Separation of carbon nanotubes by frit inlet asymmetrical flow field-flow fractionation

Flow field-flow fractionation (flow FFF), a separation technique for particles and macromolecules, has been used to separate carbon nanotubes (CNT). The carbon nanotube ropes that were purified from a raw carbon nanotube mixture by acidic reflux followed by cross-flow filtration using a hollow fiber module were cut into shorter lengths by sonication under a concentrated acid mixture. The cut carbon nanotubes were separated by using a modified flow FFF channel system, frit inlet asymmetrical flow FFF (FI AFIFFF) channel, which was useful in the continuous flow operation during injection and separation. Carbon nanotubes, before and after the cutting process, were clearly distinguished by their retention profiles. The narrow volume fractions of CNT collected during flow FFF runs were confirmed by field emission scanning electron microscopy and Raman spectroscopy. Experimentally, it was found that retention of carbon nanotubes in flow FFF was dependent on the use of surfactant for CNT dispersion and for the carrier solution in flow FFF. In this work, the use of flow FFF for the size differentiation of carbon nanotubes in the process of preparation or purification was demonstrated.     

Analytical solutions of the lattice Boltzmann BGK model

Analytical solutions of the two-dimensional triangular and square lattice Boltzmann BGK models have been obtained for the plane Poiseuille flow and the plane Couette flow. The analytical solutions are written in terms of the characteristic velocity of the flow, the single relaxation time , and the lattice spacing. The analytic solutions are the exact representation of these two flows without any approximation. Using the analytical solution, it is shown that in Poiseuille flow the bounce-back boundary condition introduces an error of first order in the lattice spacing. The boundary condition used by Kadanoffet al. in lattice gas automata to simulate Poiseuille flow is also considered for the triangular lattice Boltzmann BGK model. An analytical solution is obtained and used to show that the boundary condition introduces an error of second order in the lattice spacing.     

PLC-RF无线传输网络中的能效最优设计方法

针对电力线通信（PLC）和射频（RF）无线通信混合传输的室内通信场景,提出了一种基于角度信息的信道状态信息（AI-CSI）的能效优先传输方案。首先,Wi-Fi无线网络和PLC网络分别作为主网络和次级网络,并且采用认知无线电技术来提高频谱效率的情况下,建立次级网络总能效最大化为目标函数的优化问题。其次,为了求解该问题,通过基于AI-CSI的迫零波束成形方法,获得波束成形权矢量,并进一步提出Dinkelbach与拉格朗日乘子法相结合的优化方法,进行最优的功率分配。最后,计算机仿真结果不仅验证了所提方案的有效性和优越性,而且分析了中继天线数和用户个数等典型参数对系统能效带来的影响,从而为实际系统设计提供了参考和依据。     

基于卷积神经网络的随机因子重采样图像检测

图像重采样检测是图像取证领域的重要任务,其目的是检测图像是否经过重采样操作。现有的基于深度学习的重采样检测方法大多只针对特定的重采样因子进行研究,而较少考虑重采样因子完全随机的情况。本文根据重采样操作中所涉及的插值技术原理设计了一组高效互补的图像预处理结构以避免图像内容的干扰,并通过可变形卷积层和高效通道注意力机制(efficient channel attention, ECA)分别提取和筛选重采样特征,从而有效提高了卷积神经网络整合提取不同重采样因子的重采样特征的能力。实验结果表明,无论对于未压缩的重采样图像还是JPEG压缩后处理的重采样图像,本文方法都可以有效检测,且预测准确率相比现有方法均有较大提升。     

基于超像素暗通道和自动色阶优化的图像去雾算法

针对暗通道先验(dark channel prior, DCP)复原图像中的光晕现象、明亮区域色彩失真、环境光估计不准确等问题,提出了基于超像素暗通道和自动色阶优化的单幅图像去雾算法。首先,由改进的White Patch Retinex算法增强图像并计算精确环境光。接着,在传统暗通道去雾算法中引入超像素图像分割和引导滤波算法,使透射率估计的稳健性与精确性得以提升。然后,采用自适应容差对明亮区域的透射率进行补偿,有效抑制明亮区域色彩失真问题。最后,以自动色阶优化算法提高图像对比度。将本文去雾算法与其他算法从主观和客观两个维度进行比较,实验结果表明：采用不同算法对不同浓度的自然雾图进行对比实验,信息熵提高0.2 bit,峰值信噪比(peak signal-to-noise ratio,PSNR)提高0.8 dB,运行效率提高。该算法对不同浓度含雾图像具有良好的适应性,复原图像色彩真实、纹理清晰、细节丰富,去雾效果良好。     

基于无人机的空地信道特性测量系统

随着各种飞行器和通信一体化的发展,对空地信道特性的研究变得愈发重要且迫切。针对空地通信场景,本文研制了一套由无人机发射单元和地面接收单元组成的无人机空地信道测量系统。该系统选取具有平坦功率谱特性和大动态范围的ZC序列作为测量序列,利用硬件实现实时提取信道冲激响应(Channel Impulse Response, CIR),并通过采样偏差恢复、系统响应校正等方式,提高信道测量系统的准确性。实测验证结果表明,本文研制的信道测量系统与信道模拟器模拟结果和射线跟踪(Ray Tracing, RT)仿真结果基本一致。最后,开展了校园场景空地实测活动,分析了该场景路径损耗(Path Loss, PL)、莱斯K因子、均方根时延扩展(Root Mean Square-Delay Spread, RMS-DS)等信道特性。     

The research on channel estimation and signal-noise ratio estimation based on minimum error entropy kalman filter for single carrier frequency domain equalization system

In this paper, the channel estimation and signal-to-noise ratio (SNR) estimation technique of single-carrier frequency domain equalization (SC-FDE) system under low SNR in aeronautical multipath channel are studied, a SNR estimation algorithm which is easy to implement in engineering and an improved LS channel estimation algorithm based on Kalman filter using minimum error entropy (MEE-KF) are proposed. This paper first introduces the SC-FDE system and introduces the principle of MEE-KF, and then, the channel estimation flow based on MEE-KF is obtained by combining it with the traditional LS channel estimation algorithm, which makes the estimation results perform better. Simulation results show that after getting more accurate noise variance, the channel estimation results can better follow the changes of the channel after MEE-KF processing, so as to resist the doppler frequency offset effect and make the channel estimation results more accurate, that is the channel response results of the data part can be closer to the real situation, so that the communication performance of SC-FDE system has also been greatly improved.     

Design of high-speed data transfer turbo code for body channel communication transceivers

In this work, a digital differential transmitter based on low-power wireless compensation transceiver for body channel communication (BCC) is proposed. Further, the proposed transceiver is composed of Touch Status Detection Unit (TSDU), Wireless Status Compensation Unit (WSCU), and a reconfigurable preamplifier. Initially, the human body channel environment for wireless communication is investigated based on properties from 1 to 100 MHz. Further, the turbo code-based encoding scheme is used to encode the data before transferring the data on the transmitter side. Also, the proposed error-correcting parallel turbo decoder using a modified step-by-step algorithm is presented. The turbo code-based decoding scheme is used to recover the error-free transmitted data at the receiver side. Results demonstrate that the proposed BCC transceiver is designed using 90 nm CMOS technology and it is observed that the proposed BCC transceiver has utilized an area of 600mm². Also, the maximum data rate achieved by a proposed BCC transceiver was 100 Mbps, and the overall transceiver power consumption is 0.42 mW, and energy for communication is 0.02 nj/b.     

基于回归模型与注意力的轻量化SAR舰船检测模型

合成孔径雷达（SAR）具有不受云层干扰、可全天时、全天候对地观测的特点,基于SAR图像的舰船检测已广泛用于海洋搜救、港口侦察、领海防御等民用或军用领域。然而,与大型舰船相比,像素点少、对比度低的小型舰船存在漏检率高的问题,并且速度和精度之间的平衡成为舰船检测算法天基应用的难点。针对以上问题,本文提出了一种基于YOLOv5s模型改进的舰船检测轻量化模型（ImShips）。首先,针对船体大小差异导致的漏检问题,采取在网络底部使用感受野较小的标准卷积,提升了模型对小规模舰船空间信息的获取能力。同时,在网络顶部设计了放大感受野的扩张卷积,保留了更多的语义特征,有利于大目标的特征提取。接着,提出将轻量级的通道注意力机制应用于YOLOv5的骨干网络和特征融合网络,通过对提取到的特征按重要性分配权重,实现纹理信息的筛选。最后,在下采样时采取深度可分离卷积代替标准卷积,减少了模型参数的数量,进一步提高了模型的推理速度。实验结果表明,在SAR舰船检测SSDD和ISSID数据集中,改进后的ImShips模型在保证精度的同时,所需的浮点计算数比YOLOv5s模型减少了45.61%,检测速度提高了8.31%。ImShips模型网络规模小、检测速度快,在实时天基舰船检测中具有较大的应用潜力。