Coseismic Coulomb stress changes induced by a 2020-2021 M_(W)>7.0 Alaska earthquake sequence in and around the Shumagin gap and its influence on the Alaska-Aleutian subduction interface

Three M_(W)>7.0 earthquakes in 2020-2021 occurred in the Shumagin seismic gap and its adjacent area of the Alaska-Aleutian subduction zone,including the Mw7.8 Simeonof thrust earthquake on July 22,2020,the M_(W)7.6 Sand Point strike-slip earthquake on October 19,2020,and the M_(W)8.2 Chignik thrust earthquake on July 29,2021.The spatial and temporal proximity of these three earthquakes prompts us to probe stress-triggering effects among them.Here we examine the coseismic Coulomb stress change imparted by the three earthquakes and their influence on the subduction interface.Our results show that:(1)The Simeonof earthquake has strong loading effects on the subsequent Sand Point and Chignik earthquakes,with the Coulomb stress changes of 3.95 bars and 2.89 bars,respectively.The Coulomb stress change caused by the Sand Point earthquake at the hypocenter of the Chignik earthquake is merely around 0.01 bars,suggesting the negligible triggering effect on the latter earthquake;(2)The triggering effects of the Simeonof,Sand Point,and Chignik earthquakes on aftershocks within three months are not well pronounced because of the triggering rates of 38%,14%,and 43%respectively.Other factors may have played an important role in promoting the occurrence of these aftershocks,such as the roughness of the subduction interface,the complicated velocity structure of the lithosphere,and the heterogeneous prestress therein;(3)The three earthquakes caused remarkable coseismic Coulomb stress changes at the subduction interface nearby these mainshocks,with an average Coulomb stress change of 3.2 bars in the shallow region directly inwards the trench.

Monitoring Multiple Cropping Index of Henan Province, China Based on MODIS-EVI Time Series Data and Savitzky-Golay Filtering Algorithm

Multiple cropping index(MCI)is a very important indicator in crop production and agricultural intensification,which represents the utilizing degree of agriculture resources at time scale and the effective utilization situation of arable land.The objective of this paper is monitoring multiple cropping index of Henan province of China according to the time series of MODIS(Moderate-Resolution Imaging Spectroradiometer)EVI(Enhanced Vegetation Index)after Savitzky-Golay filter processing from the year 2006 to 2011.The results revealed that this method could provide an effective way to monitor multiple cropping index,and the method of no additional authentication data is independent and reliable.The result was accurate and stable,the slope of linear regression of the multiple cropping index between the statistical results and the remote sensing results was 1.0136(R2=0.779).The precision of sample areas validation was 97.91%.Suggesting that the time series MODIS-EVI which after Savitzky-Golay filtering processed,could provide an effective way to extract spatial information of multiple cropping index for management department of agriculture.

Individualization of Head Related Impulse Responses Using Division Analysis

For Virtual Reality(VR) to be truly immersive, it needs convincing sound to match. Due to the diversity of individual's anthropometric measurements, the individualized customization technology is needed to get convincing sound. In this paper, we proposed a simple and effective method for modeling relationships between anthropometric measurements and Head-related Impulse Response(HRIR). Considering the relationship between anthropometric measurements and different HRIR parts is complicated, we divided the HRIRs into small segments and carried out regression analysis between anthropometric measurements and each segment to establish relationship model. The results of objective simulation and subjective test indicate that the model can generate individualize HRIRs from a series of anthropometric measurements. With the individualized HRIRs, we can get more accurate acoustic localization sense than using non-individualized HRIRs.

Numerical simulation of influences of the earth medium's lateral heterogeneity on co- and post-seismic deformation

Many studies revealed that the Earth medium＇s lateral heterogeneity can cause considerable effects on the co- and post-seismic deformation field. In this study, the threedimensional finite element numerical method are adopted to quantify the effects of lateral heterogeneity caused by material parameters and fault dip angle on the co- and postseismic deformation in the near- and far-field. Our results show that： 1） the medium＇s lateral heterogeneity does affect the co-seismic deformation, with the effects increasing with the medium＇s lateral heterogeneity caused by material parameters; 2） the Lame parameters play a more dominant role than density in the effects caused by lateral heterogeneity; 3） when a fault＇s dip angle is smaller than 90, the effects of the medium＇s lateral heterogeneity on the hanging wall are greater than on the footwall; 4） the impact of lateral heterogeneity caused by the viscosity coefficient on the post-seismic deformation can affect a large area, including the near- and far-field.

On-orbit Geometric Calibration of Linear Push-broom Optical Satellite Based on Sparse GCPs

The paper presents a geometric calibration method based on the sparse ground control points (GCPs), aiming to the linear push-broom optical satellite. This method can achieve the optimal estimate of internal and external parameters with two overlapped image pair along the charge-coupled device (CCD), and sparse GCPs in the image region, further get rid of the dependence on the expensive calibration site data. With the calibrated parameters, the line of sight (LOS) of all CCD detectors can be recovered. This paper firstly establishes the rigorous imaging model of linear push-broom optical satellite based on its imaging mechanism. And then the calibration model is constructed by improving the internal sensor model with a viewing-angle model after an analysis on systematic errors existing in the imaging model is performed. A step-wise solution is applied aiming to the optimal estimate of external and internal parameters. At last, we conduct a set of experiments on the ZY-3 NAD camera and verify the accuracy and effectiveness of the presented method by comparison.

Stream-computing of High Accuracy On-board Real-time Cloud Detection for High Resolution Optical Satellite Imagery

This paper focuses on the time efficiency for machine vision and intelligent photogrammetry, especially high accuracy on-board real-time cloud detection method. With the development of technology, the data acquisition ability is growing continuously and the volume of raw data is increasing explosively. Meanwhile, because of the higher requirement of data accuracy, the computation load is also becoming heavier. This situation makes time efficiency extremely important. Moreover, the cloud cover rate of optical satellite imagery is up to approximately 50%, which is seriously restricting the applications of on-board intelligent photogrammetry services. To meet the on-board cloud detection requirements and offer valid input data to subsequent processing, this paper presents a stream-computing of high accuracy on-board real-time cloud detection solution which follows the “bottom-up” understanding strategy of machine vision and uses multiple embedded GPU with significant potential to be applied on-board. Without external memory, the data parallel pipeline system based on multiple processing modules of this solution could afford the “stream-in, processing, stream-out” real-time stream computing. In experiments, images of GF-2 satellite are used to validate the accuracy and performance of this approach, and the experimental results show that this solution could not only bring up cloud detection accuracy, but also match the on-board real-time processing requirements.

Multi-scale regionalization based mining of spatio-temporal teleconnection patterns between anomalous sea and land climate events

Climate sequences can be applied to defining sensitive climate zones, and then the mining of spatio-temporal teleconnection patterns is useful for learning from the past and preparing for the future. However, scale-dependency in this kind of pattern is still not well handled by existing work. Therefore, in this study, the multi-scale regionalization is embedded into the spatio-temporal teleconnection pattern mining between anomalous sea and land climatic events. A modified scale-space clustering algorithm is first developed to group climate sequences into multi-scale climate zones. Then, scale variance analysis method is employed to identify climate zones at characteristic scales, indicating the main characteristics of geographical phenomena. Finally, by using the climate zones identified at characteristic scales, a time association rule mining algorithm based on sliding time windows is employed to discover spatio-temporal teleconnection patterns. Experiments on sea surface temperature, sea level pressure, land precipitation and land temperature datasets show that many patterns obtained by the multi-scale approach are coincident with prior knowledge, indicating that this method is effective and reasonable. In addition, some unknown teleconnection patterns discovered from the multi-scale approach can be further used to guide the prediction of land climate.

Excitations of length-of-day seasonal variations:Analyses of harmonic and inharmonic fluctuations

Geophysical excitations of length-of-day(LOD)variations is of great significance in understanding changes in the Earth’s spin rate and interactions between geophysical fluids and the solid Earth,as well as validating the reliability of atmospheric,oceanic and hydrological models.In this study seasonal excitations of LOD variation during 06.30,1987-06.30,2017 are investigated using both harmonic and inharmonic analyses.We examined the agreements between the IERS EOP 14C04ΔLOD series and that from the previous version EOP08C04,and analyzed contributions of atmospheric,oceanic,hydrological and sea level angular momenta to seasonal excitations of LOD variations on the bases of the ESMGFZ products.We found that the sea level angular momentum plays an important role in global mass conservation and can bring better agreements between the geophysical and geodetic excitations.

A Novel Deep Learning Method for Application Identification in Wireless Network

In modern wireless communication network, the increased consumer demands for multi-type applications and high quality services have become a prominent trend, and put considerable pressure on the wireless network. In that case, the Quality of Experience(Qo E) has received much attention and has become a key performance measurement for the application and service. In order to meet the users' expectations, the management of the resource is crucial in wireless network, especially the Qo E based resource allocation. One of the effective way for resource allocation management is accurate application identification. In this paper, we propose a novel deep learning based method for application identification. We first analyse the requirement of managing Qo E for wireless communication, and review the limitation of the traditional identification methods. After that, a deep learning based method is proposed for automatically extracting the features and identifying the type of application. The proposed method is evaluated by using the practical wireless traffic data, and the experiments verify the effectiveness of our method.

Recent developments in seismological geodesy

With the advanced development of the modern geodetic techniques, the geodetic obser- vations have been proved to be more powerful to uncover the geophysical phenomena, especially the seismic one, than that in the past time. The recent developments and achievements in the seismological geodesy are summarised here. Several popular geodetic techniques, such as high-rate GNSS, InSAR and Satellite Gravimetry, are introduced first to present their recent contributions in studying the seismic deformations. The developments of the joint inversion of the seismic source parameters from multiple observations are then highlighted. Some outlooks in seismological geodesy are presented in the end.

Arbitrary Scale Super Resolution Network for Satellite Imagery

Recently,satellite imagery has been widely applied in many areas.However,due to the limitations of hardware equipment and transmission bandwidth,the images received on the ground have low resolution and weak texture.In addition,since ground terminals have various resolutions and real-time playing requirements,it is essential to achieve arbitrary scale super-resolution(SR)of satellite images.In this paper,we propose an arbitrary scale SR network for satellite image reconstruction.First,we propose an arbitrary upscale module for satellite imagery that can map low-resolution satellite image features to arbitrary scale enlarged SR outputs.Second,we design an edge reinforcement module to enhance the highfrequency details in satellite images through a twobranch network.Finally,extensive upsample experiments on WHU-RS19 and NWPU-RESISC45 datasets and subsequent image segmentation experiments both show the superiority of our method over the counterparts.

Pedestrian Attributes Recognition in Surveillance Scenarios with Hierarchical Multi-Task CNN Models

Pedestrian attributes recognition is a very important problem in video surveillance and video forensics. Traditional methods assume the pedestrian attributes are independent and design handcraft features for each one. In this paper, we propose a joint hierarchical multi-task learning algorithm to learn the relationships among attributes for better recognizing the pedestrian attributes in still images using convolutional neural networks(CNN). We divide the attributes into local and global ones according to spatial and semantic relations, and then consider learning semantic attributes through a hierarchical multi-task CNN model where each CNN in the first layer will predict each group of such local attributes and CNN in the second layer will predict the global attributes. Our multi-task learning framework allows each CNN model to simultaneously share visual knowledge among different groups of attribute categories. Extensive experiments are conducted on two popular and challenging benchmarks in surveillance scenarios, namely, the PETA and RAP pedestrian attributes datasets. On both benchmarks, our framework achieves superior results over the state-of-theart methods by 88.2% on PETA and 83.25% on RAP, respectively.

Nonlinear Prediction with Deep Recurrent Neural Networks for Non-Blind Audio Bandwidth Extension

Non-blind audio bandwidth extension is a standard technique within contemporary audio codecs to efficiently code audio signals at low bitrates. In existing methods, in most cases high frequencies signal is usually generated by a duplication of the corresponding low frequencies and some parameters of high frequencies. However, the perception quality of coding will significantly degrade if the correlation between high frequencies and low frequencies becomes weak. In this paper, we quantitatively analyse the correlation via computing mutual information value. The analysis results show the correlation also exists in low frequency signal of the context dependent frames besides the current frame. In order to improve the perception quality of coding, we propose a novel method of high frequency coarse spectrum generation to improve the conventional replication method. In the proposed method, the coarse high frequency spectrums are generated by a nonlinear mapping model using deep recurrent neural network. The experiments confirm that the proposed method shows better performance than the reference methods.

High Quality Audio Object Coding Framework Based on Non-Negative Matrix Factorization

Object-based audio coding is the main technique of audio scene coding. It can effectively reconstruct each object trajectory, besides provide sufficient flexibility for personalized audio scene reconstruction. So more and more attentions have been paid to the object-based audio coding. However, existing object-based techniques have poor sound quality because of low parameter frequency domain resolution. In order to achieve high quality audio object coding, we propose a new coding framework with introducing the non-negative matrix factorization(NMF) method. We extract object parameters with high resolution to improve sound quality, and apply NMF method to parameter coding to reduce the high bitrate caused by high resolution. And the experimental results have shown that the proposed framework can improve the coding quality by 25%, so it can provide a better solution to encode audio scene in a more flexible and higher quality way.

Super-Resolution for Face Image with an Improved K-NN Search Strategy

Recently, neighbor embedding based face super-resolution(SR) methods have shown the ability for achieving high-quality face images, those methods are based on the assumption that the same neighborhoods are preserved in both low-resolution(LR) training set and high-resolution(HR) training set. However, due to the "one-to-many" mapping between the LR image and HR ones in practice, the neighborhood relationship of the LR patch in LR space is quite different with that of the HR counterpart, that is to say the neighborhood relationship obtained is not true. In this paper, we explore a novel and effective re-identified K-nearest neighbor(RIKNN) method to search neighbors of LR patch. Compared with other methods, our method uses the geometrical information of LR manifold and HR manifold simultaneously. In particular, it searches K-NN of LR patch in the LR space and refines the searching results by re-identifying in the HR space, thus giving rise to accurate K-NN and improved performance. A statistical analysis of the influence of the training set size and nearest neighbor number is given, experimental results on some public face databases show the superiority of our proposed scheme over state-of-the-art face hallucination approaches in terms of subjective and objective results as well as computational complexity.

利用时空协同克里金方法时空估算中国新疆降水量（英文）

在对地观测中,所研究的地学变量不仅具有时间、空间特征,还受其它变量的影响,采用多元时空相关数据,可以提高时空估值的精度。以新疆区域为试验区,利用1960—2013年气象站的降水量观测数据的月平均值,采用时空克里金和时空协同克里金插值方法,估计试验区2013年1—12月降水量的时空分布情况。在使用时空协同克里金插值过程中,建立时空直接变异函数和协变异函数是时空CoKriging插值的关键一步。以该地区1960—2013年月平均降水量为主变量,引入同时间同位置的月平均空气相对湿度作为协变量,对降水量和空气相对湿度进行时空直接变异函数和时空交叉协变异函数建模。实验结果表明,引入空气相对湿度作为协变量的时空协同克里金的插值方法比时空普通克里金的插值方法的均方根误差降低了31.46%;引入空气相对湿度作为协变量的时空协同克里金的插值方法的估计值与观测值的相关系数比时空普通克里金的插值方法的相关系数提高了5.07%。因此,引入空气湿度作为协变量的时空协同克里金插值方法提高了插值精度。

Robust Background Subtraction Method via Low-Rank and Structured Sparse Decomposition

Background subtraction is a challenging problem in surveillance scenes. Although the low-rank and sparse decomposition(LRSD) methods offer an appropriate framework for background modeling, they fail to account for image's local structure, which is favorable for this problem. Based on this, we propose a background subtraction method via low-rank and SILTP-based structured sparse decomposition, named LRSSD. In this method, a novel SILTP-inducing sparsity norm is introduced to enhance the structured presentation of the foreground region. As an assistance, saliency detection is employed to render a rough shape and location of foreground. The final refined foreground is decided jointly by sparse component and attention map. Experimental results on different datasets show its superiority over the competing methods, especially under noise and changing illumination scenarios.

Minimum Cost Multi-Path Parallel Transmission with Delay Constraint by Extending Openflow

Sometimes user has the requirement to run a high bandwidth application over a low bandwidth network. But its implementation is not easy as the traditional network transmits data with only one path where its bandwidth is lower than the demand. Although the current network technology like SDN has the ability to precisely control the data transmission in the network, but till now the standard openflow protocol does not support splitting one flow to multiple flows. In this paper, a flow splitting algorithm is proposed. The algorithm splits a data flow to multiple sub-flows by extending the openflow protocol. A multiple paths routing algorithm is also proposed to implement the multi-path parallel transmission in the paper. The algorithm selects multiple paths and minimizes the cost of transmission under the constraint of maximum delay and delay variance. The simulations show the algorithms can significantly improve the transmission performance.

Precise orbit determination of Haiyang‑2D using onboard BDS‑3 B1C/B2a observations with ambiguity resolution

The Haiyang-2D altimetry mission of China is one of the first Low Earth Orbit(LEO)satellites that can receive new B1C/B2a signals from the BeiDou-3 Navigation Satellite System(BDS-3)for Precise Orbit Determination(POD).In this work,the achievable accuracy of the single-receiver ambiguity resolution for onboard LEO satellites is studied based on the real measurements of new BDS-3 frequencies.Under normal conditions,six BDS-3 satellites on average are visible.However,the multipath of the B1C/B2a code observations presents some patchy patterns that cause near-field variations with an amplitude of approximately 40 cm and deteriorate the ambiguity-fixed rate.By modeling those errors,for the B2a code,a remarkable reduction of 53%in the Root Mean Square(RMS)is achieved at high elevations,along with an increase of 8%in the ambiguity-fixed rates.Additionally,an analysis of the onboard antenna’s phase center offsets reveals that when compared to the solutions with float ambiguities,the estimated values in the antenna’s Z direction in the solutions with fixed ambiguities are notably smaller.The independent validation of the resulting POD using satellite laser ranging at 16 selected high-performance stations shows that the residuals are reduced by a minimum of 15.4%for ambiguity-fixed solutions with an RMS consistency of approximately 2.2 cm.Furthermore,when compared to the DORIS-derived orbits,a 4.3 cm 3D RMS consistency is achieved for the BDS-3-derived orbits,and the along-track bias is reduced from 2.9 to 0.4 cm using ambiguity fixing.

Low Complexity SOVA for Turbo Codes

Recently, trimming Soft-output Viterbi algorithm(T-SOVA) has been proposed to reduce the complexity of SOVA for Turbo codes. In its fi rst stage, a dynamic algorithm, lazy Viterbi algorithm, is used to indicate the minimal metric differences which brings obstacle on hardware implementation. This paper proposes a Viterbi algorithm(VA) based T-SOVA to facilitate hardware implementation. In the first stage of our scheme, a modified VA with regular structure is used to fi nd the maximum likelihood(ML) path and calculate the metric differences. Further, local sorting is introduced to trim the metric differences, which reduces the complexity of trimming operation. Simulation results and complexity analysis show that VA based T-SOVA performs as well as lazy VA based T-SOVA and is easier to be applied to hardware implementation.