基于FPGA的宽带线性调频信号的产生与实现北大核心CSCD

随着激光雷达技术的迅速发展,激光雷达的作用距离、分辨力和测量精度等性能指标不断提高。为了提升测量精度和距离分辨力,要求发射信号具有大的带宽。宽带线性调频信号有助于提高激光雷达的距离分辨力。针对远程测距中激光雷达测距精度低的问题,本文首先分析和比较了几种产生宽带线性调频信号的方法,针对远程测距的应用场景,提出并设计了一种宽带线性调频信号的实现方法。通过Matlab仿真实验,证明实验实现的可行性。最后在FPGA上进行实现,产生了带宽为150 MHz,距离分辨力为1 m的宽带线性调频信号。     

Editorial: Machine Learning and Intelligent Communications (MLICOM 2018)

Mobile Networks and Applications -     

Response map evaluation for RGBT tracking

Neural Computing and Applications - In recent years, RGB and thermal sensors are widely used. There is complementary information from these two types of sensors. A fundamental task which arises in...     

Stubborn state estimation for complex-valued neural networks with mixed time delays: the discrete time case

Neural Computing and Applications - In this paper, the state estimation problem is investigated for a class of discrete-time complex-valued neural networks (CVNNs) with mixed time delays. We...     

Neural network-based robust finite-time attitude stabilization for rigid spacecraft under angular velocity constraint

Neural Computing and Applications - In this paper, the problem of attitude stabilization control of spacecraft under angular velocity constraint is investigated. A state-constrained finite-time...     

Sparse online kernelized actor-critic Learning in reproducing kernel Hilbert space

In this paper, we develop a novel non-parametric online actor-critic reinforcement learning (RL) algorithm to solve optimal regulation problems for a class of continuous-time affine nonlinear dynamical systems. To deal with the value function approximation (VFA) with inherent nonlinear and unknown structure, a reproducing kernel Hilbert space (RKHS)-based kernelized method is designed through online sparsification, where the dictionary size is fixed and consists of updated elements. In addition, the linear independence check condition, i.e., an online criteria, is designed to determine whether the online data should be inserted into the dictionary. The RHKS-based kernelized VFA has a variable structure in accordance with the online data collection, which is different from classical parametric VFA methods with a fixed structure. Furthermore, we develop a sparse online kernelized actor-critic learning RL method to learn the unknown optimal value function and the optimal control policy in an adaptive fashion. The convergence of the presented kernelized actor-critic learning method to the optimum is provided. The boundedness of the closed-loop signals during the online learning phase can be guaranteed. Finally, a simulation example is conducted to demonstrate the effectiveness of the presented kernelized actor-critic learning algorithm.

     

Cold-Start Link Prediction via Weighted Symmetric Nonnegative Matrix Factorization with Graph Regularization

Link prediction has attracted wide attention among interdisciplinary researchers as an important issue in complex network. It aims to predict the missing links in current networks and new links that will appear in future networks. Despite the presence of missing links in the target network of link prediction studies, the network it processes remains macroscopically as a large connected graph. However, the complexity of the real world makes the complex networks abstracted from real systems often contain many isolated nodes. This phenomenon leads to existing link prediction methods not to efficiently implement the prediction of missing edges on isolated nodes. Therefore, the cold-start link prediction is favored as one of the most valuable subproblems of traditional link prediction. However, due to the loss of many links in the observation network, the topological information available for completing the link prediction task is extremely scarce. This presents a severe challenge for the study of cold-start link prediction. Therefore, how to mine and fuse more available non-topological information from observed network becomes the key point to solve the problem of cold-start link prediction. In this paper, we propose a framework for solving the cold-start link prediction problem, a joint-weighted symmetric nonnegative matrix factorization model fusing graph regularization information, based on low-rank approximation algorithms in the field of machine learning. First, the nonlinear features in high-dimensional space of node attributes are captured by the designed graph regularization term. Second, using a weighted matrix, we associate the attribute similarity and first order structure information of nodes and constrain each other. Finally, a unified framework for implementing cold-start link prediction is constructed by using a symmetric nonnegative matrix factorization model to integrate the multiple information extracted together. Extensive experimental validation on five real networks with attributes shows that the proposed model has very good predictive performance when predicting missing edges of isolated nodes.     

卫星USB测控副载波同步算法研究

吉林一号卫星测控采用USB测控体制,遥控接收采用FFT+DPLL1+DPLL2的架构,FFT主要实现对载波的粗捕,DPLL1在FFT的基础上实现对载波相位的精跟踪,DPLL2实现副载波的跟踪。对遥控副载波跟踪所用到的数字PLL技术进行了研究,首先给出了载波多普勒折算到副载波上的方法,然后对模拟PLL数字化进行了详细的论述,并设计了遥控副载波同步用的PLL2,对其稳定性、快捕带及快捕时间、捕获带和捕获时间、同步速率等进行了分析和仿真。结果表明,该方法具有捕获时间短、跟踪精度高、稳定性好等特点,易于数字化实现,已成功在吉林一号系列卫星中得到应用。     

Evolutionary state-based novel multi-objective periodic bacterial foraging optimization algorithm for data clustering

Clustering divides objects into groups based on similarity. However, traditional clustering approaches are plagued by their difficulty in dealing with data with complex structure and high dimensionality, as well as their inability in solving multi-objective data clustering problems. To address these issues, an evolutionary state-based novel multi-objective periodic bacterial foraging optimization algorithm (ES-NMPBFO) is proposed in this article. The algorithm is designed to alleviate the high-computing complexity of the standard bacterial foraging optimization (BFO) algorithm by introducing periodic BFO. Moreover, two learning strategies, global best individual (gbest) and personal historical best individual (pbest), are used in the chemotaxis operation to enhance the convergence speed and guide the bacteria to the optimum position. Two elimination-dispersal operations are also proposed to prevent falling into local optima and improve the diversity of solutions. The proposed algorithm is compared with five other algorithms on six validity indexes in two data clustering cases comprising nine general benchmark datasets and four credit risk assessment datasets. The experimental results suggest that the proposed algorithm significantly outperforms the competing approaches. To further examine the effectiveness of the proposed strategies, two variants of ES-NMPBFO were designed, and all three forms of ES-NMPBFO were tested. The experimental results show that all of the proposed strategies are conducive to the improvement of solution quality, diversity and convergence.     

低功耗异构计算架构的高光谱遥感图像分类研究

高光谱图像分类算法通常需要逐点对图像中的像素点进行迭代处理,计算复杂度及并行程度存在较大差异。随着高光谱遥感图像空间、光谱和辐射分辨率的不断提升,这些算法无法满足实时处理海量遥感图像数据的需求。通过分析NPU存储计算一体化模式与遥感图像分类算法的实现步骤,设计低功耗CPU+NPU异构资源计算架构的低秩稀疏子空间聚类（LRSSC）算法,将数据密集型计算转移至NPU,并利用NPU数据驱动并行计算和内置AI加速,对基于机器学习算法的海量遥感数据进行实时分类。受到big.LITTLE计算范式的启发,CPU+NPU异构资源计算架构由8 bit和低精度位宽NPU共同组成以提高整体吞吐量,同时减少图网络推理过程中的能量损耗。实验结果表明,与CPU计算架构和CPU+GPU异构计算架构的LRSSC算法相比,CPU+NPU异构计算架构的LRSSC算法在Pavia University遥感数据集下的计算速度提升了3~14倍。