GaN中的缺陷与杂质

GaN是一种新型的宽禁带半导体兰色发光与激光材料。GaN中的缺陷和杂质对于材料的电学输运特性和发光性能有着至关重要的影响。本文综述了近年来对于GaN中缺陷和杂质的理论与实验研究。包括对天然缺陷与非特意性掺杂的研究,以及对离子注入所产生的缺陷与深能级的研究。     

三峡工程岩石基础开挖爆破震动控制安全标准

主要介绍了三峡工程岩石基础开挖过程中爆破震动控制安全标准及爆破震动监测的主要成果 ,并针对采用的爆破震动控制安全标准在实践中遇到的一些重要问题作了进一步讨论。     

无限深阱势的非线性谱生成代数与新型相干态

利用对称一维无限深阱势的哈密顿算符和自然算符构造出该势场的非线性谱生成代数,并在此基础上得到了一种新的非线性相干态.该相干态具有时间稳定性,既可以看成本征值为算符函数的降算符本征态,也可以看成广义极小测不准状态的转动态.     

Si中Nd离子热退火行为的深能级瞬态谱研究

本文采用高能离子注入技术将两种剂量的稀土元素钕(Nd)引入外延n-Si片中,并借助非相干光快速热退火(RTA)方法使注入层再结晶并电激活;利用深能级瞬态谱(DLTS)测量方法对Si中Nd离子的深能级行为进行了研究.结果发现:经几秒钟的RTA处理,Nd能在Si中被激活并形成深能级中心,测量到较宽的DLTS谱峰.Nd在n-Si中的深能级行为与Si衬底材料的浅能级杂质基本无关.在低于1285℃的RTA时,Nd形成的深能级中心均为施主型.深能级中心的位置随注入剂量和退火温度不同而有所变化;高温退火后,Nd在硅中有一个能级位置为Ec-(0.32±0.04)eV稳定的施主型深中心,对它的成因进行了讨论.     

Nuclear Effect Study Under K Factor's Nonconstancy

A consistent approach to estimating nuclear effect functions R_v^A(x₂) and R_s^A(x₂) based on numerical iteration technique is presented in the quark-parton model when taking into account the nonconstancy of quantum chromodynamics correction factor K. R_v^A(x₂) and R_s^A(x₂) correspond respectively to the valence quark distributions for one bound nucleon within the nucleus and to the sea quark ones. Related numerical analysis is given for nuclei ₆C¹², ₂₀Ca⁴⁰, and ₂₆Fe⁵⁶. As the basis, it adopts both experimental data of the high energy proton-nucleus Drell-Yan process and of the high energy lepton-nucleus deep inelastic scattering.     

基于多尺度学习与深度卷积神经网络的遥感图像土地利用分类

土地利用信息是国土资源管理的基础和重要依据,随着高分辨率遥感图像数据的日益增多,迫切需要快速准确的土地利用分类方法。目前应用较广的面向对象的分类方法对空间特征的利用尚不够充分,在特征选择上存在一定的局限性。为此,提出一种基于多尺度学习与深度卷积神经网络（deep convolutional neural network,DCNN）的多尺度神经网络（multi-scale neural network,MSNet）模型,基于残差网络构建了100层编码网络,通过并行输入实现输入图像的多尺度学习,利用膨胀卷积实现特征图像的多尺度学习,设计了一种端到端的分类网络。以浙江省0.5 m分辨率的光学航空遥感图像为数据源进行了实验,总体分类精度达91.97%,并将其与传统全卷积网络（fully convolutional networks,FCN）方法和基于支持向量机（support vector machine,SVM）的面向对象方法进行了对比,结果表明,本文所提方法分类精度更高,分类结果整体性更强。     

Deep Coupling Recurrent Auto-Encoder with Multi-Modal EEG and EOG for Vigilance Estimation

Vigilance estimation of drivers is a hot research field of current traffic safety. Wearable devices can monitor information regarding the driver’s state in real time, which is then analyzed by a data analysis model to provide an estimation of vigilance. The accuracy of the data analysis model directly affects the effect of vigilance estimation. In this paper, we propose a deep coupling recurrent auto-encoder (DCRA) that combines electroencephalography (EEG) and electrooculography (EOG). This model uses a coupling layer to connect two single-modal auto-encoders to construct a joint objective loss function optimization model, which consists of single-modal loss and multi-modal loss. The single-modal loss is measured by Euclidean distance, and the multi-modal loss is measured by a Mahalanobis distance of metric learning, which can effectively reflect the distance between different modal data so that the distance between different modes can be described more accurately in the new feature space based on the metric matrix. In order to ensure gradient stability in the long sequence learning process, a multi-layer gated recurrent unit (GRU) auto-encoder model was adopted. The DCRA integrates data feature extraction and feature fusion. Relevant comparative experiments show that the DCRA is better than the single-modal method and the latest multi-modal fusion. The DCRA has a lower root mean square error (RMSE) and a higher Pearson correlation coefficient (PCC).     

Instance Segmentation of Multiple Myeloma Cells Using Deep-Wise Data Augmentation and Mask R-CNN

Multiple myeloma is a condition of cancer in the bone marrow that can lead to dysfunction of the body and fatal expression in the patient. Manual microscopic analysis of abnormal plasma cells, also known as multiple myeloma cells, is one of the most commonly used diagnostic methods for multiple myeloma. However, as it is a manual process, it consumes too much effort and time. Besides, it has a higher chance of human errors. This paper presents a computer-aided detection and segmentation of myeloma cells from microscopic images of the bone marrow aspiration. Two major contributions are presented in this paper. First, different Mask R-CNN models using different images, including original microscopic images, contrast-enhanced images and stained cell images, are developed to perform instance segmentation of multiple myeloma cells. As a second contribution, a deep-wise augmentation, a deep learning-based data augmentation method, is applied to increase the performance of Mask R-CNN models. Based on the experimental findings, the Mask R-CNN model using contrast-enhanced images combined with the proposed deep-wise data augmentation provides a superior performance compared to other models. It achieves a mean precision of 0.9973, mean recall of 0.8631, and mean intersection over union (IOU) of 0.9062.     

Cooperative Spectrum Sensing Based on Multi-Features Combination Network in Cognitive Radio Network

Cognitive radio, as a key technology to improve the utilization of radio spectrum, acquired much attention. Moreover, spectrum sensing has an irreplaceable position in the field of cognitive radio and was widely studied. The convolutional neural networks (CNNs) and the gate recurrent unit (GRU) are complementary in their modelling capabilities. In this paper, we introduce a CNN-GRU network to obtain the local information for single-node spectrum sensing, in which CNN is used to extract spatial feature and GRU is used to extract the temporal feature. Then, the combination network receives the features extracted by the CNN-GRU network to achieve multifeatures combination and obtains the final cooperation result. The cooperative spectrum sensing scheme based on Multifeatures Combination Network enhances the sensing reliability by fusing the local information from different sensing nodes. To accommodate the detection of multiple types of signals, we generated 8 kinds of modulation types to train the model. Theoretical analysis and simulation results show that the cooperative spectrum sensing algorithm proposed in this paper improved detection performance with no prior knowledge about the information of primary user or channel state. Our proposed method achieved competitive performance under the condition of large dynamic signal-to-noise ratio.     

An Overview of Variational Autoencoders for Source Separation,Finance, and Bio-Signal Applications

Autoencoders are a self-supervised learning system where, during training, the output is an approximation of the input. Typically, autoencoders have three parts: Encoder (which produces a compressed latent space representation of the input data), the Latent Space (which retains the knowledge in the input data with reduced dimensionality but preserves maximum information) and the Decoder (which reconstructs the input data from the compressed latent space). Autoencoders have found wide applications in dimensionality reduction, object detection, image classification, and image denoising applications. Variational Autoencoders (VAEs) can be regarded as enhanced Autoencoders where a Bayesian approach is used to learn the probability distribution of the input data. VAEs have found wide applications in generating data for speech, images, and text. In this paper, we present a general comprehensive overview of variational autoencoders. We discuss problems with the VAEs and present several variants of the VAEs that attempt to provide solutions to the problems. We present applications of variational autoencoders for finance (a new and emerging field of application), speech/audio source separation, and biosignal applications. Experimental results are presented for an example of speech source separation to illustrate the powerful application of variants of VAE: VAE, β-VAE, and ITL-AE. We conclude the paper with a summary, and we identify possible areas of research in improving performance of VAEs in particular and deep generative models in general, of which VAEs and generative adversarial networks (GANs) are examples.