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Both unresolved physics in numerical models and limited theoretical understanding of the small-scale diffusion processes occurring near the ocean surface hamper predictability of tropical cyclone (TC) wind changes. An analytical model is here developed to diagnose the short-term evolution of the TC wind profile. An effective frictional parameter is introduced to control the unknown diffusion effects. When this frictional parameter is adjusted to match the TC intensity change, solutions are consistent with observed high-resolution ocean surface wind speeds from satellite synthetic aperture radar (SAR). The initial high-resolution estimate of the near-core wind structure is then found to strongly modulate the wind profile evolution. The frictional parameter can, unfortunately, not efficiently be calibrated using outer-core wind speed changes. Low-resolution observations or standard numerical weather predictions may thus not be directly used to reinterpret and anticipate short-term TC wind changes. The expected accumulation of orbiting SAR sensors as well as improved measurements of the ocean-atmosphere boundary layer characteristics shall then become essential to more precisely monitor TC dynamics. [ABSTRACT FROM AUTHOR]

The ultrasonic echo technique is widely used in non-destructive testing (NDT) of concrete objects for thickness measurements, geometry determinations and localization of built-in components. To improve ultrasonic imaging of complex concrete structures, we transferred a seismic imaging technique, the Reverse Time Migration (RTM), to NDT in civil engineering. RTM, in contrast to the conventionally used synthetic aperture focusing technique (SAFT) algorithms, considers all wavefield types and thus, can handle complex wave propagations in any direction with no limit on velocity variations and reflector dip. In this paper, we focused on the development, application and evaluation of a two-dimensional elastic RTM algorithm considering horizontally polarized shear (SH) waves only. We applied the elastic SH RTM routine to synthetic ultrasonic echo SH-wave data generated with a concrete model incorporating several steps and circular cavities. As these features can often be found in real-world NDT use cases, their imaging is extremely important. By using elastic SH RTM, we were able to clearly reproduce almost all reflectors inside the concrete model including the vertical step edges and the cross sections of the cavities. We were also capable to show that more features could be mapped compared to SAFT, and that imaging of complex reflectors could be sharpened compared to elastic P-SV (compressional-vertically polarized shear) RTM. Our promising results illustrate that elastic SH RTM has the potential to significantly enhance the reconstruction of challenging concrete structures, representing an important step forward for precise, high-quality ultrasonic NDT in civil engineering. [ABSTRACT FROM AUTHOR]

Speckle noise corrupts synthetic aperture radar (SAR) images and limits their applications in sensitive scientific and engineering fields. This challenge has attracted several scholars because of the wide demand of SAR images in forestry, oceanography, geology, glaciology, and topography. Despite some significant efforts to address the challenge, an open-ended research question remains to simultaneously suppress speckle noise and to restore semantic features in SAR images. Therefore, this work establishes a diffusion-driven nonlinear method with edge-awareness capabilities to restore corrupted SAR images while protecting critical image features, such as contours and textures. The proposed method incorporates two terms that promote effective noise removal: (1) high-order diffusion kernel; and (2) fractional regularization term that is sensitive to speckle noise. These terms have been carefully designed to ensure that the restored SAR images contain stronger edges and well-preserved textures. Empirical results show that the proposed model produces content-rich images with higher subjective and objective values. Furthermore, our model generates images with unnoticeable staircase and block artifacts, which are commonly found in the classical Perona–Malik and Total variation models. [ABSTRACT FROM AUTHOR]

Speckle noise corrupts synthetic aperture radar (SAR) images and limits their applications in sensitive scientific and engineering fields. This challenge has attracted several scholars because of the wide demand of SAR images in forestry, oceanography, geology, glaciology, and topography. Despite some significant efforts to address the challenge, an open-ended research question remains to simultaneously suppress speckle noise and to restore semantic features in SAR images. Therefore, this work establishes a diffusion-driven nonlinear method with edge-awareness capabilities to restore corrupted SAR images while protecting critical image features, such as contours and textures. The proposed method incorporates two terms that promote effective noise removal: (1) high-order diffusion kernel; and (2) fractional regularization term that is sensitive to speckle noise. These terms have been carefully designed to ensure that the restored SAR images contain stronger edges and well-preserved textures. Empirical results show that the proposed model produces content-rich images with higher subjective and objective values. Furthermore, our model generates images with unnoticeable staircase and block artifacts, which are commonly found in the classical Perona–Malik and Total variation models. [ABSTRACT FROM AUTHOR]

Under the condition of small roof deformation before the occurrence of fractures and collapse in room and pillar mined-out areas caused by coal pillar instability, the surface deformation may be large, which threatens the safety of ground structures. Interferometric synthetic aperture radar, geophysical exploration, geotechnical exploration and physical simulation tests were conducted to analyse the deformation and development mechanism of the overlying strata in the mined-out area in this case. The results show that in terms of surface deformation, the surface deformation caused by coal pillar instability in the room and pillar mined-out area exhibits the slow deformation stage, uniform deformation stage and accelerated deformation stage. In terms of deformation of overlying strata, after the completion of room and pillar mining, a strip-shaped deformation area and trapezoidal deformation area are developed in the overlying rock. With the occurrence of coal pillar instability, a trapezoidal deformation area and inverted funnel-shaped deformation area are developed in the overlying rock. The deformation characteristics of unconsolidated formations transition from trapezoidal deformation after room and pillar mining to funnel-shaped deformation due to coal pillar instability. Moreover, the maximum surface deformation point is located at the centre of the funnel. In terms of spatial morphology of mined-out area deformation, the maximum surface deformation point corresponds to the position of the initial coal pillar instability and the crack in the mined-out area roof along the vertical direction. The mined-out area treatment method can be optimized based on the deformation characteristics of the overlying strata in the room and pillar mined-out area under the condition of coal pillar instability. [ABSTRACT FROM AUTHOR]

Full waveform inversion (FWI) has potential for quantitative ultrasound bone imaging, which can provide detailed estimation of bone internal structure. The existing ultrasound FWI methods suffer from cycle skipping and local minima and high computational costs, which limits their clinical application potential. In recent years, data-driven method for solving the problem of inversion has become a very novel approach, but we haven't seen it in the application of ultrasonic bone imaging studies. Herein, we develop an improved dual-encoder-based Unet with high-frequency feature enhancement (DEFE-Unet) for ultrasonic bone quantitative imaging to obtain more detailed information by inserting high-frequency feature extractors related to bone microstructure into the network. This method can obtain more detailed and fine results than FWI and avoid the extremely high computational cost of FWI dual-parameter modeling. We designed two types of bone model datasets based on Micro_CT images to train and test the proposed network architecture. Experimental results show that the DEFE-Unet algorithm proposed in this paper obtains competitive results with less computational cost compared to the FWI algorithm. Compared with other baseline machine learning architectures, such as Unet and attention-Unet, the proposed algorithm achieves significant performance improvement with a small computational cost increment. The results show that the DEFE-Unet has better potential in clinical detection of bone disease. [ABSTRACT FROM AUTHOR]

Nitrogen (N) nutrition index (NNI) is a reliable indicator of plant N status for field crops, but its determination is both labor- and cost-intensive. The utilization of remote sensing approaches for monitoring N, mainly in relevant crops such as of corn (Zea mays L.), will be critical for enhancing effective use of this nutrient. Therefore, the aim of this study was to assess NNI predicted from optical and C-band Synthetic Aperture Radar (C-SAR) satellite data and available soil N (Nav) at different vegetative growth stages for corn crop. Eleven field studies were conducted in the Pampas region (Argentina), applying five fertilizer N rates (0, 60, 120, 180, and 240 kg N ha−1), all at sowing time. Plant samples were collected at sixth-leaf (V6), tenth-leaf (V10), fourteen-leaf (V14), and flowering (R1). Using linear regression models, NNI was best predicted using only optical satellite data from V6 to V14, and integrating optical with C-SAR plus Nav at R1. The best monitoring model integrated vegetation spectral indices, C-SAR and Nav data at V10 with an adjusted R2 of 0.75 achieved during calibration in the northern Pampa. During validation, it predicted NNI with an RMSE of 0.14 and a MAPE of 12% in the southeastern Pampa. The red-edge spectrum and Local Incidence Angle of C-SAR were necessary to monitor the corn N status via prediction of NNI. Thus, this study provided empirical models to remotely sensed corn N status within fields during vegetative period, serving as a foundational data for guiding future N management. [ABSTRACT FROM AUTHOR]

The rotating synthetic aperture (RSA) optical imaging system employs a rectangular primary mirror for detection. During the imaging process, the primary mirror rotates around the center to achieve the aperture equivalent to the long side of the rectangle at different rotation angles. As a result, the system's point spread function changes over time, causing periodic time-varying characteristics in the acquired images' resolution. Moreover, due to the rectangular primary mirror, the images obtained by the RSA system are spatially asymmetric, with a lower resolution in the short side's direction than in the long side's direction. Hence, image processing techniques are necessary to enhance the image quality. To provide reference for the study of image quality improvement methods, we first characterize the imaging quality degradation mechanism of the RSA system and the time–space evolution law of the imaging process. We then establish an imaging experiment platform to simulate the dynamic imaging process of the RSA system. We quantify the RSA system's impact on image degradation using objective indexes. Subsequently, by comparing the imaging experiment results with theoretical analysis, we verify the spatially asymmetric and temporally periodic imaging characteristics of the RSA system. Lastly, we introduce image super-resolution experiments to assess the limitations of directly applying generic deep learning-based single image super-resolution methods to the images captured by the RSA system, thereby revealing the challenges involved in improving image quality for the RSA system. [ABSTRACT FROM AUTHOR]

The rotating synthetic aperture (RSA) optical imaging system employs a rectangular primary mirror for detection. During the imaging process, the primary mirror rotates around the center to achieve the aperture equivalent to the long side of the rectangle at different rotation angles. As a result, the system's point spread function changes over time, causing periodic time-varying characteristics in the acquired images' resolution. Moreover, due to the rectangular primary mirror, the images obtained by the RSA system are spatially asymmetric, with a lower resolution in the short side's direction than in the long side's direction. Hence, image processing techniques are necessary to enhance the image quality. To provide reference for the study of image quality improvement methods, we first characterize the imaging quality degradation mechanism of the RSA system and the time–space evolution law of the imaging process. We then establish an imaging experiment platform to simulate the dynamic imaging process of the RSA system. We quantify the RSA system's impact on image degradation using objective indexes. Subsequently, by comparing the imaging experiment results with theoretical analysis, we verify the spatially asymmetric and temporally periodic imaging characteristics of the RSA system. Lastly, we introduce image super-resolution experiments to assess the limitations of directly applying generic deep learning-based single image super-resolution methods to the images captured by the RSA system, thereby revealing the challenges involved in improving image quality for the RSA system. [ABSTRACT FROM AUTHOR]

In recent years, remote sensing images of various types have found widespread applications in resource exploration, environmental protection, and land cover classification. However, relying solely on a single optical or synthetic aperture radar (SAR) image as the data source for land cover classification studies may not suffice to achieve the desired accuracy in ground information monitoring. One widely employed neural network for remote sensing image land cover classification is the U-Net network, which is a classical semantic segmentation network. Nonetheless, the U-Net network has limitations such as poor classification accuracy, misclassification and omission of small-area terrains, and a large number of network parameters. To address these challenges, this research paper proposes an improved approach that combines both optical and SAR images in bands for land cover classification and enhances the U-Net network. The approach incorporates several modifications to the network architecture. Firstly, the encoder-decoder framework serves as the backbone terrain-extraction network. Additionally, a convolutional block attention mechanism is introduced in the terrain extraction stage. Instead of pooling layers, convolutions with a step size of 2 are utilized, and the Leaky ReLU function is employed as the network's activation function. This design offers several advantages: it enhances the network's ability to capture terrain characteristics from both spatial and channel dimensions, resolves the loss of terrain map information while reducing network parameters, and ensures non-zero gradients during the training process. The effectiveness of the proposed method is evaluated through land cover classification experiments conducted on optical, SAR, and combined optical and SAR datasets. The results demonstrate that our method achieves classification accuracies of 0.8905, 0.8609, and 0.908 on the three datasets, respectively, with corresponding mIoU values of 0.8104, 0.7804, and 0.8667. Compared to the traditional U-Net network, our method exhibits improvements in both classification accuracy and mIoU to a certain extent. [ABSTRACT FROM AUTHOR]

The ultrasonic echo technique is frequently used in non-destructive testing (NDT) of concrete structures for thickness measurements, geometry determinations as well as localization of built-in components. To improve ultrasonic imaging of complex structures in concrete, we transferred a geophysical imaging technique, the reverse time migration (RTM), to NDT in civil engineering. In contrast to the conventionally used synthetic aperture focusing technique (SAFT) algorithms, RTM is a wavefield continuation method in time and uses the full wave equation. Thus, RTM can handle complicated wave propagations in any direction without dip limitation. In this paper, we focused on the application and evaluation of a two-dimensional (2D) elastic RTM algorithm considering compressional waves, vertically polarized shear waves, and Rayleigh waves. We tested the elastic RTM routine on synthetic ultrasonic echo data generated with a 2D concrete model consisting of several steps and circular air inclusions. As these complex structures can often be found in real-world NDT use cases, their imaging is especially important. By using elastic RTM, we were able to clearly reproduce vertical reflectors and lower edges of circular air voids inside our numerical concrete model. Such structures cannot be imaged with conventional SAFT algorithms. Furthermore, the used elastic RTM approach also yielded a better reconstruction of a horizontal reflector and upper boundaries of circular air inclusions. Our encouraging results demonstrate that elastic RTM has the potential to significantly improve the imaging of complex concrete structures and, thus, is a step forward for detailed, high-quality ultrasonic NDT in civil engineering. [ABSTRACT FROM AUTHOR]

The 3D range-migration algorithm (RMA) and its 2D equivalent, the omega-k algorithm, are employed in a wide range of applications where reconstruction of synthetic aperture data is required, from satellite radar imaging of planets over seismic imaging of the earth crust, down to phased-array ultrasound and ultrasonic application, and recently in-line synthetic aperture radar for non-destructive testing. These algorithms are based on Fourier transforms and share their time-complexity. This limits highly-resolved measurement data to be processed at high speeds which would be advantageous for modern production feed lines. In this publication, we present the development and implementation of the RMA on a quantum computer that scales favourably compared to the time complexity of the classical RMA. We compare reconstruction results of simulated and measured data of the classical and quantum RMA. Hereby, the quantum RMA is run on a quantum simulator backend as well as on IBM's Q System One quantum computer. The results show that real world applications and testing tasks may benefit from future quantum computers. [ABSTRACT FROM AUTHOR]

Satellite Interferometric Synthetic Aperture Radar (InSAR) is a space-borne geodetic technique that can map ground displacement at millimetre accuracy. Via the new era for InSAR applications provided by the Copernicus Sentinel-1 SAR satellites, several open-source software packages exist for processing SAR data. These packages enable one to obtain high-quality ground deformation maps, but still require a deep understanding of InSAR theory and the related computational tools, especially when dealing with a large stack of images. Here we present an open-source toolbox, EZ-InSAR (easy-to-use InSAR), for a user-friendly implementation of InSAR displacement time series analysis with multi-temporal SAR images. EZ-InSAR integrates the three most popular and renowned open-source tools (i.e., ISCE, StaMPS, and MintPy), to generate interferograms and displacement time series by using these state-of-art algorithms within a seamless Graphical User Interface. EZ-InSAR reduces the user's workload by automatically downloading the Sentinel-1 SAR imagery and the digital elevation model data for the user's area of interest, and by streamlining preparation of input data stacks for the time series InSAR analysis. We illustrate the EZ-InSAR processing capabilities by mapping recent ground deformation at Campi Flegrei (> 100 mm·yr−1) and Long Valley (~ 10 mm·yr−1) calderas with both Persistent Scatterer InSAR and Small-Baseline Subset approaches. We also validate the test results by comparing the InSAR displacements with Global Navigation Satellite System measurements at those volcanoes. Our tests indicate that the EZ-InSAR toolbox provided here can serve as a valuable contribution to the community for ground deformation monitoring and geohazard evaluation, as well as for disseminating bespoke InSAR observations for all. [ABSTRACT FROM AUTHOR]

When ultrasounds are blocked by some obstacles or some areas cannot be illuminated by ultrasounds due to the complex shape of targets, the targets may not be detected by ultrasonic testing (UT). Thus, a simple and direct full-angle synthetic aperture focusing technique (SAFT) is proposed to conquer the above-mentioned difficulties, and its performance will be evaluated and compared with the commonly used B-scan SAFT. Two types of the full-angle scan are introduced in this study, the first one is the four-side scan which comprises top-, right-, bottom-, and left-side scans. The second is the circular scan which the transducer circulates and faces the center of the object during scanning. Then the acquired scan data is processed by SAFT. Study results show that the images of full-angle SAFT are significantly superior to the image of the traditional B-scan SAFT. This indicates the ultrasonic detection ability is largely improved by the full-angle SAFT. However, the traditional B-scan SAFT is simple and direct which is recommended to detect the existence of the shallowest targets. For detecting the deeper targets, which may be blocked by some obstacles, the four-side scan SAFT is suggested since this method has a better detection capability to detect the extreme positions of the targets. The image of the circular scan SAFT is fine, which shows this technique has a better focus capability since the transducer inspects the specimen from full-azimuth. Thus, to reveal the shape of being blocked target, the circular scan SAFT is suggested. [ABSTRACT FROM AUTHOR]

Corrosion in reinforced concrete (RC) structures is associated with a reduction of the rebar diameter, loss of interfacial bond, cracking, and eventual spalling and probable collapse of the structure. The negative effects of corrosion on structural safety, durability, and longevity imposes significant costs on the national economy. Therefore, planned non-destructive testing (NDT) of concrete structures is essential to enhance the safety and economic sustainability of infrastructure. Previous work by the research group has established the capability of the ultrasonic Synthetic Aperture Focusing Technique (SAFT) as a tool for detection of rebar corrosion. This work extends the previous research towards application of statistical learning for ascertaining the corrosion severity through analysis of SAFT images of the rebar. Using features extracted from images, a Gaussian mixture model (GMM) is implemented to classify the severity of corrosion damage to the rebar. The results from the research positively demonstrate the potential of the proposed technique as an enabler for decisions pertaining to maintenance and timely repair of concrete infrastructural assets. [ABSTRACT FROM AUTHOR]

As the demand of structural integrity in manufacturing industries is increasing, the ultrasonic array technique has drawn more attention thanks to its inspection flexibility and versatility. By taking advantage of the possibility of individual triggering of each array element, full matrix capture (FMC) data acquisition strategy has been developed that contains the entire information of an inspection scenario. Total focusing method (TFM) as one of the ultrasonic imaging algorithms, is preferably applied to FMC dataset since it uses all information in FMC to synthetically focus the sound energy at every image pixel in the region of interest. Half-skip TFM (HSTFM) is proposed in multi-mode TFM imaging that involves a backwall reflection wave path, so that the defect profile could be reconstructed for accurate defect characterization. In this paper, a method involving Snell's law-based wave mode conversion is proposed to account for more reasonable wave propagation time when wave mode conversion happens at backwall reflection in HSTFM. A series of model based simulations (in software simSUNDT) are performed for parametric studies, with the intention of investigating the capability of defect characterization using HSTFM with varying tilt angle and relative position of surface-breaking notch to array probe. The results show that certain TFM modes could help with defect characterization, but the effectiveness is limited with varying defect features. It is inappropriate to address a certain mode for all characterization perspectives but rather a combination, i.e., multi-mode TFM, should be adopted for possible interpretation and characterization of defect features. [ABSTRACT FROM AUTHOR]

Recent research works have established the importance of sarcasm detection in the domain of sentiment analysis. Automatic sarcasm detection using social media data is a challenging task in the presence of imbalanced classes. Real-life social media data often suffer from this problem of class imbalance resulting in dramatical degradation of the performance of classification models attempting to detect sarcasm. Motivated by this, in the current article, a Bi-LSTM variational autoencoder model has been proposed to alleviate the problem of imbalanced classes in social media datasets targeted to train sarcasm detection models. The proposed BVA model is trained with a large corpus of sarcastic and non-sarcastic tweets to obtain the most suitable latent space representation of the same. These inherently class-biased latent vectors are then oversampled using synthetic minority oversampling techniques. The quality of the proposed method is established by training and testing a set of well-known classifiers in terms of precision, recall, F1-score, AUC, and G-mean. Extensive experiments reveal that the proposed BVA model combined with oversampling techniques can improve classifier performance for sarcasm detection to a greater extent. [ABSTRACT FROM AUTHOR]

Aiming at improving the performance of scale invariant feature transform (SIFT) algorithm during the registration of optical and synthetic aperture radar (SAR) images, a new SIFT algorithm is proposed. Firstly, the nonlinear diffusion scale space of optical and SAR images is constructed by using nonlinear diffusion filtering, the uniform gradient information is calculated by using multi-scale Sobel operator and multi-scale exponential weighted mean ratio operator respectively. Then, after removing the first layer of the scale space with the image blocking strategy, the scale space is partitioned, and Harris feature points are extracted on the basis of consistent gradient information to obtain stable and uniform point features. Descriptors are constructed based on gradient position and direction histogram templates and normalized to overcome nonlinear radiation differences between images. Finally, the correct matching point pairs are obtained by using bilateral fast approximate nearest neighbor (FLANN) search matching method and random sampling consistent (RANSAC) method, and the affine transformation model parameters are obtained. Compared with the other two algorithms, the CMR of this algorithm is improved by 80.53%, 75.61% and 81.74% respectively in the three groups of images, and the RMSE is reduced by 0.6491, 1.0287 and 0.6306 respectively. [ABSTRACT FROM AUTHOR]

Nowadays, the increasing demands for synthetic aperture radar images are of great importance in both marine and terrestrial applications, due to their availability day and night and in all-weather conditions. Moreover, synthetic aperture radar images are characterized by much more important information than that introduced by optical images. The single image super-resolution is considered a challenging process, especially for synthetic aperture radar images which are usually degraded by high level of speckle noise. Due to the success of convolutional neural networks in many super-resolution optical image applications, a proposed regression layer-based framework is exploited for synthetic aperture radar image enhancement. The proposed regression layer is used during the learning phase to drive the back-propagation error based on the structure similarity index loss function. The structure similarity index is a quality assessment based on comparing the structural information. Consequently, the proposed loss function compares the statistical values of the network output images with that of the optimal target images. Moreover, the proposed framework introduces the concept of adjusting the proportions of the structure similarity index components (luminance, contrast and structure) according to their impacts, by changing their exponents. In addition to that, the proposed layer modifies the statistical properties of the target images, in such a way that drives learning to enhance the resulting image structure and eliminate the speckle noise. This layer is useful for noise reduction and super-resolution applications, especially for those dedicated to synthetic aperture radar images. [ABSTRACT FROM AUTHOR]

Road structure is composed of different pavement materials. These materials contain a large number of particles and pores with different sizes, shapes, dielectric properties and spatial locations, which determines the electromagnetic properties of roads. These feature multi-scale and discontinuous characteristics between layers, together with geometric irregularity and random non-uniformity characteristics within layers, therefore random structures will undoubtedly have a negative impact on GPR detection and data interpretation. As a supplement to the experimental observation, the forward modeling based on random media model can provide an economical and effective way for GPR detection of road hidden diseases. In this paper, discrete random media model and continuous random media model are established respectively by using digital image processing technology and stochastic process theory according to the structural characteristics of different layers of media in road structure. On the basis of the established random media model, the GPR response of holes and pipelines are simulated and analyzed by the Finite Difference Time Domain method, and the GPR signal is processed by the synthetic aperture focused imaging method. By comparing with homogeneous layered models, the results show that the forward modeling based on random media model can reflect the characteristics of ground penetrating radar signal of road structure more accurately. PVC pipe is accompanied by obvious multiple waves in the case of water filling. The polarity relationship between reflected wave and direct wave is the key to distinguish whether it is iron pipe or cavity. Synthetic aperture focused imaging algorithm can enhance the recognition of spatial location, size and dielectric properties of the target. The comparison with the field results shows that the simulated results are in reasonable agreement with the measured ones. [ABSTRACT FROM AUTHOR]

The back-projection algorithm can adapt to synthetic aperture radar imaging in any mode because of its time-domain imaging characteristics, so it has received increasing attention in scientific research and engineering practice. However, the large enormous of computation of the back-projection algorithm limits its application. Since the back-projection algorithm has a high degree of data parallelism, it is very suitable for GPU parallel processing. The data scale and hardware environment in different application scenarios will result in different bottlenecks which may prevent the back-projection algorithm from performing optimally. Thus, this paper proposes a series of strategies to aid the back-projection algorithm in achieving the best peak performance with any given problem scale on any given platform. A heuristic block strategy is proposed to optimize the peak performance of the back-projection algorithm on servers and miniaturized GPU devices, which can handle the differences in hardware platforms as well as the differences in data scales. To optimize the peak performance of the back-projection algorithm on miniaturized GPU devices, a memory management strategy is proposed by using unified memory and pinned host memory. The method proposed in this paper has achieved an acceleration ratio close to the device's peak performance on servers and miniaturized GPU devices. By using the proposed methods, any algorithm with data-independent chunking and any GPU device with a uniform memory architecture can achieve higher peak performance. [ABSTRACT FROM AUTHOR]

Frequency-modulated continuous wave radar techniques typically have inadequate angular resolutions due to the limited aperture sizes of antenna arrays in spite of employing multiple-input multiple-output (MIMO) techniques. Therefore, despite the existence of multiple objects, angularly close objects with similar distances and relative velocities are recognized as one single object. Autonomous driving requires the accurate recognition of road conditions. This requirement is one of the critical issues to be solved to distinguish significantly close objects. This paper proposes a technique referred to as an antenna element space pseudo-peak suppressing (APPS) method to resolve angularly close targets. The proposed APPS method aims to identify closely spaced objects on roads. These angularly close targets cause a single peak in a spatial spectrum obtained by a beamformer-based angle estimation. The APPS considers this single peak as pseudo. The APPS radar cancels this pseudo peak from the spatial spectrum. Then, the obtained residual received signal is analyzed. With these procedures, the APPS identifies the number of targets. The APPS also estimates the target angles. The proposed APPS is experimentally validated using a typical single-chip MIMO-based radar evaluation board with three transmit (TX) and four receive (RX) antennas. The experimental results confirm that the proposed APPS successfully resolves angularly close pseudo targets with an angle difference of approximately 0. 5 ∘ . [ABSTRACT FROM AUTHOR]

Systems that emit electromagnetic or sonic waves for diagnostic or interventional applications often have constraints on the size of their aperture, and thus produce an elongated focus in the axial dimension. This extended depth of focus limits imaging resolution and spatial specificity of the delivered energy. Here, we have developed a method that substantially minimizes the depth of focus. The method superimposes beams of distinct frequencies in space and time to create constructive interference at target and amplify deconstructive interference everywhere else, thus sharpening the focus. The method does not require labeling of targets or other manipulations of the medium. Using simulations, we found that the method tightens the depth of focus even for systems with a narrow bandwidth. Moreover, we implemented the method in ultrasonic hardware and found that a 46.1% frequency fractional bandwidth provides an average 7.4-fold reduction in the focal volume of the resulting beams. This method can be readily applied to sharpen the focus of interventional systems and is expected to also improve the axial resolution of existing imaging systems. [ABSTRACT FROM AUTHOR]

The results of the five-year (2017–2021) monitoring of oil pollution in the Kerch Strait and its prestrait regions are analyzed. The synthetic aperture radar (SAR) images of the European Sentinel-1A and Sentinel-1B satellites, both real-time and archived, representing valuable material for searching, detecting, and studying the character of oil pollution caused by spills of crude oil, petroleum products, and other oily substances, have been used for monitoring. During monitoring, 2597 oil slicks with a total area of 1120.4 km2 are detected on 481 SAR images. The main sources of pollution have been identified, in descending order, they are cargo transshipment areas in the strait, the port of Taman with its berthing complex, and prestrait anchorages in the Sea of Azov and the Black Sea. In addition, natural seep manifestations in the adjacent Black Sea shelf have been discovered, they create a natural hydrocarbon background that should be taken into account. Based on the monitoring results, a tendency towards an overall decrease in spills is revealed, despite their growth in 2016–2019. Monitoring with SAR using the geoinformation approach has shown its high efficiency for the problems of real-time and retrospective analysis of the satellite imagery data concerning oil pollution. [ABSTRACT FROM AUTHOR]

Wide Area Surveillance refers to the continuous monitoring and tracking of the earth's surface by using air and space-borne satellites. Many countries routinely do WAS for military and intelligence purposes. The present work is an application of the same for maritime ship detection in the seas, using the basic premise that a wake produced by a vessel can be more prominently captured than the ship alone. At the free surface, synthetic aperture radar images of a ship's wake show long dark and bright streaks known as Kelvin and turbulent wake. This Kelvin wave wake travels more than 100 kilometres away from the ship and contains important information about the ship. In our paper, we have done an assessment study on different ship's wake signatures by employing a combination of numerical simulations and validation with experiments. Though the initial part of the paper deals with straight line motion, an application of the same to a ship turning is also investigated at the end. Reverse calculations for detection of ship forward velocity using dispersion relations are carried out from their wake signature. This study is part of the research carried out by the Indian Navy with the collaboration of our department. Based on the results of our study, we can say that the method is promising and can be used for routine monitoring of the coast and open sea. [ABSTRACT FROM AUTHOR]

We consider the influence of the navigation data on the radar-image quality in a multiposition synthetic aperture radar system. We propose algorithms for adaptive synthesis with additional focusing, which provide both refinement of the navigation data using the primary radar information and the synthesis of images with improved quality. The proposed algorithms are efficient for processing the radar information obtained as a result of both the multiposition and monostatic survey. Using an example of processing the radio holograms, which were obtained from an airborne synthetic aperture radar with inaccurate navigation information, we show the efficiency of the method of a posteriori focusing, determine its efficiency conditions, and conclude on the necessity of adaptation (a priori focusing) in the case of insufficient accuracy of the navigation data. We take into account that the laws of motion of a couple of carriers can formally be reduced to the law of motion of an equivalent monostatic carrier. [ABSTRACT FROM AUTHOR]

The piston error is a phase error present in multi-element optical systems, such as coherent beam combining systems and optical synthetic aperture telescopes. It can be extracted from the interference pattern generated by a lens placed between adjacent sub-apertures. In this paper, we present the results of three piston error extraction methods based on interference patterns: the correlation coefficient method, the peak ratio method, and the peak shift method. Through a series of simulations, the piston error detection accuracy of the three methods is compared, considering multiple error sources: aberration of sub-apertures, Gaussian noise in the measured interference pattern, and offset of lens; the causes for the influence of different error sources on these three methods are analyzed. The advantage of the comparison consists in the fact that it reveals the strengths and limitations of each method and can provide a reference for the selection and improvement of piston error extraction methods in multi-element optical systems working under different conditions. [ABSTRACT FROM AUTHOR]

High angular resolution light field (LF) enables exciting applications such as depth estimation, virtual reality, and augmented reality. Although many light field angular super-resolution methods have been proposed, the reconstruction problem of LF with a wide-baseline is far from being solved. In this paper, we propose an end-to-end learning-based approach to achieve angular super-resolution of the light field with a wide-baseline. Our model consists of three components. We first train a convolutional neural network to predict the depth map for each sub-aperture view. Then the estimated depth maps are used to warp the input views. In the final component, we first use a convolutional neural network to fuse the initial warped light fields, and then we propose an edge-aware inpainting network to modify the inaccurate pixels in the near-edge regions. Accordingly, we design an EdgePyramid structure that contains multi-scale edges to perform the inpainting of near-edge pixels. Moreover, we introduce a novel loss function to reduce the artifacts and further estimate the similarity in near-edge regions. Experimental results on various light field datasets including large-baseline light field images show that our method outperforms the state-of-the-art light field angular super-resolution methods, especially in the terms of visual performance near edges. [ABSTRACT FROM AUTHOR]

This study aims to generate and also validate an automatic detection algorithm for pharyngeal airway on CBCT data using an AI software (Diagnocat) which will procure a measurement method. The second aim is to validate the newly developed artificial intelligence system in comparison to commercially available software for 3D CBCT evaluation. A Convolutional Neural Network-based machine learning algorithm was used for the segmentation of the pharyngeal airways in OSA and non-OSA patients. Radiologists used semi-automatic software to manually determine the airway and their measurements were compared with the AI. OSA patients were classified as minimal, mild, moderate, and severe groups, and the mean airway volumes of the groups were compared. The narrowest points of the airway (mm), the field of the airway (mm2), and volume of the airway (cc) of both OSA and non-OSA patients were also compared. There was no statistically significant difference between the manual technique and Diagnocat measurements in all groups (p > 0.05). Inter-class correlation coefficients were 0.954 for manual and automatic segmentation, 0.956 for Diagnocat and automatic segmentation, 0.972 for Diagnocat and manual segmentation. Although there was no statistically significant difference in total airway volume measurements between the manual measurements, automatic measurements, and DC measurements in non-OSA and OSA patients, we evaluated the output images to understand why the mean value for the total airway was higher in DC measurement. It was seen that the DC algorithm also measures the epiglottis volume and the posterior nasal aperture volume due to the low soft-tissue contrast in CBCT images and that leads to higher values in airway volume measurement. [ABSTRACT FROM AUTHOR]

Bed separation's evolution plays a crucial role in mining safety. Accurate identification of bed separation location is necessary to prevent disasters caused by bed separation. However, conventional methods such as theoretical calculation and in situ investigation cannot comprehensively reflect the dynamic evolution of the bed separation during mining. In this study, bed separations were classified as either supported or unsupported based on the stress state of the strata below a given bed separation. Furthermore, a theoretical method (PDLS-method) for identifying the dynamic location and aperture of bed separations during mining was proposed. In addition, in situ investigation was carried out to determine the internal movement of overlying strata and evolution law of bed separation. Monitoring results indicate that the internal movement of overlying strata has characteristics of "group movement" and "differential settlement". The rock layers in a strata group move synchronously, and adjacent strata groups move independently. Bed separations occur along the interfaces of those strata groups. The evolution of bed separation can be divided into four stages of emergence, rapid growth, stability, and recession. The biggest aperture of bed separation at investigation borehole was 1.56 m. The feasibility of the PDLS-method was verified by observed data. The results can be used in research regarding bed separation and prevention of secondary hazards. Highlights: A theoretical method for identifying the dynamic location and aperture of bed separations during mining was proposed. In-situ investigation was carried out to determine the internal movement of overlying strata and evolution law of bed separation. Internal movement of overlying strata caused by underground coal mining have characteristics of "group movement" and "differential settlement". [ABSTRACT FROM AUTHOR]

Increasing requirements to modern synthetic-aperture radars leads to a complication of both the radars themselves and the tools for monitoring of the radar parameters. Reflected-signal simulator is designed to check through characteristics of such radars at various stages of the life cycle, starting with the development of components and including regular operation, using the same methods. We describe a device for converting the sounding signals of synthetic-aperture radars to simulated reflected ones. A distinctive feature of the presented device is the ability to simulate point and spatially distributed targets and an arbitrary target environment (a set of observation conditions) for test, calibration, and validation. [ABSTRACT FROM AUTHOR]

The Fourth Industrial Revolution, or Industry 4.0, aims at automating traditional manufacturing and industrial practices using modern smart technology. Autonomous mobile robots equipped with different sensors and employing different techniques have been proposed in the literature in the logistics and inventory warehouse management contexts. Efficient robot motion and planning depend on precise localization, mapping, and awareness of the environment. To properly localize items, recent attempts have been made employing radio frequency identification (RFID) in a 3D environment. This manuscript introduces four least mean squares methods to estimate the 3D positions of tags employing synthetic apertures and phase unwrapping. The proposed methods approach the localization problem solving a system of equations typical of multilateration methods to find the intersections of multiple hyperboloids. The novelty introduced here is the use of unwrapped phase distances to compute pseudo ranges for the multilateration problem. The use of such a technique is feasible thanks to precise mobile robot localization and custom navigation policies. All the analyzed methods are suitable for online localization due to reduced computation timings and have been tested on three different datasets. Two of them contain virtual data that has been generated by simulation, while the other one comes from an indoor experimental setup. Simulated tests show that combined antenna motions in 3D space (including Z-axis) improve the localization obtaining errors under the centimeter for 3D localization. Experimental tests obtained results as low as 13 cm of mean accuracy for 3D localization and 0.21 cm for 2D localization. [ABSTRACT FROM AUTHOR]

The simultaneous detection of cover depth and rebar diameter are among the most frequently encountered issues for inspecting concrete for quality control and assurance, or evaluating a concrete structure. This paper presents a novel application of 2D full-waveform inversion of ultrasonic SH-waves (2D SH-FWI), for determining cover depth and size of embedded rebar. The method was applied to ultrasonic SH-wave datasets collected by a commercial shear-wave tomography system (MIRA) on four concrete specimens. Two of them had 10 steel bars of the same size (#5) embedded at various depths, and the other two had 10 steel bars of various sizes (#3 to #14) placed at the same cover depth of 65 mm. The results showed that the presented 2D SH-FWI was able to characterize both the depths and sizes of rebars. Except for the smallest rebar # 3, the difference between the inverted and true sizes is less than 30% for 9 rebars (#4 to #14 at 65-mm cover depth), and less than 33% for all 10 rebars (#5) at various depths from 25 to 140 mm. For comparison, the rebar sizes could not be obtained with the ultrasonic synthetic aperture focusing technique and GPR method, which only detected the cover thickness. [ABSTRACT FROM AUTHOR]

This paper presents a millimeter-wave direction of arrival estimation (DoA) technique powered by dynamic aperture optimization. The frequency-diverse medium in this work is a lens-loaded oversized mmWave cavity that hosts quasi-random wave-chaotic radiation modes. The presence of the lens is shown to confine the radiation within the field of view and improve the gain of each radiation mode; hence, enhancing the accuracy of the DoA estimation. It is also shown, for the first time, that a lens loaded-cavity can be transformed into a lens-loaded dynamic aperture by introducing a mechanically controlled mode-mixing mechanism inside the cavity. This work also proposes a way of optimizing this lens-loaded dynamic aperture by exploiting the mode mixing mechanism governed by a machine learning-assisted evolutionary algorithm. The concept is verified by a series of extensive simulations of the dynamic aperture states obtained via the machine learning-assisted evolutionary optimization technique. The simulation results show a 25 % improvement in the conditioning for the DoA estimation using the proposed technique. [ABSTRACT FROM AUTHOR]

The presence of cranial bones in the ultrasound propagation path seriously complicates the imaging of tissues and blood vessels of the brain since the bones distort the ultrasound field, introducing phase and amplitude aberrations. Such distortions are not always apparent since complete information about the studied object is fundamentally inaccessible. The article develops a new approach that uses the synthetic aperture method to detect wavefront aberrations. A quantitative parameter is proposed that characterizes the presence of aberrations by measuring the RMS width of the angular intensity distribution. Experimental results were obtained at a frequency of 2 MHz using phantom and in vivo transcranial data. It is shown that in the presence of aberrations, the value of the proposed parameter increases by 22–45% with respect to the theoretical value for the aberrationless case. [ABSTRACT FROM AUTHOR]

Fully autonomous drones have been demonstrated to find lost or injured persons under strongly occluding forest canopy. Airborne optical sectioning (AOS), a novel synthetic aperture imaging technique, together with deep-learning-based classification enables high detection rates under realistic search-and-rescue conditions. We demonstrate that false detections can be significantly suppressed and true detections boosted by combining classifications from multiple AOS—rather than single—integral images. This improves classification rates especially in the presence of occlusion. To make this possible, we modified the AOS imaging process to support large overlaps between subsequent integrals, enabling real-time and on-board scanning and processing of groundspeeds up to 10 m/s. [ABSTRACT FROM AUTHOR]

Volcanic mass flows, including lava, pyroclastic density currents, and lahars, account for the bulk of fatalities and infrastructure damage caused by volcanic eruptions. Mapping these flows soon after their emplacement is vital to understanding their impact and to forecasting the likely behavior of potential future flows. Synthetic aperture radar (SAR) can provide useful information about surface properties and changes regardless of environmental conditions or time of day, but no individual SAR product can unambiguously detect and map surface mass flows in all conditions. Combining SAR products, however, can capitalize on the strengths and compensate for the weaknesses of individual data types. SAR volcanic flow maps (SAR VFMs) merge cross-polarized amplitude imagery from two different dates with interferometric coherence spanning those dates. The combination of amplitude change with coherence provides a means of detecting volcanic mass flows regardless of surface conditions, and data collected by satellite provide the spatial coverage needed to detect changes over broad areas. Application to eruptions of Kīlauea (Hawaiʻi), Nyiragongo (Democratic Republic of Congo), Sinabung (Indonesia), and Fuego (Guatemala) demonstrate the value of SAR VFMs for monitoring hazardous volcanic activity, and the importance of acquiring cross-polarized satellite SAR imagery for volcano applications. The ever-growing number of public and private satellite SAR missions will provide for improved temporal resolution in SAR VFMs in the future, and the technique may be suitable for automated analysis that is capable of timely identification of changes due to volcanic activity, even in areas that are otherwise unmonitored. [ABSTRACT FROM AUTHOR]

Recent advances in satellite technologies, statistical and mathematical models, and computational resources have paved the way for operational use of satellite data in monitoring and forecasting natural hazards. We present a review of the use of satellite data for Earth observation in the context of geohazards preventive monitoring and disaster evaluation and assessment. We describe the techniques exploited to extract ground displacement information from satellite radar sensor images and the applicability of such data to the study of natural hazards such as landslides, earthquakes, volcanic activity, and ground subsidence. In this context, statistical techniques, ranging from time series analysis to spatial statistics, as well as continuum or discrete physics-based models, adopting deterministic or stochastic approaches, are irreplaceable tools for modeling and simulating natural hazards scenarios from a mathematical perspective. In addition to this, the huge amount of data collected nowadays and the complexity of the models and methods needed for an effective analysis set new computational challenges. The synergy among statistical methods, mathematical models, and optimized software, enriched with the assimilation of satellite data, is essential for building predictive and timely monitoring models for risk analysis. [ABSTRACT FROM AUTHOR]

We investigate the impact of closing a fracture with rough surfaces on the fracture hydraulic diffusivity, which controls the spatiotemporal evolution of pore-pressure perturbations in geological formations, particularly those composed of an impermeable matrix and highly permeable natural fractures. We build distributions of synthetic fracture apertures at a reservoir scale (∼ 500 m) from a self-affine model with isotropic Hurst exponents derived from field observations of fault surfaces. To quantify the hydraulic diffusivity of rough fractures, we conduct finite element simulations of transient fluid flow in a single fracture. We use a surface representation of the fracture aperture following the Reynolds lubrication approximation. We verify that our approximation is valid for a steady-state flow and a low Reynolds number (Re ≪ 1) from the comparison with a volume-represented fracture aperture model solved by the Navier–Stokes equations for incompressible fluids (INS). Subsequently, the effective hydraulic diffusivity of the rough fracture is estimated by fitting the computed pressure field with the solution of an equivalent parallel plate model. The results show that the long-range correlation aperture field (up to the fault scale) due to self-affinity significantly affects hydraulic pressure diffusion, which is manifested as a strong variability in the pressure distribution with the orientation of the imposed pressure drop. Based on a rigid-plastic rheology, when closing the fracture stepwise from the initial contact to the flow percolation threshold, a decrease in the hydraulic diffusivity over seven orders of magnitude in one direction along the fracture but over four orders of magnitude in the perpendicular direction is obtained. Our results have strong implications for the interpretation of some measured hydraulic diffusivity data as well as for the use of hydraulic diffusivity in interpreting the spatial distribution of fluid-induced seismic events in faulted reservoirs. [ABSTRACT FROM AUTHOR]

Persistent scatterer interferometric synthetic aperture radar (PS-InSAR) is a widely used technique for local ground deformation estimation due to its millimetric accuracy and full-resolution results. However, with the enhancement of data acquisition capability of the SAR sensors, larger coverage and higher temporal-spatial resolution of SAR images can cause explosive increase and uneven distribution of PS points. PS network constructed between PSs with different qualities may cause spatial-error propagation. Large PS points list with large amount of data also have high requirements on computing performance and storage space. Those problems can bring big challenge for PS-InSAR technique in processing capacity, deformation monitoring accuracy and algorithm efficiency. In order to effectively overcome those limitations, this paper proposed a novel block PS-InSAR method that uses the approach of partitioning a study area into regular blocks with overlapping regions, selecting a high-quality reference point for each block and eliminating the discontinuity of the parameter calculating results in adjacent blocks by the weighted least square method. We used this approach to analyze ground subsidence in over 13,000 km2 of southern California, and it performed successfully when 69 Sentinel-1 images were used and 21,029,968 PSs were selected. Only approximately 10 h was needed for this experiment. Comparing the deformation rate and time series of the InSAR results with 29 GPS observations, the mean and standard deviation of difference of deformation rate were − 0.63 mm/yr and 1.53 mm/yr, respectively, and the average root mean squared error of deformation time series was 3.7 mm. The experiment demonstrated that this method could achieve high accuracy results without spatial discontinuity and increase the computational efficiency significantly. [ABSTRACT FROM AUTHOR]

In this article, an active millimeter-wave imager based on multiple-input multiple-output synthetic aperture radar (MIMO-SAR) scheme is presented. In order to achieve two-dimensional (2-D) synthetic aperture, a one-dimensional (1-D) high-precision mechanical scanning frame is used to combine with a self-designed 1-D millimeter-wave sparse MIMO array. The hardware composition of the presented imager is introduced in detail. In addition, a new operation method of transmitting and receiving based on the designed 1-D millimeter-wave sparse MIMO array is proposed. Different from the full-channel mode, a minimum number of transmitters and receivers is required in our method to make the equivalent phase centers (EPCs) evenly distributed while covering the whole array aperture. In this way, the amount of the echo data can be significantly reduced, and the real-time imaging can be realized by applying fast imaging algorithms. Experimental results show that the proposed imager and the corresponding array operation method perform well in high-resolution imaging and concealed object detection. [ABSTRACT FROM AUTHOR]

In April 2013, Rio Tinto Kennecott Copper's (RTKC) Bingham Canyon Mine experienced what is arguably the world's largest ever in-pit slope failure. The failure initiated on the east wall of the mine, along major, continuous, low-strength bedding faults, which were the upper and lower contact of the Manefay hornfels bed. The failure comprised approximately 145 million tonnes of rock and waste dump material. East wall slope deformations were detected some months prior to the catastrophic slope collapse by RTKC's ground-based slope monitoring systems. Use of existing terrestrial radar and prism monitoring systems provided excellent data to manage the response to the slope collapse. The failure resulted in no injuries/loss of life, although the failure runout distance was longer than expected, resulting in the loss of mining equipment and significant production interruption. Post-failure investigations identified an RTKC-sponsored, university research project, which detected a zone of sporadic ground deformation at the crest of the Manefay failure using satellite-based Interferometric Synthetic Aperture Radar (InSAR) monitoring. When the outcomes of this research study were received in 2010, RTKC responded by conducting detailed field inspections, installing prisms in the area of concern and monitoring the area closely in the years before the event. Interferometric methods for calculating ground displacement from satellite-based synthetic aperture radar (SAR) data have improved significantly since the time leading up to the Manefay failure. These updated methods have been used in this study of the available historic SAR data with results indicating that significant ground movements were occurring over a number of years prior to failure. Detailed knowledge of these movements could potentially have led to a different interpretation of failure mechanisms and magnitude and, in hindsight, different slope management and mine development plans in the years preceding slope failure. From a business and mine operations perspective, the ability to identify subtle signs of slope failure before a catastrophic slope collapse is essential for appropriate management of such events. The use of InSAR monitoring, as discussed in this paper, provides an increased capability for further improved slope management at RTKC, and at other mining operations. [ABSTRACT FROM AUTHOR]

In this paper, an object recognition approach for synthetic aperture radar (SAR) images is addressed, which is based on the enhanced kernel sparse representation of monogenic signal. It consists of two main modules. In the first module, to capture the spatial and spectral properties of a target at the same time, a multi-scale monogenic feature extraction scheme is proposed. In the second module, an enhanced kernel sparse representation-based classifier (KSRC) is designed. Different from the traditional KSRC, in the enhanced KSRC, we first integrate the kernel principal component analysis (KPCA) as well as the kernel fisher discriminant analysis (KFDA) to generate an augmented pseudo-transformation matrix. Then, a new discriminative feature mapping approach is presented by exploiting the augmented pseudo-transformation matrix so that the dimensionality of the kernel feature space can be effectively reduced. At last, the ℓ1 -norm minimization is utilized to calculate the sparse coefficients for a test sample, and thus the inference can be reached in terms of the total reconstruction error. Experimental results on the public moving and stationary target acquisition and recognition dataset (MSTAR) demonstrate that the proposed method achieves high recognition accuracy for SAR automatic target recognition. [ABSTRACT FROM AUTHOR]

The article proposes a direct algorithm for reconstructing the electromagnetic field at the antenna aperture, which can be used to solve problems of diagnostics and spatial filtering. The algorithm is based on the solution of Maxwell's equations in the form of advanced potentials of the electromagnetic field. It is shown that advanced electromagnetic waves, which are a solution to Maxwell's equations, are equivalent to retarded electromagnetic waves when the parameters of the medium are stationary and linear. The direct algorithm is formulated as the multiplication of the transformation operator matrix by the known vector of the electromagnetic field on a closed surface. In this case, the transformation outward (to the far field) or inward (to the aperture) of the surface is described by the same operator and differs only in the signs of the input quantities. Unlike the known algorithms, the direct one does not require scanning the electromagnetic field on canonical surfaces or solving a large system of integral equations. It makes the new algorithm optimal for use in new near-field antenna measurement systems based on industrial robots, unmanned aerial vehicles, etc. Verification of the developed algorithm using experimental data has shown the possibility of reconstructing the normalized distribution of the electric field strength at the antenna aperture with an error less than 2 dB. [ABSTRACT FROM AUTHOR]

In this paper, a new model-based Bayesian compressive sensing technique for non-stationary images is proposed. Our algorithm is based on the recently addressed triplet Markov fields (TMF) model. The TMF model is well appropriate for non-stationary image processing, owing to the introduction of a third random field which reflects different non-stationarity of images. Furthermore, TMF can extract the interactions among the neighboring sites of an image in a more complete way than the classic hidden Markov models do. In this paper, the inter-scale dependencies between the wavelet coefficients is exploited explicitly in the proposed TMF model, which results in the wavelet domain TMF model. Our proposed model considers the intra- and inter-scale dependencies and the non-stationarity of images simultaneously. Also, we have developed our proposed algorithm for both Gaussian and non-Gaussian measurement noises, and we have modeled the non-Gaussianity of the noise via Laplace distribution. To approximate the posterior distributions of the hidden variables, we resort to a variational Bayesian expectation–maximization algorithm. Simulation results in both the optical and synthetic aperture radar images show that this model leads to an improvement over state-of-the-art algorithms in terms of the reconstruction error and the structural similarity. [ABSTRACT FROM AUTHOR]

The paper presents a multi-source approach tailored for the analysis of ground movements affecting the village of Barberà de la Conca (Tarragona, Catalonia, Spain), where cracks on the ground and damage of different severity to structures and infrastructure was recorded. For this purpose, monitoring of ground displacements performed by topographic survey, geotechnical monitoring and remote sensing techniques (ground-based synthetic aperture radar, GBSAR) are combined with multi-temporal damage surveys and monitoring of cracks (crackmeters) to get an insight into the kinematics of the urban slope. The obtained results highlight the correspondence between the monitoring data and the effects on the exposed facilities induced by ground displacements, which seem to occur predominantly in the horizontal plane with diverging directions (northward and southward) from the main ground fracture crossing the centre of the village. The case study stands as a further contribution to fostering this kind of integrated approaches that via cross-validations can improve data reliability as well as enrich datasets for slope instability recognition and analysis, which are crucial to plan risk mitigation works. [ABSTRACT FROM AUTHOR]

Results are presented from monitoring the current state of the area of the landslide on the Bureya River in 2018–2019 using images from synthetic aperture radars and optical sensors of the Sentinel multisatellite system. Differential radar interferometry shows the stability of the landslide's surface in the first four months after the landslide and since the end of July 2019. The small-scale dynamics of the surface are revealed within the landslide circus. It is shown that interferometry cannot be used to observe large modifications of the shoreline, in contrast to optical images, where the effects of the collapse of shoreline fragments and shoreline flooding were clearly observed. Ongoing landslide activity within the landslide circus and area of shoreline collapse is detected using satellite images. Continuous monitoring of this and other dangerous landslide zones on the Bureya River is needed. [ABSTRACT FROM AUTHOR]

Data from synthetic aperture radars (SAR) provide new opportunities for observing and studying local katabatic winds (bora and foehn) over the seas in different regions of the Russian Federation and adjacent marine basins. The impact of these winds on the characteristics of small-scale wind waves leads to the formation of characteristic manifestations on the sea surface, which in turn can be seen in SAR images. Satellite SAR data, unlike subsatellite observations, make it possible to see the phenomena in coastal zones and large lakes, estimate their spatial scales, and determine a number of quantitative characteristics. The analysis of the collected SAR images shows that bora-like and foehn-like local winds have similar characteristics and cover vast areas of the inland and marginal seas of the Russian Federation. The similarity of the manifestations of local winds clearly indicates the same physical and aerohydrodynamic mechanisms of their onset and display on SAR images. It is shown that spaceborne SAR data, which are 2D patterns of the 'frozen wind', are extremely useful for studying and monitoring local winds over the seas, especially where the network of weather stations is extremely sparse. [ABSTRACT FROM AUTHOR]

The substrate effects on aperture resonance have been widely studied because the resonance peak position is key for sensing, communications, and field enhancement applications. So far, the theoretical works have focused on the lossless infinite substrate case, which only explains the resonance peak redshift by the substrate index. The loss effect has not been investigated yet because lossy infinite substrate significantly reduces the aperture transmission. Therefore, this work studied the loss effect on aperture resonance via an analytic model of the transmission though slot antennas on a finite substrate. When the substrate thickness was very thin compared to the wavelength, the transmittance remained high even for a large imaginary part of the refractive index; furthermore, the developed model predicted a strong blueshift when this imaginary part exceeds the real part. Besides, the zero of the imaginary part of the coupling strength was crucial in determining the resonance frequency for both infinite and finite substrates. Thus, this study can contribute to the material characterization, label-free detection, and efficient design of active metamaterials. [ABSTRACT FROM AUTHOR]

The generalised time–frequency transform (GTFT) is a powerful tool to analyse a large variety of frequency-modulated signals. However, it is not adequate to represent the variation of frequency over time for non-stationary signals. To solve this problem, short-time GTFT and short-time GTFT-based adaptive generalised fractional spectrogram (AGFS) are proposed. The AGFS is capable of providing a high concentration, high resolution, cross-term-free time–frequency distribution for analysing multicomponent frequency-modulated signals. It is also a generalisation of the short-time Fourier transform-based spectrogram and the short-time fractional Fourier transform-based spectrogram. The uncertainty principle for short-time GTFT is derived, and its time-bandwidth product is compared with other time–frequency distributions. With the help of simulated data examples, the effectiveness of AGFS is demonstrated in comparison with other time–frequency distributions for resolving and extracting individual components of multicomponent quadratic chirps. Robustness of AGFS is demonstrated under different input signal-to-noise ratio conditions. A local spectrogram optimisation technique is adopted for AGFS to represent simulated and real chirp signals. Finally, an application of the AGFS is presented to resolve multiple ground moving targets in synthetic aperture radar data and obtain its focused synthetic aperture radar image. [ABSTRACT FROM AUTHOR]