基于FPGA的可变带宽基带信号回放系统设计

针对不同带宽基带信号回放到目标中频的需求，设计了一种可变带宽基带信号回放系统。该系统以FPGA为核心数据处理单元，通过配置高速数模转换芯片AD9122和时钟芯片AD9516完成基带信号的回放功能。为解决不同带宽基带信号采样率与DAC发射速率不匹配的问题，设计了一种多速率处理算法，采用多级HB滤波器、CIC滤波器和Farrow滤波器级联的结构实现了任意倍的采样率转换功能。算法仿真和实际测试结果表明该系统能够以较少的资源消耗将1 kHz~20 MHz的可变带宽基带信号回放到目标中频上，回放信号无杂散动态范围不低于60 dBc,满足实际通信系统需求。     

射频EAS系统的抗干扰研究

目前,射频EAS系统的市场应用仍十分广泛,伴随着日益严重的电磁干扰,其抗干扰性能面临着更大的挑战。此文针对一体式射频EAS系统如何提高其抗干扰性能进行研究。根据不同干扰噪声的特点,对稳态干扰和瞬时干扰在系统工作的各个环节上的影响进行分析,有针对性地给出了一系列包括硬件和软件等方面的方法,提出了一种以信噪比为基础提高系统自适应能力的算法,并结合实验仿真进行了验证,结果表明,本方法提升了系统的抗干扰能力和稳定性。     

基于单目视频路径移动机器人导航方法研究

以单目视觉中双视图定位为基础,提出了一种基于单目视频路径的导航方法.算法基于单目摄像机提取一组有序的关键图像,以代表视频路径,使用Harris角点算法提取图像特征点,利用零均值归一化互相关算法进行图像匹配,根据单目摄像机双视图匹配点之间的约束关系求出基础矩阵并优化,得到位姿参数,完成了移动机器人定位,使其在视频路径的指导下实现自主导航.验证表明:方法在室外环境中具有较好的实用性.     

Gravel coverage rate measurement in synchronous chip seal based on deep convolutional neural network

Synchronous chip seal is an advanced road constructing technology, and the gravel coverage rate is an important indicator of the construction quality. In this paper, a novel approach for gravel coverage rate measurement is proposed based on deep learning. Convolutional neural network (CNN) is used to segment the image of ground covered with gravels, and the gravel coverage rate is computed by the percentage of gravel pixels in the segmented image. The gravel coverage rate dataset for model training and testing is built. The performance of fully convolutional neural network (FCN) and U-Net model in the dataset is tested. A better model named GravelNet is constructed based on U-Net. The scaled exponential linear unit (SELU) is employed in the GravelNet to replace the popular combination of rectified linear unit (ReLU) and batch normalization (BN). Data augmentation and alpha dropout are performed to reduce overfitting. The experimental results demonstrate the effectiveness and accuracy of our proposed method. Our trained GravelNet achieves the mean gravel coverage rate error of 0.35% on test dataset.     

多移动微小型机器人编队控制与协作避碰研究

针对多移动微小型机器人系统的协作避碰和队形保持,给出了一种分布式的编队控制方法。结合移动微小型机器人的运动控制模型,提出了一种路径规划方法,使其在运动中实时避免碰撞。在此基础上利用李雅普诺夫(Lyapunov)法设计了一种编队控制器。在有界误差范围内,该控制器能够保证多机器人的轨迹跟踪和协作避碰。通过将编队控制转化为跟踪整个队形质心的轨迹,降低了控制的复杂度,从而可以较好地应用到计算资源有限的多移动微小型机器人中。通过仿真、分析和对比,对以上控制方法的稳定性和可行性进行了验证,并进行了实际的编队和避碰控制实验。实验结果表明该方法可有效地应用于多移动微小型机器人的协作避碰和编队控制。     

Traffic sign recognition based on deep convolutional neural network

Traffic sign recognition (TSR) is an important component of automated driving systems. It is a rather challenging task to design a high-performance classifier for the TSR system. In this paper, we propose a new method for TSR system based on deep convolutional neural network. In order to enhance the expression of the network, a novel structure (dubbed block-layer below) which combines network-in-network and residual connection is designed. Our network has 10 layers with parameters (block-layer seen as a single layer): the first seven are alternate convolutional layers and block-layers, and the remaining three are fully-connected layers. We train our TSR network on the German traffic sign recognition benchmark (GTSRB) dataset. To reduce overfitting, we perform data augmentation on the training images and employ a regularization method named “dropout”. The activation function we employ in our network adopts scaled exponential linear units (SELUs), which can induce self-normalizing properties. To speed up the training, we use an efficient GPU to accelerate the convolutional operation. On the test dataset of GTSRB, we achieve the accuracy rate of 99.67%, exceed-ing the state-of-the-art results.     

基于CS5484的电力参数测量装置设计

分析了电能测量芯片CS5484的电路结构和工作原理,并设计开发了一种基于CS5484的电力参数测量装置,重点分析了电压通道和电流通道的电路组成。系统采用集成以太网控制器的微处理器STM32F107为控制芯片,可以满足复杂的控制输出和联网测量的需求。采用标准交流信号源对装置进行了测试,结果表明该装置对电压测量误差均在0.2%以下,无功功率的测量误差均在0.4%以下,可以较好满足电力参数的测量要求。