卫星移动通信抗衰落技术研究

卫星移动通信信道是典型的衰落信道,信道的衰落会降低信号的接收质量,严重时可能导致通信中断。该文针对卫星移动通信在复杂信道条件下的衰落特性,简要介绍了分集接收抗衰落技术、自适应均衡抗衰落技术、编码抗衰落技术等方法,通过分析衰落产生的原理并结合抗衰落技术研究的现状,总结了相应的抗衰落性能。     

基于EPON智能电能表光通信模块的设计与实现

智能电能表光通信模块的研究,在智能电网建设中发挥着重要作用。通过对EPON(以太网无源光网络)、PLC(电力载波通信)和WiMAX(全球微波互联接入)技术的对比分析,选择EPON作为智能电能表通信接入网技术,并设计了通信流程。基于QCA8829嵌入式芯片,在Redhat Linux 2.6.x开发平台上采用可接入EPON系统的光纤接口技术,实现了智能电能表主站与从站的通信系统。经测试表明,基于EPON智能电表光通信模块实现了智能电网配电侧信息全采集、全覆盖,并使远程电费控制及负载控制到户。     

Hybrid Perovskite Quantum Dot/Non-Fullerene Molecule Solar Cells with Efficiency Over 15%

Organic-inorganic hybrid film using conjugated materials and quantum dots (QDs) are of great interest for solution-processed optoelectronic devices, including photovoltaics (PVs). However, it is still challenging to fabricate conductive hybrid films to maximize their PV performance. Herein, for the first time, superior PV performance of hybrid solar cells consisting of CsPbI₃ perovskite QDs and Y6 series non-fullerene molecules is demonstrated and further highlights their importance on hybrid device design. In specific, a hybrid active layer is developed using CsPbI₃ QDs and non-fullerene molecules, enabling a type-II energy alignment for efficient charge transfer and extraction. Additionally, the non-fullerene molecules can well passivate the QDs, reducing surface defects and energetic disorder. The champion CsPbI₃ QD/Y6-F hybrid device has a record-high efficiency of 15.05% for QD/organic hybrid PV devices, paving a new way to construct solution-processable hybrid film for efficient optoelectronic devices.     

网络中间件和NGN

未来的网络是开放的、具有统一结构的网络，中间件在未来的网络中将扮演重要的角色。在分析全球信息基础设施(GII)、信息通信架构(ICA)以及软交换系统的基础上。进一步把GII中的中间件功能分为网络中间件和面向应用的中间件，并分析了网络中间件和NGN的关系。     

“光纤通信”课程的教学改革

＂光纤通信＂是通信工程专业的主干专业课,课程的特点是理论性强、涵盖的知识面广泛。本文从理论和实践教学两个关键教学环节对本科通信工程专业的专业课＂光纤通信＂的教学进行了深入的探讨,总结了作者多年从事该课程教学的经验,提出了开展该课程教学的建议。     

改进的自适应呼吸信号均值漂移跟踪方法研究

利用呼吸门控技术进行放射治疗已成为一个热门研究问题,它能确保对健康组织细胞造成最小程度的伤害,而其中的关键问题之一是研究呼吸信号均值漂移跟踪方法,以保证门控技术在开关放射线方面的准确性。本文基于Kanatani高精度椭圆拟合分析方法,并在广义特征分解问题基础上,提出了一种改进的自适应呼吸信号均值漂移跟踪方法,利用我们提出的改进算法可以在状态空间中用椭圆对呼吸信号进行估计。在本文中我们可以得到椭圆的中心值,它对应于我们所要估计的呼吸信号均值,其对有信号损失的数据以及伪数据都具有鲁棒性,在实验中我们验证了由于利用了Kanatani提出的方法,对小数据量信号进行估计时,我们的算法同样具备良好的抗噪性,这修正了仅利用广义特征分解问题中寻找对应特征向量的最大特征值方法求解椭圆估计参数方面的不足。      

关于提高变频器调频速度的研究

本文介绍了作者的一项专利技术。对于需要比较快速、比较频繁地调整变频器频率应用场合,目前的通用变频器就很难满足这个需要,尤其是在大惯性负载上使用变频器时,须将升、降频（特别是降频）速度设置的比较慢,以适应由于惯性造成的电机转速的滞后。本专利技术克服上述传统变频器目前存在的缺陷,在传统变频器上增加比较简单并业已成熟的换相技术,为变频器的制造提供了一种新的附加技术。     

Analysis and application of new quasi-network characteristics of nonuniform mesh in FDTD simulation

The equation of computing the reflection coefficient between two meshes of different sizes is derived. Using the equation, quasi-network characteristics of nonuniform mesh for the finite-difference time-domain technique is found and analyzed. The so-called mesh network (MN) here is a kind of structure composed of the sections of mesh in cascade. The cell sizes of these sections change regularly. By means of choosing the number of mesh sections, length of each section, and cell sizes, some novel network characteristics are obtained, which can be used to match the reflecting wave of nonuniform mesh or improve the transmitted characteristics for a mesh wave to travel along the nonuniform mesh. Formulas for analyzing the MN are given. The characteristics are realized in both one- and three-dimensional cases. The applications and advantages of the MN are shown by computing three different structures, i.e., microstrip-gap capacitor, parallel-coupling filter, and microstrip slot-line transformer.     

ProtectDrive为硬盘数据加上安全密码

目前,黑客的攻击目的发生了根本的变化.黑客攻击电脑或者网络,目的不再是为了自己出名,而是想获取被攻击电脑或网络的信息和数据,而这些数据和信息义具有一定的市场价值,可为他们换取金钱.虽然许多金融机构都比较重视网络安全的建设,无论是防病毒,安全网关,还是入侵检测、防火墙,应有尽有,目的就是要把攻击或者威胁拒之网外.     

Enhancing Representation of Deep Features for Sensor-Based Activity Recognition

Sensor-based activity recognition (AR) depends on effective feature representation and classification. However, many recent studies focus on recognition methods, but largely ignore feature representation. Benefitting from the success of Convolutional Neural Networks (CNN) in feature extraction, we propose to improve the feature representation of activities. Specifically, we use a reversed CNN to generate the significant data based on the original features and combine the raw training data with significant data to obtain to enhanced training data. The proposed method can not only train better feature extractors but also help better understand the abstract features of sensor-based activity data. To demonstrate the effectiveness of our proposed method, we conduct comparative experiments with CNN Classifier and CNN-LSTM Classifier on five public datasets, namely the UCIHAR, UniMiB SHAR, OPPORTUNITY, WISDM, and PAMAP2. In addition, we evaluate our proposed method in comparison with traditional methods such as Decision Tree, Multi-layer Perceptron, Extremely randomized trees, Random Forest, and k-Nearest Neighbour on a specific dataset, WISDM. The results show our proposed method consistently outperforms the state-of-the-art methods.