Parameterized power domination complexity

The optimization problem of measuring all nodes in an electrical network by placing as few measurement units (PMUs) as possible is known as Power Dominating Set. Nodes can be measured indirectly according to Kirchhoff's law. We show that this problem can be solved in linear time for graphs of bounded treewidth and establish bounds on its parameterized complexity if the number of PMUs is the parameter.     

State complexity of unique rational operations

For each basic language operation we define its “unique” counterpart as being the operation that results in a language whose words can be obtained uniquely through the given operation. These unique operations can arguably be viewed as combined basic operations, placing this work in the popular area of state complexity of combined operations on regular languages. We study the state complexity of unique rational operations and we provide upper bounds and empirical results meant to cast light into this matter. Equally important, we hope to have provided a generic methodology for estimating their state complexity.     

State complexity of basic operations on suffix-free regular languages

We investigate the state complexity of basic operations for suffix-free regular languages. The state complexity of an operation for regular languages is the number of states that are necessary and sufficient in the worst-case for the minimal deterministic finite-state automaton that accepts the language obtained from the operation. We establish the precise state complexity of catenation, Kleene star, reversal and the Boolean operations for suffix-free regular languages.     

State complexity of some operations on binary regular languages

We investigate the state complexity of some operations on binary regular languages. In particular, we consider the concatenation of languages represented by deterministic finite automata, and the reversal and complementation of languages represented by nondeterministic finite automata. We prove that the upper bounds on the state complexity of these operations, which were known to be tight for larger alphabets, are tight also for binary alphabets.     

矿用动力电池荷电状态预测

针对最小二乘支持向量机(LSSVM)用于预测矿用动力电池荷电状态(SOC)时正则化参数和核函数参数难以优化选择,灰狼优化(GWO)算法在单独求解约束优化问题时出现早熟、稳定性差、易陷入局部最优等问题,在差分进化灰狼优化(DE-GWO)算法的基础上,采用指数函数形式的非线性收敛因子对DEGWO算法进行改进。该非线性收敛因子在迭代过程前段衰减速率低,能更好地寻找全局最优解,在迭代过程后段衰减速率高,能更精确地寻找局部最优解,有效平衡全局搜索能力和局部搜索能力。实验结果表明,利用改进DE-GWO算法优化LSSVM参数后建立的矿用动力电池SOC预测模型最大绝对误差为3.7%,最大相对误差为5.3%。     

飞思卡尔推出新系列带LCD驱动的单片机以降低系统成本功耗和复杂性

<正> 在不断增长的消费类电子和工业控制产品中,LCD显示的应用范围正在扩大。为了满足高效、经济的 LCD应用需求,飞思卡尔半导体推出了三种8位微控制器(MCU)系列,旨在降低基于 LCD 显示的嵌入式应用的系统成本和功耗。新型飞思卡尔 LCD MCU 包括 S08LL、RS08LA 和RS08LE 系列。L 系列器件以低价位提供业界领先的LCD 驱动功能和超低功耗选择。8位 MCU 的设计针对     

Linear complexity of ternary sequences formed on the basis of power residue classes

We propose a computation method for linear complexity of ternary sequences formed on the basis of power residue classes. We find the linear complexity of ternary sequences formed on the basis of two classes of biquadratic residues and the linear complexity of ternary sequences formed on the basis of two classes of sextic residues with close-to-perfect autocorrelation.     

一种低复杂度非正交多址接入功率分配算法

功率分配是非正交多址系统(NOMA)资源分配中的一个重要研究问题。最优迭代注水功率分配算法能提高系统性能,但是算法复杂度较高。提出一种低复杂度的功率分配算法,首先对子载波采用注水原理得到总的复用功率,然后在单个子载波上叠加用户间采用分数阶功率分配方法进行功率再分配。通过仿真分析,与最优迭代注水功率分配算法相比,该算法在性能损失不超过3%的情况下,大幅减低了计算复杂度。     

基于复杂网络理论的典型电力电子电路复杂性研究

基于复杂网络对电力电子电路的复杂性进行了研究,指出了复杂网络在电力电子技术中运用的结合点并以三相桥式全控整流电路及三相电压型桥式逆变电路为研究对象,提出用复杂网络的特征参数来分析该电路拓扑特性的方法,并对其鲁棒性及脆弱性进行了分析研究.最后对电力电子电路复杂性研究结果存在的问题进行了分析和总结.     

The complexity of the max word problem and the power of one-way interactive proof systems

We study the complexity of the max word problem for matrices, a variation of the well-known word problem for matrices. We show that the problem is NP-complete, and cannot be approximated within any constant factor, unless P=NP. We describe applications of this result to probabilistic finite state automata, rational series andk-regular sequences. Our proof is novel in that it employs the theory of interactive proof systems, rather than a standard reduction argument. As another consequence of our results, we characterize NP exactly in terms ofone-way interactive proof systems.     

Generalized kolmogorov complexity and other dual complexity measures

We construct and analyze some dual complexity measures that indicate the time it takes to obtain the desired object. The existence of optimal dual complexity measures is established. Various relations between dual measures and complexities are determined. The relationship of these measures to program quality is demonstrated.Translated from Kibernetika, No. 4, pp. 21–29, 54, July–August, 1990.     

Graph complexity

Summary We develop a complexity theory based on the concept of the graph instead of the Boolean function. We show its relation to the Boolean complexity and prove some lower bounds to the complexity of explicitly given graphs.The paper was written while the first author was visiting Department of Mathematics, Statistics and Computer Science, University of Illinois at Chicago     

Shape complexity

The complexity of 3D shapes that are represented in digital form and processed in CAD/CAM/CAE, entertainment, biomedical, and other applications has increased considerably. Much research was focused on coping with or on reducing shape complexity. However, what exactly is shape complexity? We discuss several complexity measures and the corresponding complexity reduction techniques. Algebraic complexity measures the degree of polynomials needed to represent the shape exactly in its implicit or parametric form. Topological complexity measures the number of handles and components or the existence of non-manifold singularities, non-regularized components, holes or self-intersections. Morphological complexity measures smoothness and feature size. Combinatorial complexity measures the vertex count in polygonal meshes. Representational complexity measures the footprint and ease-of-use of a data structure, or the storage size of a compressed model. The latter three vary as a function of accuracy.     

Kolmogorov complexity and combinatorial methods in communication complexity

We introduce a method based on Kolmogorov complexity to prove lower bounds on communication complexity. The intuition behind our technique is close to information theoretic methods.We use Kolmogorov complexity for three different things: first, to give a general lower bound in terms of Kolmogorov mutual information; second, to prove an alternative to Yao’s minmax principle based on Kolmogorov complexity; and finally, to identify hard inputs.We show that our method implies the rectangle and corruption bounds, known to be closely related to the subdistribution bound. We apply our method to the hidden matching problem, a relation introduced to prove an exponential gap between quantum and classical communication. We then show that our method generalizes the VC dimension and shatter coefficient lower bounds. Finally, we compare one-way communication and simultaneous communication in the case of distributional communication complexity and improve the previous known result.     

时间复杂性和空间复杂性研究

计算复杂性是衡量问题求解的难易程度的。研究问题的计算复杂性,可以明确该问题是否存在有效的求解算法。介绍并分析了计算理论的一些基本概念,论述了时间复杂性（包括P、NP、NP-hard、NP-complete和EXPTIME）和空间复杂性（包括PSPACE、NPSPACE、PSPACE-hard和PSAPCE-complete）中的各个主要分类。最后分析了各个复杂性类之间的关系。     

Evolving complexity

A review of efforts to implement the process of evolution in a computational medium. The review will cover prominent examples, and discuss the major classes of implementations, their successes, and the obstacles they face. This work was presented, in part, at the International Symposium on Artificial Life and Robotics, Oita, Japan, February 18–20, 1996     

Evaluating code complexity triggers,use of complexity measures and the influence of code complexity on maintenance time

Code complexity has been studied intensively over the past decades because it is a quintessential characterizer of code’s internal quality. Previously, much emphasis has been put on creating code complexity measures and applying these measures in practical contexts. To date, most measures are created based on theoretical frameworks, which determine the expected properties that a code complexity measure should fulfil. Fulfilling the necessary properties, however, does not guarantee that the measure characterizes the code complexity that is experienced by software engineers. Subsequently, code complexity measures often turn out to provide rather superficial insights into code complexity. This paper supports the discipline of code complexity measurement by providing empirical insights into the code characteristics that trigger complexity, the use of code complexity measures in industry, and the influence of code complexity on maintenance time. Results of an online survey, conducted in seven companies and two universities with a total of 100 respondents, show that among several code characteristics, two substantially increase code complexity, which subsequently have a major influence on the maintenance time of code. Notably, existing code complexity measures are poorly used in industry.     

The network complexity and the Turing machine complexity of finite functions

Summary Let L(f) be the network complexity of a Boolean function L(f). For any n-ary Boolean function L(f) let . Hereby p ranges over all relative Turing programs and ranges over all oracles such that given the oracle , the restriction of p to inputs of length n is a program for L(f). p is the number of instructions of p. T _p (n) is the time bound and S _p of the program p relative to the oracle on inputs of length n. Our main results are (1) L(f) O(TC(L(f))), (2) TC(f) O(L(f) ^{2 2+}) for every O.The results of this paper have been reported in a main lecture at the 1975 annual meeting of GAMM, April 2–5, Göttingen