Search

In this paper, a characteristic mode (CM)-based design methodology is developed for the reconfigurable microwave metasurface absorbers (MMA) with using commercially available substrates. Theoretical intrinsic connection between modal behaviors and absorption properties is first formulated for the design of the structures. As a proof of the method, a broadband MMA consisting of butterfly elements is step-by-step designed. The positions and values of the impedance loading are determined based on the modal significances and modal currents for broadband operation. The absorption performance is evaluated by the formulated theoretical model and verified by full-wave simulation. After that, a flexible implementation a employed for free-form surfaces conformal. Experimentally, the proposed MMA is fabricated and measured. The absorption fractional bandwidth with reflectivity less than –10 dB is achieved from 3.6 to 11.6 GHz (105%). Good agreements between the simulation and measurement results for both planar and cylindrical cases validate the developed CM-based design methodology. The mimicry of time-varying scattering cross section (SCS) is then further realized through real-time coding absorption states, which is a promising candidate for practical electromagnetic systems.

Abstract An efficient number estimation method is proposed for target detection by using the forward‐backward matrix pencil method. Compared with the traditional parameter estimation methods, the proposed method can work well even for sparse signals. At first, the radar cross section of the group targets arranged in linear array is obtained at some certain angles or frequencies. Then, the Hankel‐Toeplitz matrix is constructed from the known radar echoes, and the singular value decomposition is performed on this matrix. Finally, the number of the group targets arranged in linear array can be accurately estimated by setting an appropriate threshold to select the singular value with a large proportion. In addition, the influence of different signal‐to‐noise ratios (SNR) on the estimation results is also discussed. In order to enhance the accuracy, several groups of different echoes are used to estimate the number of group targets jointly. The simulation results demonstrate that the proposed method is effective and accurate in estimating the number of group targets arranged in linear array. Moreover, the estimation result of the proposed method in this paper is not affected by the noise, which shows that this method has better robustness.

The contamination of food by microplastics has garnered widespread attention, particularly concerning the health risks associated with small-sized microplastics. However, detecting these smaller microplastics in food poses challenges attributed to the complexity of food matrices and instrumental and method limitations. Here, we employed Raman imaging for visualization and identification of polystyrene particles synthesized in polymerization reactions, ranging from 400 to 2600 nm. We successfully developed a quantitative model of particle size and concentration for polystyrene, exhibiting excellent fit (R2 of 0.9946). We established procedures for spiked flavored yogurt using synthesized polystyrene, providing fresh insights into microplastic extraction efficiency. Recovery rates calculated from models validated the method’s feasibility. In practical applications, the assessment of the size, type, shape, and quantity of microplastics in unspiked flavored yogurt was conducted. The most common polymers found were polystyrene, polypropylene, and polyethylene, with the smallest polystyrene sizes ranging from 1 to 10 μm. Additionally, we conducted exposure assessments of microplastics in branded flavored yogurt. This study established a foundation for developing a universal method to quantify microplastics in food, covering synthesis of standards, method development, validation, and application.

Limited by the hardware, the bandwidth of the transmitted signal is not wide enough for super resolution; this is the same for cross resolution, which is limited by the observation angle. In this paper, we propose a technique for imaging fusion using 2D-imaging super-resolution by using multi-radar data from different observation locations, and the resultant effective band is proposed. First, a sparse 2D parametric model based on GTD theory is introduced to construct a dictionary by matching the scattering theory of the radar observation target. Then, the multi-radar fusion imaging framework is constructed. Meanwhile, the 2D model’s sparse parameters are obtained in low SNR using an expansion-compression variance-component algorithm. Finally, radar echo data is expanded to realize the fusion imaging process. The simulation results show that the image quality is improved after multi-radar fusion, which is better than that of the single radar echo, verifying the effectiveness of our method.

In this paper, a review of the low-rank factorization method is presented, with emphasis on their application to multiscale problems. Low-rank matrix factorization methods exploit the rankdeficient nature of coupling impedance matrix blocks between two separated groups. They are widely used, because they are purely algebraic and kernel free. To improve the computation precision and efficiency of low-rank based methods, the improved sampling technologies of adaptive cross approximation (ACA), post compression methods, and the nested low-rank factorizations are introduced. O(N) and O (NlogN) computation complexity of the nested equivalence source approximation can be achieved in low and high frequency regime, which is parallel to the multilevel fast multipole algorithm, N is the number of unknowns. Efficient direct solution and high efficiency preconditioning techniques can be achieved with the low-rank factorization matrices. The trade-off between computation efficiency and time are discussed with respect to the number of levels for low-rank factorizations.

This paper presents a near-infrared (near-IR) photodetector based on a gold nanoparticles-doped (AuNPs-doped), single-walled carbon nanotube–germanium (SWCNT/Ge) heterojunction. The responsivity, detectivity, and response time of the AuNPs-doped, SWCNT/Ge heterojunction photodetector measured 476 mA W−1 (a 291% improvement), 1.0 × 1012 cm Hz1/2 W−1 (a 208% improvement), and 8 μs, respectively. The mechanism of the enhanced performance originated from the surface modification by gold doping, which effectively improved the work function of the carbon nanotube films and thus increased the barrier height between the heterojunctions, as measured by the contact potential distribution (CPD) and open circuit voltage (Voc) of the SWCNT/Ge interface. In addition, we investigated the effect of various particle sizes on the performance and stability of the photodetector. The results demonstrate the promising prospects of the presented heterojunction photodetector for infrared detection applications.

A novel multi-parameter inversion method is proposed for the Advanced Integral Equation Model (AIEM) by using bi-directional deep neural network. There is a very complex nonlinear relationship between the surface parameters (dielectric constant and roughness) and radar backscattering coefficient. The traditional inverse neural network, which is constructed by using the backscattering coefficients as the input and the surface parameters as the output, leads to bad convergence and wrong results. This is because many sets of surface parameters can get the same backscattering coefficient. Therefore, the proposed bi-directional deep neural network starts with building an AIEM-based forward deep neural network (AIEM-FDNN), whose inputs are the surface parameters and outputs are the backscattering coefficients. In this way, the weights and biases of the forward deep neural network can be optimized and predicted, which can be used for the backward deep neural network (AIEM-BDNN). Then, the multi-parameters are updated by minimizing the loss between the output backscattering coefficients with the measured ones. By inserting a sigmoid function between the input and the first hidden layer, the input multi-parameters can be efficiently approximated and continuously updated. As a result, both the forward and backward deep neural networks can be built with these weights and biases. By sharing the weights and biases of the forward network, the training of the inverse network is avoided. The bi-directional deep neural network can not only predict the backscattering coefficient but can also inverse the surface parameters. Numerical results are given to demonstrate that the RMSE of the backscattering coefficients calculated by the proposed bi-directional neural network can be reduced to 0.1%. The accuracy of the inversion parameters, including the real and imaginary parts of the dielectric constant, the root mean square height and the correlation length, can be improved to 97.56%, 91.14%, 99.04% and 98.45%, respectively. At the same time, the bi-directional neural network also has good accuracy for the inversion of the POLARSCAT measured data.

In this paper, we have designed, analyzed, and characterized a hybrid-type MEMS device for X-band phase shift measurement. The signal related to a phase shift of the inputs is fractionally in-line coupled by a MEMS beam and delivered to a thermoelectric power sensor, where the phase is ultimately converted into DC voltage output. With the hybrid of the MEMS beam and the thermoelectric power sensor, both in-line detection process and phase-DC voltage conversion is reserved, which is a benefit for large power capacity, good linearity property, and high-level integration density. In order to get a deep insight into the physical mechanisms involved in the phase detection process, a comprehensive analysis model is presented. The beam is modeled as a precise RLC circuit component, where the capacitance is related to the input power. The fabrication is compatible with GaAs monolithic microwave integrated circuit (MMIC) technology. Experimental results show that return loss is smaller than −11.3 dB and isolation is better than −9.3 dB over X-band. Phase shift detection from 0 to 180 degrees is verified for a large power range of 200–1600 mW (23–32 dBm). The perfect linearity property of the phase-detection sensitivity is demonstrated in the same power range. Low intermodulation distortion is also confirmed through measurement. It is revealed from the comparison between this work and other published results in the literature that this presented hybrid-type structure shows superiorities in both power handling ability and phase-detection linearity. It can be adopted in medium power signal applications with a high level of integration.

This study proposes a method of observing the same target from different radars to extract more useful target information, solving the problem of target occlusion in monostatic radar. This study uses the parameter estimation method of two-dimension compressed sensing algorithm based on the attribute scattering centre model to get the location, length and attitude angle of the target from different radars. The algorithm can reduce the memory, solving the problem of increasing data volume caused by multi-radar observations. Simulation results are used to verify the effectiveness of the method. The multi-radar imaging processing can also improve operation efficiency and enhance the visibility of the image, which is conducive to target recognition.

Radar imaging of the ship is one of the important means for surveillance and reconnaissance of targets. Here, the K-distribution is used for the statistical characterisation of echo model in sea-clutter environment. FEKO software is used to simulate the ship model. The multi-radar data fusion technique and Sparse Bayesian learning algorithm are used to generate ISAR target images in the different levels of sea condition. The simulation results show that the multi-radar data fusion algorithm not only has a significant inhibitory effect on sea-clutter but also improves the radar image resolution.

Cysteinyl cathepsin K (CatK) is one of the most potent mammalian collagenases involved in cardiovascular disease. Here, we investigated the clinical predictive value of serum CatK levels in patients with chronic heart failure (CHF). We examined 134 patients with CHF, measuring their serum CatK, troponin I, high-sensitive C-reactive protein, and pre-operative N-terminal pro-brain natriuretic peptide levels. The patients were divided into two groups: the 44 patients who showed a left ventricular (LV) ejection fraction (LVEF) < 40% (the "lowLVEF" group) and the 90 patients showing LVEF values ≥ 40% (the "highLVEF" group). The lowLVEF patients had significantly higher serum CatK levels compared to the highLVEF patients (58.4 ± 12.2 vs. 44.7 ± 16.4, P < 0.001). Overall, a linear regression analysis showed that CatK levels correlated negatively with LVEF (r = -0.4, P < 0.001) and positively with LV end-diastolic dimensions (r = 0.2, P < 0.01), LV end-systolic dimensions (r = 0.3, P < 0.001), and left atrial diameters (r = 0.3, P < 0.01). A multiple logistic regression analysis showed that CatK levels were independent predictors of CHF (odds ratio, 0.90; 95% confidence interval, 0.84-0.95; P < 0.01). These data indicate that elevated levels of CatK are closely associated with the presence of CHF and that the measurement of circulating CatK provides a noninvasive method of documenting and monitoring the extent of cardiac remodeling and dysfunction in patients with CHF.