ISAR autofocus imaging algorithm for maneuvering targets based on deep learning and keystone transform

The issue of small-angle maneuvering targets inverse synthetic aperture radar(ISAR)imaging has been successfully addressed by popular motion compensation algorithms.However,when the target’s rotational velocity is sufficiently high during the dwell time of the radar,such compensation algorithms cannot obtain a high quality image.This paper proposes an ISAR imaging algorithm based on keystone transform and deep learning algorithm.The keystone transform is used to coarsely compensate for the target’s rotational motion and translational motion,and the deep learning algorithm is used to achieve a super-resolution image.The uniformly distributed point target data are used as the data set of the training u-net network.In addition,this method does not require estimating the motion parameters of the target,which simplifies the algorithm steps.Finally,several experiments are performed to demonstrate the effectiveness of the proposed algorithm.

Theoretical calculations of hardness and metallicity for multibond hexagonal 5d transition metal diborides with ReB_2 structure

The hardness, electronic, and elastic properties of 5d transition metal dibofides with ReB2 structure are studied theoretically by using the first principles calculations. The calculated results are in good agreement with the previous experimental and theoretical results. Empirical formulas for estimating the hardness and partial number of effective free electrons for each bond in multibond compounds with metallicity are presented. Based on the formulas, IrB2 has the largest hardness of 21.8 GPa, followed by OsB2 （21.0 GPa） and ReB2 （19.7 GPa）, indicating that they are good candidates as hard materials.

Wavelength and sensitivity tunable long period gratings fabricated in fluid-cladding microfibers

We report the fabrication of long period gratings in fluid-cladding microfibers by directly focusing a femtosecond laser beam on the microfibers surface to induce periodical modification a long one side of the microfibers.A long period grating is fabricated in a water-cladding microfiber with a diameter of~5μm,which demonstrates a resonant attenuation of 28.53 dB at wavelength of 1588.1 nm with 10 pitches.When water cladding is changed to be refractive index oil of n=1.33 and alcohol solution with concentration of 5%,the resonance wavelength shifts to 1575.1 nm with resonant attenuation of 24.91 dB and 1594.1 nm with resonant attenuation of 35.9 dB,respectively.The long period grating demonstrates different temperature sensitivities of-0.524 nm/℃,-0.767 nm/℃and-1.316 nm/℃for water,alcohol solution and refractive index oil cladding microfibers,respectively,which means the alterable liquid cladding allows the availability of tunable wavelength and sensitivity.The fluid-cladding protects the microfibers from external disturbance and contamination and allows more flexibility in controlling the transmission property and sensing characteristics of long period gratings,which can be used as fiber devices and sensors for chemical,biological,and environmental applications.

A cloud-edge-device collaborative offloading scheme with heterogeneous tasks and its performance evaluation

How to collaboratively offload tasks between user devices,edge networks(ENs),and cloud data centers is an interesting and challenging research topic.In this paper,we investigate the offoading decision,analytical modeling,and system parameter optimization problem in a collaborative cloud-edge device environment,aiming to trade off different performance measures.According to the differentiated delay requirements of tasks,we classify the tasks into delay-sensitive and delay-tolerant tasks.To meet the delay requirements of delay-sensitive tasks and process as many delay-tolerant tasks as possible,we propose a cloud-edge device collaborative task offoading scheme,in which delay-sensitive and delay-tolerant tasks follow the access threshold policy and the loss policy,respectively.We establish a four-dimensional continuous-time Markov chain as the system model.By using the Gauss-Seidel method,we derive the stationary probability distribution of the system model.Accordingly,we present the blocking rate of delay-sensitive tasks and the average delay of these two types of tasks.Numerical experiments are conducted and analyzed to evaluate the system performance,and numerical simulations are presented to evaluate and validate the effectiveness of the proposed task offloading scheme.Finally,we optimize the access threshold in the EN buffer to obtain the minimum system cost with different proportions of delay-sensitive tasks.

Design Guideline of Hollow-Core Fibres with Cobweb Cladding Structure

由使用一个飞机波浪扩大方法，与蜘蛛网 cladding 结构设计空核心的纤维的一些重要参数被分析。例如，拿绝缘的材料 PMMA 参数的忍耐被讨论。结果证明结构的参数整齐和限制拥有自己，并且在制造为结构的参数有更大的忍耐。

A Robust Collaborative Recommendation Algorithm Based on k-distance and Tukey M-estimator

The existing collaborative recommendation algorithms have lower robustness against shilling attacks.With this problem in mind,in this paper we propose a robust collaborative recommendation algorithm based on k-distance and Tukey M-estimator.Firstly,we propose a k-distancebased method to compute user suspicion degree(USD).The reliable neighbor model can be constructed through incorporating the user suspicion degree into user neighbor model.The influence of attack profiles on the recommendation results is reduced through adjusting similarities among users.Then,Tukey M-estimator is introduced to construct robust matrix factorization model,which can realize the robust estimation of user feature matrix and item feature matrix and reduce the influence of attack profiles on item feature matrix.Finally,a robust collaborative recommendation algorithm is devised by combining the reliable neighbor model and robust matrix factorization model.Experimental results show that the proposed algorithm outperforms the existing methods in terms of both recommendation accuracy and robustness.

An Ensemble Detection Method for Shilling Attacks Based on Features of Automatic Extraction

Faced with the evolving attacks in recommender systems, many detection features have been proposed by human engineering and used in supervised or unsupervised detection methods. However, the detection features extracted by human engineering are usually aimed at some specific types of attacks. To further detect other new types of attacks, the traditional methods have to re-extract detection features with high knowledge cost. To address these limitations, the method for automatic extraction of robust features is proposed and then an Adaboost-based detection method is presented. Firstly, to obtain robust representation with prior knowledge, unlike uniform corruption rate in traditional mLDA(marginalized Linear Denoising Autoencoder), different corruption rates for items are calculated according to the ratings’ distribution. Secondly, the ratings sparsity is used to weight the mapping matrix to extract low-dimensional representation. Moreover, the uniform corruption rate is also set to the next layer in mSLDA(marginalized Stacked Linear Denoising Autoencoder) to extract the stable and robust user features. Finally, under the robust feature space, an Adaboost-based detection method is proposed to alleviate the imbalanced classification problem. Experimental results on the Netflix and Amazon review datasets indicate that the proposed method can effectively detect various attacks.

Single Image Rain Removal Using Image Decomposition and a Dense Network

Removing rain from a single image is a challenging task due to the absence of temporal information. Considering that a rainy image can be decomposed into the low-frequency(LF) and high-frequency(HF) components, where the coarse scale information is retained in the LF component and the rain streaks and texture correspond to the HF component, we propose a single image rain removal algorithm using image decomposition and a dense network. We design two task-driven sub-networks to estimate the LF and non-rain HF components of a rainy image. The high-frequency estimation sub-network employs a densely connected network structure, while the low-frequency sub-network uses a simple convolutional neural network(CNN).We add total variation(TV) regularization and LF-channel fidelity terms to the loss function to optimize the two subnetworks jointly. The method then obtains de-rained output by combining the estimated LF and non-rain HF components.Extensive experiments on synthetic and real-world rainy images demonstrate that our method removes rain streaks while preserving non-rain details, and achieves superior de-raining performance both perceptually and quantitatively.

Broadband tunable Raman soliton self-frequency shift to mid-infrared band in a highly birefringent microstructure fiber

Raman soliton self-frequency shifted to mid-infrared band（λ 〉 2 μm） has been achieved in an air-silica microstructure fiber（MF）. The MF used in our experiment has an elliptical core with diameters of 1.08 and 2.48 μm for fast and slow axis. Numerical simulation shows that each fundamental orthogonal polarization mode has two wide-spaced λZDW and theλZDW pairs located at 701/2110 nm and 755/2498 nm along the fast and slow axis, respectively. Using 810-nm Ti：sapphire femtosecond laser as pump, when the output power varies from 0.3 to 0.5 W, the furthest red-shift Raman solitons in both fast and slow axis shift from near-infrared band to mid-infrared band, reaching as far as 2030 and 2261 nm. Also, midinfrared Raman solitons can always be generated for pump wavelength longer than 790 nm if output pump power reaches0.5 W. Specifically, with pump power at 0.5 W, the mid-infrared soliton in slow axis shifts from 2001 to 2261 nm when the pump changes from 790 nm to 810 nm. This means only a 20 nm change of pump results in 260 nm tunability of a mid-infrared soliton.

Mid-infrared supercontinuum and optical frequency comb generations in a multimode tellurite photonic crystal fiber

We numerically investigate the mid-infrared(MIR)supercontinuum(SC)and SC-based optical frequency comb(OFC)generations when the three optical modes(LP_(01),LP_(02),and LP_(12))are considered in a multimode tellurite photonic crystal fiber(MM-TPCF).The geometrical parameters of the MM-TPCF are optimized to support the multimode propagation and obtain the desired dispersion characteristics of the considered three optical modes.When the pump pulse with center wavelengthλ=2.5μm,width T=80 fs,and peak power P=18 kW is coupled into the anomalous dispersion region of the LP_(01),LP_(02),and LP_(12)modes of the MM-TPCF,the-40-dB bandwidth of the generated MIR SCs can be up to 2.56,1.39,and 1.12 octaves,respectively,along with good coherence.Moreover,the nonlinear dynamics of the generated SCs are analyzed.Finally,the MIR SCs-based OFCs are demonstrated when a train of 50 pulses at 1-GHz repetition rate is used as the pump source and launched into the MM-TPCF.

Brillouin scattering spectrum character extraction based on genetic algorithm and seeker optimization algorithm

A new hybrid optimization method based on genetic algorithm(GA)and seeker optimization algorithm(SOA)is presented in this paper.The hybrid algorithm optimizes SOA by using crossover and mutation operations in GA in order to improve the global search ability of SOA.Four algorithms,i.e.particle swarm optimization(PSO),SOA,GA and quantum-behaved particle swarm optimization(GA-QPSO)and GA-SOA are used to process the simulation and experimental data of Brillouin scattering spectrum(BSS)at different temperatures.The results show that GA-SOA improves the accuracy of extracting the center frequency shift and the minimum center frequency of Brillouin scattering spectrum compared with other three algorithms.The shift error is 0.203 MHz.Therefore,GA-SOA can be applied to the accurate extraction of BSS characteristics.

Mixing matrix estimation of underdetermined blind source separation based on the linear aggregation characteristic of observation signals

Under the underdetermined blind sources separation(UBSS) circumstance,it is difficult to estimate the mixing matrix with high-precision because of unknown sparsity of signals.The mixing matrix estimation is proposed based on linear aggregation degree of signal scatter plot without knowing sparsity,and the linear aggregation degree evaluation of observed signals is presented which obeys generalized Gaussian distribution(GGD).Both the GGD shape parameter and the signals' correlation features affect the observation signals sparsity and further affected the directionality of time-frequency scatter plot.So a new mixing matrix estimation method is proposed for different sparsity degrees,which especially focuses on unclear directionality of scatter plot and weak linear aggregation degree.Firstly,the direction of coefficient scatter plot by time-frequency transform is improved and then the single source coefficients in the case of weak linear clustering is processed finally the improved K-means clustering is applied to achieve the estimation of mixing matrix.The proposed algorithm reduces the requirements of signals sparsity and independence,and the mixing matrix can be estimated with high accuracy.The simulation results show the feasibility and effectiveness of the algorithm.

Analysis of entropies based on empirical mode decomposition in amnesic mild cognitive impairment of diabetes mellitus

EEG characteristics that correlate with the cognitive functions are important in detecting mild cognitive impairment(MCI)in T2DM.To investigate the complexity between aMCI group and age-matched non-aMCI control group in T2DM,six entropies combining empirical mode decomposition(EMD),including Approximate entropy(ApEn),Sample entropy(SaEn),Fuzzy entropy(FEn),Permutation entropy(PEn),Power spectrum entropy(PsEn)and Wavelet entropy(WEn)were used in the study.A feature extraction technique based on maximization of the area under the curve(AUC)and a support vector machine(SVM)were subsequently used to for features selection and classi¯cation.Finally,Pearson's linear correlation was employed to study associations between these entropies and cognitive functions.Compared to other entropies,FEn had a higher classification accuracy,sensitivity and specificity of 68%,67.1% and 71.9%,respectively.Top 43 salient features achieved classification accuracy,sensitivity and speci¯city of 73.8%,72.3% and 77.9%,respectively.P4,T4 and C4 were the highest ranking salient electrodes.Correlation analysis showed that FEn based on EMD was positively correlated to memory at electrodes F7,F8 and P4,and PsEn based on EMD was positively correlated to Montreal cognitive assessment(MoCA)and memory at electrode T4.In sum,FEn based on EMD in righttemporal and occipital regions may be more suitable for early diagnosis of the MCI with T2DM.

A survey of occlusion detection method for visual object

Occlusion problem is one of the challenging issues in vision field for a long time,and the occlusion phenomenon of visual object will be involved in many vision research fields. Once the occlusion occurs in a visual system,it will affect the effects of object recognition,tracking,observation and operation,so detecting occlusion autonomously should be one of the abilities for an intelligent vision system. The research on occlusion detection method for visual object has increasingly attracted attentions of scholars. First,the definition and classification of the occlusion problem are presented.Then,the characteristics and deficiencies of the occlusion detection methods based on the intensity image and the depth image are analyzed respectively,and the existing occlusion detection methods are compared. Finally,the problems of existing occlusion detection methods and possible research directions are pointed out.

Regularization by Multiple Dual Frames for Compressed Sensing Magnetic Resonance Imaging With Convergence Analysis

Plug-and-play priors are popular for solving illposed imaging inverse problems. Recent efforts indicate that the convergence guarantee of the imaging algorithms using plug-andplay priors relies on the assumption of bounded denoisers. However, the bounded properties of existing plugged Gaussian denoisers have not been proven explicitly. To bridge this gap, we detail a novel provable bounded denoiser termed as BMDual,which combines a trainable denoiser using dual tight frames and the well-known block-matching and 3D filtering(BM3D)denoiser. We incorporate multiple dual frames utilized by BMDual into a novel regularization model induced by a solver. The proposed regularization model is utilized for compressed sensing magnetic resonance imaging(CSMRI). We theoretically show the bound of the BMDual denoiser, the bounded gradient of the CSMRI data-fidelity function, and further demonstrate that the proposed CSMRI algorithm converges. Experimental results also demonstrate that the proposed algorithm has a good convergence behavior, and show the effectiveness of the proposed algorithm.

Dual-Prior Integrated Image Reconstruction for Quanta Image Sensors Using Multi-Agent Consensus Equilibrium

Quanta image sensors(QIS) are a new type of singlephoton imaging device that can oversample the light field to generate binary bit-streams. The reconstruction for QIS refers to the recovery of original scenes from these binary measurements.Conventional reconstruction algorithms for QIS generally depend solely on one instantiated prior and are certainly insufficient for capturing the statistical properties over high-dimensional space.On the other hand, deep learning-based methods have shown promising performance, due to their excellent ability to learn feature representations from relevant databases. However, most deep models only focus on exploring local features while generally overlooking long-range similarity. In view of this, a dual-prior integrated reconstruction algorithm for QIS(DPI-QIS) is proposed, which combines a deep prior with a non-local self-similarity one using the multi-agent consensus equilibrium(MACE)framework. In comparison to the approaches that utilize a single prior, DPI-QIS fits the reconstruction model sufficiently by leveraging the respective merits of both priors. An effective yet flexible MACE framework is employed to integrate the physical forward model allying with the two prior-based models to achieve an overall better result. Extensive experiments demonstrate that the proposed algorithm achieves state-of-the-art performance in terms of objective and visual perception at multiple oversampling factors, while having stronger robustness to noise.

Determination of ethanol content in ethanol-gasoline based on derivative absorption spectrometry and information fusion

The ethanol content in ethanol-gasoline is respectively detected by the first-order derivative UV/vis absorption spectrum, the first-order derivative near infrared(NIR) absorption spectrum and the information fusion method. The backward interval partial least squares(BiPLS) algorithm is used as the feature extraction method, which is established by the partial least squares(PLS) regression model. Based on the information fusion theory, the low level data fusion(LLDF) and mid-level data fusion(MLDF) models are established by the first-order derivative UV/vis and NIR spectra. The analytical results are compared with the related textual references. Thereby, the single-spectral model based on the first-order derivative NIR absorption spectrum has the optimal results, where R_p^2 =0.999 1 and RMSEP=0.324 5, while the LLDF after vector normalization(LLDF-VN2) is the optimal multi-spectral fusion model, where R_p^2 =0.998 3 and RMSEP=0.498 2. The proposed method can be used to detect the ethanol content in ethanol-gasoline rapidly and provides a better choice for the component detection in mixed oils.

ISAR imaging based on improved phase retrieval algorithm

Traditional inverse synthetic aperture radar(ISAR)imaging methods for maneuvering targets have low resolution and poor capability of noise suppression. An ISAR imaging method of maneuvering targets based on phase retrieval is proposed,which can provide a high-resolution and focused map of the spatial distribution of scatterers on the target. According to theoretical derivation, the modulus of raw data from the maneuvering target is not affected by radial motion components for ISAR imaging system, so the phase retrieval algorithm can be used for ISAR imaging problems. However, the traditional phase retrieval algorithm will be not applicable to ISAR imaging under the condition of random noise. To solve this problem, an algorithm is put forward based on the range Doppler(RD) algorithm and oversampling smoothness(OSS) phase retrieval algorithm. The algorithm captures the target information in order to reduce the influence of the random phase on ISAR echoes, and then applies OSS for focusing imaging based on prior information of the RD algorithm. The simulated results demonstrate the validity of this algorithm, which cannot only obtain high resolution imaging for high speed maneuvering targets under the condition of random noise, but also substantially improve the success rate of the phase retrieval algorithm.

Wavelength-selective coupling of dual-core photonic crystal fiber and its application

We investigate the coupling characteristics of photonic crystal fiber composed of two cores with different index profiles. The index curves of the fundamental modes of the two cores can cross at a desired wavelength by choosing appropriate index profiles of the cores. As a result, wavelength-selective coupling is achieved. The designed coupler can be applied as an oDtical fiber bandstop filter with 26-nm bandwidth.

Cascading failure in the wireless sensor scale-free networks

In the practical wireless sensor networks （WSNs）, the cascading failure caused by a failure node has serious impact on the network performance. In this paper, we deeply research the cascading failure of scale-free topology in WSNs. Firstly, a cascading failure model for scale-free topology in WSNs is studied. Through analyzing the influence of the node load on cascading failure, the critical load triggering large-scale cascading failure is obtained. Then based on the critical load, a control method for cascading failure is presented. In addition, the simulation experiments are performed to validate the effectiveness of the control method. The results show that the control method can effectively prevent cascading failure.