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Multimode microwave remote sensors are a new type of radar system that operates in SAR (synthetic aperture radar), scatterometer, altimeter, and spectrometer modes in a time-sharing manner according to the current task scheduled on small satellites. High flexibility through mode switching is the outstanding advantage of this new microwave remote sensor. Two or more small satellites operating in different operation modes can be networked to perform observations synchronously to provide excellent remote sensing performance. This paper focuses on a method to determine the radar parameters of the scatterometer mode since the frequency band and the platform height of this new of kind sensor are determined according to the requirement of the SAR mode, and their values are evidently different from the values of current scatterometers in orbit. The proposed method systematically presents the complete steps and related formulae of the radar parameter design of scatterometers and determines the signal bandwidth by optimization. A system simulation method of the scatterometer mode is further studied to determine the wind field inversion error and thus evaluate the rationality of the parameter design results. In contrast to the existing methods, which directly add noise to the backscatter coefficients, our method simulates the noisy echo of the scatterometer and then retrieves the wind field by estimated backscatter coefficients from the noisy echo. The performed simulation shows that, by using the designed system parameters, the multimode microwave remote sensor obtains wind field inversion accuracy, resolution and swath width comparable to those of the HY-2 satellite and the QuikSCAT satellite.

Type 2 diabetes (T2DM), one of the most prevalent chronic diseases, affects the glucose metabolism of the human body, which decreases the quantity of life and brings a heavy burden on social medical care. Patients with T2DM are more likely to suffer bone fragility fracture as diabetes affects bone mineral density (BMD). However, the discovery of the determinant factors of BMD in a medical way is expensive and time-consuming. In this paper, we propose a novel algorithm, Prior-Knowledge-driven local Causal structure Learning (PKCL), to discover the underlying causal mechanism between BMD and its factors from the clinical data. Since there exist limited data but redundant prior knowledge for medicine, PKCL adequately utilize the prior knowledge to mine the local causal structure for the target relationship. Combining the medical prior knowledge with the discovered causal relationships, PKCL can achieve more reliable results without long-standing medical statistical experiments. Extensive experiments are conducted on a newly provided clinical data set. The experimental study of PKCL on the data is proved to highly corresponding with existing medical knowledge, which demonstrates the superiority and effectiveness of PKCL. To illustrate the importance of prior knowledge, the result of the algorithm without prior knowledge is also investigated.

Multimodal small satellite synthetic aperture radar (SAR) is a new radar system under development that integrates an SAR, altimeter, scatterometer, and spectrometer. When applied to marine scenes, this system can be used to measure both marine targets and marine dynamic environments with high precision. This study addresses system parameter design for multimodal small satellite SAR operating in scan mode, and this design provides system parameters such as antenna size, signal bandwidth, pulse repetition frequency, scanning wavenumber and wave position for system simulation in scan mode. A method for optimizing the transmit power when illuminating a marine scene based on the wind speed and wind direction above the sea surface is studied. The goal is to fully use the characteristics of strong sea surface microwave scattering under a suitable wind speed and wind direction to reduce the required transmit power, thus improving the available data sampling time per orbit of a multimodal radar when working in SAR mode. Various simulation experiments were conducted, and the system parameter design results are given under scan mode. Furthermore, imaging simulation results of ocean scenes are also given under conventional and decreased power after optimization. The results show that good ocean scene imaging results are obtained when the designed system parameters are used for system simulation. In addition, the simulation results also verify that when the sea surface wind speed is relatively high and the wind direction is suitable, an acceptable ocean scene imaging result can still be obtained by using reduced transmit power.

Moving target detection is of vital importance to maritime security and maritime resource protection. However, the detection of slow or weak targets is difficult based on traditional methods. A new detection method is proposed by using the different motion variations of radar moving target and sea clutter in the range-Doppler spectrum sequence. The first step in implementing this method is the separation of moving target and sea clutter by the low-rank representation, in which the target and clutter are modeled as foreground and background components. Subsequently, a sea clutter discriminator is constructed within the sea clutter bandwidth to further remove the sea clutter (false alarms) that exists in the foreground. The proposed method can reduce the sea clutter power while maintaining the target power and improve the detection rate of moving targets, especially slow or weak targets. Data collected with airborne maritime surveillance radar in maritime moving target indication (MMTI) mode are used to validate the performance of the proposed method. The experimental results demonstrate that the improvement in the signal-to-clutter ratio (SCR) obtained with the proposed method is better than that obtained with space-time adaptive processing (STAP, including 1DT-STAP, 3DT-STAP and sparse-STAP) and principal component pursuit (PCP) methods; additionally, the figure of merit (FOM) of the proposed method is higher than that of the constant false alarm rate (CFAR) and PCP method. Furthermore, the tracks of ships are obtained by applying a location constraint to the foreground sequence.

With the increasing of the grid-connected capacity of renewable energy such as wind energy and solar energy, its instability, intermittence and uncontrollability have a serious impact on the stable operation of power system, such as voltage fluctuation, frequency flicker, etc. Therefore, Electric Spring (ES) system has become a current research hotspot. Aiming at the deficiencies of the resonance control strategy, a control method that combines quasi-proportional resonance (QPR) control and repetitive control is proposed. The quasi proportional resonance control is used to realize the tracking without static error, and the internal model principle of repetitive control is used to suppress the periodic disturbance of the grid voltage, so as to improve the power quality of the grid. Compared with the traditional quasi proportional resonant control, the introduction of repetitive control can effectively improve the steady-state accuracy, reduce the system harmonic and enhance the ability of anti-harmonic interference. The modeling of ES and the design method of repetitive controller parameters are discussed in detail. The simulation model is built using MATLAB / Simulink, and the ES experimental platform is built with dSPACE as the control core, the results verify the feasibility and effectiveness of the proposed control strategy.

With the high growth rates of railway transportation, it is extremely important to detect railway obstacles ahead of the train to ensure safety. Manual and traditional feature-extraction methods have been utilized in this scenario. There are also deep learning-based railway object detection approaches. However, in the case of a complex railway scene, these object detection approaches are either inefficient or have insufficient accuracy, particularly for small objects. To address this issue, we propose a feature-enhanced single-shot detector (FE-SSD). The proposed method inherits a prior detection module of RON and a feature transfer block of FB-Net. It also employs a novel receptive field-enhancement module. Through the integration of these three modules, the feature discrimination and robustness are significantly enhanced. Experimental results for a railway traffic dataset built by our team indicated that the proposed approach is superior to other SSD-derived models, particularly for small-object detection, while achieving real-time performance close to that of the SSD. The proposed method achieved a mean average precision of 0.895 and a frame rate of 38 frames per second on a railway traffic dataset with an input size of 320 × 320 pixels. The experimental results indicate that the proposed method can be used for real-world railway object detection.

This paper studies the application of compact polarimetric (CP) SAR in the detection and identification of ocean internal solitary waves (ISWs). First, based on full-polarimetric ALOS PALSAR images, we construct CP SAR images and extract 26 CP features. Then, the ISWS-sea surface differentiation capability for the different polarization features is analyzed by using the Jeffries and Euclidean distances. The results show that λ1, Entropy (H), Lambda, the polarimetric total power (Span) and the Stokes parameters (Stokesg0, and Stokesg3) improve the ISWs detection results. On this basis, a k-means clustering algorithm based on CP features is introduced, and the results show that the ISWs detection and identification performance of the algorithm are superior to that of the traditional Wishart polarization clustering algorithm, which suggests that CP SAR has good application prospects in the detection and identification of ocean ISWs.

Concept Factorization (CF) improves Nonnegative matrix factorization (NMF), which can be only performed in the original data space, by conducting factorization within proper kernel space where the structure of data become much clear than the original data space. CF-based methods have been widely applied and yielded impressive results in optimal data representation and clustering tasks. However, CF methods still face with the problem of proper kernel function design or selection in practice. Most existing Multiple Kernel Clustering (MKC) algorithms do not sufficiently consider the intrinsic neighborhood structure of base kernels. In this paper, we propose a novel Discriminative Multiple Kernel Concept Factorization method for data representation and clustering. We first extend the original kernel concept factorization with the integration of multiple kernel clustering framework to alleviate the problem of kernel selection. For each base kernel, we extract the local discriminant structure of data via the local discriminant models with global integration. Moreover, we further linearly combine all these kernel-level local discriminant models to obtain an integrated consensus characterization of the intrinsic structure across base kernels. In this way, it is expected that our method can achieve better results by more compact data reconstruction and more faithful local structure preserving. An iterative algorithm with convergence guarantee is also developed to find the optimal solution. Extensive experiments on benchmark datasets further show that the proposed method outperforms many state-of-the-art algorithms.

This paper devotes to the image compression and encryption problems. We develop a novel hybrid scheme based on block compressive sensing. Concentrate on taking full advantage of the different frequency coefficients sparsity, the nonuniform sampling strategy is adopted to improve the compression efficiency. First, the discrete cosine transform coefficients matrices of blocks are transformed into vectors by zigzag scanning. The different frequency components are extracted in the front, middle, and back of vectors, respectively. Using the measurement matrices with different dimensions, the combination of low- and high-frequency components, together with the medium-frequency coefficients are compressed simultaneously. Second, the recombinational block measurements are re-encrypted by the permutation-diffusion framework. The logistic map is introduced for key stream generation. In order to accomplish a sensitive and effective cryptosystem, the control strategy for secret keys is employed. The simulation results indicate that the proposed scheme forms a high balance between reconstruction performance, storage and computational complexity, and hardware implementation. Moreover, the security analyses demonstrate the satisfactory performance and effectiveness of the proposed cryptosystem. The scheme can work efficiently in the parallel computing environment, especially for the images with medium and large size.

The growing demand for lithium-ion (Li-ion) battery in electric vehicles has expedited the need for new optimal charging approaches to improve the speed and reliability of the charging process without deteriorating battery performances. Many efforts have been deployed to develop optimal charging strategies for commercial Li-ion batteries over the last decade. The active optimal charging strategies have great potential to meet the requirement. This paper is a review of the studies on constructing the optimal charging algorithms for Li-ion batteries. The battery models on which these protocols rest are stated, the generalized structures are examined, the advantages and the drawbacks of the mathematical controller algorithms are discussed, and their applications are presented. Suggestions for overcoming the shortcomings of the proposed strategies are proposed. Challenges and future directions in the development of optimal charging strategies for commercial Li-ion batteries are also discussed.

Epilepsy is one of the most common neurological disorders generally characterized by sudden and recurrent unprovoked seizures, which is commonly diagnosed through visual inspection of electroencephalography (EEG) signals. However, since EEG signals are highly complex, nonlinear and nonstationary in nature, both visual inspection and the existing computed aid detection approaches fail to capture the intrinsic dynamics of seizure events, leading to unsatisfactory detection performance. Therefore, an accurate computer-aided automatic epileptic seizure diagnosis system is valuable and urgently needed in clinical application. In this study, we propose an automatic classification method using nonlinear dynamics features and nonlinear classifiers for determining whether the EEG signals are in interictal or ictal activities, respectively. The main novelty of this paper is that investigating the performance of discernable entropy-based nonlinear dynamics features used for automatic diagnosis of an epileptic seizure. First, the discrete wavelet transform is applied to obtain de-noised and sub-band signals. Second, the nonlinear dynamics features are extracted based on information entropy theory. Subsequently, one-way analysis of variance and forward sequential feature selection technique are employed to identify the most explainable features for seizure detection. Then the selected features are fed to six different classifiers, namely, least square-support vector machine (LS-SVM), K-nearest neighbors, logistic regression, linear discriminant analysis, Naive Bayes classifier, and random forest, for automatic diagnosis of the epileptic seizure. Finally, a public EEG dataset from Bonn University, Germany, is utilized to investigate the performance of the proposed method. The experimental results show that the LS-SVM yields the optimal performance with an accuracy of 99.50%, a sensitivity of 100.00%, and a specificity of 99.40%. Our proposed method shows great potential for real-time diagnosis of the epileptic seizures, which can be deployed in hospitals to assist clinicians in accurate diagnosis of seizures.

In real-world networks, an edge (or vertex) may be associated with a transaction database, where each transaction consists of a set of items to describe the attributes or behaviors of the edge (or vertex). This type of graph is named as a transactional graph, and the problem of mining sequential patterns from transactional graphs is to discover the subsequences of transaction sequences that frequently appear in many paths of the graph. Solving this problem can facilitate broad applications ranging from social network analysis to urban computing. However, this task is not trivial due to a large number of sequences existed in the graph. In particular, each path of the graph may induce multiple sequences of transactions, and gathering the transaction sequences induced by all the paths may lead to an extremely large volume. To efficiently approximate the top-k patterns, we propose a Parallelized Sampling-based Approach For Mining Top-k Sequential Patterns, PSMSP, which involves two key techniques: (a) a parallelized unbiased sequence sampling approach based on a sequence balanced graph partition strategy, and (b) a novel PSP-Tree structure to efficiently mine the patterns based on the anti-monotonicity properties. The experimental results on synthetic and real-world datasets demonstrate that the PSMSP can successfully find the sequential patterns with superior efficiency.

A reentry object covered by a plasma sheath will produce two reflective signals in a radar echo, which seriously affects the detection of the reentry object by radar and results in positioning error and even tracking loss. On the basis of the scattering center model and the non-uniform plasma stratification model, the present paper establishes a radar scattering model of a reentry object covered by a plasma sheath. The model effectively retains the plasma distribution and electromagnetic wave propagation characteristics of the plasma sheath. Analysis of the signal components of radar echoes in an example shows to some extent that the non-mirror reflection signal is not generated by the plasma wake.

Resiliency of distribution systems under extreme operating conditions is critical, especially when the utility is not available. With the large-scale deployment of distributed resources, it becomes possible to restore critical loads with local non-utility resources. Distribution system operators (DSOs) need to determine the critical loads to be restored, considering limited resources and distribution facilities. Several studies on resiliency have been conducted for the restoration of distribution systems. However, the inherent asynchronous characteristic of the information availability has not been incorporated. With incomplete and asynchronous information, decisions may be made that result in underutilization of generation resources. In this paper, a new distribution system restoration approach is proposed, considering uncertain devices and associated asynchronous information. It uses a two-module architecture that efficiently optimizes restoration actions using a binary linear programming model and evaluates their feasibility with unbalanced optimal power flow. Networked microgrids are included in the model. The IEEE 123-node test feeder is used for validation. The results show that asynchronous messages may affect the restoration actions significantly and the impacts can be mitigated by the proposed decision support tool for the DSOs.

Based on the resonance absorption effect of plasma, a novel time-varying plasma passive jamming method against one dimensional range profile of radar detection is proposed in this paper. The jamming mechanism of time-varying plasma structure is presented and verified. Based on the transmission line analogy, an instantaneous method is developed to calculate the radar time domain echo and the pulse compression of a target coated by the time-varying plasma. Then, the distortions of radar waveform and spectrum are numerically calculated. Finally, the jamming effect of the time-varying plasma on the one-dimensional range profile of a target is studied systematically for different regulating amplitude and frequency of electron density, radar bandwidth and pulse width parameters. The range profile result indicates that single radar target coating by time-varying plasma structure will generate cloaking effects and produce two or more false radar targets, and the distance between real and false target as well as reduction of echo intensity are related to the radar and regulating plasma parameters. The results show that this jamming method could have a great impact on the one-dimensional range profile, and the results could provide many useful reference to radar countermeasure.

Forecasting the stock market movements is an important and challenging task. As the Web information grows, researchers begin to extract effective indicators (e.g., the events and sentiments) from the Web to facilitate the prediction. However, the indicators obtained in previous studies are usually based on only one data source and thus may not fully cover the factors that can affect the stock market movements. In this paper, to improve the prediction for stock market composite index movements, we exploit the consistencies among different data sources, and develop a multi-source multiple instance model that can effectively combine events, sentiments, as well as the quantitative data into a comprehensive framework. To effectively capture the news events, we successfully apply a novel event extraction and representation method. Evaluations on the data from the year 2015 and 2016 demonstrate the effectiveness of our model. In addition, our approach is able to automatically determine the importance of each data source and identify the crucial input information that is considered to drive the movements, making the predictions interpretable.

A high-frequency injection (HFI) sensorless control for interior permanent magnet synchronous motors with enhanced precision and widened speed range is proposed in this paper. The injection frequency reaches up to 2 kHz under a 50~100 kHz silicon carbide (SiC)-based three-phase inverter. In addition to the high switching frequency, the field-programmable gate array (FPGA) is utilized to achieve high control bandwidth (>200 kHz) when implementing the field-oriented control algorithm. The benefits of high switching frequency and high control bandwidth in senseless controls are explained theoretically, i.e., leaving enough room for the injection frequency by using SiC while tuning down the noise-to-signal ratio by using the FPGA. Experimental results verified that such manners improved the position estimation and lifted the injection frequency effectively, which further allows us to widen the motor speed range under the HFI sensorless control from 0 to 500 r/min with the conventional Si+DSP design to 0~1200 r/min with the proposed SiC+FPGA.

This paper reports the finding of bi-stability phenomenon in a constant on-time (COT) controlled buck converter with a small output capacitor equivalent series resistance (ESR). In the previously published literatures, it has been reported that the COT controlled buck converter with a small output capacitor ESR operates in the reduced-frequency periodic oscillation state. However, it is newly validated by numerical simulations that for some specified initial states, such a buck converter with the same circuit parameters can operate in a chaotic oscillation state as well, reflecting the existence of bi-stable oscillation states. Based on the piecewise continuous model of COT controlled buck converter, the bi-stability phenomenon associated with two sets of initial states is revealed by numerical simulations and verified by PSIM circuit simulations. Consequently, when a small output capacitor ESR is used, the coexisting attractors' behavior of two bi-stable oscillation states appears in such a COT controlled buck converter, leading to the emergence of bi-stability phenomenon.

This paper proposes a novel method for traffic flow detection using distributed optical fiber acoustic sensing (DAS). Different from the traditional traffic flow detection method, this method detects the traffic vibration signal using a fiber optic cable. Distributed fiber-optic acoustic sensing technology can provide fully distributed acoustic information along the entire fiber link, and thus external acoustic signals from an arbitrary point can be detected and located. This paper uses DAS to obtain traffic vibration data. Using the characteristics of traffic vibration data, this paper proposes an improved wavelet threshold algorithm and an improved dual-threshold algorithm and verifies the feasibility and effectiveness of these methods. Finally, the experimental results from a vehicle-counting test show that the counting error is smaller for a single vehicle passing through the detection area and that the counting error is larger if multiple vehicles pass through the detection area continuously. In vehicle speed estimation, the results show good accuracy, and the error range is controlled to less than 6%.

We propose a method for improving the classification performance of a classifier, termed the classifier graph, by embedding it in a graph of classifiers. Our graph-based method has the advantage of enabling delicate classification from different levels of interpretation and abstraction. For the problem that thresholds corresponding to different classifiers are correlated and thus have mutual effects on the final performance, we provide a generalization of the receiver operator characteristic curve that properly tunes them jointly to obtain optimal performance. This method is successfully applied to the detection of noise artifacts (glitches) in the gravitational-wave data. We thus obtain an improvement up to 10% on the classification performance compared with that of a single classifier. The methods of this paper provide an effective way to improve the classification performance with multiple classifiers.

As the phase of the interferometric synthetic aperture radar (InSAR) contains abundant information for many earth observation activities, the interferometric phase denoising is an important step before InSAR processing and application because of its significant influence on the following steps. And this paper focused on the interferometric phase filtering and proposed a new filter based on the Stein's unbiased risk estimate (SURE)-based nonlocal means method. The SURE formula is more convenient for estimating the mean square error (MSE) from the noisy image only. Due to advantages of both the nonlocal means and the Stein's unbiased risk estimate, the proposed filter is exploited to the interferometric phase in the complex domain in the paper. Except for the denoising performance, the proposed method is more focused on preserving more useful information in the InSAR phase fringe patterns, such as the phase jumps utilized for urban information retrieval. The experiments on both the simulated data sets and the real data sets were implemented and analyzed to demonstrate the effectiveness of the proposed filtering method.

A 1-D electronic beam-steering partially reflective surface (PRS) antenna using a new reconfigurable PRS unit cell is proposed in this paper. The proposed work addresses the challenge to achieve a large beam steering angle with small gain variation and a small number of active/lumped elements by using a reconfigurable PRS superstrate only. The PRS unit cell consists of two back-to-back T-shaped strips with one PIN diode inserted between them and a pair of trapezoid patches (a rectangular patch and a pair of triangle parasitic patches). Beam steering is achieved by controlling the different states of PIN diodes. Thanks to the trapezoid patches, the proposed unit cell can generate a larger phase difference between different states, thereby leading to a larger beam steering angle. Furthermore, due to the addition of more degrees of freedom in the proposed unit cell, the phase difference can be easily manipulated. Moreover, since the T-shaped strips in each unit cell is connected with adjacent ones, the biasing network is very simple without needing a large number of lumped elements and dc biasing lines. The beam steering property is analyzed by using phased array theory. An antenna prototype with a main beam direction towards 0°, -18° and 18° operating at 5.5 GHz in the H-plane is fabricated and measured. Good agreement between the predicted simulation and measurement results for the input reflection coefficients and radiation patterns is achieved, which validates the feasibility of the design. The measured realized gains are over 11 dBi for all states with a 0.8 dBi gain variation.

For the geosynchronous-low earth orbit (GEO-LEO) bi-static multi-channel high-resolution and wide-swath synthetic aperture radar (HRWS SAR) system, a novel azimuth Doppler signal reconstruction algorithm is developed based on an equivalent baseline approximating in the azimuth Doppler chirp Fourier transform domain. The main difficulty for the GEO-LEO bistatic multi-channel HRWS SAR system is that the baseline is variant with the scene in the conventional Doppler Fourier transform domain during azimuth signal reconstruction. The developed approach first employs the linear range cell migration correction and chirp Fourier transform to obtain the coarse-focused ambiguity SAR image. More importantly, the baseline component can be approximated a uniform baseline length for the whole image scene and the ambiguous Doppler spectrum can be reconstructed using the available Doppler signal reconstruction approach. Finally, the experimental results for a simulation GEO-LEO bi-static multi-channel HRWS SAR system are employed to validate the theoretical investigations and the proposed approach.

An imaging processing for the squint mode multi-channel in azimuth high-resolution and wide-swath (MC-HRWS) synthetic aperture radar (SAR) system is presented in this paper. First, the linear range cell migration correction (RCMC) is employed to correct the skew Doppler spectrum for each channel echo. Then, a robust Doppler spectrum reconstruction method is proposed, which is based on the robust Capon beamforming and multi-Doppler-direction restriction technique. After the reconstruction, a two-step focus approach is utilized to focus the image, where the first step is utilized to deal with the azimuth-invariance components and the second addresses the azimuth-variance component introduced by the linear RCMC. Since the Doppler centroid estimation is crucial to squint mode SAR imaging, this paper also presents a robust entropy-based Doppler centroid estimation algorithm for an MC-HRWS SAR system, where the correlation function approach is employed to estimate the baseband Doppler centroid. More importantly, an entropy-based Doppler ambiguity number resolving approach is proposed and the bisection algorithm is utilized to accelerate the searching process. Some simulation experiments are conducted to verify our proposal. Besides, the results of some real MC-HRWS SAR data also show that the proposed method works well.