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The self-tapping screw connectors are widely used in Cold-formed Steel (CFS) construction due to their rapid fastening with automatic power driving and ease of installation via a self-drilling process. However, there is a lack of a robust numerical method for the modelling of behaviours of screw fasteners bearing against steel sheets, where the connections can undergo different failure modes including fracture, bearing, tilting, pull-out and shear failure, particularly for CFS thin plates. A nonlinear explicit dynamic finite element (FE) model is developed in this paper to address this issue by validating the modelled load-deformation relationships and failure modes with available tests in literature. The proposed numerical model is improved in two aspects comparing to available research: firstly, a ductile fracture criterion was proposed that could address the fracture of ply metals subjected to complex stress state with less trial-and-error process. Secondly, the threads in the screw fasteners and ply sheets were carefully modelled where the inter-locking capacity between the screws and steel sheets were better taken into account, eliminating the need for an artificial friction factor. The FE modelled results were compared against the test results and showed good agreement in terms of connection strength and failure modes, including fracture, bearing, tilting, pull-out and shear failure. Finally, parametric studies were carried out to investigate the influence of steel sheet thickness and the screw outer diameter on the force–displacement relationships of the steel lap joints.

Forward-looking imaging and target detection are highly desirable in many military and civilian fields, such as search and rescue, sea surface surveillance, airport surveillance, and guidance. However, there is a blind zone of forward-looking imaging for conventional Doppler beam sharpening and synthetic aperture radar. Scanning radar can be utilized to obtain a real beam image of a forward-looking area and implement target detection, while its azimuth resolution is poor due to the limitation of antenna size. Besides, during the processing procedure, imaging and target detection are usually regarded as two independent parts, which means that the imaging result will directly affect the detection performance. In this article, an integrated algorithm of super-resolution imaging and target detection for forward-looking scanning radar is proposed. In this algorithm, first of all, low-rank and sparse constraints as regularization norms are incorporated into the forward-looking scanning radar imaging and the objective function is established. Subsequently, the convex theory is utilized to solve the objective function and transform the problem of simultaneous super-resolution imaging and target detection into an optimization problem. Lastly, the super-resolution imaging and the target detection results are obtained simultaneously by solving the optimization problem using the alternating direction method of multipliers. In addition, simulation and experiment results are given to verify the effectiveness of the proposed algorithm.

Real-aperture radar can be utilized to realize forward-looking imaging by antenna scanning the imaging region. However, low azimuth resolution seriously affects its practical application. Although traditional super-resolution methods could enhance azimuth resolution to a certain extent, effective preservation of contour information for important targets still remains to be a problem. In this article, a method of total variation-sparse (TV-sparse) multiconstraint deconvolution is proposed to improve azimuth resolution of forward-looking imaging as well as preserve contour information of important targets. Since our interested targets usually appear to be sparse, the sparse constraint of the target is introduced first to achieve high resolution of forward-looking images, which may cause the loss of target contour information in the meantime. Second, total variation (TV) constraint is introduced based on the sparse constraint, converting traditional single-constraint super-resolution problem to a multiconstraint problem. We then use the split Bregman algorithm (SBA) to solve the multiconstraint problem, whose solution is the super-resolution image of radar forward-looking region. Compared with traditional super-resolution methods, the proposed method can improve the azimuth resolution of radar forward-looking imaging as well as better restore target contour information by adjusting respective weights of sparse constraint and TV constraint. Finally, the performance of the proposed method is validated with the simulation and measured data.

Scanning radar can be used to obtain images of targets in forward-looking area, and has attracted much attention in many fields, such as ocean monitoring, air-to-ground attack, navigation, and so on. However, its azimuth resolution is extremely poor due to the limitation of the antenna size. In order to break through the limitation, many superresolution algorithms have been proposed, and iterative shrinkage-thresholding algorithm (ISTA) is one of the most famous methods because of its antinoise ability and simplicity. In the meantime, the slow convergence of iterative shrinkage-thresholding algorithm is also known to all. In this article, a second-order accelerated ISTA for scanning radar forward-looking superresolution imaging is proposed. In this algorithm, a prediction vector is constructed before each iteration by using the first and the second-order difference information of iteration vectors to reduce the number of iterations and get a faster convergence speed. In the end, simulations and experimental results are given to illustrate the effectiveness of the accelerated imaging algorithm.

We propose a method for low-concentration detection of sodium chloride based on surface plasmon resonance (SPR). A novel prism coupling structure is designed for the sodium chloride concentration sensor, where a TiO2–ZnO composite film and a MoS2–GO hybrid nanosheet are chosen to enhance the surface plasmon resonance based on an Au nanowire array because of their unique properties in metal nanowire arrays and two-dimensional nanomaterials. Using the finite element method, we analyze the thickness of each medium layer for the structure designed in order to obtain the optimum value, so that the sensitivity of the sodium chloride concentration sensor designed is substantially enhanced. The optimum thickness of each medium layer is given, and the sensitivity of the sodium chloride concentration sensor based on the surface plasmon resonance can reach 0.260°/ (g/100 ml) concentration in the detection range of the sodium chloride concentration from zero to 15 g/100 ml. We derive the relationship between the sodium chloride concentration and the surface plasmon resonance angle in the detection range of the sodium chloride concentration.

Forward-looking scanning radar is capable of obtaining the real beam image of terrain in front of the flight platform, and can be used in military and civilian fields, such as sea surface surveillance, search and rescue, etc. However, it is difficult to extract multiple targets from the real beam image due to its poor azimuth resolution. A multiple-target extraction scheme based on low-rank and sparse matrix decomposition is proposed for forward-looking scanning radar. In the scheme, an image with good azimuth resolution is obtained first by the deconvolution preprocessing, and then it is used to map a patch-image. Second, using the low-rank property of the patch-image and the sparse property of the targets of interest, the target extraction is converted into an optimization problem of low-rank and sparse matrix decomposition. Third, the extraction results are obtained by solving this optimization problem using the alternating direction method of multipliers. Finally, simulation and experiment results are given to verify the effectiveness of the proposed scheme.

In this paper, the electromagnetically induced transparency (EIT) effect of graphene nanostructures was studied by numerical methods. Numerous branch structures were added to the sides of the graphene/medium/graphene waveguide, and the Fabry-Perot resonators were formed between adjacent branches due to the phase coupling effect. The back and forth oscillation of electromagnetic waves in the cavity significantly enhanced the transmitted light in a narrow band. Thus, an output spectrum similar to the EIT effect was produced. The output spectrum can be further adjusted by the gate voltage with the unique electrically adjustable properties of graphene. The surface of plasmon exhibited stronger locality in graphene nanostructures than metals. Thus, graphene-based electro-optical devices were more compact and integrated in the deep sub-wavelength scale. The proposed graphene nanostructures facilitated the implementation of a variety of high performance electro-optical devices, including multi-channel selective filters, sensors, and modulators.

The problem of low azimuth resolution has restricted the applicability for radar forward-looking imaging in practice. In this paper, a sparse with fast majorization-minimization (SFMM) superresolution algorithm was proposed to realize fast superresolution imaging of sparse targets in radar forward-looking area. First, we analyzed the azimuth signal of the radar forward-looking area and modeled the azimuth signal as a convolution of antenna pattern and targets distribution. Second, the superresolution problem was converted into an L1 regularization issue by introducing the L1 norm to represent the distribution of the targets under the regularization framework. Third, according to the principle of majorization-minimization (MM) algorithm, a simple L2 regularization issue was obtained to replace the difficult L1 one, and the real target distribution was obtained by solving the L2 regularization problem (We named it sparse with MM (SMM) superresolution algorithm for convenience). Then, in order to improve the computational efficiency of the algorithm, we adopted the second-order vector extrapolation idea to accelerate the conventional MM algorithm and solve the L2 regularization problem. The simulation and real data verified that the proposed SFMM algorithm not only improves the azimuth resolution in radar forward-looking imaging but also increases convergence speed on the basis of SMM superresolution algorithm.

An in-fiber integrated sensor array with the embedded weakly reflective joint surface is proposed and demonstrated. The weak reflector is formed of a single-mode fiber (SMF) and a double-cladding fiber (DCF), whose cores have different refractive index. A sensor array based on this structure is sensitive to strain and temperature, and it has the ability to achieve quasi-distributed measurements. Therefore, it can be used for structural health monitoring. Besides that, without any connector, the sensors were integrated in a fiber. Consequently, they are more suitable for a harsh test environment (space limitation or high temperature).

In general, autofocus methods integrated with frequency-domain imaging algorithms are instrumental to obtain a well-focused bistatic forward-looking synthetic aperture radar (BFSAR) image in the presence of motion errors. Nevertheless, before applying autofocus methods to correct the azimuth phase errors, range cell migration (RCM) should be eliminated by the RCM correction (RCMC) procedure in frequency-domain imaging algorithms. With motion errors being taken into account, there always exists some residual nonsystematic RCM (NsRCM), which refers to the residual migration components after the RCMC procedure. For the conventional side-looking SAR, NsRCM is caused by motion errors. On the other hand, NsRCM of BFSAR is originated from motion errors before RCMC and the NsRCM amplified by the RCMC procedure. In this paper, we analyze the different types of NsRCM in BFSAR imaging and their relationship. Based on the analyses, we propose an autofocus NsRCM correction scheme for BFSAR imagery using frequency-domain imaging algorithms that can eliminate the range-dependent NsRCM. The proposed scheme consists of three steps. First, for the BFSAR data after range compression and RCMC, a division procedure is carried out in the azimuth direction. The subblocks with the highest signal-to-clutter ratio along the range direction are selected after the azimuth segmentation procedure. Second, for the selected subblocks, the total NsRCM is estimated based on the minimum-entropy criterion. Based on the estimation results, different parts of the NsRCM are obtained by solving an ordinary differential equation. Third, a two-step compensation of the NsRCM is executed to reach the spatially variant correction. Simulations and experimental results are provided to demonstrate that our proposed scheme is effective for BFSAR imaging.

Graft reconstruction of the mandible is an important approach that aims at improving the appearance and functionality of defected mandibles. The traditional implant materials are generally bioinert, non-degradable, and that they lack favorable pore structures for cell proliferation, which limit their clinical application. In this study, we used boron-containing bioactive glass which was combined with a three-dimensional (3D) printing technology to construct an osteoinductive implant scaffold, according to the imaging instructions of CT scan on bone defects. Here, the boron-containing bioglass scaffold (B-BGs) was prepared through sol-gel processing and a 3D print technique. Different boron content of borosilicate bioglass was prepared by incorporating B2O3 (molar: 19.4 and 38.8%) into 58S bioglass to replace parts of SiO2. For fabricated mandible implants through three-dimensional 3D printing of B-BGs (size: 8 × 2 mm; pore size: 250 μm) modified with borosilicate bioglass powder and sodium alginate. Notably, the compressive strength of the B-BGs was about 3.8 Mpa, which supported mandibular activity. Subsequently, the excellent biocompatibility of B-BGs was confirmed using cytotoxicity in vitro studies. Finally, data from in vivo experiments demonstrated that the B-BGs could promote bone regeneration and they could almost get completely degraded within 4 weeks. Our results showed that the boron-containing bioglass could repair mandibular defects.

A tunable Fano resonance based on a microring resonator is proposed. The double microring is nested in parallel in the U-shaped feedback waveguide with the graphene embedded in part of the waveguide as the active modulation region of Fano resonance. When different voltages are applied to the graphene in the feedback waveguide or microrings, the Fano resonance exhibits different and asymmetrical shapes and has a larger extinction ratio than the traditional Lorentz shape. The maximum extinction ratio of the proposed device is up to 71.50 dB. The proposed structure will have a wide application in sensors, switches, and integrated optoelectronic devices.

Total variation (TV) method has been utilized to realize super-resolution and preserve contour information of target in radar forward-looking imaging. However, its real-time ability is restricted to matrix inversion. In this paper, a fast TV (FTV) superresolution method is proposed to improve the real-time superresolution ability of traditional TV method. The proposed FTV method utilizes the low displacement rank features of Toplitz matrix and realizes fast matrix inversion by Gohberg-Semencul (GS) representation. It not only effectively improves the azimuth resolution and preserve the contour information of target, but also reduced the computational complexity of traditional TV method to improve its real-time superresolution ability. The superior performance of the proposed FTV method is verified by simulation and measured data processing.

Sparse regularization method has been widely used to realize super-resolution imaging in radar forward-looking imaging. However, most of existed methods directly minimize a nondifferentiable L1 regularization problem. In this paper, a Majorize-Minimization (MM) based super-resolution method is proposed to realize super-resolution for radar forward-looking imaging. According to MM principle, the proposed method converts the non-differentiable L1 regularization problem into a differentiable L2 regularization problem, and the real target distribution is obtained by solving the L2 regularization problem. Due to the introduction of the sparse prior, the proposed method can better improve the azimuth resolution of radar forward-looking imaging. In addition, the application of MM principle makes the non-differentiable L1 regularization easier to be solved. Finally, the superior performance of the proposed method is verified by simulation.

In this paper, an improved target extraction scheme is proposed for forward-looking scanning radar. In the scheme, an image with good azimuth resolution is obtained firstly by deconvolution technique, and then it is used to map a patch-image. Secondly, low rank and sparse matrix decomposition is conducted on the patch image. Thirdly, constant false-alarm rate (CFAR) detection method is used to extract the targets from the sparse matrix. Finally, simulation results are given to verify the effectiveness of the proposed scheme.

Motion error and compensation is a crucial problem for bistatic SAR. In this paper, based on the geometry model of bistatic SAR considering motion error and topographic variation, the concept of bistatic SAR iso-MoCo curve is proposed, which refers to the contour line of motion error compensation for bistatic SAR. Analytical expression of bistatic SAR iso-MoCo curves under flat terrain conditions is deduced via a conic section model. Properties of motion errors considering spatial variation with and without topographic variation are analysed based on simulation results.

Due to the limitation of antenna size, the azimuth resolution of radar forward-looking imaging is limited. Conventional technologies improved the azimuth resolution and obtained good effects, but they are poor in contour recovery of targets. In this paper, we employed the total variation (TV) superresolution method to improve the azimuth resolution and recover the contour of the targets in radar forward-looking imaging. Firstly, the signal model of radar forward-looking imaging was deduced. Secondly, the TV operator was employed as the penalty term under regularization framework. Thirdly, the TV regularization model was solved by split Bregman algorithm. The TV regularization method not only improved the azimuth resolution, but also better recovered the contour of the targets. The performance was verified by simulations lastly.

Deconvolution techniques provide efficient implementations for super-resolution imaging for forward-looking scanning radar. However, deconvolution is normally an ill-posed problem, and the solution is extremely sensitive to noise. From a statistical perspective, maximum likelihood (ML) methods are able to condition the ill-posed problem into a well-posed one. Nevertheless, traditional ML methods only consider the amplitude of the echo and by ignoring the phase that do not adequately model the radar imaging system. In this letter, an I/Q-channel modeling ML method is proposed for forward-looking scanning radar. First, the probability model of the echo is deduced by jointly considering noise in the I and Q channels. Then, a probability density function of the received data is deduced and used to formulate the likelihood function. Finally, the targets can be precisely estimated by maximizing this likelihood function. The results of simulations and experiments are provided to illustrate the effectiveness of the proposed method.

High-resolution scanning radar mapping of the surface is an effective tool for addressing concerns in local environmental and social investigation fields. Regrettably, the azimuth resolution of a scanning radar is constrained by the antenna beamwidth. Multiple super-resolution approaches have been applied to the scanning radar to enhance the azimuth resolution, but they suffer from limited resolution improvement. In this paper, a methodology to derive surface estimates from the scanning radar at an improved azimuth resolution is proposed. We first consider the truncated spectrum by discarding the unreliable frequencies to suppress the noise amplification. Then, based on the iterative adaptive approach (IAA), a novel inverse filtering method is formulated to obtain lower sidelobes and a higher resolution. Finally, by taking advantage of the Fourier property of the steering matrix and the Toeplitz structure of the covariance matrix, we exploit the Gohberg-Semencul representation and the data-dependent trigonometric polynomials to derive a fast IAA (FIAA)-based inverse filtering to mitigate the computational burden. Simulation results and real data processing demonstrate that the proposed FIAA-based inverse filtering outperforms the existing super-resolution approaches in resolution improvement and results in a higher computational efficiency.

Graphene-based electrocatalytic materials are potential low-cost electrocatalysts for the oxygen evolution reaction (OER). However, substantial overpotentials above thermodynamic requirements limit their efficiency and stability in OER-related energy conversion and storage technologies. Here, we embedded CrN crystals into graphene and in situ electrochemically oxidized them to construct graphene materials with encapsulated Cr6+ ions (Cr6+@G). These Cr6+@G materials exhibit the lowest OER overpotential of 197 mV at 10 mA cm−2 and excellent stability over 200 h at a high current density of about 120 mA cm−2 in an alkaline electrolyte. Spectroscopic and computational studies confirm a stable ion coordination environment significantly benefiting the downshift of the graphene Fermi level via hybridization of C p orbitals with d orbitals of Cr6+ ions that enhances the OER activity and stability.

The control of interfacial interaction between polymers and fillers is essential for the fabrication of high-performance polymer composites. In this work, poly(ether-ether-ketone)/silica (PEEK/SiO2) and PEEK/SiO2/graphene oxide (GO) composite were prepared by ball milling-ultrasonic dispersion combined with melt extrusion injection molding. GO nanosheets were introduced as the interfacial enhancer to improve interfacial binding between SiO2 and PEEK. Mechanical tests showed that the incorporation of SiO2 and GO greatly optimized the modulus, strength, and fracture toughness of the composites. The tensile strength and Young's modulus of the PEEK/SiO2 composites increases with the increase of SiO2 content. The maximum tensile strength and Young's modulus of the PEEK/SiO2 composites are approximate 95.9 ± 0.6 MPa and 4.007 ± 0.005 GPa at 30 wt% of SiO2, an increase of 6.4% and 21.2% than that of pure PEEK. The maximum tensile strength and Young's modulus of the PEEK/SiO2/GO composite are further improved to approximate 101.5 ± 0.7 MPa and 4.62 ± 0.08 GPa at a GO content of 1.5% wt, which is 12.6% and 39.4% higher than that of pure PEEK. In addition, SEM images show that numerous HA formed on the surface of the PEEK/SiO2/GO composite after immersion in SBF for 7 days, and the HA layer becomes gradually thicker after 14 days, implying the good osteogenic activity of PEEK/SiO2/GO composites. Therefore, these results suggest that the use of GO as a novel filler surface modifier for the preparation of high-performance composites may become a novel interfacial design strategy for the development of high-performance composites.

Usual design practice for distortional buckling considers a lower bound solution as the actual buckling load. In reality, this practice is inconsistent with actual case since the obtained buckling load is a constant value no matter how long the column is and whatever the end condition is. According to available literature, the research dealt with such a problem is found quite rare. In this scenario, this paper presents an analytical approach to establish a new distortional buckling formula, which takes both the effects of column length and end condition into consideration. The formula was derived based on an edge stiffened plate model. The model was assumed to be pin-ended and fix-ended so as to investigate their effects. The Galerkin method was employed to derive the distortional buckling formula. Further, simplifications to the rigorous formula were made to allow them to be easily used by the engineers. Subsequently, in order to verify the accuracy of the derived formula, the results obtained from the derived formula were compared with the numerical results obtained from the computer software GBTUL. In addition, the performance of the derived formula was further verified by comparing the corresponding ultimate strength based on Shafer's DSM expressions with numerical result from the literature. The comparison and validation result shows that the derived formula (i) can be used successfully in estimating the distortional buckling load for both pin-ended and fix-ended columns with practical length and (ii) can general more rational buckling strength estimation due to the consideration of column length and end condition effect.

In bistatic forward-looking synthetic aperture radar (BFSAR), conventional autofocus algorithms cannot estimate the phase errors accurately when the range walk is compensated in the azimuthal time domain. This problem stems from the influence of the azimuth-variant Doppler coefficients after linear range cell migration correction in azimuthal time domain. In principle, nonlinear chirp scaling processing can be carried out to remove the azimuth variance. Nevertheless, Doppler azimuth variations of the Doppler parameters induced by the motion errors are not taken into consideration and serious defocus would emerge in the final image. To cope with such a problem, an estimation–evaluation–equalization scheme is proposed before conventional autofocus for BFSAR. Different from the conventional autofocus method, the azimuth-variant Doppler coefficients are additionally estimated and equalized before autofocus, and as a consequence, phase errors can be precisely estimated. The BFSAR data processing results demonstrate the validity of the proposed method on the improvement of autofocus in BFSAR.

This paper aims at investigating the structural response and predicting the ultimate strength of the cold-formed built-up I-section columns affected by local, distortional, global and in particular by the local-distortional (LD) interactive and local-distortional-global (LDG) interactive buckling modes. For this purpose, a total of 18 single C-section columns and 18 built-up I-section columns were tested under uniaxial compression load, respectively. The cross-sectional dimension, the thickness and the length of the tested members were varied in the test so as to cover a wide range of local, distortional and overall slenderness. It was shown in the test that noticeable LD interaction was observed for a built-up column with short length as well as LDG interaction for a built-up column with intermediate length. Due to the clear evidence obtained in the test that LD and LDG interactions cause substantial ultimate strength erosion in cold-formed built-up I-section column, a novel direct strength based method was proposed in this paper to quantify such an erosion effect. The validity of the proposed method was then verified by comparing the results obtained from the proposed method with the test results in this paper as well as several other test results in the literature. The comparison results proved that the proposed method can be used successfully in estimating the ultimate strength of cold-formed built-up I-section column affected by pure buckling mode as well as interactive buckling mode.

The Chinese remainder theorem (CRT) is often used to find unwrap signal phase over multiple wavelengths as a means of estimating total travel time of a signal. Existing approaches, implemented efficiently in closed form using either standard CRT or methods derived from lattice theory, require the wavelengths involved to be coprime. This can be a limiting factor in real applications, especially where the available signal bandwidth is limited. In this letter, we derive a new algorithm to extend the closed-form lattice algorithm to accommodate non-coprime wavelengths. The benefit of this development is demonstrated with a Wi-Fi wireless device localization example.

Target detection is an important function of forward-looking scanning radar search and tracking applications. However, it is difficult to detect the targets using the real beam image with low azimuth resolution. In this paper, a target detection scheme is proposed for forward-looking scanning radar. First, an image with better resolution is obtained by deconvolution technique, and it is used to map a patch-image. Then, according to the low rank characteristic of the patch-image and the sparse characteristic of the targets, the target detection is converted into an optimization problem of low rank and sparse matrix decomposition. Finally, the targets are obtained by solving this optimization problem. Simulations are given to verify its effectiveness.

In this paper, an accelerated maximum a posteriori (AMAP) algorithm is proposed to realize fast and effective super resolution imaging of real beam scanning radar. The main idea of this algorithm is to construct a prediction vector based on the first and the second order of difference information before iteration. By using Taylor expansion series and second-order vector extrapolation technique, it aims to enhance the convergence speed of maximum a posteriori algorithm. Finally, the proposed algorithm is verified by simulations.

Regularization technology can be utilized to improve the azimuth resolution for forward-looking scanning radar. In this paper, low-rank and sparse constraints as regularization norms are incorporated into the forward-looking scanning radar imaging. This method can achieve azimuth superresolution and noise suppression. Simulations are given to verify the effectiveness of the method.

Iterative shrinkage-thresholding algorithm (ISTA) is a simple and robust method to realize super-resolution of real-beam scanning radar. However, slow convergence is a drawback. This work proposes an accelerated iterative shrinkage-thresholding algorithm based on Taylor series expansion. Simulation results are presented to validate the effectiveness the validity and high efficiency of the proposed algorithm.

Micro-Doppler signals analysis has been emerging as an important topic in target identification, and relative research has been focusing on features extraction and separation of the radar signals. As a time-frequency representation, the Hilbert-Huang transform (HHT) could extract the accurate instantaneous micro-Doppler signature from the radar signals by empirical mode decomposition and Hilbert transform. However, HHT has the shortcoming that it cannot decompose the signals with close-frequency components. To solve this problem, an innovative decomposition method for multicomponent micro-Doppler signals based on Hilbert–Huang transform and analytical mode decomposition (HHT-AMD) is proposed. In this method, the multicomponent micro-Doppler signals are firstly decomposed by empirical mode decomposition, and the decomposed signal components are transformed by Hilbert transform to get the Hilbert-Huang spectrum and marginal spectrum. Through the spectrum processing, we get the frequency distribution of each signal component. The next step is to judge whether there exists frequency aliasing in each signal component. If there is aliasing, the AMD method is used to decompose the signal until all the decomposed signals are mono-component signals. Evaluation considerations are covered with numerical simulations and experiments on measured radar data. The results demonstrate that compared with conventional HHT, the proposed method yields accurate decomposition for multicomponent micro-Doppler signals and improves the robustness of decomposition. The method presented here can also be applied in various settings of non-stationary signal analysis and filtering.

In this letter, an improved regularized iterative adaptive approach (IAA) is proposed for scanning radar angular superresolution. Because the IAA requires matrix inversion, the increasing condition number of the covariance matrix leads to the ill-posed problem of the IAA. Based on this reality, the diagonal loading method is introduced to solve the ill-posed problem. Because the loading value controls the tradeoff between the azimuth resolution and noise amplification, we use the radiometer uncertainty principle to determine the optimum loading value. When compared with the existing angular superresolution approaches, the proposed regularized IAA is shown to provide significant resolution improvement. Numerical results illustrate the superior performance of the proposed regularized IAA.

The present work is concerned with the application of linear friction welding (LFW) process accessible for the weld-jointing of 9Cr reduced activation ferrite/martensite (RAFM) steels. Optical and electron microscopic characterization of microstructures were performed in various regions of the weld joint. Hardness, tensile and Charpy impact tests on the joined samples were processed to examine the mechanical property and reliability of the weld joints. It indicates that, hot plastic deformation induced by linear friction triggers continual dynamic recrystallization in the weld zone, along with high-density dislocation substructures formed by such severe deformation, which leads to a good combination of mechanical performances in the weld joint. Such linear friction welding comes beyond the rotational process restriction in conventional friction stir welding, and avoid significant oxide inclusions, porosities and coarsened grains brought by heat input as well. The work proves that the present LFW technique works well in the welding of 9Cr RAFM steels and inspires us of a future study on optimizing process parameters of the welding process for a better performance.

A method of sucrose concentration detection based on surface plasmon resonance is proposed. Thus, what we believe to be a novel prism coupling structure is designed for a sucrose concentration sensor utilizing the unique feature of graphene oxide to sucrose in water, and a ${{\text{MoS}}_2}$MoS2-graphene oxide hybrid nanofilm is chosen to enhance surface plasmon resonance based on Au nanoparticles. The thickness of each layer for the designed structure is analyzed to get the optimal value by the finite element method, so that the sensitivity of the sucrose concentration sensor is able to be improved greatly. The optimal thickness of each layer is given, for which the sensitivity of the surface plasmon resonance sucrose concentration sensor can reach as high as 0.16°/% concentration within the detection range of sucrose concentration, 0%–70% concentration. The relationship between the sucrose concentration and the surface plasmon resonance angle are obtained within the detection range of the sucrose concentration.

Micromanipulation is an interdisciplinary technology that integrates advanced knowledge of microscale/nanoscale science, mechanical engineering, electronic engineering, and control engineering. Over the past two decades, it has been widely applied in the fields of MEMS (microelectromechanical systems), bioengineering, and microdevice integration and manufacturing. Microvision servoing is the basic tool for enabling the automatic and precise micromanipulation of microscale/nanoscale entities. However, there are still many problems surrounding microvision servoing in theory and the application of this technology’s micromanipulation processes. This paper summarizes the research, development status, and practical applications of critical components of microvision servoing for micromanipulation, including geometric calibration, autofocus techniques, depth information, and visual servoing control. Suggestions for guiding future innovation and development in this field are also provided in this review.

This paper proposes a space variant-based maximum a posteriori (MAP) angular super-resolution algorithm for highspeed moving real-beam scanning radar. Firstly, the aberration model of the antenna modulation function is established through analyzing the relationship between the scanning angle and the sight angle. Afterwards, an efficient piecewise constant model is formulated for the sake of reducing the computational complexity and restoring cost. Finally, the space variant-based MAP algorithm is derived based on the aberrant model. Simulation experiments demonstrate that the proposed method can improve the super-resolution performance of the high-speed platforms more efficiently than the traditional MAP method.

Regularization is an efficient technology for radar forward-looking imaging. Nevertheless, for high-speed moving platforms, the antenna pattern will normally be distorted, and therefore the imaging performance of the traditional regularization method will be seriously deteriorated. This paper proposes a space variant sparse regularization super-resolution imaging method for high-speed moving forward-looking scanning radar. By analyzing the time-variant relationship between the scanning angle and the sight angle, the pattern aberration is firstly analyzed. Then an efficient piecewise constant model is established for the sake of low computational complexity and restoring cost. Finally, the space variant sparse regularization method has been derived based on the aberrant model. Simulation experiments demonstrate that the proposed method can improve the super-resolution performance of the high-speed platforms more efficiently than the traditional regularization method.

The Chinese Remainder Theorem (CRT) enables phase unwrapping for measurement of a distance using multiple wrapped phases of sinusoidal transmissions, such as in radar, sonar, or wireless geolocation. In the presence of measurement noise, the existing CRT algorithm assumes that the correct number of wrapping wavelengths can be obtained after a round off operation by ignoring the received signal noise. In this paper, additional hypotheses for the output of the round off process are formulated and a modified CRT algorithm for range estimation is proposed. We show that an improved distance reconstruction rate over the existing CRT algorithm is achieved from both analytical calculations and numerical simulations.

Deconvolution provides an efficient technology to implement angular super-resolution for scanning radar forward-looking imaging. However, deconvolution is an ill-posed problem, of which the solution is not only sensitive to noise, but also would be easily deteriorate by the noise amplification when excessive iterations are conducted. In this paper, a penalized maximum likelihood angular super-resolution method is proposed to tackle these problems. Firstly, a new likelihood function is deduced by separately considering the noise in I and Q channels to enhance the accuracy of the noise modeling for radar imaging system. Afterwards, to conquer the noise amplification and maintain the resolving ability of the proposed method, a joint square-Laplace penalty is particularly formulated by making use of the outlier sensitivity property of square constraint as well as the sparse expression ability of Laplace distribution. Finally, in order to facilitate the engineering application of the proposed method, an accelerated iterative solution strategy is adopted to solve the obtained convex optimal problem. Experiments based on both synthetic data and real data demonstrate the effectiveness and superior performance of the proposed method.

The mechanical dynamics in addition to its bulk translation of the target or any structure on the target is called micro-motion, which yields new features in the target's signature that are distinct from its signature in the absence of micro-motion. Micro-motion evokes a frequency modulation in radar echo known as micro-Doppler (m-D) effect which may help to detect specific intrinsic structures of the target, leading a potential method to perform target discrimination and identification by extracting micro-Doppler features. At present, it has drawn a lot of attention to extract the micro-motion target's m-D information for target classification and identification. The various m-D classification approaches require first the extraction of salient features from the radar signal. The micro-Doppler usually manifests curves characteristic in the time-frequency (T-F) domain. Thus the features are calculated from the joint T-F domain. Most of these techniques treat the spectrogram in T-F domain as an image, and obtain features through some image processing techniques. In this paper, we investigated statistical classification and recognition methods for target classification using their micro-Doppler signatures. In our work, micro-Doppler signatures for targets represented by point scattering model with four different micro-motions (Vibration, Coning, Spinning, and Precession) are studied. We propose use of principle component analysis (PCA) and 2-D PCA as the data driven feature extraction approaches that captures vital statistics of the input at a reduced dimension. Simulation analysis by using the simulated data is performed to confirm the effectiveness of the proposal. Experiment results show that with the proposed methods, perfect classification of four different motions can be attained when training and testing set has data from different targets.

Micro-Doppler effect refers to the additional Doppler frequencies around main Doppler frequency which is caused by the micro-motion. Radar targets' motion parameters are useful for characterizing the target and become inputs to automatic target recognition algorithms, which means parameters estimation is fundamental and vital for the target recognition. To improve the instance frequency (IF) estimation accuracy, this paper proposes a new time-frequency analysis (TFA) method combining Choi-Williams Distribution (CWD) and cross Wigner-Ville distribution (XWVD). Firstly, Hadamard product of T-F distribution matrix from T-F distributions of CWD and XWVD is calculated. Then we apply peak value detection (PVD) to get the IF estimation. The simulation results prove its effectiveness and good performance under low SNR. The proposed method not only has better T-F congregation, but also works well even at low SNR, compared to the traditional method of WVD based PVD.

Regularization technology can be utilized to improve the azimuth resolution for forward-looking scanning radar (FLSR). Among various regularization methods, L1 norm constrained method is usually adopted for its strong ability in resolving the sparse targets. Nevertheless, the solution of L1-norm constrained regularization method (L1-CRM) is sensitive to noise and the iterations would quickly diverge from the desired result if too many iterations are performed. In this paper, a regularization imaging method via joint L1-L2 norm constraint is proposed. By combing the L1 norm constraint and L2 norm constraint together, a new objective function is obtained and then the popular fast iterative threshold/shrinkage (FIST) method is adopted to minimize this nonsmooth convex function. This new method can not only have strong ability in resolving the sparse targets, but also be more robust to the noise. Simulations are carried out to demonstrate the effectiveness of the proposed method in terms of resolving ability and noise robustness.

Fast Factorized Back-projection (FFBP) is a new way to fix the high computation problem in Back-projection (BP) but it still keeps high-resolution and strong adaptability in synthetic aperture radar (SAR). However, Applying conventional autofocus methods to both BP and FFBP requires a special consideration. In this letter, we present a new method to solve the phase error problem. We establish a evaluation function based on maximum image sharpness in FFBP. Then we utilize Coordinate Descent (CD) optimization to transform multi-variables into a single variable. To deal with the single variable problem we introduce a secant method. The proposed method keeps the rapid imaging performance of FFBP and well solves the problems caused by phase errors, and has faster convergence speed. Using the simulation data to verify the effectiveness of the proposed algorithm.

The mechanical dynamics in addition to its bulk translation of the target or any structure on the target is called micro-motion. Micro-motion evokes a frequency modulation in radar echo known as micro-Doppler (m-D) effect which may help to detect specific structures of a target with micro-motion, leading a potential way to realize target identification by extracting the micro-Doppler features. In most literatures, the micro-motions were modeled as the sinusoidal forms. In fact the micro-motions in reality should include more types of movement forms besides the sinusoidal one. This paper derives the foundation of universal micro-motion model for radar target other than the popular sinusoidal model. The mathematical formulas of micro-Doppler induced by targets with arbitrary micro-motion are derived, and some examples of several basic and typical micro-motion types (vibration, rotation, sinusoidal, pendulum) are given. At last the simulation studies based on the point scattering model are conducted to validate the proposed theoretical model.

The micro-Doppler induced by micro-motion in radar target represents an important characteristic made available for target recognition, which is usually manifest a sinusoid in the time-frequency (T-F) domain. Inverse Radon transform has been validated as an effective sinusoid detection method. Considered on the condition that multiple sinusoids existed in the T-F plane especially one is weaker relatively, this paper proposes an enhancing normalized inverse Radon transform (ENIRT) method to detect sinusoids in T-F plane as to extract the micro-Doppler parameters reflecting the target's intrinsic character. The improved method enhances weak sinusoidal curves firstly, and then inverse Radon transformed data is normalized by a normalization matrix. Preliminary results illustrates that the proposed algorithm improves sinusoidal curves detection performance in T-F plane.

Because a scanning radar system works as a noncoherent sensor, it is suitable for any geometry situation, and it has significant and extensive applications, such as surveillance, autonomous landing of aircraft, navigation, and guidance. After the pulse compression technique of improving range resolution was presented, angular resolution became crucial for a scanning radar system. In this paper, a scheme of angular superresolution based on maximum a posteriori (MAP) framework is proposed. First, the received signal in azimuth is modeled as a mathematical convolution of the antenna pattern and the targets' scattering. Then, the principle of the angular superresolution algorithm, superresolution performance analysis, and computational implementation are presented. The algorithm can endure more significant disturbance of noise than conventional approaches. Simulation validates that the method can improve the radar angular resolution at least four times, even when the signal-to-noise ratio (SNR) is 10 dB. Furthermore, real data processing has proved the effectiveness of the proposed method.

Integrated circuits (ICs) are now designed and fabricated in a globalized multivendor environment making them vulnerable to malicious design changes, the insertion of hardware Trojans/malware, and intellectual property (IP) theft. Algorithmic reverse engineering of digital circuits can mitigate these concerns by enabling analysts to detect malicious hardware, verify the integrity of ICs, and detect IP violations. In this paper, we present a set of algorithms for the reverse engineering of digital circuits starting from an unstructured netlist and resulting in a high-level netlist with components such as register files, counters, adders, and subtractors. Our techniques require no manual intervention and experiments show that they determine the functionality of ${>}{45\%}$ and up to 93% of the gates in each of the test circuits that we examine. We also demonstrate that our algorithms are scalable to real designs by experimenting with a very large, highly-optimized system-on-chip (SOC) design with over 375000 combinational elements. Our inference algorithms cover 68% of the gates in this SOC. We also demonstrate that our algorithms are effective in aiding a human analyst to detect hardware Trojans in an unstructured netlist.

For bistatic forward-looking synthetic aperture radar (BFSAR), images are often blurred by uncompensated radar motion errors. To get refocused images, autofocus is a useful postprocessing technique. However, a severe drawback of the autofocus algorithms is that they are only capable of removing one-dimensional azimuth phase errors. In BFSAR, motion errors and approximations of imaging algorithms introduce residual range cell migration (RCM) on BFSAR data as well. When residual RCM is within a range resolution cell, it can be neglected. However, the residual migration, which exceeds a range cell, is increasingly encountered as resolution becomes finer and finer. A novel residual RCM correction method is proposed in this paper. By fitting the low-frequency phase difference of adjacent azimuth cells, residual RCM of each azimuth cell can be corrected precisely and effectively. Simulations and real data experiments are carried out to validate the effectiveness of the proposed method.

Convergence speed is quite important for a iterative superresolution algorithm, especially in real time processing. A vector extrapolation acceleration maximum a posteriori (VEAMAP) superresoltuion algorithm is proposed in this paper. In the algorithm a predicted point is made before initial multiplicative iteration and this can make the iterations go faster along the convergence path. As the prediction is implemented by simply extrapolating the obtained vector, only little additional computation is needed. Thus, the time required to obtained the optimal solution can be greatly reduced. Simulations and real data experiments verify the effectiveness of proposed algorithm.

A method of ethanol concentration detection based on surface plasmon resonance is proposed. Therefore, a novel prism coupling structure is designed for the ethanol concentration sensor by use of the unique feature of graphene oxide (GO) to act as a sieve for ethanol in water, and a TiO2-ZnO composite film is chosen to enhance the surface plasmon resonance with a molybdenum disulfide–graphene oxide hybrid nano-sheet. The thickness of each layer of the structure is analyzed by the finite element method to obtain the optimal value in order to improve the sensitivity greatly. The optimal thickness of each layer is given, and the sensitivity of the surface plasmon resonance ethanol concentration sensor can reach as high as 0.089°/% within the detection range of ethanol concentration (0%–80%). The relationship between the surface plasmon resonance angle and the ethanol concentration is obtained within the detection range of ethanol concentration.