Numerous control and decision problems in networked systems can be posed as optimization problems. Examples include the framework of network utility maxi-mization for resource allocation in communication networks, multi-agent coordina-tion in robotics, and collaborative estimation in wireless sensor networks (WSNs). In contrast to classical distributed optimization, which focuses on improving compu-tational efficiency and scalability, these new applications require simple mechanisms that can operate under limited communication. In this thesis, we develop several novel mechanisms for distributed optimization under communication constraints, and apply these to several challenging engineering problems

As the main building block of the smart grid systems, microgrid (MG) integrates a number of local distributed generation units, energy storage systems, and local loads to form a small-scale, low- and medium-voltage level power system. In gen- eral, an MG can operate in two modes, i.e., the grid-connected and islanded mode. Recently, in order to standardize its operation and functionality, hierarchical con- trol for islanded MG systems has been proposed. It divides the control structure into three layers, namely, primary, secondary, and tertiary control. The primary control is based on each local distributed generation (DG) controller and is realized in a de- centralized way. In the secondary layer, the frequency and voltage restoration control as well as the power quality enhancement is usually carried out. In the tertiary con- trol, economic dispatch and power flow optimization issues are considered. However, conventionally both the secondary and tertiary control are realized in a centralized way. There are certain drawbacks to such centralized control, such as high compu- tation and communication cost, poor fault tolerance ability, lack of plug-and-play properties, and so on. In order to overcome the above drawbacks, distributed control is proposed in the secondary control and tertiary optimization in this book. In the secondary control, restorations for both voltage and frequency in the droop- controlled inverter-based islanded MG are addressed. A distributed finite-time con- trol approach is used in the voltage restoration which enables the voltages at all the DGs to converge to the reference value in finite time, and thus, the voltage and frequency control design can be separated. Then, a consensus-based distributed fre- quency control is proposed for frequency restoration, subject to certain control input constraints. The proposed control strategy can restore both voltage and frequency to their respective reference values while having accurate real power sharing, under a sufficient local stability condition established. Then the distributed control strategy is also employed in the secondary voltage unbalance compensation to replace the conventional centralized controller. The con- cept of contribution level (CL) for compensation is first proposed for each local DG to indicate its compensation ability. A two-layer secondary compensation architecture consisting of a communication layer and a compensation layer is designed for each xvii xviii Distributed Control and Optimization Technologies in Smart Grid Systems local DG. A totally distributed strategy involving information sharing and exchange is proposed, which is based on finite-time average consensus and newly developed graph discovery algorithm. In the tertiary layer, a distributed economic dispatch (ED) strategy based on pro- jected gradient and finite-time average consensus algorithms is proposed. By de- composing the centralized optimization into optimizations at local agents, a scheme is proposed for each agent to iteratively estimate a solution for the optimization problem in a distributed manner with limited communication among neighbors. It is shown that the estimated solutions of all the agents reach consensus of the optimal solution asymptomatically. Besides, two distributed multi-cluster optimization meth- ods are proposed for a large-scale multi-area power system. We first propose to divide all the generator agents into clusters (groups) and each cluster has a leader to com- municate with the leaders of its neighboring clusters. Then two different schemes are proposed for each agent to iteratively estimate a solution of the optimization prob- lem in a distributed manner. It is theoretically proved that the estimated solutions of all the agents reach consensus of the optimal solution asymptomatically. In addition, a novel hierarchical decentralized optimization architecture is proposed to solve the ED problem. Similar to distributed algorithms, each local generator only solves its own problem based on its own cost function and generation constraint. An extra co- ordinator agent is employed to coordinate all the local generator agents. Besides, it also takes the responsibility for handling the global demand supply constraint. In this way, different from existing distributed algorithms, the global demand supply con- straint and local generation constraints are handled separately, which would greatly reduce the computational complexity. It is theoretically shown that under proposed hierarchical decentralized optimization architecture, each local generator agent can obtain the optimal solution in a decentralized fashion. A distributed optimal energy scheduling strategy is also proposed in the tertiary layer, which is based on a newly proposed pricing strategy named PD pricing. Con- ventional real-time pricing strategies only depend on the current total energy con- sumption. In contrast to this, our proposed pricing strategy also takes the incremen- tal energy consumption into consideration, which aims to further fill the valley load and shave the peak load. An optimal energy scheduling problem is then formulated by minimizing the total social cost of the overall power system. Two different dis- tributed optimization algorithms with different communication strategies are pro- posed to solve the problem.

The book is structured in two parts: principles and paradigms. The first chapter is a general introduction to the subject. Then come seven chapters on individual principles we consider most important: communication, processes, naming, synchronization, consistency and replication, fault tolerance, and security.

Building Embedded Linux Systems shows you how to design and build your own embedded systems using Linux® as the kernel and freely available open source tools as the framework. Written by an active member of the open source community, the book is structured to gradually introduce readers to the intricacies of embedded Linux, with detailed information and examples in each chapter that culminate in describing how Linux is actually put on an embedded device

具有宽带可调范围和微型尺寸的K波段微机电系统（MEMS）可调带通滤波器能够满足多波段卫星通信系统的要求。 设计，分析，制造和测量了新颖的21.69–24.36 GHz MEMS可调带通滤波器。 本文还设计并分析了一个电感调谐慢波谐振器，该谐振器由MEMS电容开关，MEMS电容器和短金属线组成。 通过改变MEMS开关的电容值，所提出的滤波器具有四个不同的工作状态。 测量结果表明，对于所有四种状态，插入损耗分别为24.36、23.2、22.24和21.69 GHz时为2.81、3.27、3.65和4.03 dB。 激励电压分别为0、20、16和26V。 可调滤波器的3 dB带宽分别为5.4％，6.2％，5.7％和5.9％。 这项研究为具有宽频率可调范围的微型毫米可调滤波器的设计做出了贡献。

Communication Systems Engineering(2nd Edition)

l-系统的简单实现 更多信息： : 。建造 只需输入 make 。跑步 只需输入 ./lsys .依赖 只是 opengl 和（免费）过剩

本文着重研究高阶非线性多智能体系统的自适应自适应模糊控制。 通信网络是具有固定拓扑的无向图。 每个代理由高阶积分器建模，该积分器具有未知的非线性动力学和未知的干扰。 在backstepping框架下，为每个代理设计了一个鲁棒的自适应模糊控制器，以使所有代理最终达成共识。 而且，从每个代理程序的控制器设计仅需要其自身及其邻居之间的相对状态信息的意义上说，这些控制器是分布式的。 一个四阶仿真实例证明了该算法的有效性。

Signals and Systems (信号与系统) Second Edition Oppenheim Solutions 英文原版 本书全面系统地论述了信号与系统分析的基本理论和方法。全书共11章，内容包括：信号与系统、线性时不变系统，周期信号的傅里叶级数表示，连续和离散时间傅里叶变换，信号与系统的时域和频域特性，采样，通信系统，拉普拉斯和z变换以及线性反馈系统。每章都有足够数量的例题和大量精选的习题；并将习题分列为4种栏目，分属3种不同的层次，便于使用。 　　 本书是在第1版基础之上经重新组织，重新改写并作补充而成，除保留原书结构新颖，选材得当，论述严谨，条理清楚等特色外，在某些方面更有所加强，恰似锦上添花，堪称反映信号与系统分析当代水平的一部佳作。 　　 本书可作为通信与电子系统类，自动化类以及全部电类专业信号与系统课程的教材，也可以供任何从事信息获取、转换、传输及处理工作的其它专业研究生，教师和广大科技工作者参考。 part1 地址http://download.csdn.net/source/1220163

Computer Systems- A Programmer’s Perspective, 1st,2nd,3rd, Global Edition