A New Approach for Filtering and Derivative Estimation of Noisy Signals

Filtering and estimation of derivatives in a single step from noisy signals is an important and challenging task in signal processing. The aim of this paper is to propose a new tracking differentiator based on only one parameter; this differentiator is able to synchronously filter noise and estimate the derivative of the input signal. The new tracking differentiator design is based on an inverse Taylor series approach. Both error and stability analyses of the tracking differentiator design are provided. Theoretical analysis and computer simulation results show that this tracking differentiator cannot only obtain better filtering results than previous approaches but can also estimate the derivatives with high accuracy.     

Lightweight opportunistic routing forwarding strategy based on Markov chain

A lightweight opportunistic routing forwarding strategy (MOR) was proposed based on Markov chain.In the scheme,the execute process of network was divided into a plurality of equal time period,and the random encounter state of node in each time period was represented by activity degree.The state sequence of a plurality of continuous time period constitutes a discrete Markov chain.The activity degree of encounter node was estimated by Markov model to predict its state of future time period,which can enhance the accuracy of activity degree estimation.Then,the method of comprehensive evaluating forwarding utility was designed based on the activity degree of node and the average encounter interval.MOR used the utility of node for making a routing forwarding decision.Each node only maintained a state of last time period and a state transition probability matrix,and a vector recording the average encounter interval of nodes.So,the routing forwarding decision algorithm was simple and efficient,low time and space complexity.Furthermore,the method was proposed to set optimal number of the message copy based on multiple factors,which can effectively balance the utilization of network resources.Results show that compared with existing algorithms,MOR algorithm can effectively increase the delivery ratio and reduce the delivery delay,and lower routing overhead ratio.     

Localization Algorithm for Large Scale Wireless Sensor Networks Based on Fast-SVM

Sensor node localization is one of research hotspots in the applications of wireless sensor networks (WSNs) field. In recent years, many scholars proposed some localization algorithms based on machine learning, especially support vector machine (SVM). Localization algorithms based on SVM have good performance without pairwise distance measurements and special assisting devices. But if detection area is too wide and the scale of wireless sensor network is too large, the each sensor node needs to be classified many times to locate by SVMs, and the location time is too long. It is not suitable for the places of high real-time requirements. To solve this problem, a localization algorithm based on fast-SVM for large scale WSNs is proposed in this paper. The proposed fast-SVM constructs the minimum spanning by introducing the similarity measure and divided the support vectors into groups according to the maximum similarity in feature space. Each group support vectors is replaced by linear combination of “determinant factor” and “adjusting factor” which are decided by similarity. Because the support vectors are simplified by the fast-SVM, the speed of classification is evidently improved. Through the simulations, the performance of localization based on fast-SVM is evaluated. The results prove that the localization time is reduce about 48 % than existing localization algorithm based on SVM, and loss of the localization precision is very small. Moreover, fast-SVM localization algorithm also addresses the border problem and coverage hole problem effectively. Finally, the limitation of the proposed localization algorithm is discussed and future work is present.     

基于多层次模糊综合评判模型的软件质量评价方法研究

本文介绍了一种基于多层次模糊综合评判模型的软件质量评价方法。针对层次化的软件质量模型,建立因素集、权重集和评语集。对经测试后的软件从多个角色进行打分,然后应用多层次模糊综合评判法对软件质量特性给出科学的评价结果。     

新颖的正则NFA引擎构造方法

提出了一种新颖的正则NFA引擎构造方法——PFA构造法。PFA构造法包括3个主要算法：预处理算法、解析树编码算法和基于编码树的NFA构造算法。采用PFA构造法能够构造出只含有一个开始状态和一个终止状态的规模更小的NFA,称其为NFAp。NFAp的规模与正则表达式组的长度线性相关,较Thompson自动机、后跟自动机、位置自动机以及部分派生自动机的规模都要小,是Thompson NFA的1/3,比已经接近最优的后跟自动机构造法所获得的NFA还要小。     

不等长十字形谐振器双频带带通滤波器设计

提出一种简单结构的双频带带通滤波器,由不等长十字形谐振器和平行耦合线馈电结构组成.由于滤波器分布电路具有对称性,首先利用奇偶模分析法对带通滤波器奇偶模输入阻抗、传输零点和传输极点进行分析.所设计的滤波器具有3个传输零点和4个传输极点.传输零点的位置不随阻抗参数的变化而变化.在4个传输极点中,第1个偶模传输极点和第1个奇模传输极点构成了第1个通带,剩下2个传输极点构成了第2个通带.与等长十字形谐振器滤波器相比,该结构多了一个极点.滤波器的通带带宽可以通过调节传输极点位置进行调整.给出了滤波器实物的结构参数,并且利用仿真软件进行仿真优化,得到了滤波器插入损耗和回波损耗的仿真结果.制作和测试了滤波器,给出了测试结果.仿真结果和测试结果基本上一致,验证了设计理论的正确性.     

Supporting real-time distributed computer-controlled systems with multi-hop P-NET networks

Fieldbus communication networks aim to interconnect sensors, actuators and controllers within process control applications. Therefore, they constitute the foundation upon which real-time distributed computer-controlled systems can be implemented. P-NET is a fieldbus communication standard, which uses a virtual token-passing medium-access-control mechanism. In this paper pre-run-time schedulability conditions for supporting real-time traffic with P-NET networks are established. Essentially, formulae to evaluate the upper bound of the end-to-end communication delay in P-NET messages are provided. Using this upper bound, a feasibility test is then provided to check the timing requirements for accessing remote process variables. This paper also shows how P-NET network segmentation can significantly reduce the end-to-end communication delays for messages with stringent timing requirements.     

基于 MultiRes+UNet 网络的车道线检测算法

无人驾驶技术改变人类生活方式,带车道线属性的高精地图,是无人驾驶领域的重要一环。 针对现有算法在车道线检 测时存在准确率低、效率低等问题提出基于 MultiRes+UNet 检测方法。 该方法通过空洞卷积扩大卷积感受野,从而对全局信息 统筹,运用 MultiRes block 和 Res path 结构减轻编码器-解码器特征之间的差异,大大降低了内存的需求。 实验结果表明,此算 法在保证检测准确率的同时, 提高了算法的稳定性和运行速率,在纯车道、复合车道、阴影污损车道等多情况下,调和平均值分 数分别为 0. 959、0. 942、0. 891,该算法存在高效性、高鲁棒性。     

PFA在FPGA正则引擎上的设计和仿真

将后缀自动机构造方法应用到现场可编程门阵列的正则引擎设计上,能够有效地压缩状态空间,提高"速度"和"面积"这2个最主要的现场可编程门阵列的引擎性能指标,并能利用有限的现场可编程门阵列资源来实现更多正则表达式的匹配处理. Testbench模拟仿真结果表明,所设计的正则引擎完全实现了预期匹配要求,而其支持的正则表达式的数量和匹配速度都因有效的规模压缩而得到了很大的提升,对比传统的基于Thompson不确定的有限自动机实现的硬件引擎,其所需要的硬件逻辑资源更少,能够并行执行的正则表达式数量也就越多,有效地提高了匹配效率.     

A Router Based Packet Filtering Scheme for Defending Against DoS Attacks

The filter-based reactive packet filtering is a key technology in attack traffic filtering for defending against the Denial-of- Service （DOS） attacks. Two kinds of relevant schemes have been proposed as victim- end filtering and source-end filtering. The first scheme prevents attack traffic from reaching the victim, but causes the huge loss of legitimate flows due to the scarce filters （termed as collateral damages）; the other extreme scheme can obtain the sufficient filters, but severely degrades the network transmission performance due to the abused filtering routers. In this paper, we propose a router based packet filtering scheme, which provides relatively more filters while reducing the quantity of filtering touters. We implement this scheme on the emulated DoS scenarios based on the synthetic and real-world Internet topologies. Our evaluation results show that compared to the previous work, our scheme just uses 20% of its filtering routers, but only increasing less than 15 percent of its collateral damage.