag movements. A dedicated radar signal processing framework analyzes these dual-polarized signals to extract essential root parameters. These parameters are then used in an optimized slice relation clustering (OSRC) algorithm, specifically designed for improving the reconstruction of complex RSA. The efficacy of integrating root parameters derived from dual-polarized GPR signals into the OSRC algorithm is initially evaluated through simulations to assess its capability in RSA reconstruction. Subsequently, the GPR system and processing methodology are validated under real-world conditions using natural Angsana tree root systems. The findings demonstrate a promising methodology for enhancing the accurate reconstruction of intricate 3-D tree RSA structures.

Tree defect detection is crucial for the structural health screening of trees. The existing nondestructive testing (NDT) techniques for tree defect detection require time-consuming and labor-intensive measurement campaigns. This discourages their application for the routine structural health screening of whole populations of managed urban trees. To address this issue, this study proposes a deep-learning augmented stand-off radar scheme for contactless scanning of tree trunks and rapid detection of tree defects. In this scheme, the antenna is moved along a straight trajectory at a distance from the tree trunk to obtain the trunk’s B-scan. The obtained raw B-scan is then processed by a signal-processing framework specifically developed for revealing the scattering signatures of defects in B-scan, which achieves a 30 and 22 dB increase in the signal-to-clutter and noise ratio of the measurement data of tree trunk samples and living trees, respectively. Finally, the processed B-scan is input into a multilevel feature fusion neural network particularly designed for extracting the signature of the defect in the processed B-scan in real time. The developed scheme’s applications to the detection of defects in real fresh-cut tree trunks show that the stand-off radar scheme can detect tree defects with 96% accuracy. This stand-off radar scheme is the first contactless NDT technique for tree defect detection while operated on a straight trajectory and potentially can be integrated into the routine tree inspection workflow, which is part of urban tree management.

Precipitable water vapor (PWV) is a crucial atmospheric parameter in meteorological studies as it evinces a clear correlation with precipitation which makes it incumbent for rainfall prediction. Nevertheless, the rainfall is not solely dependent on PWV, other dynamic factors also initiate the occurrence of rain events. Hence, the research on weather forecast demands the investigation of other atmospheric parameters as well which can better predict the rain events with improved accuracy and better lead time, particularly for tropical region. Atmospheric gradient is another imperative parameter which changes its direction with the movement of impending weather event and provides real atmospheric information. Till date, previous studies have been conducted to predict rainfall for individual stations. Now, we have put a step forward to carry out our research for a region to analyze an imminent weather front and predict it accurately with sufficient lead time. The atmospheric gradient shows a converging nature at the time of precipitation exhibiting the similar behavior as its wet component, however, the hydrostatic gradient increases in magnitude when the rain event starts to occur.

Cloud image classification has been extensively studied in the literature, as it has several radio-meteorological and remote sensing applications. Recently, images from ground-based sky imagers (GSIs) are being widely used because of their high temporal and spatial resolution and low infrastructure cost as compared to satellites. To classify sky/cloud images obtained from such GSIs, this paper1examines the application of transfer learning using the standard VGG-16 architecture. The paper further analyzes the importance of adjusting the number of neurons in the top dense layers to improve the performance of the model. The reasons for the same are traced by conducting extensive experiments on multiple datasets exhibiting varied properties.

The reconstruction of the 3D permittivity map from ground-penetrating radar (GPR) data is of great importance for mapping subsurface environments and inspecting underground structural integrity. Traditional iterative 3D reconstruction algorithms suffer from strong non-linearity, ill-posedness, and high computational cost. To tackle these issues, a 3D deep learning scheme, called 3DInvNet, is proposed to reconstruct 3D permittivity maps from GPR C-scans. The proposed scheme leverages a prior 3D convolutional neural network with a feature attention mechanism to suppress the noise in the C-scans due to subsurface heterogeneous soil environments. Then a 3D U-shaped encoder-decoder network with multi-scale feature aggregation modules is designed to establish the optimal inverse mapping from the denoised C-scans to 3D permittivity maps. Furthermore, a three-step separate learning strategy is employed to pre-train and fine-tune the networks. The proposed scheme is applied to numerical simulation as well as real measurement data. The quantitative and qualitative results show the networks’ capability, generalizability, and robustness in denoising GPR C-scans and reconstructing 3D permittivity maps of subsurface objects.

Tree root system 3D reconstruction and spatial distribution analysis are prevalent aspects of tree root investigation using ground penetrating radar (GPR). Precedent 3D reconstruction methods are found to be effective in mapping simple, smooth root structures. However, repetitive and dense B-scans are needed, otherwise, the retrieved roots’ spatial distribution and growth extension trend accuracy would deteriorate with the increase in the root systems’ complexity. To address these issues, this paper, for the first time, explores the possibility of integrating the horizontal angle information of the tree roots and a slice-relation-clustering (SRC) algorithm to reconstruct the complex tree root systems in a 3D manner. The proposed framework, which takes the roots’ horizontal angle as an analyzing condition instead of biological properties that are similar among neighboring branches used in existing methods, clusters pre-processed and focused 2D reflection patterns from the same single root together. The whole roots system is the combination of every single root cluster. Real measurement results show that our proposed method achieves a high efficiency in accurate root system reconstruction.

The application of GPS technology has been pervasive in meteorological science besides point positioning for weather prediction with high spatio-temporal resolution. In meteorology, rainfall forecasting is highly imperative to mitigate the destruction of public properties and lives. In recent years, precipitable water vapor has caught the interest of the scientists in the field of research on rainfall prediction. In this article, we have presented the correlation of precipitable water vapor with time and space during the transition of weather condition from a rainy period to dry one or vice-versa. The enhancement in PWV values in a larger region prior to rainfall makes it a potential predictor for rain for a particular area. The amendment in PWV slope with time before precipitation is also significant to forecast the impending rain event. These results substantiate the usefulness of precipitable water vapor as a potential indicator of weather forecasting.

Accurate monitoring of tree roots using ground penetrating radar (GPR) is very useful in assessing the trees' health. In high moisture tropical areas such as Singapore, tree fall due to root rot can cause loss of lives and properties. The tropical complex soil characteristics due to the high moisture content tends to affect penetration depth of the signal. This limits the depth range of the GPR. Typically, a wide band signal is used to increase the penetration depth and to improve the resolution of the GPR. However, this broad band frequency tends to be noisy and selective frequency filtering is required for noise reduction. Therefore, in this paper, we adapt the stepped frequency continuous wave (SFCW) GPR and propose the use of a Joint time frequency analysis (JTFA) method called short-time Fourier transform (STFT), to reduce noise and enhance tree root detection. The proposed methodology is illustrated and tested with controlled experiments and real tree roots testing. The results show promising prospects of the method for tree roots detection in tropical areas.

The forward full-wave modeling of ground-penetrating radar (GPR) facilitates the understanding and interpretation of GPR data. Traditional forward solvers require excessive computational resources, especially when their repetitive executions are needed in signal processing and/or machine learning algorithms for GPR data inversion. To alleviate the computational burden, a deep learning-based 2D GPR forward solver is proposed to predict the GPR B-scans of subsurface objects buried in the heterogeneous soil. The proposed solver is constructed as a bimodal encoder-decoder neural network. Two encoders followed by an adaptive feature fusion module are designed to extract informative features from the subsurface permittivity and conductivity maps. The decoder subsequently constructs the B-scans from the fused feature representations. To enhance the network’s generalization capability, transfer learning is employed to fine-tune the network for new scenarios vastly different from those in training set. Numerical results show that the proposed solver achieves a mean relative error of 1.28%. For predicting the B-scan of one subsurface object, the proposed solver requires 12 milliseconds, which is 22,500x less than the time required by a classical physics-based solver.

The reliability of European Remote Sensing 5 (ERA5) satellite-based air temperature data is under investigation in this letter. To evaluate this, the ERA5 data will be compared with land-based data obtained from weather stations on the global historical climatology network (GHCN). Two climate regions are taken into consideration, temperate and tropical. Five years worth of data is collected and compared through box plots, regression models, and statistical metrics. The results show that the satellite temperature performs better in the temperate region than the tropical region. This suggests that the time of year and climate region have an impact on the accuracy of the satellite data as milder temperatures produce better approximations.

The horizontal orientation angle and the vertical inclination angle of an elongated subsurface object are key parameters for object identification and imaging in ground-penetrating radar (GPR) applications. Conventional methods can only extract the horizontal orientation angle or estimate both angles in narrow ranges due to limited polarimetric information and detection capability. To address these issues, this letter, for the first time, explores the possibility of leveraging neural networks with multipolarimetric GPR data to estimate both angles of an elongated subsurface object in the entire spatial range. Based on the polarization-sensitive characteristic of an elongated object, we propose a multipolarization aggregation and selection network (MASNet), which takes the multipolarimetric radargrams as inputs, integrates their characteristics in the feature space, and selects discriminative features of reflected signal patterns for accurate orientation estimation. Numerical results show that our proposed MASNet achieves high estimation accuracy with an angle estimation error of less than 5°. The promising results obtained by the proposed method encourage one to think of new solutions for GPR-related tasks by integrating multipolarization information with deep learning techniques. The data and code implemented in the letter can be found athttps://haihan-sun.github.io/GPR.html.

In recent studies, precipitable water vapor (PWV) has caught the interest of researchers in predicting rainfall. However, rainfall depends on several other atmospheric factors that play a vital role in its initiation. With only one atmospheric parameter, the false prediction is high, especially for long-term prediction. In this article, a new method for rainfall forecasting is proposed using horizontal tropospheric gradient and atmospheric residual that are important weather features. It is observed that the gradient orientation defines the weather front for a larger region, and the gradient slope, gradient magnitude, and atmospheric residual play a crucial role in rainfall prediction. The algorithm is based on global positioning system (GPS) PWV data from stations in the tropical region. This proposed algorithm obtains average false alarm (FA) and true detection (TD) rates of 36.6% and 87%, respectively, for a prediction window of 6 h. The proposed threshold values are found to be similar for three different tropical stations that make the algorithm location independent. The comparison of this approach with several other data suggests that this algorithm is suitable in the practical scenario for a long-term rainfall prediction with a better TD rate and a minimal FA rate.

Ground-penetrating radar (GPR) has been used as a nondestructive tool for tree root inspection. Estimating root-related parameters from GPR radargrams greatly facilitates root health monitoring and imaging. However, the task of estimating root-related parameters is challenging as the root reflection is a complex function of multiple root parameters and root orientations. Existing methods can only estimate a single root parameter at a time without considering the influence of other parameters and root orientations, resulting in limited estimation accuracy under different root conditions. In addition, soil heterogeneity introduces clutter in GPR radargrams, making the data processing and interpretation even harder. To address these issues, a novel neural network architecture, called mask-guided multi-polarimetric integration neural network (MMI-Net), is proposed to automatically and simultaneously estimate multiple root-related parameters in heterogeneous soil environments. The MMI-Net includes two subnetworks: a MaskNet that predicts a mask to highlight the root reflection area to eliminate interfering environmental clutter and a parameter estimation subnetwork (ParaNet) that uses the predicted mask as guidance to integrate, extract, and emphasize informative features in multi-polarimetric radargrams for accurate estimation of five key root-related parameters. The parameters include the root depth, diameter, relative permittivity, and horizontal and vertical orientation angles. Experimental results demonstrate that the proposed MMI-Net achieves high estimation accuracy in these root-related parameters. This is the first work that takes the combined contributions of root parameters and spatial orientations into account and simultaneously estimates multiple root-related parameters. The data and code implemented in this article can be found athttps://haihan-sun.github.io/GPR.html.

In recent years, the research on atmospheric gradient for weather forecasting as well as GPS positioning has significantly increased as it contains real atmospheric information. In this paper, the seasonal dependence of the atmospheric gradient orientation consistent with wind direction has been presented vividly. We have illustrated the gradient time series of four different monsoon seasons for the tropical station Singapore. The tropospheric gradient alters its direction from season to season and mostly remains oriented along the wind flow. Also, the cumulative distribution plot between the abrupt change in gradient and precipitation gives rise to an indicative feature for rainfall forecasting. The results substantiate the fact that gradient can be contemplated as an imperative atmospheric parameter for predicting a weather event for a larger region.

Air pollution has been a long-existing problem for most of the major metropolitan cities of the world. Several measures including strict climate laws and reduction in the number of vehicles were implemented by several nations. However, in the recent wake of the COVID19 pandemic, there has been a renewed interest in revisiting the problem of low air quality. Several countries implemented strict lockdown measures halting the vehicular traffic and other economic activities, in order to reduce the spread of COVID19. In this paper, we analyze the impact of such COVID19-induced lockdown on the air quality of the atmosphere. Our case study is based in the city of Dublin, Ireland. We analyze the average concentration of common gaseous pollutant majorly responsible for industrial and vehicular pollution, viz. nitrogen dioxide (NO2). These concentrations are obtained from the tropospheric column of the atmosphere collected by Sentinel-5P, which is an earth observation satellite of European Space Agency. We observe that Dublin had a significant drop in the level of NO2concentration, owing to the strict lockdown measures implemented across the nation.

The curvature of tunnels introduces an extra loss in the wave propagation. A simulation and measurement study are performed on the straight and the curved tunnels to investigate the extra loss in the curved tunnels in comparison with the straight tunnels at a frequency of 2.4 GHz. This study suggests the existence of two wave propagation mechanisms in the curved tunnel;enhanced waveguiding mechanisminduced by rich multipath components from the curved tunnels anddegraded waveguiding mechanismdue to the blockage from the curved tunnel walls. For efficient radio planning, a new propagation model with curvature dependent break point is proposed. The proposed break point indicates the end of theenhanced waveguiding mechanismand the beginning of thedegraded waveguiding mechanism. A two-slope radio wave propagation model is then proposed for radio communications inside curved tunnels by using the determined break point, with performance evaluation.

We analyze clouds in the earth's atmosphere using ground-based sky cameras. An accurate segmentation of clouds in the captured sky/cloud image is difficult, owing to the fuzzy boundaries of clouds. Several techniques have been proposed, which use color as the discriminatory feature for cloud detection. In the existing literature, however, analysis of daytime and nighttime images is considered separately, mainly because of differences in image characteristics and applications. In this letter, we propose a lightweight deep-learning architecture called CloudSegNet. It is the first that integrates daytime and nighttime (also known as nychthemeron) image segmentation in a single framework and achieves state-of-the-art results on public databases.

In recent years, there has been growing interest in using precipitable water vapor (PWV) derived from global positioning system (GPS) signal delays to predict rainfall. However, the occurrence of rainfall is dependent on a myriad of atmospheric parameters. This paper proposes a systematic approach to analyze various parameters that affect precipitation in the atmosphere. Different ground-based weather features such as Temperature, Relative Humidity, Dew Point, Solar Radiation, PWV along with Seasonal and Diurnal variables are identified, and a detailed feature correlation study is presented. While all features play a significant role in rainfall classification, only a few of them, such as PWV, Solar Radiation, Seasonal, and Diurnal features, stand out for rainfall prediction. Based on these findings, an optimum set of features are used in a data-driven machine learning algorithm for rainfall prediction. The experimental evaluation using a 4-year (2012-2015) database shows a true detection rate of 80.4%, a false alarm rate of 20.3%, and an overall accuracy of 79.6%. Compared to the existing literature, our method significantly reduces the false alarm rates.

Numerous weather parameters affect the occurrence and amount of rainfall. Therefore, it is important to study these parameters and their interdependency. In this paper, different weather and time-related variables - relative humidity, solar radiation, temperature, dew point, day-of-year, and time-of-day are analyzed systematically using Principal Component Analysis (PCA). We found that four principal components explain a cumulative variance of 85%. The first two principal components are applied to distinguish rain and no-rain scenarios as well. We conclude that all 7 variables have similar contribution towards rainfall detection.

Precipitation is dependent on a myriad of atmospheric conditions. In this paper, we study how certain atmospheric parameters impact the occurrence of rainfall. We propose a data-driven, machine-learning based methodology to detect precipitation using various meteorological sensor data. Our approach achieves a true detection rate of 87.4% and a moderately low false alarm rate of 32.2%.

In this paper, the Precipitable Water Vapor (PWV) content of the atmosphere is derived using the Global Positioning System (GPS) signal delays. The PWV values from GPS are calculated at different elevation cut-off angles. It was found that the significant range of elevation cut-off angles is from 5° to 50°. The PWV values calculated from GPS using varying cutoff angles from this range were then compared to the PWV values calculated using the radiosonde data. The correlation coefficient and the Root Mean Square (RMS) error between the GPS and radiosonde derived PWV decreases with the increasing cut-off angle and the distance between the two. The seasonal parameters also effect the relation between the two.

In this paper, a simple algorithm is proposed to perform the nowcasting of rainfall in the tropical region. The algorithm applies global positioning system-derived precipitable water vapor (PWV) values and its second derivative for the short-term prediction of rainfall. The proposed algorithm incorporates the seasonal dependency of PWV values for the prediction of a rain event in the coming 5 min based on the past 30 min of PWV data. This proposed algorithm is based on the statistical study of four-year PWV and rainfall data from a station in Singapore and is validated using two-year independent data for the same station. The results show that the algorithm can achieve an average true detection rate and a false alarm rate of 87.7% and 38.6%, respectively. To analyze the applicability of the proposed algorithm, further validations are done using one-year data from one independent station from Singapore and two-year data from one station from Brazil. It is shown that the proposed algorithm performs well for both the independent stations. For the station from Brazil, the average true detection and false alarm rates are around 84.7% and 37%, respectively. All these observations suggest that the proposed algorithm is reliable and works well with a good detection rate.

Imaging the atmosphere using ground-based sky cameras is a popular approach to study various atmospheric phenomena. However, it usually focuses on the daytime. Nighttime sky/cloud images are darker and noisier, and thus harder to analyze. An accurate segmentation of sky/cloud images is already challenging because of the clouds' non-rigid structure and size, and the lower and less stable illumination of the night sky increases the difficulty. Nonetheless, nighttime cloud imaging is essential in certain applications, such as continuous weather analysis and satellite communication. In this paper, we propose a superpixel-based method to segment nighttime sky/cloud images. We also release the first nighttime sky/cloud image segmentation database to the research community. The experimental results show the efficacy of our proposed algorithm for nighttime images.

Cloud base height and volume estimation is needed in meteorology and other applications. We have deployed a pair of custom-designed Whole Sky Imagers, which capture stereo pictures of the sky at regular intervals. Using these images, we propose a method to create rough 3D models of the base of clouds, using feature point detection, matching, and triangulation. The novelty of our method lies in the fact that it locates the cloud base in all three dimensions, instead of only estimating cloud base height. For validation, we compare the results with measurements from weather radar.

Color channel selection is essential for accurate segmentation of sky and clouds in images obtained from ground-based sky cameras. Most prior works in cloud segmentation use threshold-based methods on color channels selected in an ad hoc manner. In this letter, we propose the use of rough sets for color channel selection in visible-light images. Our proposed approach assesses color channels with respect to their contribution for segmentation and identifies the most effective ones.

In this paper, a simplified latitude and day-of-year (DoY)-based model is proposed for the retrieval of precipitable water vapor (PWV) from global positioning system (GPS) signal. Conventionally, PWV, the total amount of water in a vertical column of a unit cross-sectional area, is estimated from the GPS signal delay and a dimensionless conversion factor PI. This PI value is found to rely on a water vapor weighted mean temperature (Tm) value which varies widely across the day, month, and year for different regions. It is, therefore, both time specific and site specific. Analysis of the PI value and its effect on the retrieved PWV from the data obtained for tropical, subtropical, and temperate regions show that although the PI value is time and site specific, the change in the median value of PI for different years is minimal and is dependent only on factors like the latitude coordinates of the particular site and the DoY. Therefore, using the data obtained from 174 different sites, a latitude-coordinate and DoY-based PI value model for the retrieval of PWV is proposed in this paper. The proposed model has been successfully validated using data from different databases: the International GNSS Service Global Positioning System National Aeronautics and Space Administration (IGS GPS NASA) database, the International GNSS Service Global Positioning System Global Geodetic Observing System (IGS GPS GGOS) database, and the very-long-baseline interferometry (VLBI) database. Results show strong agreement between PWV values calculated using the proposed model and those calculated using the temperature dependent models with 99%, 98%, and 93% of error within ±1 mm for IGS GPS NASA, IGS GPS GGOS, and VLBI databases, respectively. Moreover, the proposed model allows for the ease of PWV retrieval, which is useful in meteorological studies and also applicable in satellite communications.

Ground-based whole sky imagers (WSIs) can provide localized images of the sky of high temporal and spatial resolution, which permits fine-grained cloud observation. In this paper, we show how images taken by WSIs can be used to estimate solar radiation. Sky cameras are useful here because they provide additional information about cloud movement and coverage, which are otherwise not available from weather station data. Our setup includes ground-based weather stations at the same location as the imagers. We use their measurements to validate our methods.

Precipitable Water Vapor (PWV) is a good source to monitor precipitation. It is defined by the amount of water vapor present in atmosphere. Traditionally, radiosondes and microwave radiometers were used to derive PWV. However, these devices have poor temporal resolutions and high operational costs. Therefore, GPS signal delay is now widely used for such purposes. The main aim of this paper is to study relationship between GPS derived PWV and precipitation. We present an analysis which shows that PWV increases before any rainfall event, while it decreases after the rainfall event. We also derive a threshold PWV that detects the occurrence of rainfall, once PWV exceeds the threshold value. PWV and rainfall data of June 2010 and 2011 are used for validation.

Ground-based Whole Sky Imagers (WSIs) are now commonly used for cloud observations. Upon deployment, they may not be exactly level or precisely face north. This significantly affects subsequent processing of the images, especially for applications where two or more imagers are required, e.g. 3D volumetric cloud reconstruction. We present a method to remove this mis-alignment using the sun position in images captured over a whole day. Coupled with precise coordinates of the device locations, this method also improves the geo-referencing accuracy of the captured images. We detect the sun in the images and compute the corresponding 3D vectors using the lens calibration function. These vectors are compared to the actual sun direction. The mismatch between the two sets of vectors is then corrected using a 3D rotation matrix. The method can also be applied to other celestial bodies, such as stars or the moon.

Efficiency in spectrum utility has been a concern in wireless communications for a long time. Cognitive radios have been seen as a solution to occupy the gaps in the licensed spectrum through opportunistic spectrum access and simultaneous spectrum sharing techniques. For this purpose, spectrum sensing has been vital in providing accurate statistical information regarding licensed or primary user (PU) activity on its spectrum. In this paper, we design new sensing thresholds that take into account the outage caused to the PU as a consequence of cognitive or secondary users (SUs) accessing or sharing the said spectrum. With these new thresholds, we can see more protection to the PU from SU spectrum access transmissions based on missed detections, and eliminate most common assumptions made with spectrum sharing systems. Our thresholds also work with a dynamic decision-making algorithm that allows the SUs to use only the statistical sensing information to understand the network dynamics, and determine its transmission opportunities and corresponding power consumption, in a decentralized and uncooperative cross-layer network.

A new method using water vapor pressure (WVP) from a radiosonde profile to determine the cloud vertical structure for the tropical region is proposed in this paper. This includes both the cloud base height and the cloud occurrences at different levels in the atmosphere. Our study shows that the presence of clouds depends on the following criterion: the measured WVP is larger than the critical WVP at the same level. The applicable level is found to be within the range of 300-12 000 m. The estimated cloud vertical structure using the proposed method is compared with the Salonen and Uppala (SU) model, the ceilometer data, and two kinds of meteorological observation data, namely, SYNOP and METAR. The proposed model shows a higher accuracy level of prediction of the cloud vertical structure as compared with the existing SU model.

We propose a modified texton-based classification approach that integrates both color and texture information for improved classification results. We test our proposed method for the task of cloud classification on SWIMCAT, a large new database of cloud images taken with a ground-based sky imager, with very good results. We perform an extensive evaluation, comparing different color components, filter banks, and other parameters to understand their effect on classification accuracy. Finally, we release the SWIMCAT dataset that was created for the task of cloud categorization.

Sky/cloud images captured by ground-based Whole Sky Imagers (WSIs) are extensively used now-a-days for various applications. In this paper, we learn the semantics of sky/cloud images, which allows an automatic annotation of pixels with different class labels. We model the various labels/classes with a continuous-valued multi-variate distribution. Using a set of training images, the distributions for different labels are learnt, and subsequently used for labeling test images. We also present a method to determine the number of clusters. Our proposed approach is the first for multi-class sky-cloud image annotation and achieves very good results.

Ground-based whole sky imagers are popular for monitoring cloud formations, which is necessary for various applications. We present two new Wide Angle High-Resolution Sky Imaging System (WAHRSIS) models, which were designed especially to withstand the hot and humid climate of Singapore. The first uses a fully sealed casing, whose interior temperature is regulated using a Peltier cooler. The second features a double roof design with ventilation grids on the sides, allowing the outside air to flow through the device. Measurements of temperature inside these two devices show their ability to operate in Singapore weather conditions. Unlike our original WAHRSIS model, neither uses a mechanical sun blocker to prevent the direct sunlight from reaching the camera; instead they rely on high-dynamic-range imaging (HDRI) techniques to reduce the glare from the sun.

Fine scale cloud monitoring using ground-based imagers is becoming popular for a variety of applications and domains. We present a framework for cloud base height estimation using two such imagers; our method is based on stereoscopic scene flow. We demonstrate the feasibility of our approach and use computer-generated images with controlled cloud height to validate the accuracy of our method.

A new method is proposed in this paper to determine the cloud vertical structure using water vapor pressure estimated from radiosonde profile. The presence of a cloud depends on the following criteria: the measured water vapor pressure being larger than the critical water vapor pressure at the same level. The estimated results of cloud vertical structures using the proposed method are compared with the Salonen and Uppala model and ceilometer (CL31) data. Results show good agreement between the proposed model, the existing model and the measured data.

Spectrum utility efficiency is key in designing systems that can meet the heavier demands of bandwidth and data rate of future communication technologies. Shared spectrum techniques and collaborative protocols have thus been studied to better utilize already existing spectrum resources. In this paper, we present a spectrum trading approach that allows the licensed user's (LU) resources to be efficiently shared with the secondary user (SU) network in exchange for a monetary cost. The model is based on demand and supply economics, wherein the highest bidder for spectrum resource is awarded with transmission rights over licensed spectrum. The transmission opportunities for the SU consider every state of the licensed link, in the form of dynamic spectrum access (DSA), spectrum sharing, and relaying, each of which has an optimized cost that will maximize the returns for the LU. The numerical results backed by the analytical study show that this spectrum trading scheme allows for significant improvements in data rate and spectrum transmission opportunities than previous work conducted in either DSA or the spectrum sharing fields.

Sky/cloud imaging using ground-based Whole Sky Imagers (WSI) is a cost-effective means to understanding cloud cover and weather patterns. The accurate segmentation of clouds in these images is a challenging task, as clouds do not possess any clear structure. Several algorithms using different color models have been proposed in the literature. This paper presents a systematic approach for the selection of color spaces and components for optimal segmentation of sky/cloud images. Using mainly principal component analysis (PCA) and fuzzy clustering for evaluation, we identify the most suitable color components for this task.

Drill string acoustic telemetry is an effective transmission method to retrieve downhole data. Finite-difference simulations produce the comb-filter-like channel response (patterns of pass bands and stop bands) due to the presence of coupling joints in the metallic drill string. Practical pipes used for drilling deep wells have slight variation in length. The selection and arrangement of downhole pipes is important for improving the transmission efficiency of extensional waves transmitted through the drill string. Downhole drill string channel is studied using the transmission coefficients calculated from the transmission matrix method, and the resultant transfer function produces identical results similar to the finite-difference simulations. Reciprocity of the drill string structure is proved by comparing the pass band responses using the ascend-only (AO) and descend-only pipe arrangements. Transferred energies calculated up to 180 pipes of random length at the end of the drill strings using transmission coefficients for the three different pipe arrangements, namely, AO, descend-then-ascend, and ascend-then-descend (ATD), are compared to find the optimal pipe arrangement for single measurement. For the situations when pipes are distributed in sets, multiple measurements are required. In this paper, two sets of AO and two sets of ATD arrangements are analyzed for multiple measurements. ATD and$n$xATD arrangements are proposed as optimal pipe arrangements to produce the best possible telemetry performance in terms of optimal acoustic energy transfer via one- and two-way acoustic communication for single and multiple measurements, respectively.

Vein biometrics is emerging and gaining popularity over other types of biometric systems due to its strengths of low forgery risk, aliveness detection, non-invasive data acquisition as well as stable over long period. This paper presents a novel design of an embedded finger vein biometric recognition hardware system targeted for implementation on a field-programmable gate array (FPGA) platform. A novel streaming architecture for hardware acceleration of window-based image processing application is proposed. To ensure high quality image capture and high recognition accuracy, we introduce an image acquisition subsystem that uses an embedded camera with dynamic illumination based on quality assessment. Experimental results show that the proposed finger vein verification system achieves 0.87% equal error rate (EER) on a database of 500 finger vein images.

The characterization of a Line of Sight (LOS) and a Non-Line of Sight (NLOS) link is performed over the military Ultra High Frequency (UHF) band (225 to 450 MHz). This is done using experimental results collected inside the cargo hold of a merchant ship. By analyzing the guiding effect associated with the cargo hold and its sub-structures, the channel characteristics of the environment can be determined. This important propagation mechanism is analyzed using the 3-D ray tracing simulator. Path loss for both topologies is studied and linear path loss models are proposed. Small-scale channel characteristics, such as the number of multipath components, decay factor of the multipath components and Root-Mean-Square (RMS) delay spread are studied and compared for the LOS and NLOS scenarios. Due to the guiding effect and multiple reflections along the cargo hold, both the LOS and NLOS scenarios are found to exhibit similar channel characteristics. A linear decay function is proposed to model the average Power Delay Profiles (PDPs). It is concluded that the linear model provides a good representation for the shape of the impulse responses for a cluttered environment with reflective substructures. This study is useful for the implementation of wireless sensor networks for status monitoring in maritime applications.

Heavy convective rain events are often experienced in tropical countries such as Singapore. The operation of high-speed satellite transmission in the Ka-band is therefore susceptible to attenuation. We present the setup of a high-speed link via the WINDS satellite using an ultra small aperture terminals (USAT). Video streaming is performed over the high-speed link so as to investigate the effects of rainfall on the signal strength of the link and the video quality. The video streaming is performed at two locations 40km apart in order to examine site diversity as a mitigation technique.

Gamma model is fitted using the second, fourth, and sixth moments to model the rain drop size distribution (DSD) of Singapore. As the Joss distrometer measures the number of rain drops between the drop diameters from 0.3 to 5 mm, the truncated moment fitting between these drop diameter ranges is also used for modeling the DSD. Gamma DSD requires three-parameter estimation:N0, the intercept parameter; μ, the shape parameter; and Λ, the slope parameter. The aim of this paper is to find a suitable fixed μ and derive an appropriate μ-Λ relation for the tropical region in order to form a two-parameter gamma model. To find an appropriate μ value, observed DSDs are fitted with different μ values to estimate the rain rates, which are assessed by rain rate observations of the distrometer. Shape-slope relationships are fitted for different categories according to the rain rate and the number of drops. The derived μ-Λ relationships for the Singapore region are compared to the published results from two other regions, and the analysis is presented. Two-parameter gamma models are compared by retrieving the rain rate using the polarimetric radar variables. The effect of truncation on rain rate retrieval is also studied, and the use of the μ-Λ relationship for rain retrieval is recommended for the tropical region. The μ-Λ relation using the truncated moment method for the rain categoryR≥ 5 mm/hr andrain counts≥ 1000 drops retrieves the rain rates well compared to other μ-Λ relations.

In this paper, two relatively novel synthetic aperture radar (SAR) imaging techniques, namely the back-projection algorithm and range migration algorithm, are discussed. The back-projection algorithm originates from the medical imaging reconstruction technique called computer-aided tomography whereas the range migration algorithm is derived from seismic migration techniques. In this paper, both the back-projection and range migration algorithms are applied to foliage penetration (FOPEN) SAR imaging and performance comparisons are made. The simulations and experimental data processing results show that both algorithms are suitable for FOPEN radar imaging and that theoretical performances can be achieved.