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In this article, a high-security three-dimensional (3D) probabilistic shaping optical transmission system based on prefix-free code distribution matching (PCDM) and Polar code joint coding is proposed. The bit error rate (BER) performance of the communication system is optimized by using the probabilistic shaping effect of PCDM and the strong correction performance of Polar code. Meantime the Logistic and Lorenz chaotic models are used to mask the constellation map for multiple times to ensure the security transmission of data. The experiment verifies the transmission of 28.64Gb/s encrypted PCDM encoded signals on 2km 7-core fiber. At a BER is 1×10-3, the encrypted PCDM encoded signals improve the BER performance about 3dB compared to the conventional unencrypted 3D 32QAM signals. In terms of security performance, the key space of the proposed encryption scheme is up to 10119, and the BER of the illegal receiver is more than 0.4. The experimental results show that the proposed chaotic optical transmission scheme based on the joint encoding of PCDM and Polar can effectively improve the BER and security performance, which is a promising optical communication transmission scheme.

The topography can be very important for passive microwave remote sensing of soil moisture due to its complex influence on the emitted brightness temperature observed by a satellite microwave radiometer. In this study, a methodology of using the first brightness Stokes parameter (i.e., the sum of vertical and horizontal polarization brightness temperature) observed by the soil moisture and ocean salinity (SMOS) was proposed to improve the soil moisture retrieval under complex topographic conditions. The applicability of the proposed method is validated using in-situ soil moisture measurements collected at four networks (Pali, Naqu, Maqu, and Wudaoliang) on the Qinghai–Tibetan Plateau. The results over Pali, which is a typical mountainous area, showed that soil moisture retrievals using the first brightness Stokes parameter are in better agreement with the in-situ measurements (the correlation coefficient R > 0.75 and unbiased root mean square error 3/m3) compared with that using the single-polarization brightness temperature. At the other three networks with relatively flatter terrains, soil moisture retrievals using the first brightness Stokes parameter are found to be comparable to the single-polarization retrievals. On the contrary, the maximum bias of the retrieved soil moisture caused by topographic effects exceeds 0.1 m3/m3 when using vertical or horizontal polarization alone, which is far beyond the expected accuracy (0.04 m3/m3) of SMOS satellite. In the regions on the Qinghai–Tibetan Plateau where the vegetation effect can be ignored, soil moisture retrieved using horizontal polarization brightness temperature is generally underestimated, overestimated when using vertical polarization brightness temperature. It is reasonable due to the polarization rotation effect (depolarization) caused by the topographic effects. It is concluded that the proposed method for soil moisture retrieval using the first brightness Stokes parameter has a great potential in reducing the influence of topographic effects.

Logs are a combination of static message type fields and dynamic variable fields, and the accuracy of log parsing affects the result of subsequent log analysis tasks. In this regard, an offline log parsing method based on frequent words is introduced: PatCluster. This method first generates root nodes by preprocessing; secondly, the frequency of words is counted, and the word with the largest frequency is extracted as the segmentation condition to refine the template generated by the root node. So on recursively, pattern nodes are formed for all elements of the nodes, and corresponding templates are generated to finally achieve the purpose of log pattern mining. The mining process of the log patterns is from coarse to fine which is based on fewer assumptions, and the pattern fitting depth can be controlled by adjusting the termination condition. In optimized algorithm model, we also consider the maximum extent of the log template matching the token in the log message. The experimental results show that this method effectively improves the log parsing quality and has higher log parsing accuracy than other methods, and is more suitable for handling logs with complex structures.

This paper proposes a high-security physical layer encryption scheme for sparse code multiple access filter bank multi-carrier (SCMA-FBMC) in weakly coupled four-mode fiber. Due to the multiplexing of four modes and the overload of SCMA, the number of users connected by the proposed method is increased by six times. To improve the security of the SCMA-FBMC system, the Logistic mapping parameters are employed as the public key that is distributed to all users and is utilized for frequency masking to protect against illegal receivers' attacks. The 6D Duffing_Lu model acts as a private key of optical network units (ONUs) to prevent other ONUs from eavesdropping. A 60 Gb/s (4 × 15 Gb/s) encrypted SCMA-FBMC signal transmission over 5 km weakly coupled four-mode fiber is experimentally demonstrated. The experimental results show that there is no discernible difference in the signal's bit error rate (BER) performance before and after encryption. The sensitivity between different ONUs is less than 0.5 dB. In addition, the proposed scheme's key space can reach up to 10110, which effectively guarantee the physical layer security of the system. This scheme has application prospects in future short-distance optical access due to the growing number of connections and good security performance.

In this paper, transmission of orthogonal chirp division multiplexing (OCDM) based on asymmetric pair mapping over a seven core fiber is proposed. High-order 32 quadrature amplitude modulation (QAM) is reduced to quadrature phase-shift keying (QPSK) and 8 QAM signal employing asymmetric pair mapping, which will not affect the rate of the system. Combined with geometric shaping (GS), the average transmission power is successfully lowered, realizing signal damage suppression. In order to verify the performance of the proposed asymmetric pair mapping scheme, a 140 Gb/s asymmetric pair mapping OCDM signal transmission over 2 km seven core fiber is experimentally demonstrated. The experimental results show that the proposed transmission scheme has the best performance when the power distribution ratio (PDR) is 4 dB. Meanwhile, compared with GS 32 QAM with QPSK+rectangle 8 QAM, GS 32 QAM with QPSK+star 8 QAM and GS 32 QAM, the proposed asymmetric pair mapping OCDM signal has improved by 1.55 dB, 1.45 dB, and 0.7 dB, respectively. It has a wide range of applications in the future optical transmission system due to its outstanding performance in the suppression of high-order modulation signal degradation.

A highly secure and reliable optical orthogonal frequency division multiplexing passive optical network (OFDM-PON) based on the chaos encryption inside polar code scheme is proposed. The 3-dimensional Chua's circuit model is adopted to realize chaotic encryption during polar encoding with low complexity. The three chaotic sequences generated by Chua's circuit model are used to encrypt information index bit matrix, frozen bits (during polar encoding) and subcarrier sequence, respectively. The BER and security performance of the optical transmission system are enhanced by our method. A 70 Gb/s polar encoded encrypted 16 quadrature amplitude modulation OFDM signal transmission over a 2 km seven-core fiber is further experimentally demonstrated. The experimental results show that the polar encoded encrypted 16 QAM-OFDM obtains a 2.1 dB gain in receiver sensitivity at a BER of 10−3 under the same bit rate compared to the normal 16 QAM-OFDM without FEC. Moreover, the key space of the proposed scheme is 1084, which is large enough to guard against any malicious attacks from illegal ONUs effectively. The combined superiority of BER and security performance enable such OFDM-PON based on chaos encryption inside polar code, to have a high prospect of application in low-cost and reliable optical access system.

Aiming to address the problems of arbitrary orientations, large aspect ratios, and dense arrangements in ship detection, an arbitrary-oriented ship detection method based on RetinaNet is proposed. Our proposed method includes a rotated RetinaNet, a refined network, a feature alignment module, and an improved loss function. First, the rotated RetinaNet achieves rotation detection by using a feature pyramid network, rotated anchors, the skew intersection-over-union (IoU), and skew nonmaximum suppression. Then, the refined network and feature alignment module are introduced to achieve better detection accuracy. Finally, to address the boundary discontinuity, the loss function is improved by introducing the IoU constant factor. Considering the problems with the HRSC2016 dataset, we establish a new dataset with more accurate labels and more images and object samples. Through an ablation study, we thoroughly analyze the validity of the proposed rotated RetinaNet, feature alignment module, and improved loss function. The experimental results show that our method is superior to other state-of-the-art methods.

Urban thermal condition has seriously affected the quality of residents’ daily life and triggered some environmental issues. Understanding spatial patterns of land surface temperature (LST) and its driving mechanism is important for the sustainable development of cities. Taking Beijing as an example, this study employed spatial econometric models to investigate spatial and temporal heterogeneity of LST from 2014 to 2018 based on multisource remote sensing and statistical data. The global autocorrelation Moran's I index showed the existence of significant positive correlations of LST among regions, indicating the regions with high thermal environments are spatially adjacent. The temperature of a region would increase by more than 0.6% for every 1% increase in LST of surrounding areas based on the spatial Durbin model. In terms of spatial interactions of influencing factors, elevation, normalized difference vegetation index, modified normalized difference water index, nighttime light, and fossil energy consumption of neighbors exhibited significantly positive spatial agglomeration effects on local LST, whereas albedo, GDP, and population density of adjacent areas had negative effects on LST in local areas. Particularly, the indirect effects of drivers were greater than their direct effects, indicating urban thermal condition was an interregional issue and joint control measures should be adopted to mitigate the urban heat island effects as a whole.

Many existing refactoring tools reduce the possibility of lock conflicts and improve the concurrency of the system by reducing lock granularity and narrowing the scope of locked objects. However, such refactorings can lead to changes in concurrent program behavior, introduce concurrency errors, and often even produce code that does not compile or can be compiled but has changed semantics. To address the problem of changes in concurrent program behavior caused by transferring from coarse-grained locking to fine-grained lock refactoring, a refactoring consistency validation method for fine-grained locking is proposed. Firstly, the types of behavioral changes caused by the existing refactoring engine are analyzed in terms of thread interactions. Secondly, the relevant consistency checking rules are summarized according to the types. Finally, with the help of various program analysis techniques such as call graph analysis, alias analysis and side-effect analysis, the corresponding checking algorithms are designed according to the consistency checking rules to check the consistency of the program before and after refactoring. We implement an automatic validation tool as an Eclipse plug-in. Our approach is verified by ten open-source projects including HSQLDB, Xalan and Cassandra, etc. A total of 1,483 refactoring methods were tested, and 60 inconsistent synchronization behaviors were found, which improved the robustness of refactoring in terms of data dependence and execution order.

Specific Emitter Identification (SEI) detects the individual emitter according its varied signal characteristics. The method operates in the physical layer of the internet and can effectively improve the security of the Internet of Things (IoT). Generally, SEI identifies the uniqueness of the transmitting platform by using the unintentional modulation information of the emitter such as radar, which has “fingerprint” characteristics. Existing SEI methods are based on hand-crafted features to distinguish different emitters. In this paper, traditional feature extraction methods are studied and a new recognition method is proposed. To determine the effectiveness of the method, the output signals of eight amplifiers are collected as the research object. The power spectrum characteristics and adjacent channel power ratio (ACPR) of the signal are then extracted and eight amplifiers are distinguished. Finally, the quadrature-phase signals are converted into pictures, and convolutional neural networks are used to automatically extract features for classification and recognition. The results show that the recognition rate of converting signals into pictures can reach 95%, when SNR is 20dB.

Low-carbon technology innovation of power grid is vital for grid enterprises to improve their competitiveness and resource utilization efficiency. In this paper, a novel tripartite evolutionary game theory is proposed to examine the behavioral strategies of government, banks, and the grid enterprises in the low-carbon power grid technology innovation cooperation. The evolutionary replication dynamics equations are presented to study evolutionary stable strategies (ESS) of participants. The meaningful simulation results are as follows: from the subsidy perspective, even if the government subsidies are phasing out, the ESS of the low-carbon grid technology cooperation still converges to the Pareto optimal equilibrium; from the cost perspective, the higher low-carbon technology innovation cost only slows down the evolution rate, while the higher business cost of carbon asset pledge credit and the lower incentive cost not only slow down the evolution rate but also change the evolution results. It shows that the business cost of carbon asset pledge credit has a greater impact on the evolution of the system than the incentive cost; from the benefit perspective, increasing the green revenue and the successful probability of the low-carbon technology innovation can both prompt the ESS to evolve to Pareto optimal state, and the effect of the former is greater than the latter. These results provide a theoretical guidance for government to promote the development of low-carbon technology innovation of power grid.

A correntropy induced loss (C-loss) function is a loss function developed based on entropy theory. Due to the non-convexity of the C-loss function, ELM based on C-loss has been proven to have better regression effects and robustness. Inspired by generalized quantile learning, we find that there is a further improvement in the C-loss function. The main work of this paper is to propose a new improved C-loss function, which lead to a novel algorithm ICELM, and the half-optimized algorithm is used to solve the ICELM. Experiments on several benchmark data sets prove the superiority of the ICELM. Compared with CELM, ICELM has more efficient regression performance and robustness, and is obviously more suitable for complex aeroengines remaining useful life prediction field which affected by noise and outliers. Experiments on a benchmark C-MAPSS dataset also validate the theory.

Motion blur is a quite tricky issue in object tracking community. In recent years, many trackers have been explored to address this issue without sensational performance. This paper proposes a novel correlation tracking framework with Recurrent Neural Network (RNN) deblurnet and proposal detection to handle motion blur and heavy occlusion in object tracking tasks. We take advantage of high efficient Kernelized Correlation Filter (KCF) tracker, a typical method that exploits the circulant structure and the kernel trick to enhance the performance, and furthermore incorporate two regression methods in it. We employ RNN as our baseline of deblurnet, and introduce residual block and ConvLSTM in our deblur network to improve the result of deblurring. In addition, we suggest an edge information based rectification system to overcome the challenge of target occlusion. Finally, we update the model adaptively in term of the feedback from high-confidence tracking results to avoid the model degradation. Extensive experimental results demonstrate our tracker outperforms several state-of-the-art trackers on the OTB-2015 and VOT-2016 datasets.

The ELM constructed based on the least squares loss function and ±1 label has poor generalization in the classification of data containing noise. The introduction of square pinball loss function can improve the robustness of ELM. However, the algorithm based on the squared loss function and the ±1 label imposes a margin of 1 for all training samples. At the same time, due to the unbounded nature of the loss function, the generalization of the algorithm in the classification problem is reduced. This paper proposes a soft threshold square pinball loss (SSP-Loss) function. This function can set more flexible thresholds for training samples while maintaining the robustness of the square pinball loss function. The soft-threshold square pinball loss function can approximate the bounded loss function in stages to further improve the classification performance of the algorithm. The performance of ELM based on the soft pinball loss function on several benchmark data sets proves the effectiveness of our proposed algorithm. More importantly, the excellent robustness and classification performance of the algorithm is very suitable for aeroengine gas path fault diagnosis, and is expected to become its candidate technology.

Currently, search engines are widely used to address the information overload problem. Different from the existing client-and-sever-based frameworks, edge computing (EC) technology can provide a new architecture for personalized searching services. The issue of how to measure the similarities among entities by using the context information generated by user behavior in the edge environment is vital in the task of entity-related personal searching. To analyze and measure the similarities among entities, existing methods are mainly based on either the textual content or relationships unilaterally, and the results usually have a fixed degree of similarity. However, the similarities among entities depend on the set of properties that belong to the entities. This approach should be used in determining the similarity or dissimilarity associated with the surrounding context. To address this limitation, we propose a novel semantic augmentation method with a double attention mechanism. The method refers to a dynamic representation learning process that maps an entity to a real number vector in semantic space. In this article, different from the existing similarity measurement methods, we propose a thematic similarity measure approach to analyze the connotation and denotation similarities among entities. The experimental results show that the double attention mechanism leads to a significant improvement in the entity thematic similarity measurement tasks. The model can make a separation among the entities from different domains effectively. In addition, it can take similar entities that are closer in the same domain. It also shows excellent performance on the task of entity thematic similarity, which makes the recommendation results more explainable.

Passive microwave remote sensing is an effective way to obtain global soil moisture (SM) measurements, and many studies have explored the uncertainty inherent in microwave-based SM products. However, SM product accuracy has not been evaluated in northeast China, a national and global production base for commodity grain. In this study, a ground-based wireless sensor network with 28 observation nodes that were spatially distributed within 36 × 36 km was established to achieve satellite-scale “true” SM values through sensor calibration for specific soil types, senor consistency testing, and spatial scale transformation. The uncertainties of four passive microwave SM products (SMAP L3, SMOS L3, the Japan Aerospace Exploration Agency/JAXA AMSR2, and FY3C) were investigated and the following conclusions were obtained: 1) SMAP SM accuracy was very close to the expected application accuracy of 0.04 cm3/cm3, followed by SMOS, FY3C, and AMSR2; 2) for SMOS and SMAP, there were no significant temporal changes in SM errors, except for the larger error of descending SMOS SM and June SM values for descending SMAP and ascending SMOS. AMSR2 SM generally underestimated field SM, while FY3C SM values under low vegetation conditions were more consistent with field data, with an error of about 0.06 cm3/cm3; 3) agricultural activities and rainfall caused the soil surface roughness to increase or decrease within a growing season, which may have been an important source of satellite-scale SM error indicated by high bias values in July for both SMAP and SMOS; and 4) the standard deviation of field SM (0.06 cm3/cm3) produced a SMAP SM error of about 0.06 cm3/cm3 in low vegetation water content conditions, indicating that SM spatial heterogeneity cannot be ignored in the retrieval algorithm. This article investigated the accuracy and error sources of four satellite SM products in the farmland area of northeast China, and identified future research directions for further improving SM algorithms.

During recent years, researchers throughout academia and industry have been advancing the theory, designing, and applications of mobile service computing through the Internet of Things (IoT). Research interest in mobile service computing stems from its performance, security, reliability, and power consumption. Hence, ultra-low power integrated circuits are essential for mobile service computing that can offer the advantages of low power for computational tasks in the IoT that is driven by the restricting constraints of power consumption and autonomy in both computation and idle phases. To attain the benefits of ultra-low power circuits, the energy-consuming and computational intense demands are imposed by the underlying processing and memory devices on which the conventional ultra-low power integrated circuit can benefit substantially from innovative hardware designs. Logic-in-Memory (LIM) architectures are considered as the potential approaches to attain goals within area and energy constraints starting with the lowest layers of the hardware stack. In this paper, we propose and implement the LIM asynchronous computing paradigm for energy-efficient mobile service computing. The results indicate that the proposed design achieves 38% leakage reduction and 30% accuracy improvement compared to the state-of-the-art non-volatile asynchronous circuits. At the system level, we compare our designs with various commercial microprocessors. The experimental results show that the asynchronous processors attain a four-fold throughput increase relative to their synchronous counterparts under these operating constraints. Therefore, the proposed design offers an approach toward tangible benefits of the battery-constrained embedded mobile service computing.

Online learning platforms provide an opportunity to better understand students' weaknesses by tracking both their learning behavior and knowledge. This information can then be used to assist teachers in making instructional decisions and to further guide those who are at risk of failure. In this paper, we tracked student learning data from a C++ programming course over a whole semester of their freshman year via the Trustie platform. A total of 17,854 code submissions were collected. We then used CppCheck, SonarQube and Trustie to capture the quality characteristics and submission characteristics of the code, including lineOfCode, cyclomaticComplexity, codeSmell, syntacticError, averageScore, submission, and logicError, and analyzed the impact of code quality on the assignment work results. Several factors were discovered that we believe can help teachers to develop more effective teaching strategies.

Recently, Siamese network based trackers have been greatly developed and achieved state-of-the-art performance on multiple benchmarks. However, the decision-making mechanism needs to be studied more deeply in order to obtain higher accuracy. In this paper, we propose a novel Siamese network based visual tracking method, which enhances decision-making ability by Spatially Constrained Correlation Filter (SCCF) and Saliency Prior Context (SPC) model. We use the deep features extracted from Siamese networks to train the SCCF via the efficient Alternating Direction Method of Multipliers (ADMM), and our SCCF applies a penalizing matrix to suppress the boundary effect well. Meanwhile, we regard the end-to-end output of Siamese networks as a priori probability and utilize the spatio-temporal relationship to establish the SPC model. The SPC model can handle the various cases of feature distributions generated from different targets and their contexts. Further, we also take measures to solve some challenging problems in visual tracking, such as target scale change and target occlusion. We conduct extensive experiments to demonstrate the effectiveness of the proposed method, which obtains currently the best results on three large tracking benchmarks, including OTB-2013, OTB-2015, and VOT-2016.

For the grid-connected system, the unbalanced and harmonic components of three-phase grid voltage have a significant impact on the accuracy of grid synchronization. And the positive, negative sequences, harmonics, as well as dc component of the three-phase grid voltage are difficult to separate and extract in a fast and precise way. In this paper, a new fully digital phase-locked loop (FDPLL) is proposed. Compared with the existing continuous-domain methods, it is simple to implement, and able to quickly and accurately extract the positive and negative sequence components for grid synchronization. In order to verify the effectiveness and feasibility of the proposed method, the time-domain simulation is carried out. Finally, the feasibility of the proposed method is experimentally verified with a 32-bit floating-point TMS320F28335 DSP. The experimentally results verify the effectiveness of the proposed solution.

The conventional single-phase multilevel current source inverters suffer from the high count of switches for increasing the levels. Therefore, how to generate more levels with reduced-count of switches is attractive. In this paper, a programmable single-phase current source inverter is presented. It can achieve the programmable level output with only five switches. Different from the existing single-phase reduced-count multilevel inverters, it has the advantages of a simple structure and programmable level output. Finally, the simulation and experimental tests are carried out. And, the experimental results verify the effectiveness of the proposed programmable multilevel inverter.

In order to mitigate investments, stock price forecasting has attracted more attention in recent years. Aiming at the discreteness, non-normality, high-noise in high-frequency data, a support vector machine regression (SVR) algorithm is introduced in this paper. However, the characteristics in different periods of the same stock, or the same periods of different stocks are significantly different. So, SVR with fixed parameters is difficult to satisfy with the constantly changing data flow. To tackle this problem, an adaptive SVR was proposed for stock data at three different time scales, including daily data, 30-min data, and 5-min data. Experiments show that the improved SVR with dynamic optimization of learning parameters by particle swarm optimization can get a better result than compared methods including SVR and back-propagation neural network.

Explosive growth of applications in hyperspectral image (HSI) has made HSI classification a hot topic in the remote sensing community. The key to improve classification accuracy is how to make full use of the spectral and spatial information. We combine k-nearest neighbor (KNN) algorithm with guided filter which can extract spatial context information and denoise the classification results by edge-preserving filtering. To solve the problem of dimension disaster, we also take dimensionality reduction into account for HSI classification. To verify the feasibility of our proposed methods, we evaluate the performance over four widely used hyperspectral data sets. The experimental results show that with only 5% of samples, our method obtained better performance than improved support vector machine and KNN methods.

Magnetic field effects on tris-(8-hydroxyquinoline) aluminum-based organic light-emitting devices (OLEDs) by employing Fe3O4 as a magnetic dopant in the hole-transport layer (HTL) have been studied. The magnetic doped OLEDs exhibit efficient injection and transport of holes, and its performances are further enhanced after a magnetic field is applied. The enhancement of luminance and current efficiency of 20% and 24% has been obtained from the magnetic doped devices, while they are only 8% and 9%, respectively, for the nondoped devices under an applied magnetic field of 500 mT. Organic magnetoresistance induced by the magnetic doped HTL is the main origin of increased electroluminescence for the magnetic doped OLEDs.