Search

Soil organic matter (SOM) plays an important role in the field of climate change and terrestrial ecosystems. SOM in large areas, especially in urban areas, is difficult to monitor and estimate by traditional methods. Urban land structure is complex, and soil is a mixture of organic and inorganic constituents with different physical and chemical properties. Previous studies showed that remote sensing techniques that provide diverse data in the visible-near-infrared (VNIR)-shortwave infrared (SWIR) spectral range, are promising in the prediction of SOM content on a large scale. Sentinel-2 covers the important spectral bands (VNIR-SWIR) for SOM prediction with a short revisit time. Thus, this article aimed to evaluate the capacity of Sentinel-2 for SOM prediction in an urban area (i.e., Shanghai). 103 bare soil samples filtrated from 398 soil samples at a depth of 20 cm were selected. Three methods, partial least square regression (PLSR), artificial neural network (ANN), and support vector machine (SVM), were applied. The root mean square error (RMSE) of modelling (mRMSE) and the coefficient of determination ( $R^{2}$ ) of modelling $(mR^{2})$ were used to reflect the accuracy of the model. The results show that PLSR has the poorest performance. ANN has the highest modelling accuracy (mRMSE = 7.387 g kg $^{-1}$ , $mR^{2}=0.446$ ). The ANN prediction accuracy of RMSE (pRMSE) is 4.713 g kg $^{-1}$ and the prediction accuracy of $R^{2}~(pR^{2})$ is 0.723. For SVR, the pRMSE is 4.638 g kg $^{-1}$ , and the $pR^{2}$ is 0.732. The prediction accuracy of SVR is slightly higher than that of ANN. The spatial distribution of SOM demonstrates that the value obtained by ANN is the closest to the range of the bare soil samples, and ANN performs better in vegetation-covered areas. Therefore, Sentinel-2 can be used to estimate SOM content in urban areas, and ANN is a promising method for SOM estimation.

A cost-effective chromatic dispersion (CD) estimation method is proposed, which is based on the delay-tap sampling (DTS) technique in combination with periodic training sequences (PTS). By performing equivalent sampling of PTS, DTS-based CD estimation at ultra-low sampling rate can be realized, significantly reducing the cost and high-speed requirements of analog to digital converters (ADCs). In contrast to the existing Nyquist DTS-based CD estimation method, we construct the CD lookup table in the CD over-compensated region, which can be suitable for a dynamic range of OSNR conditions. The CD lookup table built from the simulation can be used in the experimental system with similar configurations directly, indicating that the suggested method is reasonable and highly feasible. Besides, with only 1.25 GSa/s sampling rate of ADCs (about 1/30 of the Nyquist sampling rate which is 40.28 GSa/s), both 20.14 GBaud quadrature-phase-shift-keying (QPSK) simulation and experimental systems demonstrate the feasibility of the method, with a 28 ps/nm maximum CD estimation error for the simulation and a 35 ps/nm maximum CD estimation error for the experiment when OSNR is fixed to 18 dB. Furthermore, we also investigate the robustness of our method under the condition of different amplifier spontaneous emission (ASE) noise and nonlinear noise. The proposed method shows great potential for making a remarkable cost reduction in CD monitoring.

Early stage of underwater electrical wire explosion was observed by shadowgraph. By decreasing initial energy, the early stage of wire explosion was slowed down and possible to capture shadow photos during phase transitions. The vaporization process of wire is usually axially non-uniform due to random local micro explosions along the wire. The degree of non-uniform will be significantly decreased when increasing the initial energy. During explosion of the wire, more than one shock waves were generated not only from phase transition, but also from different pulses of deposition power. A modified piston model was introduced to explain the phenomenon.

In recent years, with the popularity of smartphones, mobile sensor technologies have been widely used in indoor localization. Its advantage is that data can be collected directly from smartphones without installing any other equipment. Meanwhile, it has the disadvantages of high requirements on positioning conditions and large deviations in positioning results. A technology of crowd-sourcing landmarks-assisted smartphone in indoor localization (CLS-IL) is proposed in this paper so as to solve these disadvantages. Firstly, the accelerometer, magnetometer and gyroscope sensors are used to acquire initial positioning of users. The characteristics of magnetometer and gyroscope sensors are analyzed through a series of experiments in allusion to the key problem of heading estimation errors, then corresponding data is matched for different experimental conditions. After that, the corresponding landmark database is established for experimental environment and an adaptive optimization algorithm of landmarks is proposed to correct drift errors caused by the readings of sensors influenced by surrounding environment and long positioning time excessively during traditional pedestrian dead reckoning. Experimental results show that the location accuracy of CLS-IL system is significantly improved compared with traditional technologies, which provides strong support for subsequent studies.

The hesitant fuzzy set (HFS) is widely applied in actual multi-attribute group decision-making (MAGDM) problems. It can depict experts' hesitant evaluation information with the membership degree consisting of several possible values. Most existing methods based on HFSs only focus on the final integrated information by different kinds of aggregation operators but fail to provide detailed comparisons between alternatives. They are essentially result-oriented static decision-making methods, based on which, the decision-making results may be inconsistent with reality. However, there is no process-oriented research on hesitant fuzzy information. The decision field theory (DFT) is a dynamic decision-making method and can better simulate the uncertain decision-making process. Thus, this paper integrates the HFS into the DFT and proposes a new decision-making method named as hesitant fuzzy decision field theory (HFDFT) to fill this vacancy. First, we define the hesitant fuzzy momentary preference function and other parameters in HFDFT. After that, for the MAGDM problems with incompletely known attribute weight information, the programming model is used to determine the weights of attributes. Then, the group decision-making method based on HFDFT is presented. Moreover, we apply the proposed HFDFT method to a case about route selection of the Arctic Northwest Passage. Two traditional methods based on the score function and the correlation coefficient, respectively, are further implemented for comparisons to illustrate the validity of the proposed HFDFT method.

Compressed sensing (CS) has provided a novel way for inverse synthetic aperture radar (ISAR) imaging. In CS based ISAR imaging, the continuous range-Doppler plane is divided into grids, and the strong scattering points are assumed on the grids. However, the strong scattering points may not be on the grids, which will degrade the performance of CS greatly. This is the off-grid problem. To solve the problem, most of existing methods aim at estimating off-grid error and sparse solution jointly. But the computational cost is relatively high. To reduce the computational cost, a fast and accurate algorithm has been proposed in this paper. Interestingly, the joint optimization problem can be solved efficiently through two least squares problems based on first order Taylor approximation. When applied into simulated chirp signal and quasi real ISAR data, the proposed algorithm has got much better imaging results than existing algorithms. Therefore, it is a promising off-grid CS based ISAR imaging algorithm.