The phonon scattering mechanism and its effect on temperature dependent thermal and thermoelectric properties of the silver nanowire

In this work, the electron-phonon, the phonon-phonon, and phonon structure scattering mechanisms and the effect on the thermal and thermoelectric properties of the silver nanowire (AgNW) are investigated in temperature...

A Lightweight Detection Model for SAR Aircraft in a Complex Environment

Recently, deep learning has been widely used in synthetic aperture radar (SAR) aircraft detection. However, the complex environment of the airport—consider the boarding bridges, for instance—greatly interferes with aircraft detection. Besides, the detection speed is also an important indicator in practical applications. To alleviate these problems, we propose a lightweight detection model (LDM), mainly including a reuse block (RB) and an information correction block (ICB) based on the Yolov3 framework. The RB module helps the neural network extract rich aircraft features by aggregating multi-layer information. While the RB module brings more effective information, there is also redundant and useless information aggregated by the reuse block, which is harmful to detection precision. Therefore, to accurately extract more aircraft features, we propose an ICB module combining scattering mechanism characteristics by extracting the gray features and enhancing spatial information, which helps suppress interference in a complex environment and redundant information. Finally, we conducted a series of experiments on the SAR aircraft detection dataset (SAR-ADD). The average precision was 0.6954, which is superior to the precision values achieved by other methods. In addition, the average detection time of LDM was only 6.38 ms, making it much faster than other methods.

Polarimetric SAR Speckle Filtering Using a Nonlocal Weighted LMMSE Filter

Despeckling is a key preprocessing step for applications using PolSAR data in most cases. In this paper, a technique based on a nonlocal weighted linear minimum mean-squared error (NWLMMSE) filter is proposed for polarimetric synthetic aperture radar (PolSAR) speckle filtering. In the process of filtering a pixel by the LMMSE estimator, the idea of nonlocal means is employed to evaluate the weights of the samples in the estimator, based on the statistical equalities between the neighborhoods of the sample pixels and the processed pixel. The NWLMMSE estimator is then derived. In the preliminary processing, an effective step is taken to preclassify the pixels, aiming at preserving point targets and considering the similarity of the scattering mechanisms between pixels in the subsequent filter. A simulated image and two real-world PolSAR images are used for illustration, and the experiments show that this filter is effective in speckle reduction, while effectively preserving strong point targets, edges, and the polarimetric scattering mechanism.

The Electrical and Thermal Transport Properties of La-Doped SrTiO3 with Sc2O3 Composite

Donor-doped strontium titanate (SrTiO3) is one of the most promising n-type oxide thermoelectric materials. Routine doping of La at Sr site can change the charge scattering mechanism, and meanwhile can significantly increase the power factor in the temperature range of 423–773 K. In addition, the introduction of Sc partially substitutes Sr, thus further increasing the electron concentration and optimizing the electrical transport properties. Moreover, the excess Sc in the form of Sc2O3 composite suppresses multifrequency phonon transport, leading to low thermal conductivity of κ = 3.78 W·m−1·K−1 at 773 K for sample Sr0.88La0.06Sc0.06TiO3 with the highest doping content. Thus, the thermoelectric performance of SrTiO3 can be significantly enhanced by synergistic optimization of electrical transport and thermal transport properties via cation doping and composite engineering.

Localization to delocalization probed by magnetotransport of hBN/graphene/hBN stacks in the ultra-clean regime

We report on magnetotransport in a high-quality graphene device, which is based on monolayer graphene (Gr) encapsulated by hexagonal boron nitride (hBN) layers, i.e., hBN/Gr/hBN stacks. In the vicinity of the Dirac point, a negative magnetoconductance is observed for high temperatures > ~ 40 K, whereas it becomes positive for low temperatures ≤ ~ 40 K, which implies an interplay of quantum interferences in Dirac materials. The elastic scattering mechanism in hBN/Gr/hBN stacks contrasts with that of conventional graphene on SiO2, and our ultra-clean graphene device shows nonzero magnetoconductance for high temperatures of up to 300 K.

Polarimetric Model-Based Decomposition with Refined Double-Bounce Orientation Angle and Scattering Model

Oriented manmade targets can produce significant cross-polarization power. The scattering mechanism interpretation of them is still challenging. Within the framework of traditional scattering models, the scattering mechanism of oriented manmade targets will be interpreted as volume scattering. Recently, many advanced approaches have been proposed to mitigate the cross-polarization terms of the coherency matrix or distribute the power of cross-polarization to new scattering models, such as orientation angle compensation and multiple scattering components decomposition. Among these methods, the general model-based decomposition with physically meaningful double-bounce and odd-bounce scattering models has been proposed by modeling their independent orientation angles and becomes a widely accepted method. However, the two vital parameters of generalized scattering models: double- and odd-bounce orientation angles are derived through nonlinear optimization procedure. These generalized models lead to a heavy computation burden for parameters inversion. In this paper, we disclose the latent relationship between the double-bounce orientation angle and polarization orientation angle by data fitting experiments. With this simplified relationship, a refined double-bounce scattering model is established. Then, the odd-bounce orientation angle can be derived through equations. In this way, the nonlinear optimization procedure can be converted to a linear solution. A fast generalized model-based decomposition is developed thereafter. The main contribution of this work is to inherit the generalized models while speeding up the parameter calculation procedure. The comparison studies are carried out with X-band airborne PiSAR, L-band spaceborne ALOS-2, and C-band spaceborne Radarsat-2 PolSAR datasets. Compared with the state-of-the-art approaches, the proposed decomposition achieves improved interpretation performance from both visual and quantitative investigations especially for oriented built-up areas.