基于二维最小Tsallis交叉熵的图像阈值分割方法

利用Tsallis熵的非广延性,提出了二维最小Tsallis交叉熵阈值分割方法.首先给出了二维Tsallis交叉熵的定义,并以最小二维Tsallis交叉熵为准则,利用粒子群优化算法来搜索最优二维阈值向量.该方法不仅进一步考虑了像素之间的空间邻域信息,而且考虑了目标和背景之间的相互关系,其分割性能优于基于Shannon熵的交叉熵阈值法和一维最小Tsallis交叉熵阈值法,并且具有很强的抗噪声能力.实验结果表明,该方法可以实现快速、准确的分割. 关键词： Tsallis交叉熵 二维直方图 粒子群优化算法 图像分割     

Neural network-based segmentation of dynamic MR mammographic images

The usefulness of neural networks for the classification of signal-time curves from dynamic MR mammography was recently demonstrated by our group. The multi-layer perceptron under study consists of 28 input, 4 hidden, and 3 output nodes, and was trained to classify signal-time curves into three tissue classes: "carcinoma," "benign lesion," and "parenchyma." Extending this approach, it was the aim of the present study to evaluate the performance of the developed network in the segmentation of dynamic MR mammographic images in comparison to a pixel-by-pixel two-compartment pharmacokinetic analysis. The population investigated in this pilot study comprised 15 women with suspicious lesions in the breast, which were confirmed histologically after the MR examination. The neural network classified the same areas as malignant as those which were marked as being highly suspicious by the pharmacokinetic mapping approach but with the advantage that no a priori knowledge on tissue microcirculation was needed, that computation proved to be much faster, and that it yielded a unique classification into just three tissue classes.     

A data augmentation approach to train fully convolutional networks for left ventricle segmentation

Left ventricle (LV) segmentation plays an important role in the diagnosis of cardiovascular diseases. The cardiac contractile function can be quantified by measuring the segmentation results of LVs. Fully convolutional networks (FCNs) have been proven to be able to segment images. However, a large number of annotated images are required to train the network to avoid overfitting, which is a challenge for LV segmentation owing to the limited small number of available training samples. In this paper, we analyze the influence of augmenting training samples used in an FCN for LV segmentation, and propose a data augmentation approach based on shape models to train the FCN from a few samples. We show that the balanced training samples affect the performance of FCNs greatly. Experiments on four public datasets demonstrate that the FCN trained by our augmented data outperforms most existing automated segmentation methods with respect to several commonly used evaluation measures.     

Layer-number determination of two-dimensional materials by optical characterization

Initiated by graphene, two-dimensional(2D) layered materials have attracted much attention owing to their novel layer-number-dependent physical and chemical properties. To fully utilize those properties, a fast and accurate determination of their layer number is the priority. Compared with conventional structural characterization tools, including atomic force microscopy, scanning electron microscopy, and transmission electron microscopy, the optical characterization methods such as optical contrast, Raman spectroscopy, photoluminescence, multiphoton imaging, and hyperspectral imaging have the distinctive advantages of a high-throughput and nondestructive examination. Here, taking the most studied 2D materials like graphene, MoS_2, and black phosphorus as examples, we summarize the principles and applications of those optical characterization methods. The comparison of those methods may help us to select proper ones in a cost-effective way.     

二维材料的磁性研究

二维材料在自旋电子学器件中具有良好的应用潜力,高磁化强度、室温铁磁性二维材料的制备与研究,成为近年来自旋电子学领域的研究热点.文章简要回顾了二维材料磁性研究的背景,主要介绍了几种具有代表性的二维材料磁性研究的现状,并对二维材料的磁性研究进行了展望.     

二维材料的转移方法

二维材料及其异质结在电子学、光电子学等领域具有潜在应用,是延续摩尔定律的候选电子材料.二维材料的转移对于物性测量与器件构筑至关重要.本文综述了一些具有代表性的转移方法,详细介绍了各个方法的操作步骤,并基于转移后样品表面清洁程度、转移所需时间以及操作难易等方面对各个转移方法进行了对比归纳.经典干、湿法转移技术是进行物理堆...     

Thermal properties of two-dimensional materials

Two-dimensional(2D) materials, such as graphene, phosphorene, and transition metal dichalcogenides(e.g., Mo S2 and WS2), have attracted a great deal of attention recently due to their extraordinary structural, mechanical, and physical properties. In particular, 2D materials have shown great potential for thermal management and thermoelectric energy generation. In this article, we review the recent advances in the study of thermal properties of 2D materials. We first review some important aspects in thermal conductivity of graphene and discuss the possibility to enhance the ultra-high thermal conductivity of graphene. Next, we discuss thermal conductivity of Mo S2 and the new strategy for thermal management of Mo S2 device. Subsequently, we discuss the anisotropic thermal properties of phosphorene. Finally, we review the application of 2D materials in thermal devices, including thermal rectifier and thermal modulator.     

Raman spectroscopy characterization of two-dimensional materials

Two-dimensional(2D) materials have become a hot study topic in recent years due to their outstanding electronic,optical, and thermal properties. The unique band structures of strong in-plane chemical bonds and weak out-of-plane van der Waals(vdW) interactions make 2D materials promising for nanodevices and various other applications. Raman spectroscopy is a powerful and non-destructive characterization tool to study the properties of 2D materials. In this work, we review the research on the characterization of 2D materials with Raman spectroscopy. In addition, we discuss the application of the Raman spectroscopy technique to semiconductors, superconductivity, photoelectricity, and thermoelectricity.     

二维材料在生物传感器中的应用

自石墨烯发现以来,大量二维层状材料被相继发现.二维材料中载流子被限制在界面1 nm空间内,使其对化学掺杂非常敏感,有望引起生物传感领域的技术变革.生物传感过程无论基于何种传感机制,都包含了检测物识别和信号转化过程.检测物识别通常依靠传感界面的生物探针来完成,信号转换依靠二维材料来实现信号输出.在传感界面处对生物探针和二维材料进行原子级精准构筑,则可精确调控传感过程中的物理化学过程,优化器件的各项指标.本文综述了二维生物传感界面构筑领域的研究进展,重点介绍了目前几种常见的二维生物传感器的传感机制和不同类型的生物探针精准构筑方法,探讨了未来生物传感界面研究的发展方向.     

Controllable growth of two-dimensional quantum materials

Two-dimensional(2D) quantum materials have attracted extensive attention due to their superior properties at the atomic thickness. Numerous novel physical phenomena, including the quantum Hall effect, fractional quantum Hall effect, quantum anomalous Hall effect and topological superconductor phase or topological insulator phase, have been discovered on different2D quantum materials. Over the past decades, various technologies have been developed to prepare the 2D quantum materials and all of th...     

Emerging properties of two-dimensional twisted bilayer materials

Recent studies in van der Waals coupled two-dimensional(2D) bilayer materials have demonstrated a new freedom for material engineering by the formation of moiré pattern. By tuning the twist angle between two layers, one can modulate their electronic band structures and therefore the associated electrical transport and optical properties, which are distinct from the original ones of each individual layer. These new properties excite great passion in the exploration of new quantum states and possible applications of 2D bilayers. In this article, we will mainly review the prevailing fabrication methods and emerging physical properties of twisted bilayer materials and lastly give out a perspective of this topic.     

Emerging of two-dimensional materials in novel memristor

The rapid development of big-data analytics (BDA), internet of things (IoT) and artificial intelligent Technology (AI) demand outstanding electronic devices and systems with faster processing speed, lower power consumption, and smarter computer architecture. Memristor, as a promising Non-Volatile Memory (NVM) device, can effectively mimic biological synapse, and has been widely studied in recent years. The appearance and development of two-dimensional materials (2D material) accelerate and boost the progress of memristor systems owing to a bunch of the particularity of 2D material compared to conventional transition metal oxides (TMOs), therefore, 2D material-based memristors are called as new-generation intelligent memristors. In this review, the memristive (resistive switching) phenomena and the development of new-generation memristors are demonstrated involving grapheme (GR), transition-metal dichalcogenides (TMDs) and hexagonal boron nitride (h-BN) based memristors. Moreover, the related progress of memristive mechanisms is remarked.     

Autonomous reconstruction and segmentation of tomographic data

A Bayesian approach to reconstruction and segmentation of tomographic data is outlined and further detailed for the case of absorption tomography. The algorithm allows the quantification of reconstruction errors and segmentation confidence. Calculation results for various experimental settings (number of projections, incident dose, different materials) are shown and discussed.     

Ellipsometry of anisotropic graphene-like two-dimensional materials on transparent substrates

New possibilities for determining anisotropic properties of the dielectric constants of two-dimensional materials by ellipsometry are developed. Graphene-like 2D materials are considered within the framework of macroscopic electrodynamics as ultrathin absorbing anisotropic films where the optical axis is perpendicular to the film surface. The ellipsometric inversion problem is resolved analytically. The resulting inversion formulas are very fast because they allow you to directly calculate the complex anisotropic dielectric constants without the use of sophisticated regression analysis or iterative root-finding procedures. In particular, the method offers an interest in graphene and related 2D materials because the anisotropic properties of such materials have not been studied to date.     

Chaotic mixing of granular materials in two-dimensional tumbling mixers

We consider the mixing of similar, cohesionless granular materials in quasi-two-dimensional rotating containers by means of theory and experiment. A mathematical model is presented for the flow in containers of arbitrary shape but which are symmetric with respect to rotation by 180 degrees and half-filled with solids. The flow comprises a thin cascading layer at the flat free surface, and a fixed bed which rotates as a solid body. The layer thickness and length change slowly with mixer rotation, but the layer geometry remains similar at all orientations. Flow visualization experiments using glass beads in an elliptical mixer show good agreement with model predictions. Studies of mixing are presented for circular, elliptical, and square containers. The flow in circular containers is steady, and computations involving advection alone (no particle diffusion generated by interparticle collisions) show poor mixing. In contrast, the flow in elliptical and square mixers is time periodic and results in chaotic advection and rapid mixing. Computational evidence for chaos in noncircular mixers is presented in terms of Poincare sections and blob deformation. Poincare sections show regions of regular and chaotic motion, and blobs deform into homoclinic tendrils with an exponential growth of the perimeter length with time. In contrast, in circular mixers, the motion is regular everywhere and the perimeter length increases linearly with time. Including particle diffusion obliterates the typical chaotic structures formed on mixing; predictions of the mixing model including diffusion are in good qualitative and quantitative (in terms of the intensity of segregation variation with time) agreement with experimental results for mixing of an initially circular blob in elliptical and square mixers. Scaling analysis and computations show that mixing in noncircular mixers is faster than that in circular mixers, and the difference in mixing times increases with mixer size. (c) 1999 American Institute of Physics.     

Diamagnetic susceptibility of carbon materials with two-dimensional graphite structure

The diamagnetic susceptibility (DMS) of an electron gas in two-dimensional graphite (TDG) is computed with the impurity potential taken into account. Known experimental dependences of the DMS of modern carbon materials with TDG structure on the Fermi energy of the carrier system and the temperature are analyzed. The computed expressions are in satisfactory agreement with experimental results.Translated from Izvestiya Vysshikh Uchebnykh Zavedenii, Fizika, No. 11, pp. 81–85, November, 1989.     

Epitaxy of III-nitrides on two-dimensional materials and its applications

III-nitride semiconductor materials have excellent optoelectronic properties, mechanical properties, and chemical stability, which have important applications in the field of optoelectronics and microelectronics. Two-dimensional (2D) materials have been widely focused in recent years due to their peculiar properties. With the property of weak bonding between layers of 2D materials, the growth of III-nitrides on 2D materials has been proposed to solve the mismatch problem caused by heterogeneous epitaxy and to develop substrate stripping techniques to obtain high-quality, low-cost nitride materials for high-quality nitride devices and their extension in the field of flexible devices. In this progress report, the main methods for the preparation of 2D materials, and the recent progress and applications of different techniques for the growth of III-nitrides based on 2D materials are reviewed.     

二维材料及其异质结的声子物理研究

声子是固体最重要的元激发之一,是理解材料摩尔热容、德拜温度以及热膨胀系数等热力学性质的基础,同时电声子相互作用也决定了固体的电导和超导等特性。拉曼光谱是表征固体声子物理的重要实验手段,不仅能表征材料的结构和质量,还能提供材料声子性质、电子能带结构、电声耦合等信息。文章将拉曼光谱应用于二维材料及其范德瓦尔斯异质结的声子物理研究。先简单介绍二维材料的层间振动声子模式和层内振动声子模式,其中层间振动声子模式的频率可用线性链模型来计算,而强度则可用层间键极化率模型来解释;同类层内振动声子模式的Davydov劈裂峰之间的频率差异可用范德瓦尔斯模型拟合。随后,将这些模型推广到二维范德瓦尔斯异质结中,以转角多层石墨烯、MoS₂/石墨烯和hBN/WS₂为例介绍了范德瓦尔斯异质结的声子谱,阐述如何应用线性链模型和经典键极化率模型计算层间振动模的频率和强度,并由此给出二维范德瓦尔斯异质结的界面耦合强度和各层间呼吸模的电声耦合强度等重要参数。     

复杂地理环境的图像分割及电波传播特性

复杂地理环境是电波传播不可避免的传播环境,不仅不规则地形会对电波传播产生影响,不同的地表媒质对电波传播也会产生不同的影响。为了使得电波传播特性的预测结果更加地准确有效,通过图像分割算法实现地表环境的简单分类,同时对不同的媒质赋予不同的电磁参数,并结合数字高程模型（DEM)数据,实现了既具有地形起伏信息又具有地表电磁环境参数的复杂地理环境建模。在此基础上,对地表电磁环境信息做了网格剖分处理,利用抛物方程（PE）模型对复杂地理环境下的电波传播特性进行了预测。     

基于颜色和纹理特征的黄瓜果实图像分割

温室黄瓜的识别是黄瓜采摘机器人研究的重要环节,要解决此问题首先必须对采集的黄瓜图像进行分割。提出了一种结合颜色特征和纹理特征进行分割的方法。根据CIE-XYZ颜色模型及其色度图,在RGB模型中先通过颜色滤去不相关物体,然后利用灰度共生矩阵提取纹理特征,得到可区分果实和背景的两大纹理特征,即熵和能量,有效地解决了果实和背景颜色相似的识别问题。