ALBERT

Description: Publication date: Available online 9 July 2016 Source: Geodesy and Geodynamics Author(s): Ruisha Li, Xi Zhang, Shouwen Gong, Hongtao Tang, Peng Jia The equation for determining cross-fault representative value is calculated based on hanging wall and foot wall reference level surfaces. The cross-fault data reliability are analyzed base on the stability of reference datum and observation points, thereby facilitating plotting of the representative value curves after removing interference. The spatial and temporal characteristics of fault deformation abnormalities before the 2016 Menyuan M s6.4 earthquake, as well as the fault-movement characteristics reflected by representative value, are summarized. The results show that many site trends had changed 1–3 years before the Menyuan M s6.4 earthquake in the Qilian Fault, reflecting certain background abnormalities. The short-term abnormalities centrally had appeared in the 6 months to 1 year period before the earthquake near and in the neighborhood of the source region, demonstrating a significantly increased number of short-term abnormalities. Many sites near and in the neighborhood of the source region had strengthened inverse activities or had changed from positive to inverse activities in the most recent 2–3 years, which reflect stress-field enhancements or adjustment features.

Description: This paper presents a method to estimate steady wind and a maneuvering strategy for loitering under a strong, steady wind. The wind estimation uses Global Positioning System velocity only through a novel filter design without airspeed measurement. The wind estimate is then used to guide the aircraft to crab in a direction perpendicular to the wind, thereby avoiding large changes in flight variables if an orbit-type maneuver is attempted. The proposed method is demonstrated through flight tests.

Description: We propose biased estimators to find the direction of arrival of emitters present in the mainlobe of a spinning antenna-based electronic intelligence system. The proposed estimators were constructed by using Bayesian techniques and by performing a linear transformation and an affine transformation on the maximum likelihood estimator. From a Monte Carlo simulation and experimental results, we demonstrate that the proposed estimators outperform the limit set by the popular performance benchmark, the Cramer-Rao lower bound.

Description: A new method was proposed for chirp signal detection and estimation built on the frame-based fast Fourier transform (FFT). The proposed method uses the peak frequency difference between FFT frames to detect a chirp signal and estimate chirp rate. This approach differs from conventional methods and is easy to implement. It generates more accurate chirp rate estimation especially under a low signal-to-noise ratio. Simulation and experimental data are used to verify the proposed methods.

Description: The quasimaximum likelihood (QML) estimator of polynomial-phase signals (PPSs) is based on the maximization of the short-time Fourier transform and suffers from aliasing when signals are sampled below the Nyquist sampling rate. In this paper, a phase unwrapping procedure has been proposed as an additional step in the QML to estimate parameters of such signals. Statistical study has shown excellent performance of the proposed approach.

Description: Publication date: Available online 18 July 2016 Source: Journal of Asian Ceramic Societies Author(s): J. Raj Mohamed, L. Amalraj The present work investigates the effect of precursor concentration ( m c ) on the structural, optical, morphological and electrical conductivity properties of In 2 S 3 thin films grown on amorphous glass substrates by nebulized spray pyrolysis (NSP) technique. The mixed phase of cubic and tetragonal structure of In 2 S 3 thin films at higher concentration has been observed by X-ray diffraction pattern. The reduced strain by increasing the precursor concentration increased the average crystallite from 17.8 to 28.9 nm. The energy dispersive analysis by X-ray (EDAX) studies confirmed the presence of In and S. The transmittance, optical direct band gap energy, Urbach energy and skin depth of In 2 S 3 films have been analyzed by optical absorption spectra. The better conductivity and mobility noticed at m c = 0.15 M are explained by carrier concentration and crystallite. Better optical and electrical conductivity behaviour of In 2 S 3 thin film sample proposes for effective solar cell fabrication.

Description: The inverse problem of synthetic aperture imaging radiometers (SAIRs) has been demonstrated to be not well posed. The regularization methods are crucial for providing unique and stable solutions in the reconstruction of radiometric brightness temperature (BT) maps. Different to deterministic ones, a new approach is presented by referring to the rule of Bayesian inference, providing a probability model of regularized constraints to combat the ill-posedness of finite-dimensional discrete inverse problems. In addition, the SAIR inverse problem can be converted into the probability estimation of the reconstructed BT. Furthermore, in application to both uniformly and nonuniformly spaced arrays, our method can obtain the optimal solution adaptively and avoid the dilemma of choosing the optimal regularization parameter. Finally, simulation results illustrating the effectiveness and performance of the proposed method are provided.

Description: A range-cell-focusing algorithm is proposed in order to improve the quality of the target image. In a synthetic aperture radar (SAR) imaging system, the range resolution depends on the frequency bandwidth and determines the ability to distinguish between targets that are very close to each other. In cases where the resolution and the SNR from the environment are not adequate, targets cannot be accurately visualized. In order to successively classify targets that are close, we are combining an enhanced-multiple-signal-classification spectrum as a weighting function to reproduce the raw data. The proposed algorithm improves classification and separation for close targets while suppressing artifacts in the final images. The targets of interest are stationary point scatterers. The results are obtained from both simulated and experimental data to demonstrate that the proposed algorithm provides better performance than a conventional SAR imaging algorithm, the range migration algorithm.

Description: This letter presents a multiscale edge detection method for multilook polarimetric synthetic aperture radar (PolSAR) images based on the nonsubsampled contourlet transform (NSCT). The NSCT can provide flexible multiscale and directional decomposition. In the multiscale decomposition, the coefficients of the nonsubsampled pyramid in the NSCT are calculated via maximizing the polarimetric contrast between the adjacent subband levels, instead of using the difference of the adjacent subbands as used in the additive noise model. By this way, we make the NSCT applicable to PolSAR data and multiband data. Then, the edges are detected in the NSCT domain based on a fusion of the directional subband coefficients at different scales. Experimental results with both simulated and real PolSAR data show that the present approach is robust to noise and the extracted edges are complete and continuous.

Description: A method for defining the spatial resolution of a Global Navigation Satellite System reflectometry delay–Doppler map (DDM) and of any derived geophysical product is proposed. An effective spatial resolution is derived as a function of measurement geometry and delay–Doppler (DD) interval, and as a more appropriate representation of resolution than the geometric resolution previously used in the literature. The definition more accurately accounts for variations in the scattered power across different pixels of the DDM and more accurately includes the power spreading effect caused by the Woodward ambiguity function. The dependence of the effective resolution on incidence angle, receiver altitude, and DD interval is analyzed and compared with the dependence of the geometric resolution with similar parameters.

Description: Laser scanner-captured 3-D point cloud data analysis is becoming more commonly used for remote sensing and plant science applications. Because of nonrigidity and complexity, reconstructing a 3-D model of a plant is extremely challenging. Existing algorithms often fail to find correct correspondences for plantlike thin structures. We address the problem of finding 3-D junction points in plant point cloud data as a first step of this correspondence matching process. Temporarily, we transform the 3-D problem into 2-D by performing appropriate coordinate transformations to the neighborhood of each 3-D point. Our proposed method has two steps. First, a statistical dip test of multimodality is performed to detect the nonlinearity of the local 2D structure. Then, each branch is approximated by sequential random-sample-consensus line fitting and a Euclidean clustering technique. The straight line parameters of each branch are extracted using total-least-squares estimation. Finally, the straight line equations are solved to determine if they intersect in the local neighborhood. Such junction points are good candidates for subsequent correspondence algorithms. Using these detected junction points, we formulate a correspondence algorithm as a subgraph matching problem and show that, without using traditional descriptor similarity-based matching, good correspondences can be obtained by simply considering geodesic distances among graph nodes. Experiments on synthetic and real ( Arabidopsis plant) data show that the proposed method outperforms the state of the art.

Description: Repeat-track analysis is commonly utilized to generate elevation change time series from satellite radar altimetry over ice sheets. It requires surface gradient (SG) correction due primarily to orbital drifts and radar-related empirical corrections caused by radar scatters from ice surface and potential subsurface. In this letter, two approaches, namely, the use of a digital elevation model (DEM) and the modified repeat-track analysis, which uses the accumulated Envisat altimetry profiles, are applied to correct the SG over both Greenland ice sheet (GrIS) and Antarctic ice sheet (AIS). By comparing the root mean square (rms) of elevation change time series after SG correction, the percentage of data (rms< 1 m) obtained by using modified repeat-track analysis is found to be 85% and 88% for the GrIS and AIS, respectively, as opposed to 45% and 44% if the DEM method is used. Furthermore, three cases are studied to assess empirical corrections for elevation retrieved from both ice-1 and ice-2 algorithms over the AIS. We conclude that the modified repeat-track analysis is more effective to remove topographic induced error. For the ice-2 algorithm, waveform shape parameters are needed in addition to applying corrections from changes in backscatter coefficients. The trend of elevation changes from the ice-1 algorithm with only backscatter analysis agrees with that from the ice-2 algorithm with corrections from backscatter coefficient changes and waveform shape parameters. This study could provide a potential data processing recipe for generating improved satellite radar altimetry elevation time series over ice sheets.

Description: Extracting ships from complex backgrounds is the bottleneck of ship detection in high-resolution optical satellite images. In this letter, we propose a nearly closed-form ship rotated bounding box space used for ship detection and design a method to generate a small number of highly potential candidates based on this space. We first analyze the possibility of accurately covering all ships by labeling rotated bounding boxes. Moreover, to reduce search space, we construct a nearly closed-form ship rotated bounding box space. Then, by scoring for each latent candidate in the space using a two-cascaded linear model followed by binary linear programming, we select a small number of highly potential candidates. Moreover, we also propose a fast version of our method. Experiments on our data set validate the effectiveness of our method and the efficiency of its fast version, which achieves a close detection rate in near real time.

Description: In this letter, a two-stage method for airport detection on remote sensing images is proposed. In the first stage, a new algorithm composed of several line-based processing steps is used for extraction of candidate airport regions. In the second stage, the scale-invariant feature transformation and Fisher vector coding are used for efficient representation of the airport and nonairport regions and support vector machines employed for classification. In order to evaluate the performance of the proposed method, extensive experiments are conducted on airports around the world with different layouts. The measures used in the evaluation are accuracy, sensitivity, and specificity. The proposed method achieved an accuracy of 94.6%, which was benchmarked with two previous methods to prove its superiority.

Description: High-resolution wide-swath synthetic aperture radar (SAR) systems are very attractive for the observation of dynamic processes on the Earth's surface, but they require the downlink of a huge volume of data. In order to comply with azimuth ambiguity requirements, in fact, a pulse repetition frequency much higher than the required processed Doppler bandwidth is often desirable. The volume of downlinked data, however, can be drastically reduced by performing Doppler filtering and decimation on board. A finite-impulse-response filter with a relatively small number of taps suffices to suppress the additional ambiguous components and to recover the original impulse response. This strategy is of special relevance for staggered SAR systems, which are typically characterized by a high oversampling factor. The proposed data reduction technique is also baseline for Tandem-L, where onboard Doppler filtering, resampling, and decimation will be jointly implemented.

Description: In this letter, we generate anisotropic bicontinuous media with different vertical and horizontal correlation functions. With the computer-generated bicontinuous medium, we then use numerical solutions of Maxwell equations in 3-dimensions (NMM3D) to calculate the anisotropic effective permittivities and the effective propagation constants of V and H polarizations. The copolarization phase difference (CPD) of VV and HH is then derived. The CPDs have recently been applied to the retrieval of snow water equivalent, snow depth, and anisotropy. The NMM3D simulation results are also compared with the results of the strong permittivity fluctuations in the low frequency limit and compared against the Maxwell–Garnett mixing formula.

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Description: Publication date: Available online 19 July 2016 Source: Journal of Rock Mechanics and Geotechnical Engineering Author(s): Aref al-Swaidani, Ibrahim Hammoud, Ayman Meziab Clayey soils in Syria cover a total area of more than 20,000 km 2 of the country, most of which are located in the southwestern region. In many places of the country, the clayey soils caused severe damage to infrastructures. Extensive studies have been carried out on the stabilization of clayey soils using lime. Syria is rich in both lime and natural pozzolana. However, few works have been conducted to investigate the influence of adding natural pozzolana on the geotechnical properties of lime-treated clayey soils. The aim of this paper is to understand the effect of adding natural pozzolana on some geotechnical properties of lime-stabilized clayey soils. Natural pozzolana and lime are added to soil within the range of 0%−20% and 0%−8%, respectively. Consistency, compaction, California bearing ratio (CBR) and linear shrinkage properties are particularly investigated. The test results show that the investigated properties of lime-treated clayey soils can be considerably enhanced when the natural pozzolana is added as a stabilizing agent. Analysis results of scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX) show significant changes in the microstructure of the treated clayey soil. A better flocculation of clayey particles and further formation of cementing materials in the natural pozzolana-lime-treated clayey soil are clearly observed.

Description: Publication date: Available online 15 July 2016 Source: Journal of Rock Mechanics and Geotechnical Engineering Author(s): Sadok Benmebarek, Samir Attallaoui, Naïma Benmebarek Back-to-back mechanically stabilized earth walls (BBMSEWs) are encountered in bridge approaches, ramp ways, rockfall protection systems, earth dams, levees and noise barriers. However, available design guidelines for BBMSEWs are limited and not applicable to numerical modeling when back-to-back walls interact with each other. The objective of this paper is to investigate, using PLAXIS code, the effects of the reduction in the distance between BBMSEW, the reinforcement length, the quality of backfill material and the connection of reinforcements in the middle, when the back-to-back walls are close. The results indicate that each of the BBMSEWs behaves independently if the width of the embankment between mechanically stabilized earth walls is greater than that of the active zone. This is in good agreement with the result of FHWA design guideline. However, the results show that the FHWA design guideline underestimates the lateral earth pressure when back-to-back walls interact with each other. Moreover, for closer BBMSEWs, FHWA design guideline strongly overestimates the maximum tensile force in the reinforcement. The investigation of the quality of backfill material shows that the minor increase in embankment cohesion can lead to significant reductions in both the lateral earth pressure and the maximum tensile force in geosynthetic. When the distance between the two earth walls is close to zero, the connection of reinforcement between back-to-back walls significantly improves the factor of safety.

Description: Publication date: Available online 28 June 2016 Source: Journal of Rock Mechanics and Geotechnical Engineering Author(s): Hossein Mola-Abasi, Issa Shooshpasha It is well known that the cemented sand is one of economic and environmental topics in soil stabilization. In this instance, a blend of sand, cement and other materials such as fiber, glass, nanoparticle and zeolite can be commercially available and effectively used in soil stabilization in road construction. However, the influence and effectiveness of zeolite on the properties of cemented sand systems have not been completely explored. In this study, based on an experimental program, the effects of zeolite on the characteristics of cemented sands are investigated. Stabilizing agent includes Portland cement of type II and zeolite. Results show the improvements of unconfined compressive strength (UCS) and failure properties of cemented sand when the cement is replaced by zeolite at an optimum proportion of 30% after 28 days. The rate of strength improvement is approximately between 20% and 78%. The efficiency of using zeolite increases with the increases in cement amount and porosity. Finally, a power function of void-cement ratio and zeolite content is demonstrated to be an appropriate method to assess UCS of zeolite-cemented mixtures.

Description: Publication date: Available online 28 June 2016 Source: Journal of Rock Mechanics and Geotechnical Engineering Author(s): Atsushi Sainoki, Hani S. Mitri Fault-slip taking place in underground mines occasionally causes severe damage to mine openings as a result of strong ground motion induced by seismic waves arising from fault-slip. It is indicated from previous studies that intense seismic waves could be generated with the shock unloading of fault surface asperities during fault-slip. This study investigates the shock unloading with numerical simulation. A three-dimensional (3D) numerical model with idealized asperities is constructed with the help of discrete element code 3DEC. The idealization is conducted to particularly focus on simulating the shock unloading that previous numerical models, which replicate asperity degradation and crack development during the shear behavior of a joint surface in previous studies, fail to capture and simulate. With the numerical model, static and dynamic analyses are carried out to simulate unloading of asperities in the course of fault-slip. The results obtained from the dynamic analysis show that gradual stress release takes place around the center of the asperity tip at a rate of 45 MPa/ms for the base case, while an instantaneous stress release greater than 80 MPa occurs near the periphery of the asperity tip when the contact between the upper and lower asperities is lost. The instantaneous stress release becomes more intense in the vicinity of the asperity tip, causing tensile stress more than 20 MPa. It is deduced that the tensile stress could further increase if the numerical model is discretized more densely and analysis is carried out under stress conditions at a great depth. A model parametric study shows that in-situ stress state has a significant influence on the magnitude of the generated tensile stress. The results imply that the rapid stress release generating extremely high tensile stress on the asperity tip can cause intense seismic waves when it occurs at a great depth.

Description: Publication date: Available online 18 June 2016 Source: Journal of Rock Mechanics and Geotechnical Engineering Author(s): Morteza Esmaeili, Hamid Khajehei Deep mixed column (DMC) is known as one of the effective methods for stabilizing the natural earth beneath road or railway embankments to control stability and settlements under traffic loads. The load distribution mechanism of embankment overlying on loose subgrades stabilized with DMCs considerably depends on the columns' mechanical and geometrical specifications. The present study uses the laboratory investigation to understand the behavior of embankments lying on loose sandy subgrade in three different conditions: (1) subgrade without reinforcement, (2) subgrade reinforced with DMCs in a triangular pattern and horizontal plan, and (3) subgrade reinforced with DMCs in a square pattern and horizontal plan. For this purpose, by adopting the scale factor of 1:10, a reference embankment with 20 cm height, 250 cm length, and 93% maximum dry density achieved in standard Proctor compaction test was constructed over a 70 cm thick loose sandy bed with the relative density of 50% in a loading chamber, and its load-displacement behavior was evaluated until the failure occurred. In the next two tests, DMCs (with 10 cm diameter, 40 cm length, and 25 cm center-to-center spacing) were placed in groups in two different patterns (square and triangular) in the same sandy bed beneath the embankment and, consequently, the embankments were constructed over the reinforced subgrades and gradually loaded until the failure happened. In all the three tests, the load-displacement behaviors of the embankment and the selected DMCs were instrumented for monitoring purpose. The obtained results implied 64% increase in failure load and 40% decrease in embankment crest settlement when using the square pattern of DMCs compared with those of the reference embankment, while these values were 63% and 12%, respectively, for DMCs in triangular pattern. This confirmed generally better performance of DMCs with a triangular pattern.

Description: Publication date: Available online 15 July 2016 Source: Journal of Rock Mechanics and Geotechnical Engineering Author(s): Brian G. Sexton, Bryan A. McCabe, Minna Karstunen, Nallathamby Sivasithamparam The recently developed elasto-viscoplastic Creep-SCLAY1S model has been used in conjunction with PLAXIS 2D to investigate the effectiveness of vibro-replacement in a creep-prone clay. The Creep-SCLAY1S model accounts for anisotropy, bonding, and destructuration, and uses the concept of a constant rate of viscoplastic multiplier to calculate creep strain rate. A comparison of settlement improvement factors with and without creep indicates that ‘total’ settlement improvement factors (primary plus creep) are lower than their ‘primary’ counterparts (primary settlement only). The lowest settlement improvement factors arise for analyses incorporating the effect of bonding and destructuration. Examination of the variations of vertical stress with time and depth has indicated that vertical stress is transferred from the soil to the column as the soil creeps. This results in additional column yielding. In addition, the radial and hoop stresses in the soil are lower for the ‘creep’ case. The reduced radial stresses lead to additional column bulging and hence more settlement, whereas the hoop stress reductions appear to be a secondary effect, caused by additional plastic deformation for the ‘creep’ case.

Description: Publication date: Available online 27 July 2016 Source: Journal of Rock Mechanics and Geotechnical Engineering Author(s): Xudong Wang, Qinlin Guo, Shanlong Yang, Dexuan Zhang, Yanwu Wang arthen sites are widely distributed throughout China, and most of them belong to archaeological sites with significant values, which not only directly witness the origin, formation and development of Chinese civilization, but also possess important values for conservation and exhibition. Many researches and practices on their conservation and consolidation have been carried out; however, the consolidation effect is mainly judged by visual observation and expert evaluation. Scientific assessment of conservation and consolidation effects is a challenging issue. Many instruments in other fields cannot be directly applied to the conservation of cultural relics due to their peculiarity. In order to assess the effects of field conservation experiments, this paper tries to understand the consolidation effects at Liangzhu site using nondestructive or micro-damage methods, including thermo-physical parameters testing, infrared thermal imaging, high-density microelectrode resistivity testing, portable microscope observation, and hydrophilic and hydrophobic testing, and thereby explores the practicable methods for evaluating the properties of consolidation materials for earthen sites treatment.

Description: Publication date: Available online 25 June 2016 Source: Journal of Rock Mechanics and Geotechnical Engineering Author(s): Hongwei Wang, Yaodong Jiang, Sheng Xue, Lingtao Mao, Zhinan Lin, Daixin Deng, Dengqiang Zhang This paper presents an investigation on the characteristics of overlying strata collapse and mining-induced pressure in fault-influenced zone by employing the physical modeling in consideration of fault structure. The precursory information of fault slip during the underground mining activities is studied as well. Based on the physical modeling, the optimization of roadway support design and the field verification in fault-influenced zone are conducted. Physical modeling results show that, due to the combined effect of mining activities and fault slip, the mining-induced pressure and the extent of damaged rock masses in the fault-influenced zone are greater than those in the uninfluenced zone. The sharp increase and the succeeding stabilization of stress or steady increase in displacement can be identified as the precursory information of fault slip. Considering the larger mining-induced pressure in the fault-influenced zone, the new support design utilizing cables is proposed. The optimization of roadway support design suggests that the cables can be anchored in the stable surrounding rocks and can effectively mobilize the load bearing capacity of the stable surrounding rocks. The field observation indicates that the roadway is in good condition with the optimized roadway support design.

Description: Publication date: Available online 18 June 2016 Source: Journal of Rock Mechanics and Geotechnical Engineering Author(s): Aiqing Wu, Jimin Wang, Zhong Zhou, Shuling Huang, Xiuli Ding, Zhihong Dong, Yuting Zhang Based on the analyses of data obtained from the underground powerhouse at Jinping I hydropower station, a comprehensive review of engineering rock mechanics practice in the underground powerhouse is first conducted. The distribution of strata, lithology, and initial geo-stress, the excavation process and corresponding rock mass support measures, the deformation and failure characteristics of the surrounding rock mass, the stress characteristics of anchorage structures in the cavern complex, and numerical simulations of surrounding rock mass stability and anchor support performance are presented. The results indicate that the underground powerhouse of Jinping I hydropower station is characterized by high to extremely high geo-stresses during rock excavation. Excessive surrounding rock mass deformation and high stress of anchorage structures, surrounding rock mass unloading damage, and local cracking failure of surrounding rock masses, etc., are mainly caused by rock mass excavation. Deformations of surrounding rock masses and stresses in anchorage structures here are larger than those found elsewhere: 20% of extensometers in the main powerhouse record more than 50 mm with the maximum at around 250 mm observed in the downstream sidewall of the transformer hall. There are about 25% of the anchor bolts having recorded stresses of more than 200 MPa. Jinping I hydropower plant is the first to have an underground powerhouse construction conducted in host rocks under extremely high geo-stress conditions, with the ratio of rock mass strength to geo-stress of less than 2.0. The results can provide a reference to underground powerhouse construction in similar geological conditions.

Description: Publication date: Available online 3 August 2016 Source: Journal of Rock Mechanics and Geotechnical Engineering Author(s): Shengwu Song, Xuemin Feng, Chenggang Liao, Dewen Cai, Zhongxu Liu, Yunhao Yang The Jinping I hydropower station is a huge water conservancy project consisting of the highest concrete arch dam to date in the world and a highly complex and large underground powerhouse cavern. It is located on the right bank with extremely high in-situ stress and a few discontinuities observed in surrounding rock masses. The problems of rock mass deformation and failure result in considerable challenges related to project design and construction and have raised a wide range of concerns in the fields of rock mechanics and engineering. During the excavation of underground caverns, high in-situ stress and relatively low rock mass strength in combination with large excavation dimensions lead to large deformation of the surrounding rock mass and support. Existing experiences in excavation and support cannot deal with the large deformation of rock mass effectively, and further studies are needed. In this paper, the geological conditions, layout of caverns, and design of excavation and support are first introduced, and then detailed analyses of deformation and failure characteristics of rocks are presented. Based on this, the mechanisms of deformation and failure are discussed, and the support adjustments for controlling rock large deformation and subsequent excavation procedures are proposed. Finally, the effectiveness of support and excavation adjustments to maintain the stability of the rock mass is verified. The measures for controlling the large deformation of surrounding rocks enrich the practical experiences related to the design and construction of large underground openings, and the construction of caverns in the Jinping I hydropower station provides a good case study of large-scale excavation in highly stressed ground with complex geological structures, as well as a reference case for research on rock mechanics.

Description: Cloud storage offers a popular environment for users to store and share content. Yet, content sharing leads to multiple downloads of the same content when users synchronize devices. These downloads contribute to bandwidth waste and increase server workloads. To what extent does this occur? Would network caches alleviate the problem? The authors address these questions by investigating the traffic generated by Dropbox, a popular cloud storage service. Using data collected from four networks, they show that a large fraction (57-70 percent) of downloads generated by Dropbox users is associated with content shared among multiple devices. Moreover, despite optimizations such as the LAN Sync protocol, up to 25 percent of Dropbox download traffic comes from content replicas. The authors then evaluate an alternative synchronization architecture that uses caches to offload storage servers from such downloads. Their experiments show that the approach cost-effectively avoids most repetitive downloads, benefiting service providers, the network, and end users.

Description: Bot detection -- identifying a software program that's using a computer system -- is an increasingly necessary security task. Existing solutions balance proof of human identity with unobtrusiveness in users' workflows. Cognitive modeling and natural interaction might provide stronger security and less intrusiveness.

Description: The IPv6 Internet is an important component of the Internet's continued growth and evolution. By several metrics, IPv6 has grown exponentially and now carries nontrivial amounts of production traffic. Less well-understood, however, is IPv6's topology and the way in which providers are using their IPv6 address allocations. Rather than relying on passive measurements or heuristics, which can bias inferences, here the authors use uniform active probing. They execute Internet Control Message Protocol (ICMP)-Paris traceroute probes to an address in each /48 in all /32's advertised in the global IPv6 routing table (approximately 400 million traces from 26 globally distributed vantage points). At this granularity, they characterize the distribution of IPv6 interface addresses in the wild, and find significant differences among providers and regions. By providing insight into the structure of the current IPv6 Internet router interface addressing method, their hope is to better inform efforts to design future intelligent, active IPv6 topology-mapping algorithms and systems.

Description: Along-track multichannel synthetic aperture radar is usually used to achieve ground moving target detection and imaging. Nevertheless, there is a design dilemma between azimuth high resolution and wide swath (HRWS). To solve this problem in HRWS mode, we introduce a virtual multichannel (VMC) scheme. For each virtual channel, the low real pulse repetition frequency (PRF) improves the ability of resolving range ambiguity for wide-swath, and the high virtual PRF improves the capability of resolving Doppler ambiguity for azimuth high resolution. For multiple virtual channels, strong ground clutter is eliminated by the joint VMC processing. Furthermore, a detailed signal model of a moving target in the virtual channel is given, and the special false-peak effect in the azimuthal image is analyzed. Moreover, we propose a novel ground moving target processing method based on the VMC scheme and the clutter suppression interferometry (CSI) technique, which is called VMC-CSI. The integration of detection, location, velocity estimation, and imaging for ground moving targets can be achieved. Accounting for the unresolved main peak and false peak for a moving target, in the VMC-CSI method, we adopt a two-step scheme to estimate the radial velocity and along-track velocity, namely, rough estimation and precise estimation. Meanwhile, considering the same interferometric phases of the main peak and the false peak, we use false peaks first for the robustness of initial azimuth location estimation and remove false peaks afterward. Numerical simulations are provided for testing the effect of the false peak and the effectiveness of VMC-CSI.

Description: This paper presents an open-source canopy height profile (CHP) toolkit designed for processing small-footprint full-waveform LiDAR data to obtain the estimates of effective leaf area index (LAIe) and CHPs. The use of the toolkit is presented with a case study of LAIe estimation in discontinuous-canopy fruit plantations. The experiments are carried out in two study areas, namely, orange and almond plantations, with different percentages of canopy cover (48% and 40%, respectively). For comparison, two commonly used discrete-point LAIe estimation methods are also tested. The LiDAR LAIe values are first computed for each of the sites and each method as a whole, providing “apparent” site-level LAIe, which disregards the discontinuity of the plantations' canopies. Since the toolkit allows for the calculation of the study area LAIe at different spatial scales, between-tree-level clumping can be easily accounted for and is then used to illustrate the impact of the discontinuity of canopy cover on LAIe retrieval. The LiDAR LAIe estimates are therefore computed at smaller scales as a mean of LAIe in various grid-cell sizes, providing estimates of “actual” site-level LAIe. Subsequently, the LiDAR LAIe results are compared with theoretical models of “apparent” LAIe versus “actual” LAIe, based on known percent canopy cover in each site. The comparison of those models to LiDAR LAIe derived from the smallest grid-cell sizes against the estimates of LAIe for the whole site has shown that the LAIe estimates obtained from the CHP toolkit provided values that are closest to those of theoretical models.

Description: Multifrequency and multioffset ground-penetrating radar data acquisition modes are used to maximize the information content and parameter retrieval capabilities. However, they also increase the computational cost dedicated to the inversion procedure. In this paper, the impact of the number of frequencies and the multistatic configurations on the information retrieval capabilities is investigated through the response surface topographies of the objective functions. We resort to a full-wave-inversion procedure and a recently developed electromagnetic model which takes advantage of a closed-form solution of Maxwell's equations to describe the antenna–medium system. We show with numerical and laboratory experiments the possibility of reducing the number of frequencies from several hundreds to one or several tens of components without affecting the information retrieval capabilities. We also show through several scenarios that the presence of a perfect electrical conductor increases the number of frequencies required to ensure an acceptable retrieval of the subsurface properties whereas the conductivity of the first layer and the relative permittivity of the second layer do not affect it. The results highlight that information content analyses are important in order to study and optimize data acquisition and inversion procedures, and thereby the computation time.

Description: This paper addresses some important aspects for the spaceborne/stationary bistatic synthetic aperture radar (SAR) (SS-BiSAR) imaging with the transmitter, TerraSAR-X, operated in staring spotlight (ST) mode. With the large integration time reaching 7.5 s and the azimuth steering span reaching $pm 2.2^{0}$ , several significant effects occur, including troposphere delay, precision phase and time synchronization, the curved orbit effect, azimuth spectrum aliasing problem, and efficient frequency domain focusing algorithm. To circumvent the main effects, corresponding solutions are proposed, including a precise synchronization strategy with troposphere delay correction based on the direct signal from the transmitter and a modified and integrative bistatic polar format algorithm (PFA). This paper covers the theoretical development, implementation, and analysis of the SS-BiSAR PFA based on 2-D fast Gaussian gridding nonuniform fast Fourier transform with wavefront curvature correction. Furthermore, the high-resolution ST-mode SS-BiSAR image processed by the proposed algorithm is acquired, and the differences of scattering behaviors between monostatic and bistatic SAR images are analyzed in detail.

Description: The ground penetrating radar (GPR) is a popular and successful remote sensing modality that has been investigated for landmine detection. GPR offers excellent detection performance, but it is limited by a low rate of advance (ROA) due to its short sensing standoff distance. Standoff distance refers to the distance between the sensing platform and the location in front of the platform where the GPR senses the ground. Large standoff (high ROA) sensing modalities have been investigated as alternatives to the GPR, but they do not yet achieve comparable detection performance. This paper proposes a new sensor management approach, called multistate management (MSM), which combines large and short standoff sensors on the same platform in a way that leverages their respective advantages, yielding a system with better ROA and detection performance. MSM is more difficult to analyze than traditional systems because it allows sensor activity and system velocity to change over time. Therefore, a new probabilistic model based on queuing theory, called Q-MSM, is also proposed for analyzing and designing detection systems operating with MSM. Simulations were conducted using real field-collected data for a system with a large standoff forward-looking infrared camera and a GPR. The system is operated with MSM, and the results show that this leads to better ROA and detection performance than can be attained otherwise. Furthermore, the results show that Q-MSM can accurately predict the behavior of the MSM system, validating its utility for analyzing and designing such systems.

Description: The concept of simultaneous source has recently become of interest in seismic exploration, due to its efficient or economic acquisition or both. The blended data overlapped between shot records are acquired in simultaneous source acquisition. Separating the blended data and recovering the single-shot seismic signals (the recovery) are of great importance in the scenario of current workflows, which can be called seismic simultaneous source separation. In the context of general random time-dithering firing, we propose an alternative method to separate the blended data by combining patchwise dictionary learning with sparse inversion, in which the dictionary is directly learned from the measured blended data. Apart from the sparse coding used for the coefficients, an additional regularization term on the dictionary is particularly designed to remove the severe interference noise. The efficient and flexible alternating direction method of multipliers (ADMM) is used to update the dictionary in the used alternating optimization scheme. The results obtained from the synthetic and real examples reasonably suggest that the separated seismic signals by using dictionary learning are more accurate and robust compared with that using the fixed transform basis, such as the local discrete cosine transform. The learned dictionary tailors for the recovery and is similar to the local seismic waveform, which improves the sparsity of the recovery substantially and is highly advantageous for producing the promised results.

Description: This paper investigates the impact of the soil moisture distribution in the top layer on the accuracy of soil moisture retrieval by microwave remote sensing methods. We modeled soil emission at L-band by coherent and noncoherent models for the different moisture distributions in the top layer. As a result, it is found that, at high moisture gradients, the difference between average moisture within the sensing depth at L-band and the moisture retrievable from remote sensing data can be more than 20% in absolute terms. In addition, high differences between Soil Moisture and Ocean Salinity (SMOS) Level 2 data and the in situ measurements were revealed in cases of high gradients. Such high gradients may be observed during some time in the top layer of the drying soil after rainfall. These differences are significantly more than the accuracy declared by SMOS development team. We proposed a simple method that allows the assessment of the type of soil moisture profile by SMOS and Global Change Observation Mission‐Water “SHIZUKU” (GCOM-W1) satellites data. The procedure for simple processing of data of the two satellites is described. In addition, we compared the type of soil moisture profile retrieved from satellite data and the soil moisture profile found by in situ measurements.

Description: An iterative alternating direction method of multipliers (ADMM) is proposed for inverse finite-element–boundary-integral (FE–BI) problem with total variation (TV) regularization. The inverse FE–BI fits to a wide class of penetrable sensing applications, where this study specifically targets the problem of radio tomography of asteroid interior structure using orbiting spacecraft. The TV regularizer enforces sparsity on the gradient of reconstructed permittivity, which agrees well with the “piecewise constant” reality of “rocks embedded in soil” scenario and, meanwhile, addresses the inherent ill-posedness. For large-scale asteroid problems, the distributed ADMM algorithm is adapted to solve the linear TV inversion at each iteration. The 2-D inversion is validated with the Fresnel Institute measurement data. Simulated cases of asteroid internal imaging are also presented. The proposed iterative ADMM can be also applied to similar penetrable imaging applications.

Description: GNSS Reflectometry, Scatterometry and Radio Occultation aboard ISS is the mission concept under study within the European Space Agency. Its core payload consists of an interferometric GNSS-Reflectometry ocean altimeter/scatterometer which does not need to generate any clean replicas of the GNSS codes for its operation. This paper describes a new interferometric technique by which such payload could also perform radio occultation as an add-on, without requiring any additional hardware resources, like the generation of clean code replicas or a storage of them. Two possibilities are studied. The first one consists of performing the complex autocorrelation function of the received signal transmitted by a rising or setting GNSS satellite. The autocorrelation function is evaluated around time epochs that are multiples of the period of suitable codes found in the modulation of the navigation signals. Satellite discrimination has to be performed spatially, through the antenna pattern. The second possibility consists in acquiring the reference signal separately from the occultation event which, in turn, has two options depending upon the geometry at which the reference is recorded: zenithal and top of the atmosphere. The signal-to-noise ratio, the satellite discrimination, and the impact of clock errors are assessed.

Description: Geophysics experts are interested in understanding the behavior of volcanoes and forecasting possible eruptions by monitoring and detecting the increment on volcano-seismic activity, with the aim of safeguarding human lives and material losses. This paper presents an automatic volcanic event detection and classification system, which considers feature extraction and feature selection stages, to reduce the processing time toward a reliable real-time volcano early warning system (RT-VEWS). We built the proposed approach in terms of the seismicity presented in 2009 and 2010 at the Cotopaxi Volcano located in Ecuador. In the detection stage, the recordings were time segmented by using a nonoverlapping 15-s window, and in the classification stage, the detected seismic signals were 1-min long. For each detected signal conveying seismic events, a comprehensive set of statistical, temporal, spectral, and scale-domain features were compiled and extracted, aiming to separate long-period (LP) events from volcano-tectonic (VT) earthquakes. We benchmarked two commonly used types of feature selection techniques, namely, wrapper (recursive feature extraction) and embedded (cross-validation and pruning). Each technique was used within a suitable and appropriate classification algorithm, either the support vector machine (SVM) or the decision trees. The best result was obtained by using the SVM classifier, yielding up to 99% accuracy in the detection stage and 97% accuracy and sensitivity in the event classification stage. Selected features and their interpretation were consistent among different input spaces in simple terms of the spectral content of the frequency bands at 3.1 and 6.8 Hz. A comparative analysis showed that the most relevant features for automatic discrimination between LP and VT events were one in the time domain, five in the frequency domain, and nine in the scale domain. Our study provides the framework for an event classification system with high - ccuracy and reduced computational requirements, according to the orientation toward a future RT-VEWS.

Description: Classification of hyperspectral images usually suffers from high dimensionality and few reference data, which limits the performance of the pixelwise classifiers. The spectral–spatial classifiers, which integrate the spectral data and the spatial information during the classification, perform impressively in terms of the high classification accuracy and the homogeneous appearance of the classification map. In this paper, we propose a new probabilistic framework for spectral–spatial classification (PFSSC), which integrates the spectral data and the spatial information from the probabilistic point of view. Both the spectral data and the spatial information are used to estimate the per-pixel probability, which gives the likelihood that one pixel belongs to one class, respectively. The classification map can then be directly derived from the joint probability. In the proposed framework, a pixelwise probabilistic classifier can be extended as a spectral–spatial one since it can integrate spatial information easily. Furthermore, these spectral–spatial classifiers in the proposed framework are realized in an iterative way to avoid the problem caused by the limited reference data to some extent. In each iterative step, some unassigned pixels are classified by considering the pixels assigned in previous iterative steps. In this iterative process, pixels are assigned to specific labels step by step gradually. In the proposed framework, the probabilistic support vector machine (SVM) and random forest (RF) are extended to be two spectral–spatial classifiers. In short, we denote them as SVM-PFSSC and RF-PFSSC, respectively. The experimental results show that SVM-PFSSC and RF-PFSSC outperform some pixelwise and spectral–spatial classifiers.

Description: Land cover/land use (LCLU) information extraction from multitemporal sequences of remote sensing imagery is becoming increasingly important. Mixed pixels are a common problem in Landsat and MODIS images that are used widely for LCLU monitoring. Recently developed subpixel mapping (SPM) techniques can extract LCLU information at the subpixel level by dividing mixed pixels into subpixels to which hard classes are then allocated. However, SPM has rarely been studied for time-series images (TSIs). In this paper, a spatiotemporal SPM approach was proposed for SPM of TSIs. In contrast to conventional spatial dependence-based SPM methods, the proposed approach considers simultaneously spatial and temporal dependences, with the former considering the correlation of subpixel classes within each image and the latter considering the correlation of subpixel classes between images in a temporal sequence. The proposed approach was developed assuming the availability of one fine spatial resolution map which exists among the TSIs. The SPM of TSIs is formulated as a constrained optimization problem. Under the coherence constraint imposed by the coarse LCLU proportions, the objective is to maximize the spatiotemporal dependence, which is defined by blending both spatial and temporal dependences. Experiments on three data sets showed that the proposed approach can provide more accurate subpixel resolution TSIs than conventional SPM methods. The SPM results obtained from the TSIs provide an excellent opportunity for LCLU dynamic monitoring and change detection at a finer spatial resolution than the available coarse spatial resolution TSIs.

Description: The ability to classify urban objects in large urban scenes from point clouds efficiently and accurately still remains a challenging task today. A new methodology for the effective and accurate classification of terrestrial laser scanning (TLS) point clouds is presented in this paper. First, in order to efficiently obtain the complementary characteristics of each 3-D point, a set of point-based descriptors for recognizing urban point clouds is constructed. This includes the 3-D geometry captured using the spin-image descriptor computed on three different scales, the mean RGB colors of the point in the camera images, the LAB values of that mean RGB, and the normal at each 3-D point. The initial 3-D labeling of the categories in urban environments is generated by utilizing a linear support vector machine classifier on the descriptors. These initial classification results are then first globally optimized by the multilabel graph-cut approach. These results are further refined automatically by a local optimization approach based upon the object-oriented decision tree that uses weak priors among urban categories which significantly improves the final classification accuracy. The proposed method has been validated on three urban TLS point clouds, and the experimental results demonstrate that it outperforms the state-of-the-art method in classification accuracy for buildings, trees, pedestrians, and cars.

Description: Target detection in hyperspectral images (HSIs) is an active area of research; it seeks to detect objects that are small in both number and size within a scene. The proposed work presents a new methodology for target detection in HSIs by combining kurtosis, level sets, and a size-based thresholding strategy. Kurtosis is used as a preprocessing step to initially enhance the targets in an image. Then, level sets identify and mark associations of pixels with similar spectral information as candidate targets. Finally, the size-based thresholding strategy detects true targets and discards false alarms that do not fit with target dimensions set as input parameter. In addition, we propose a novel version of level sets, which is suitable for target detection tasks in HSIs. Results show that the proposed algorithm could successfully detect targets in HSIs, and it gave better performance in terms of the receiver operating characteristic curve than other techniques widely used in target detection such as orthogonal subspace projection, constrained signal detector, constrained energy minimization, adaptive cosine/coherent estimator algorithm, and generalized-likelihood ratio test.

Description: A high-pulse-repetition-frequency (PRF) radar can handle the high Doppler frequencies of clutter echoes received by a fast-moving airborne radar. However, high-PRF radar causes range ambiguity. In addition, the clutter is range dependent when the airborne radar works in a forward-looking geometry. The range ambiguity and range dependence will lead to severe performance degradation of the traditional space-time adaptive processing (STAP) methods. In this paper, a vertical frequency diverse array (FDA), which applies frequency diversity in the vertical of a planar array, is explored to circumvent the range ambiguity problem in STAP radar. A range-ambiguous clutter suppression approach is devised, which consists of vertical spatial frequency compensation and pre-STAP filtering. In the vertical-FDA radar, the vertical spatial frequency depends not only on the depression angle but also on the slant range. By using this characteristic, the range-ambiguous clutter can be separated in the vertical spatial frequency domain, and then, clutter suppression is achieved for each separated range region. As a result, both problems of range ambiguity and range dependence are solved. Simulation results are provided to demonstrate the effectiveness of the proposed method.

Description: Synthetic aperture radar (SAR) interferometric baseline parameters form important input for SAR interferometry. In this paper, a nonlinear error model is established for the SAR interferometric baseline and parameterized as a polynomial based on the natural nonlinearity of the orbit of a satellite. Unlike conventional models, the proposed model takes into account the nonlinear part of the baseline error. A theoretical derivation is performed based on the imaging geometry of interferometric SAR, and the results of the analysis show that the parameters of the nonlinear baseline error model can be obtained from the relationship between the orbit, the nominal baseline, the baseline error, and the residual interferogram phase. A sample data set from the Japanese Earth Resources Satellite-1 (JERS-1) L-band SAR is used to validate the proposed model, and the results indicated that the compensation of the residual interferogram phase of the test data is superior to that provided by conventional models.

Description: Leaf area index (LAI) is an important vegetation biophysical variable and has been widely used for crop growth monitoring and yield estimation, land-surface process simulation, and global change studies. Several LAI products currently exist, but most have limited temporal coverage. A long-term high-quality global LAI product is required for greatly expanded application of LAI data. In this paper, a method previously proposed was improved to generate a long time series of Global LAnd Surface Satellite (GLASS) LAI product from Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data. The GLASS LAI product has a temporal resolution of eight days and spans from 1981 to 2014. During 1981–1999, the LAI product was generated from AVHRR reflectance data and was provided in a geographic latitude/longitude projection at a spatial resolution of 0.05°. During 2000–2014, the LAI product was derived from MODIS surface-reflectance data and was provided in a sinusoidal projection at a spatial resolution of 1 km. The GLASS LAI values derived from MODIS and AVHRR reflectance data form a consistent data set at a spatial resolution of 0.05°. Comparison of the GLASS LAI product with the MODIS LAI product (MOD15) and the first version of the Geoland2 (GEOV1) LAI product indicates that the global consistency of these LAI products is generally good. However, relatively large discrepancies among these LAI products were observed in tropical forest regions, where the GEOV1 LAI values were clearly lower than the GLASS and MOD15 LAI values, particularly in January. A quantitative comparison of temporal profiles shows that the temporal smoothness of the GLASS LAI product is superior to that of the GEOV1 and MODIS LAI products. Direct validation with the mean values of high-resolution LAI maps demonstrates that the GLASS LAI values were closer to the mean values of the high-resolution LAI maps ( $text{RMSE}=0.7848$ and $R^{2}=0.8095$ ) than the GEOV1 LAI values ( $text{RMSE}=0.9084$ and $R^{2}=0.7939$ ) and the MOD15 LAI values ( $text{RMSE}=1.1173$ and $R^{2}=0.6705$ ).

Description: A novel approach called Spectral–Spatial 1-D Manifold Embedding (SS1DME) is proposed in this paper for remotely sensed hyperspectral image (HSI) classification. This novel approach is based on a generalization of the recently developed smooth ordering model, which has gathered a great interest in the image processing area. In the proposed approach, first, we employ the spectral–spatial information-based affinity metric to learn the similarity of HSI pixels, where the contextual information is encoded into the affinity metric using spatial information. In our derived model, based on the obtained affinity metric, the created multiple 1-D manifold embeddings (1DMEs) consist of several different versions of 1DME of the same set of all HSI points. Since each 1DME of the data is a 1-D sequence, a label function on the data can be obtained by applying the simple 1-D signal processing tools (such as interpolation/regression). By collecting the predicted labels from these label functions, we build a subset of the current unlabeled points, on which the labels are correctly labeled with high confidence. Next, we add a proportion of the elements from this subset to the original labeled set to get the updated labeled set, which is used for the next running instance. Repeating this process for several loops, we get an extended labeled set, where the new members are correctly labeled by the label functions with much high confidence. Finally, we utilize the extended labeled set to build the target classifier for the whole HSI pixels. In the whole process, 1DME plays the role of learning data features from the given affinity metric. With the incrementation of learning features during iteration, the proposed scheme will gradually approximate the exact labels of all sample points. The proposed scheme is experimentally demonstrated using four real HSI data sets, exhibiting promising classification performance when compared with other recently introduced - patial analysis alternatives.

Description: We evaluate the potential of troposphere models derived from ground meteorological data (pressure, temperature, and relative humidity) and Global Positioning System (GPS) data to improve InSAR measurements and models derived from them. We test this approach on an ERS-2/Envisat data set collected during a transient surface deformation episode that occurred from January to July 2005 in the San Gabriel Valley, southern California, USA. We find that the interferometric phase change observed over the corresponding period cannot be solely attributed to hydrological uplift associated with rising groundwater levels but also includes a significant contribution from differential tropospheric delay due to differing quantities of water vapor in the troposphere on the two SAR observation dates. We show that, if the tropospheric phase contribution is mistakenly interpreted as the range change associated with changes in groundwater storage, both the surface displacement and the groundwater storage coefficient may be overestimated by up to 30%. This method could be applied in real time where meteorological measurements are available near one or more GPS permanent site(s).

Description: In the booming era of high-resolution synthetic aperture radar (SAR) technology, SAR advanced information retrieval is critical for effective utilization of huge-volume SAR data. One important aspect of high-resolution SAR interpretation is to explore the anisotropic and dispersive information embedded among subaperture and subband SAR images. This paper formulates the polarimetric subaperture analysis as a singular-value decomposition problem, where polarimetric and anisotropic features can be simultaneously decomposed. The decomposed singular values and left singular vectors are equivalent to eigenanalysis-based polarimetric target decomposition, whereas the right singular vectors give the corresponding anisotropic feature vectors. A physics-based parameterization is proposed for anisotropic patterns, where two anisotropic entropy parameters, namely, compactness and directivity, are proposed. Both simulation results and real SAR image analyses demonstrate that these proposed anisotropic entropies can effectively identify specific types of scatterers depending on their geometric scale, curvature, and form of spatial distribution. The proposed anisotropic entropies could be applied to single- and dual-polarization high-resolution SAR data as well.

Description: This paper presents a method for estimating the solar transmittance of urban trees using airborne light detection and ranging (LiDAR), and the radiative transfer simulation of vegetation. The leaf area density (LAD) distribution of trees with voxel size $1 text{m}times 1 text{m}times 0.5 text{m}$ is estimated using high-resolution and multireturn airborne LiDAR data. The LAD of voxels having few incident laser beams is corrected from the surrounding voxels. The LAD of the periphery of the crown is discretized into $0.5 text{m}times 0.5 text{m}times 0.5 text{m}$ voxels to accurately calculate the shaded area. The resulting LAD distribution is used in a radiative transfer simulation to calculate the solar transmittance of the trees. We verified the accuracy of the calculated transmittance by comparing it with empirical data for a Zelkova serrata . The comparisons were conducted under different angles of incidence of laser beams and solar radiation. When the angle between the incident laser beams and solar radiation was small, the transmittance could be accurately estimated. The LAD correction enabled the method to be applied to a broader range of the angle between beams and solar radiation. When the zenith angle of the incident laser beams was small $(< 10^{circ})$ and the LAD correction was carried out, the errors in transmittance were within 0.06 for solar altitudes greater than 40°. Next, we examined the difference in solar transmittance among streets caused by the layout of trees and buildings and the growth condition of the trees. It was shown that the present method is able to quantify the solar shading provided by urban trees and take into account LAD, tree layout, and the spatial geometry of the surr- unding buildings.

Description: This paper investigates several optimum graph-cut techniques for pruning binary partition trees (BPTs) and their usefulness for the low-level processing of polarimetric synthetic aperture radar (PolSAR) images. BPTs group pixels to form homogeneous regions, which are hierarchically structured by inclusion in a binary tree. They provide multiple resolutions of description and easy access to subsets of regions. Once constructed, BPTs can be used for a large number of applications. Many of these applications consist in populating the tree with a specific feature and in applying a graph cut called pruning to extract a partition of the space. In this paper, different pruning examples involving the optimization of a global criterion are discussed and analyzed in the context of PolSAR images for segmentation. Through the objective evaluation of the resulting partitions by means of precision-and-recall-for-boundaries curves, the best pruning technique is identified, and the influence of the tree construction on the performances is assessed.

Description: Building on existing techniques for satellite remote sensing of fires, this paper takes advantage of the day–night band (DNB) aboard the Visible Infrared Imaging Radiometer Suite (VIIRS) to develop the Firelight Detection Algorithm (FILDA), which characterizes fire pixels based on both visible-light and infrared (IR) signatures at night. By adjusting fire pixel selection criteria to include visible-light signatures, FILDA allows for significantly improved detection of pixels with smaller and/or cooler subpixel hotspots than the operational Interface Data Processing System (IDPS) algorithm. VIIRS scenes with near-coincident Advanced Spaceborne Thermal Emission and Reflection (ASTER) overpasses are examined after applying the operational VIIRS fire product algorithm and including a modified “candidate fire pixel selection” approach from FILDA that lowers the 4- $mutext{m}$ brightness temperature (BT) threshold but includes a minimum DNB radiance. FILDA is shown to be effective in detecting gas flares and characterizing fire lines during large forest fires (such as the Rim Fire in California and High Park fire in Colorado). Compared with the operational VIIRS fire algorithm for the study period, FILDA shows a large increase (up to 90%) in the number of detected fire pixels that can be verified with the finer resolution ASTER data (90 m). Part (30%) of this increase is likely due to a combined use of DNB and lower 4- $mutext{m}$ BT thresholds for fire detection in FILDA. Although further studies are needed, quantitative use of the DNB to improve fire detection could lead to reduced response times to wildfires and better estimate of fire characteristics (smoldering and flaming) at night.

Description: Hyperspectral image (HSI) denoising is a crucial preprocessing task that is used to improve the quality of images for object detection, classification, and other subsequent applications. It has been reported that noise can be effectively removed using the sparsity in the nonnoise part of the image. With the appreciable redundancy and correlation in HSIs, the denoising performance can be greatly improved if this redundancy and correlation is utilized efficiently in the denoising process. Inspired by this observation, a noise reduction method based on joint spectral–spatial distributed sparse representation is proposed for HSIs, which exploits the intraband structure and the interband correlation in the process of joint sparse representation and joint dictionary learning. In joint spectral–spatial sparse coding, the interband correlation is exploited to capture the similar structure and maintain the spectral continuity. The intraband structure is utilized to adaptively code the spatial structure differences of the different bands. Furthermore, using a joint dictionary learning algorithm, we obtain a dictionary that simultaneously describes the content of the different bands. Experiments on both synthetic and real hyperspectral data show that the proposed method can obtain better results than the other classic methods.

Description: Miniaturized hyperspectral imaging sensors are becoming available to small unmanned airborne vehicle (UAV) platforms. Imaging concepts based on frame format offer an attractive alternative to conventional hyperspectral pushbroom scanners because they enable enhanced processing and interpretation potential by allowing for acquisition of the 3-D geometry of the object and multiple object views together with the hyperspectral reflectance signatures. The objective of this investigation was to study the performance of novel visible and near-infrared (VNIR) and short-wave infrared (SWIR) hyperspectral frame cameras based on a tunable Fabry–Pérot interferometer (FPI) in measuring a 3-D digital surface model and the surface moisture of a peat production area. UAV image blocks were captured with ground sample distances (GSDs) of 15, 9.5, and 2.5 cm with the SWIR, VNIR, and consumer RGB cameras, respectively. Georeferencing showed consistent behavior, with accuracy levels better than GSD for the FPI cameras. The best accuracy in moisture estimation was obtained when using the reflectance difference of the SWIR band at 1246 nm and of the VNIR band at 859 nm, which gave a root mean square error (rmse) of 5.21 pp (pp is the mass fraction in percentage points) and a normalized rmse of 7.61%. The results are encouraging, indicating that UAV-based remote sensing could significantly improve the efficiency and environmental safety aspects of peat production.

Description: This paper analyzes the availability and accuracy of coastal altimetry sea level products in the Strait of Gibraltar. All possible repeats of two sections of the Envisat and AltiKa ground-tracks were used in the eastern and western portions of the strait. For Envisat, along-track sea level anomalies (SLAs) at 18-Hz posting rate were computed using ranges from two sources, namely, the official Sensor Geophysical Data Records (SGDRs) and the outputs of a coastal waveform retracker, the Adaptive Leading Edge Subwaveform (ALES) retracker; in addition, SLAs at 1 Hz were obtained from the Centre for Topographic studies of the Ocean and Hydrosphere (CTOH). For AltiKa, along-track SLA at 40 Hz was also computed both from SGDR and ALES ranges. The sea state bias correction was recomputed for the ALES-retracked Envisat SLA. The quality of these altimeter products was validated using two tide gauges located on the southern coast of Spain. For Envisat, the availability of data close to the coast depends crucially on the strategy followed for data screening. Most of the rejected data were due to the radar instrument operating in a low-precision nonocean mode. We observed an improvement of about 20% in the accuracy of the Envisat SLAs from ALES compared to the standard (SGDR) and the reprocessed CTOH data sets. AltiKa shows higher accuracy, with no significant differences between SGDR and ALES. The use of products from both missions allows longer times series, leading to a better understanding of the hydrodynamic processes in the study area.

Description: This paper describes the analysis performed on coherent simultaneously recorded monostatic and bistatic sea clutter data. The data were generated using a networked pulsed radar system, namely, NetRAD. This analysis is completed in both the temporal and Doppler domains, and the parameters characterized are compared between multiple bistatic angles and different polarizations. The K-distribution model is used to assess the variation in the clutter amplitude statistics between multiple bistatic data and the corresponding monostatic data. Key characteristics of the Doppler data, such as the spectrum width, center of gravity (CoG), and variance of the spectral width, are evaluated as a function of bistatic angle allowing novel relationships to be defined. The results conclude that the bistatic Doppler data have a lower K-distribution shape parameter in the majority of bistatic angles compared with the simultaneous monostatic data. In addition, novel trends in the relationship between the clutter spectrum CoG and the clutter intensity are presented.

Description: Icebergs represent hazards to maritime traffic and offshore operations. Satellite synthetic aperture radar (SAR) is very valuable for the observation of polar regions, and extensive work was already carried out on detection and tracking of large icebergs. However, the identification of small icebergs is still challenging especially when these are embedded in sea ice. In this paper, a new detector is proposed based on incoherent dual-polarization SAR images. The algorithm considers the limited extension of small icebergs, which are supposed to have a stronger cross-polarization and higher cross- over copolarization ratio compared to the surrounding sea or sea ice background. The new detector is tested with two satellite systems. First, RADARSAT-2 quad-polarimetric images are analyzed to evaluate the effects of high-resolution data. Subsequently, a more exhaustive analysis is carried out using dual-polarization ground-detected Sentinel-1a extra wide swath images acquired over the time span of two months. The test areas are in the east coast of Greenland, where several icebergs have been observed. A quantitative analysis and a comparison with a detector using only the cross-polarization channel are carried out, exploiting grounded icebergs as test targets. The proposed methodology improves the contrast between icebergs and sea ice clutter by up to 75 times. This returns an improved probability of detection.

Description: A novel wavelet-based scheme to increase coefficient independence in hyperspectral images is introduced for lossless coding. The proposed regression wavelet analysis (RWA) uses multivariate regression to exploit the relationships among wavelet-transformed components. It builds on our previous nonlinear schemes that estimate each coefficient from neighbor coefficients. Specifically, RWA performs a pyramidal estimation in the wavelet domain, thus reducing the statistical relations in the residuals and the energy of the representation compared to existing wavelet-based schemes. We propose three regression models to address the issues concerning estimation accuracy, component scalability, and computational complexity. Other suitable regression models could be devised for other goals. RWA is invertible, it allows a reversible integer implementation, and it does not expand the dynamic range. Experimental results over a wide range of sensors, such as AVIRIS, Hyperion, and Infrared Atmospheric Sounding Interferometer, suggest that RWA outperforms not only principal component analysis and wavelets but also the best and most recent coding standard in remote sensing, CCSDS-123.

Description: Since Jeff Howe introduced the term Crowdsourcing in 2006, this human-powered problem-solving paradigm has gained a lot of attention and has been a hot research topic in the field of computer science. Even though a lot of work has been conducted on this topic, so far we do not have a comprehensive survey on most relevant work done in the crowdsourcing field. In this paper, we aim to offer an overall picture of the current state of the art techniques in general-purpose crowdsourcing. According to their focus, we divide this work into three parts, which are: incentive design, task assignment, and quality control. For each part, we start with different problems faced in that area followed by a brief description of existing work and a discussion of pros and cons. In addition, we also present a real scenario on how the different techniques are used in implementing a location-based crowdsourcing platform, gMission. Finally, we highlight the limitations of the current general-purpose crowdsourcing techniques and present some open problems in this area.

Description: Any important data management and analytics tasks cannot be completely addressed by automated processes. These tasks, such as entity resolution, sentiment analysis, and image recognition can be enhanced through the use of human cognitive ability. Crowdsouring platforms are an effective way to harness the capabilities of people (i.e., the crowd) to apply human computation for such tasks. Thus, crowdsourced data management has become an area of increasing interest in research and industry. We identify three important problems in crowdsourced data management. (1) Quality Control: Workers may return noisy or incorrect results so effective techniques are required to achieve high quality; (2) Cost Control: The crowd is not free, and cost control aims to reduce the monetary cost; (3) Latency Control: The human workers can be slow, particularly compared to automated computing time scales, so latency-control techniques are required. There has been significant work addressing these three factors for designing crowdsourced tasks, developing crowdsourced data manipulation operators, and optimizing plans consisting of multiple operators. In this paper, we survey and synthesize a wide spectrum of existing studies on crowdsourced data management. Based on this analysis we then outline key factors that need to be considered to improve crowdsourced data management.

Description: Principal component analysis and the residual error is an effective anomaly detection technique. In an environment where anomalies are present in the training set, the derived principal components can be skewed by the anomalies. A further aspect of anomaly detection is that data might be distributed across different nodes in a network and their communication to a centralized processing unit is prohibited due to communication cost. Current solutions to distributed anomaly detection rely on a hierarchical network infrastructure to aggregate data or models; however, in this environment, links close to the root of the tree become critical and congested. In this paper, an algorithm is proposed that is more robust in its derivation of the principal components of a training set containing anomalies. A distributed form of the algorithm is then derived where each node in a network can iterate towards the centralized solution by exchanging small matrices with neighboring nodes. Experimental evaluations on both synthetic and real-world data sets demonstrate the superior performance of the proposed approach in comparison to principal component analysis and alternative anomaly detection techniques. In addition, it is shown that in a variety of network infrastructures, the distributed form of the anomaly detection model is able to derive a close approximation of the centralized model.

Description: Many applications deal with moving object datasets, e.g., mobile phone social networking, scientific simulations, and ride-sharing services. These applications need to handle a tremendous number of spatial objects that continuously move and execute spatial queries to explore their surroundings. To manage such update-heavy workloads, several throwaway index structures have recently been proposed, where a static index is rebuilt periodically from scratch rather than updated incrementally. It has been shown that throwaway indices outperform specialized moving-object indices that maintain location updates incrementally. However, throwaway indices suffer from scalability due to their single-server design and the only distributed throwaway index (D-MOVIES), extension of a centralized approach, does not scale out as the number of servers increases, especially during query processing phase.

Description: Collaborative filtering (CF) is out of question the most widely adopted and successful recommendation approach. A typical CF-based recommender system associates a user with a group of like-minded users based on their individual preferences over all the items, either explicit or implicit, and then recommends to the user some unobserved items enjoyed by the group. However, we find that two users with similar tastes on one item subset may have totally different tastes on another set. In other words, there exist many user-item subgroups each consisting of a subset of items and a group of like-minded users on these items. It is more reasonable to predict preferences through one user's correlated subgroups, but not the entire user-item matrix. In this paper, to find meaningful subgroups, we formulate a new Multiclass Co-Clustering (MCoC) model, which captures relations of user-to-item, user-to-user, and item-to-item simultaneously. Then, we combine traditional CF algorithms with subgroups for improving their top- $N$ recommendation performance. Our approach can be seen as a new extension of traditional clustering CF models. Systematic experiments on several real data sets have demonstrated the effectiveness of our proposed approach.

Description: With the rapid development of Web 2.0 and Online To Offline (O2O) marketing model, various online e vent- b ased s ocial n etwork s (EBSNs) are getting popular. An important task of EBSNs is to facilitate the most satisfactory event-participant arrangement for both sides, i.e., events enroll more participants and participants are arranged with personally interesting events. Existing approaches usually focus on the arrangement of each single event to a set of potential users, or ignore the conflicts between different events, which leads to infeasible or redundant arrangements. In this paper, to address the shortcomings of existing approaches, we first identify a more general and useful event-participant arrangement problem, called G lobal E vent-participant A rrangement with C onflict and C apacity ( $GEACC$ ) problem, focusing on the conflicts of different events and making event-participant arrangements in a global view. We find that the GEACC problem is NP-hard due to the conflicts among events. Thus, we design two approximation algorithms with provable approximation ratios and an exact algorithm with pruning technique to address this problem. In addition, we propose an online setting of GEACC, called OnlineGEACC, which is also practical in real-world scenarios. We further design an online algorithm with provable performance guarantee. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments on real and synthetic datasets.

Description: Given a point $p$ and a set of points $S$ , the kNN operation finds the $k$ closest points to in $S$ . It is a computational intensive task with a large range of applications such as knowledge discovery or data mining. However, as the volume and the dimension of data increase, only distributed approaches can perform such costly operation in a reasonable time. Recent works have focused on implementing efficient solutions using the MapReduce programming model because it is suitable for distributed large scale data processing. Although these works provide different solutions to the same problem, each one has particular constraints and properties. In this paper, we compare the different existing approaches for computing kNN on MapReduce, first theoretically, and then by performing an extensive experimental evaluation. To be able to compare solutions, we identify three generic steps for kNN computation on MapReduce: data pre-processing, data partitioning, and computation. We then analyze each step from load balancing, accuracy, and complexity aspects. Experiments in this paper use a variety of datasets, and analyze the impact of data volume, data dimension, and the value of k from many perspectives like time and space complexity, and accuracy. The experimental part brings new advantages and shortcomings that are discussed for each algorithm. To the best of our knowledge, this is the first pape- that compares kNN computing methods on MapReduce both theoretically and experimentally with the same setting. Overall, this paper can be used as a guide to tackle kNN-based practical problems in the context of big data.

Description: Mining communities or clusters in networks is valuable in analyzing, designing, and optimizing many natural and engineering complex systems, e.g., protein networks, power grid, and transportation systems. Most of the existing techniques view the community mining problem as an optimization problem based on a given quality function(e.g., modularity), however none of them are grounded with a systematic theory to identify the central nodes in the network. Moreover, how to reconcile the mining efficiency and the community quality still remains an open problem. In this paper, we attempt to address the above challenges by introducing a novel algorithm. First, a kernel function with a tunable influence factor is proposed to measure the leadership of each node, those nodes with highest local leadership can be viewed as the candidate central nodes. Then, we use a discrete-time dynamical system to describe the dynamical assignment of community membership; and formulate the serval conditions to guarantee the convergence of each node's dynamic trajectory, by which the hierarchical community structure of the network can be revealed. The proposed dynamical system is independent of the quality function used, so could also be applied in other community mining models. Our algorithm is highly efficient: the computational complexity analysis shows that the execution time is nearly linearly dependent on the number of nodes in sparse networks. We finally give demonstrative applications of the algorithm to a set of synthetic benchmark networks and also real-world networks to verify the algorithmic performance.

Description: When a microblogging user adopts some content propagated to her, we can attribute that to three behavioral factors, namely, topic virality , user virality , and user susceptibility . Topic virality measures the degree to which a topic attracts propagations by users. User virality and susceptibility refer to the ability of a user to propagate content to other users, and the propensity of a user adopting content propagated to her, respectively. In this paper, we study the problem of mining these behavioral factors specific to topics from microblogging content propagation data. We first construct a three dimensional tensor for representing the propagation instances. We then propose a tensor factorization framework to simultaneously derive the three sets of behavioral factors. Based on this framework, we develop a numerical factorization model and another probabilistic factorization variant. We also develop an efficient algorithm for the models’ parameters learning. Our experiments on a large Twitter dataset and synthetic datasets show that the proposed models can effectively mine the topic-specific behavioral factors of users and tweet topics. We further demonstrate that the proposed models consistently outperforms the other state-of-the-art content based models in retweet prediction over time.

Description: We propose an algorithm for detecting patterns exhibited by anomalous clusters in high dimensional discrete data. Unlike most anomaly detection (AD) methods, which detect individual anomalies, our proposed method detects groups ( clusters ) of anomalies; i.e., sets of points which collectively exhibit abnormal patterns. In many applications, this can lead to a better understanding of the nature of the atypical behavior and to identifying the sources of the anomalies. Moreover, we consider the case where the atypical patterns exhibit on only a small (salient) subset of the very high dimensional feature space. Individual AD techniques and techniques that detect anomalies using all the features typically fail to detect such anomalies, but our method can detect such instances collectively, discover the shared anomalous patterns exhibited by them, and identify the subsets of salient features. In this paper, we focus on detecting anomalous topics in a batch of text documents, developing our algorithm based on topic models. Results of our experiments show that our method can accurately detect anomalous topics and salient features (words) under each such topic in a synthetic data set and two real-world text corpora and achieves better performance compared to both standard group AD and individual AD techniques. All required code to reproduce our experiments is available from https://github.com/hsoleimani/ATD .

Description: Multilabel classification is prevalent in many real-world applications where data instances may be associated with multiple labels simultaneously. In multilabel classification, exploiting label correlations is an essential but nontrivial task. Most of the existing multilabel learning algorithms are either ineffective or computationally demanding and less scalable in exploiting label correlations. In this paper, we propose a co-evolutionary multilabel hypernetwork (Co-MLHN) as an attempt to exploit label correlations in an effective and efficient way. To this end, we firstly convert the traditional hypernetwork into a multilabel hypernetwork (MLHN) where label correlations are explicitly represented. We then propose a co-evolutionary learning algorithm to learn an integrated classification model for all labels. The proposed Co-MLHN exploits arbitrary order label correlations and has linear computational complexity with respect to the number of labels. Empirical studies on a broad range of multilabel data sets demonstrate that Co-MLHN achieves competitive results against state-of-the-art multilabel learning algorithms, in terms of both classification performance and scalability with respect to the number of labels.

Description: Query auto completion (QAC) methods recommend queries to search engine users when they start entering a query. Current QAC methods mostly rank query completions based on their past popularity, i.e., on the number of times they have previously been submitted as a query. However, query popularity changes over time and may vary drastically across users. Accordingly, the ranking of query completions should be adjusted. Previous time-sensitive and user-specific QAC methods have been developed separately, yielding significant improvements over methods that are neither time-sensitive nor personalized. We propose a hybrid QAC method that is both time-sensitive and personalized. We extend it to handle long-tail prefixes, which we achieve by assigning optimal weights to the contribution from time-sensitivity and personalization. Using real-world search log datasets, we return top $N$ query suggestions ranked by predicted popularity as estimated from popularity trends and cyclic popularity behavior; we rerank them by integrating similarities to a user's previous queries (both in the current session and in previous sessions). Our method outperforms state-of-the-art time-sensitive QAC baselines, achieving total improvements of between 3 and 7 percent in terms of mean reciprocal rank (MRR). After optimizing the weights, our extended model achieves MRR improvements of between 4 and 8 percent.

Description: Wrappers are pieces of software used to extract data from websites and structure them for further application processing. Unfortunately, websites are continuously evolving and structural changes happen with no forewarning, which usually results in wrappers working incorrectly. Thus, wrappers maintenance is necessary for detecting whether wrapper is extracting erroneous data. The solution consists of using verification models to detect whether wrapper output is statistically similar to the output produced by the wrapper itself when it was successfully invoked in the past. Current proposals present some weaknesses, as the data used to build these models are supposed to be homogeneous, independent, or representative enough, or following a single predefined mathematical model. In this paper, we present MAVE, a novel multilevel wrapper verification system that is based on one-class classification techniques to overcome previous weaknesses. The experimental results show that our proposal outperforms accuracy of current solutions.

Description: Provides a listing of board members, committee members, editors, and society officers.

Description: Certain inner feelings and physiological states like pain are subjective states that cannot be directly measured, but can be estimated from spontaneous facial expressions. Since they are typically characterized by subtle movements of facial parts, analysis of the facial details is required. To this end, we formulate a new regression method for continuous estimation of the intensity of facial behavior interpretation, called Doubly Sparse Relevance Vector Machine (DSRVM). DSRVM enforces double sparsity by jointly selecting the most relevant training examples (a.k.a. relevance vectors) and the most important kernels associated with facial parts relevant for interpretation of observed facial expressions. This advances prior work on multi-kernel learning, where sparsity of relevant kernels is typically ignored. Empirical evaluation on challenging Shoulder Pain videos, and the benchmark DISFA and SEMAINE datasets demonstrate that DSRVM outperforms competing approaches with a multi-fold reduction of running times in training and testing.

Description: During at-speed test of high performance sequential ICs using scan-based Logic BIST, the IC activity factor (AF) induced by the applied test vectors is significantly higher than that experienced during its in field operation. Consequently, power droop (PD) may take place during both shift and capture phases, which will slow down the circuit under test (CUT) signal transitions. At capture, this phenomenon is likely to be erroneously recognized as due to delay faults. As a result, a false test fail may be generated, with consequent increase in yield loss. In this paper, we propose two approaches to reduce the PD generated at capture during at-speed test of sequential circuits with scan-based Logic BIST using the Launch-On-Shift scheme. Both approaches increase the correlation between adjacent bits of the scan chains with respect to conventional scan-based LBIST. This way, the AF of the scan chains at capture is reduced. Consequently, the AF of the CUT at capture, thus the PD at capture, is also reduced compared to conventional scan-based LBIST. The former approach, hereinafter referred to as Low-Cost Approach (LCA), enables a 50 percent reduction in the worst case magnitude of PD during conventional logic BIST. It requires a small cost in terms of area overhead (of approximately 1.5 percent on average), and it does not increase the number of test vectors over the conventional scan-based LBIST to achieve the same Fault Coverage (FC). Moreover, compared to three recent alternative solutions, LCA features a comparable AF in the scan chains at capture, while requiring lower test time and area overhead. The second approach, hereinafter referred to as High-Reduction Approach (HRA), enables scalable PD reductions at capture of up to 87 percent, with limited additional costs in terms of area overhead and number of required test vectors for a given target FC, over our LCA approach. Particularly, compared to two of the three recent alternative solutions mentioned above, HRA en- bles a significantly lower AF in the scan chains during the application of test vectors, while requiring either a comparable area overhead or a significantly lower test time. Compared to the remaining alternative solutions mentioned above, HRA enables a similar AF in the scan chains at capture (approximately 90 percent lower than conventional scan-based LBIST), while requiring a significantly lower test time (approximately 4.87 times on average lower number of test vectors) and comparable area overhead (of approximately 1.9 percent on average).

Description: Publication date: Available online 11 July 2016 Source: Journal of Asian Ceramic Societies Author(s): Chaouki Sadik, Omar Moudden, Abdselam El Bouari, Iz-Eddine El Amrani One of the most important elements of furnaces, boilers and other heating units is the structure (lining), usually made of silica–alumina, basic or special refractories. The basic refractories are materials that are increasingly in demand and whose manufacturing involves necessarily the use of MgO and CaO. In this article, the description and characterization of magnesite (MgCO 3 ) and dolomite (Mg,Ca(CO 3 ) 2 ) and their contribution in industrial ceramics-refractories have been reviewed.

Description: Sonar images are formed by transmitting acoustical pulses and measuring the reflected sound power from the scene surfaces. The recorded signal by a sonar device encodes information about the shape and material properties of these surfaces. In this paper, we present a detailed derivation of an image model for a new class of high-resolution lens-based 2-D forward-looking sonar systems, when the diffuse reflectance of scene/target can be characterized by the Lambertian model. A simplified single patch model is generalized to account for the finite pulse width of the transmitted beam and the simultaneous arrival of scattered signals from multiple patches at the same range in a given azimuthal direction. Validating the model using intensity measurements of isolated cylindrical targets, we then demonstrate application for multipath reflections from bottom surfaces with cylindrical and spherical targets.

Description: In India, several experimental coastal aquaculture cages have been installed on sites along the Indian coast. Many of them have been commercially successful, while some have faced technical difficulties. The construction of the Indian coastal aquaculture cage is based on ad hoc designs adopted from other countries. The performance of the coastal aquaculture cage shows that there is a need for development of a cage design with a location-specific mooring system to withstand local wave conditions for a longer period in different coastal zones of the country. This requires that designs of the cages suiting Indian conditions be made based on sound engineering principles. To design the cage, the current- and wave-induced tensions in the mooring chain and net twine and the cage motions need to be estimated. In this study, a model cage was fabricated and tested in a towing tank under different waves and towing conditions. The tension on the mooring chain was measured during model experiments along with the towing speed and wave parameter. A prototype coastal aquaculture cage with single point mooring near the coast of Visakhapatnam in South East India was identified for measurement of forces and motions. On the prototype cage, measurements of cage motions, tensions on mooring chain, and net twine and cage orientation were carried out. Characteristics of the cage response, observed in model and prototype experiments, are presented in this paper. The model cage is not a geometrically scaled down version of the prototype cage. The diameter/depth ratios of the model and the prototype are different. The mooring pattern and net twine of the model cage is the same as that of the prototype. The data presented in this paper could be used as a reference for validating the numerical model for simulating the cage forces and motions under wave and current loads.

Description: One of the challenges presented in using autonomous underwater vehicles (AUVs) for remote data collection is accurate time synchronization. In the case of bistatic acoustics, synchronization is required between AUV and source so that the time in which each ping is sent out is exactly known. There are three key obstacles to achieving this. First, the vehicle is submerged and therefore unable to access common time references directly. Second, the required accuracy in data acquisition far exceeds that possible using a computer clock to trigger data collection. Finally, to achieve accuracy in microseconds, the system must be characterized to eliminate delays introduced by filtering and analog-to-digital conversion. This paper describes the implementation and characterization of an accurate and precise timing and data acquisition system used on an AUV to collect acoustic array data. The timing was achieved using a combination of global positioning system (GPS) pulse-per-second (PPS) for synchronization on the surface and the Microsemi (Aliso Viejo, CA, USA) chip scale atomic clock (CSAC) for timing while submerged, with a PPS triggered data acquisition system. Characterization and calibration procedures were developed to ensure that the system met the experiment requirements, which included less than one percent of a wavelength error in phase, and one tenth of a meter accuracy in range. Analog and digital delays in the system were determined, and a method was demonstrated to further improve accuracy by dynamically estimating digital delays. The steps outlined in this paper for achieving precision data acquisition could be applied to many other remote systems that require similar microsecond accuracy.

Description: Underwater images suffer from blurring effects, low contrast, and grayed out colors due to the absorption and scattering effects under the water. Many image enhancement algorithms for improving the visual quality of underwater images have been developed. Unfortunately, no well-accepted objective measure exists that can evaluate the quality of underwater images similar to human perception. Predominant underwater image processing algorithms use either a subjective evaluation, which is time consuming and biased, or a generic image quality measure, which fails to consider the properties of underwater images. To address this problem, a new nonreference underwater image quality measure (UIQM) is presented in this paper. The UIQM comprises three underwater image attribute measures: the underwater image colorfulness measure (UICM), the underwater image sharpness measure (UISM), and the underwater image contrast measure (UIConM). Each attribute is selected for evaluating one aspect of the underwater image degradation, and each presented attribute measure is inspired by the properties of human visual systems (HVSs). The experimental results demonstrate that the measures effectively evaluate the underwater image quality in accordance with the human perceptions. These measures are also used on the AirAsia 8501 wreckage images to show their importance in practical applications.

Description: In this paper, an automated change detection technique is presented that compares new and historical seafloor images created with sidescan synthetic aperture sonar (SAS) for changes occurring over time. The method consists of a four-stage process: a coarse navigational alignment that relates and approximates pixel locations of reference and repeat–pass data sets; fine-scale coregistration using the scale-invariant feature transform (SIFT) algorithm to match features between overlapping data sets; local coregistration that improves phase coherence; and finally, change detection utilizing a canonical correlation analysis (CCA) algorithm to detect changes. The method was tested using data collected with a high-frequency SAS in a sandy shallow-water environment. Successful results of this multistage change detection method are presented here, and the robustness of the techniques that exploit phase and amplitude levels of the backscattered signals is discussed. It is shown that the coherent nature of the SAS data can be exploited and utilized in this environment over time scales ranging from hours through several days. Robustness of the coregistration methods and analysis of scene coherence over time is characterized by analysis of repeat pass as well as synthetically modified data sets.

Description: This paper addresses the problem of optimal acoustic sensor placement for underwater target localization in 3-D using range measurements only. By adopting an estimation theoretical framework, the optimal geometric sensor formation that will yield the best achievable performance in terms of target positioning accuracy is computed by maximizing the determinant of an appropriately defined Fisher information matrix (FIM). For mathematical tractability, it is assumed that the measurements of the ranges between the target and a set of acoustic sensors are corrupted with white Gaussian noise. For the sake of completeness, an explicit analytical result for a generic $n$ -sensor network is first obtained for the case when there is no uncertainty in the prior knowledge about the target position. The result is then extended to the practical case where the target is known to lie inside a region of uncertainty. The optimal sensor configuration thus obtained lends itself to an interesting and useful geometrical interpretation. In addition, the “spreading” of the configuration is shown to depend on the number of range measurements, target depth, and the probability distribution function that characterizes the prior knowledge about the target position. Results are also obtained for the problem of optimal sensor placement with constraints, namely, by considering that the sensors can be either located at the sea surface or distributed between the surface and the seabed. The connection between 2-D and 3-D scenarios is clarified. Simulation examples illustrate the key results derived.

Description: In this paper, experimental broadband propagation measurements in a typical sea port scenario at 5.8 GHz are presented. Measurements were performed in Cádiz Bay, Spain, by sounding the channel with a periodic pulsed signal, from which power delay profiles (PDPs) were estimated for several locations. The study focuses on propagation scenarios based on low-height antenna deployments. Experimental PDPs are found with a spiky shape that can be explained from the nature of the environment. This result is significantly different from the one expected in other regular environments (urban, indoor, etc.) where continuous shaped profiles are common. Furthermore, time-dispersive parameters, such as mean delay, delay spread, and coherence bandwidth, are extracted. The estimated values for mean delay and delay spread remain small, which indicates that the channel can be regarded as flat for typical low-bandwidth signals employed over the sea. In fact, the median value for coherence bandwidth is found to be 8 MHz, hence these channels are suitable to support relatively high data rates by using simple communication systems.

Description: In this paper, we study the problem of detection of underwater minefields amidst dense clutter as that of statistical inference under a spatial point-process model. Specifically, we model the locations (mine and clutter) as samples of a Thomas point process with parent locations representing mines and children representing clutter. Accordingly, the parents are distributed according to a homogeneous Poisson process and, given the parent locations, the children are distributed as independent Poisson processes with intensity functions that are Gaussian densities centered at the parents. This provides a likelihood function for parent locations given the observed clutter (children). Under this model, we develop a framework for penalized maximum-likelihood (ML) estimation of model parameters and parent locations. The optimization is performed using a combination of analytical and Monte Carlo methods; the Monte Carlo part relies on a birth–death–move procedure for adding/removing points in the parent set. This framework is illustrated using both simulated and real data sets, the latter obtained courtesy of Naval Surface Warfare Center Panama City Division (NSWC-PCD), Panama City, FL, USA. The results, evaluated both qualitatively and quantitatively, underscore success in estimating parent locations and other parameters, at a reasonable computation cost.

Description: The accurate modeling of underwater acoustical reflection from a wind-roughened ocean surface is a challenging problem. Some complicating factors are the presence of near-surface bubbles and the potential for shadowing of acoustical energy by parts of the surface itself. One essential factor, which is the subject of this paper, is the specular reflection of coherent plane waves at an ocean-like rough surface. We tested the accuracy of one rough surface reflection model, the small-slope approximation (SSA) approach as used by Williams (J. Acoust. Soc. Amer., vol. 116, no. 4, pp. 1975–1984, Oct. 2004), for scenarios for which scattering was entirely in the vertical plane. The SSA model was used to compute values of the coherent plane wave reflection loss per bounce for wind speeds between 5 and 12.5 m/s, frequencies between 1.5 and 9 kHz, and grazing angles between about 1 $^{circ}$ and 10 $^{circ}$ . These values were compared to those obtained from a Monte Carlo approach based on the parabolic equation (PE) method, where realistic ocean surfaces were generated based on the Pierson–Moskowitz spectrum for ocean surface heights. The SSA model compared favorably with the more rigorous PE method for most of the range of parameters considered. An approximation to the SSA model was derived for application to grazing angles less than particular values, and this approximation was shown to compare well with results from PE modeling.

Description: A general numerical model has been developed to predict the probability density function (pdf) of the magnitudes of complex pulse-compressed broadband echoes (broadband echo pdf) due to arbitrary aggregations of scatterers that are detected with a single-beam echosounder. The model is based on physics principles and rigorously accounts for the broadband frequency-dependent characteristics of the system, signal, scatterers, and the beampattern modulation effects of the sonar/radar transceiver. A key aspect to modeling the statistics of broadband echoes is accounting for the scenarios where the pulse-compressed echoes may only partially overlap. The echo statistics under those conditions will be significantly different than those associated with a narrowband system with the same center frequency but whose echoes will completely overlap given the same density of scatterers. As a result, pulse-compressed broadband echoes will generally deviate more from the Rayleigh distribution (i.e., be more “heavy-tailed”) than narrowband echoes—a feature that is critical to analyzing real-world data. As in the case of narrowband signals, the shape of the broadband echo pdf is shown to vary from strongly non-Rayleigh to Rayleigh as the number of dominant scatterers in the beam increases. The model is applied to sonar in the ocean in which the numerical density of fish is inferred using broadband echoes (30–70 kHz). The results are compared with those from conventional echo energy methods. As with narrowband systems, statistics of broadband echoes can be used to estimate the numerical density of scatterers without the need for absolute calibration of the system.

Description: All types of seismic sources have a common feature in which large energy is released into water in a short time to produce an intense acoustic pulse. For plasma sparker, the discharge time is mainly determined by the discharge circuit parameters. Providing an accurate prediction of the acoustic pulse is difficult because of the complex plasma discharge process and unclear initial bubble condition. This paper presents the first experimental study on the influence of electric pulse width on acoustic pulses, especially the expansion pulse. Three comparative experiments are conducted to investigate the effect of electric pulse and to determine the minimum acoustic pulse width produced by a plasma sparker. One of the main results is that only the amplitude of the expansion pulse can obviously be changed if the electric circuit parameters are fixed, especially the capacitance of the energy storage capacitor. Meanwhile, the smaller the energy storage capacitor is, the shorter is the electric pulse and the narrower is the expansion pulse. The maximum pressure of an expansion pulse generated by a small capacitor is larger than that generated by a large capacitor under the same energy. The expansion pulse width decreases quickly when the energy dissipation on the load speeds up, and approaches the minimum value around several microseconds when the energy dissipation rate on the load is over 2 J/ $mu$ s, and the corresponding electric pulse width is on the order of hundreds of nanoseconds.

Description: This paper considers propagation of low-frequency broadband pulses in shallow water. It focuses on across-slope propagation in wedge-shaped oceanic waveguides and on single hydrophone receiving configurations. In a low-frequency shallow-water context, propagation is dispersive and usually described by modal theory. However, in the presence of a tilted bottom, propagation is also affected by 3-D effects (horizontal refractions). The paper shows that time-frequency analysis is a suitable tool to illustrate and understand 3-D propagation. It illustrates the pertinence of the time-frequency analysis of 3-D signal by proposing an algorithm to estimate the seabed slope using a single receiver. The method is benchmarked on numerical simulations, and successfully applied on small-scale experimental data.

Description: In this paper, a new design principle is proposed for the frontend-hardware (FEHW) chain of broadband passive sonars. This principle embraces the ultimate purpose of broadband passive sonars, which is to maximize the detection of passive targets. FEHW chains were conventionally designed as noise whiteners to guarantee the data bit dynamic range in analog-to-digital converters (ADCs) rather than being optimized for target detection. The conventional design was due to the restriction of electronic hardware in the early days and is still used in modern broadband passive sonars even with the great relaxation of hardware restriction today. The proposed principle optimizes the frequency response function (FRF) of FEHW chains by means of maximizing the output signal-to-noise ratio (SNR). The optimal FRF obtained using the design principle counts for the spectral characteristics of the entire sonar system (hydrophones, FEHW chain, and beamformer) as well as those of broadband passive sources and background noise. It turns out that maximizing the SNR is equivalent to maximizing the so-called deflection ratio (DR) in broadband passive sonars, but the former is directly used as a statistical test in the sonar engineering. The relationship between the optimal FRF and the Eckart filter is also discussed, with both of them counting for spectral shapes at their input, but with the former also counting for the change of spectral shapes after its output. Design examples are included to demonstrate the merit of the optimal FRF over the noise whitener with both theoretical and simulation results.

Description: In this paper, we investigate the power amplification for underwater acoustic orthogonal frequency-division multiplexing (OFDM) systems. The maximum power delivery (MPD) and the pilot signal-to-noise ratio (PSNR) specific to the OFDM modulation are adopted as design criteria. We study the impact of key parameters associated with the pulse-width modulation (PWM), such as the modulation frequency, the number of quantization bits, and the input clipping threshold, and provide a suitable procedure to determine those parameters to increase the MPD while meeting a certain PSNR constraint. Experimental setup has been established, where the experimental results validate the key findings from the analytical and simulation results.

Description: Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

Description: A sphere is often used as a target shape for various underwater scattering experiments. The scattering from a sphere is aspect independent (monostatically), and it can be efficiently modeled. Elastic-shelled spheres can have a complicated scattering response due to the various types of waves which can propagate within the shell. In practice, “spherical” targets are rarely perfectly spherical due to the manufacturing process. For example, the shell may be slightly thicker at the top and bottom of the sphere. In this paper, we present a straightforward numerical method to model the scattering from almost-spherical objects. It is assumed that the target is still azimuthally symmetric about the ${z}$ -axis, but the shell boundary profiles may vary as a function of polar angle. The effect that this angular dependence has on the backscattered spectra is examined for some simple examples.

Description: In this paper, a direct approach of designing robust weighted fusion steady-state Kalman predictors with uncertain noise variances is presented. Based on the steady-state Kalman filtering theory, using the minimax robust estimation principle, the local and six weighted fusion robust steady-state Kalman predictors are proposed based on the worst case systems with the conservative upper bounds of noise variances. They include the three robust weighted state fusers, two robust weighted measurement fusers, and a modified robust covariance intersection (CI) fuser. Their actual prediction error variances are guaranteed to have the corresponding minimal upper bounds for all admissible uncertainties. A Lyapunov equation approach for robustness analysis and the concept of the robust accuracy are presented and their robust accuracy relations are proved. A simulation example verifies the accuracy relations and robustness.

Description: In multitarget tracking, the problem of track labeling (assigning labels to tracks) is an ongoing research topic. The existing literature, however, lacks an appropriate measure of uncertainty related to the assigned labels that has a sound mathematical basis as well as clear practical meaning to the user. This is especially important in a situation where well separated targets move in close proximity with each other and thereafter separate again; in such a situation, it is well known that there will be confusion on target identities, also known as "mixed labeling." In this paper, we specify comprehensively the necessary assumptions for a Bayesian formulation of the multitarget tracking and labeling (MTTL) problem to be meaningful.We provide a mathematical characterization of the labeling uncertainties with clear physical interpretation.We also propose a novel labeling procedure that can be used in combination with any existing (unlabeled)MTT algorithm to obtain a Bayesian solution to the MTTL problem. One advantage of the resulting solution is that it readily provides the labeling uncertainty measures. Using the mixed labeling phenomenon in the presence of two targets as our test bed, we show with simulation results that an unlabeled multitarget sequential Monte Carlo (M-SMC) algorithm that employs sequential importance resampling (SIR) augmented with our labeling procedure performs much better than its "naive" extension, the labeled SIR M-SMC filter.

Description: Track-before-detect methods jointly detect and track one or several targets from raw sensor measurements. They often require the computation of the measurement likelihood conditional on the hidden state, which depends on the complex amplitudes of the targets. Since these amplitudes are unknown and fluctuate over time, this likelihood must be marginalized over the complex amplitude (i.e., phase and modulus). It has been demonstrated that this marginalization can be done analytically over the phase in the monotarget case. In this article, we first propose to extend the marginalization to the modulus in a monotarget setting, and we show that closed forms can be obtained for fluctuations of type Swerling 1 and 3. Second, we demonstrate that, in a multitarget setting, a closed form can be obtained for the Swerling 1 case. For Swerling 0 and 3 models, we propose some approximation to alleviate the computation. Since many articles consider the case of squared modulus measurements, we also consider this specific case in monoand multitarget settings with Swerling 0, 1, and 3 fluctuations. Finally, we compare the performance in estimation and detection for the different cases studied, and we show the gain, both in detection and estimation, of the complex measurement method over the squared modulus method, for any fluctuation model.

Description: When a bistatic inverse synthetic aperture radar (ISAR) system fails to collect complete radar cross section (RCS) datasets, bistatic ISAR (Bi-ISAR) images are usually corrupted using the conventional Fourier transform (FT)-based imaging algorithm. To overcome this problem, this paper proposes a new Bi-ISAR image reconstruction method that includes three steps: suboptimal estimation of parameters regarding the bistatic angle in the Bi-ISAR signal model via an orthogonal matching pursuit-type group-searching scheme, Bi-ISAR signal reconstruction using the estimated parameters, and Bi-ISAR image generation using the FT-based imaging algorithm applied to the reconstructed Bi-ISAR signal. To validate the reconstruction capability of the proposed method, bistatic-scattered field data using the physical optics technique as well as the point-scatterer model are used for Bi-ISAR image reconstruction. The results show that the proposed sparse-recovery-interpolation approach based on the Bi-ISAR signal model reconstruction combined with the classical FT-based algorithm can yield high reconstruction accuracy for incomplete bistatic RCS data compared to conventional numerical interpolation methods and existing direct sparse reconstruction techniques.

Description: Bistatic inverse synthetic aperture radar (ISAR) is an efficient geometric configuration of ISAR to image targets traveling along radar transmitter's line of sight, but it has the problem of defocusing and distortion because of the time-varying bistatic angle. In this paper, we derive the defocusing term and the distortion term by expanding the bistatic angle using first-order Taylor expansion. In addition, necessary constraints to neglect the defocusing term are provided via point spread function analysis, and a novel method to eliminate distortion based on linked scatterers is proposed. Simulation results validate our theoretical analysis and show the effectiveness of our method.

Description: Micro-Doppler (MD)-based target classification capabilities of automotive radars can provide high reliability and short latency to future active safety automotive features. A large number of pedestrians surrounding a vehicle in practical urban scenarios mandate prioritization of their treatment level. Distinguishing between relevant pedestrians that are crossing the street or are within the vehicle path and those that are on the sidewalks and are moving along the vehicle route can significantly minimize the number of vehicle-to-pedestrian accidents. This work proposes a novel technique for estimating a pedestrian direction of motion that treats pedestrians as complex distributed targets and utilizes their MD radar signatures. The MD signatures are shown to be indicative of pedestrian direction of motion, and a supervised regression is used to estimate the mapping between the directions of motion and the corresponding MD signatures. In order to achieve higher regression performance, a state-of-the-art sparse dictionary learning-based feature extraction algorithm was adopted from the field of computer vision by drawing a parallel between the Doppler effect and the video temporal gradient. The performance of the proposed approach is evaluated in practical automotive scenario simulations, where a walking pedestrian is observed by a multiple-input/multiple-output automotive radar with a two-dimensional rectangular array. The simulated data was generated using the statistical Boulic-Thalman human locomotion model. Accurate direction of motion estimation was achieved by using support vector regression and multilayer perceptron-based regression algorithms. The results show that the direction estimation error is less than 10° in 95% of the tested cases for pedestrian at a range of 100 m from the radar.