02_1 Deterministic and Random Signal Analysis
通信原理(英文版)
2.1 Classification of Signals
2.1.1 Deterministic signals and random signals
• What is deterministic signal? • What is random signal?
2.1.2 Energy signals and power signals
f (t) f (t T) t
Its frequency spectrum is
/2
C( jn0 )
1 T
/ 2 Ve j n0t dt
/ 2
1 T
V
jn 0
e
j n0 t
/ 2
V e j n0 / 2 e j n0 / 2
f (t) sin(t) Its frequency spefct(rtu)m: f (t 1)
0 t 1 t
C(
jn 0 )
1 T0
T0 / 2 s(t )e jn0t dt
T0 / 2
1 sin(t )e j 2nt dt
Solution: Let the expression of the rectangular pulse be
Then its frequency spectral density is
its
Fourier
tragns(fto)rm:
1
0
t /2 t /2
G() / 2 e jt dt 1 (e j / 2 e j / 2 ) sin( / 2)
signal and system 英文原版书
signal and system 英文原版书Title: An Overview of the Book "Signal and System"Introduction:The book "Signal and System" is an essential resource for anyone interested in understanding the fundamentals of signal processing and system analysis. It provides a comprehensive and in-depth exploration of the concepts, theories, and applications related to signals and systems. This article aims to provide a detailed overview of the book, highlighting its key points and relevance.I. Fundamental Concepts of Signals and Systems:1.1 Definition and Properties of Signals:- Explanation of signals as time-varying or spatially varying quantities.- Discussion on continuous-time and discrete-time signals.- Description of signal properties such as amplitude, frequency, and phase.1.2 Classification of Signals:- Overview of different types of signals including periodic, aperiodic, deterministic, and random signals.- Explanation of energy and power signals.- Introduction to common signal operations such as time shifting, scaling, and time reversal.1.3 System Classification and Properties:- Definition and classification of systems as linear or nonlinear, time-invariant or time-varying.- Explanation of system properties like causality, stability, and linearity.- Introduction to system representations such as differential equations, transfer functions, and state-space models.II. Time-Domain Analysis of Signals and Systems:2.1 Convolution and Correlation:- Detailed explanation of convolution and its significance in system analysis.- Discussion on correlation as a measure of similarity between signals.- Application of convolution and correlation in practical scenarios.2.2 Fourier Series and Transform:- Introduction to Fourier series and its representation of periodic signals.- Explanation of Fourier transform and its application in analyzing non-periodic signals.- Discussion on the properties of Fourier series and transform.2.3 Laplace Transform:- Overview of Laplace transform and its use in solving differential equations.- Explanation of the relationship between Laplace transform and frequency response of systems.- Application of Laplace transform in system analysis and design.III. Frequency-Domain Analysis of Signals and Systems:3.1 Frequency Response:- Definition and interpretation of frequency response.- Explanation of magnitude and phase response.- Analysis of frequency response using Bode plots.3.2 Filtering and Filtering Techniques:- Introduction to digital and analog filters.- Discussion on different filter types such as low-pass, high-pass, band-pass, and band-stop filters.- Explanation of filter design techniques including Butterworth, Chebyshev, and Elliptic filters.3.3 Sampling and Reconstruction:- Explanation of sampling theorem and its importance in signal processing.- Overview of sampling techniques and their impact on signal reconstruction.- Discussion on anti-aliasing filters and reconstruction methods.IV. System Analysis and Stability:4.1 System Response and Impulse Response:- Explanation of system response to different input signals.- Introduction to impulse response and its relationship with system behavior.- Analysis of system stability based on impulse response.4.2 Transfer Function and Frequency Domain Analysis:- Definition and interpretation of transfer function.- Explanation of frequency domain analysis using transfer function.- Application of transfer function in system design and analysis.4.3 Feedback Systems and Control:- Overview of feedback systems and their role in control theory.- Explanation of stability analysis and design using control theory.- Discussion on PID controllers and their applications.V. Applications of Signal and System Theory:5.1 Communication Systems:- Explanation of modulation techniques and their role in communication systems.- Overview of demodulation techniques and their significance.- Discussion on error control coding and channel equalization.5.2 Digital Signal Processing:- Introduction to digital signal processing and its applications.- Explanation of digital filters and their role in signal processing.- Overview of image and speech processing techniques.5.3 Signal Processing in Biomedical Engineering:- Application of signal processing in biomedical signal analysis.- Discussion on medical imaging techniques such as MRI and CT scans.- Explanation of signal processing methods used in ECG and EEG analysis.Conclusion:The book "Signal and System" provides a comprehensive and detailed exploration of the fundamental concepts, theories, and applications related to signals and systems. It covers a wide range of topics including signal classification, system analysis, frequency-domain analysis, stability, and various applications. By studying this book, readers can gain a solid understanding of signal and system theory, which is essential in various fields such as communication, digital signal processing, and biomedical engineering.。
lec01
15
• Recording of speech
Acoustic pressure varies as a function of time: 1D signal 16
8
• Monochromatic picture
Brightness as a function of two spatial variables: 2D signal In this course, we focus on signals involving a single independent variable.
11
What can you learn from this course?
• Basic concepts and theories of signals and systems • Basic methods for analyzing deterministic signals passing through a system • Relationship between various concepts and methods, and their difference • The ability to deal with real problem from the perspectives of signals and systems
24
12
Examples
25
Periodic and Aperiodic Signals
• CT signal,T >0,x(t + nT) = x(t) • DT signal,N>0, f[n+kN] = f[n] x(t), f[n] are periodic signals. T, N are the period of x(t) and f[n], respectively。 • Signals that are not periodic are referred to as aperiodic signals
Signals and Systems-Ch1-2
t , t 0 t ramp t u d t u t 0 , t 0
•The unit ramp function is the integral of the unit step function. •It is called the unit ramp function because for positive t, its slope is one amplitude unit per time.
R F I C
Properties of the Impulse Function
The Sampling Property
g t t t dt g t
0 0
The Scaling Property
a t t0
The Replication Property
Example: Given x(t) and we are to find y(t) = x(2t).
The period of x(t) is 2 and the period of y(t) is 1,
R F I C
Time scaling Contd.
Given y(t),
find w(t) = y(3t) and v(t) = y(t/3).
Deterministic signals
• Behavior of these signals is predictable w.r.t time • There is no uncertainty with respect to its value at any time. • These signals can be expressed mathematically. For example x(t) = sin(3t) is deterministic signal.
通信原理(英文版)
【Example 2.4】Find the waveform and the frequency spectral density of a sample function. Solution: The definition of the sample function is
sin t Sa ( t ) t
d(t)
1
(f)
0
t
0
f
meaning of d function: It is a pulse with infinite height, infinitesimal width, and unit area. Sa(t) has the following property:
Physical
F ( ) lim
/2 / 2
cos 0 te
jt
sin[( 0 ) / 2] sin[( 0 ) / 2] dt lim 2 ( ) / 2 ( ) / 2 0 0
The frequency spectral density of d(t):
( f ) d (t )e
jt
d (t ) 0
t 0
dt 1 d (t )dt 1
7
d(t)
and its frequency spectral density:
f (t ) f (t 1) t
1
Its frequency spectrum:
1 C ( jn 0 ) T0
T0 / 2
T0 / 2
s(t )e
通信原理(英文版)
➢ Fourier series of signal s(t):
s(t) C( jn0 )e jn0t n
V / 2 t / 2 f (t) 0 / 2 t (T / 2) f (t) f (t T) t
Its frequency spectrum is
/2
C( jn0 )
1 T
/ 2 Ve jn0tdt
/ 2
1 T
V
jn0
e
j
n0
t
/ 2
V e e jn0 / 2
Chapter 2 Signalsห้องสมุดไป่ตู้
2.1 Classification of Signals
2.1.1 Deterministic signals and random signals
➢ What is deterministic signal? ➢ What is random signal?
2.1.2 Energy signals and power signals
j n0 / 2
T
jn0
2V
n0T
s
in
n0
2
3
Frequency spectrum figure
4
【Example 2.2】Find the frequency spectrum of a sinusoidal wave after full-wave rectification. Solution:Assume the expression of the signal is
Digital Communication ——slides1
Course Lay-out
Lec1: Introduction. Important concepts to comprehend. Difficulty: 2. Importance: 2. Lec2: Formatting and transmission of baseband signals. (Sampling, Quantization, baseband modulation). Difficulty: 6. Importance: 7. Lec3: Receiver structure (demodulation, detection, matched filter receiver). Diff.: 5. Imp: 5. Lec4: Receiver structure (detection, signal space). Diff: 4. Imp.=:4 Lec5: Signal detection; Probability of symbol errors. Diff: 7. Imp: 8. Lec6: ISI, Nyquist theorem. Diff: 6. Imp: 6. Lec7: Modulation schemes; Coherent and non-coherent detection. Diff: 8. Imp: 9. Lec8: Comparing different modulation schemes; Calculating symbol errors. Diff: 7. Imp: 9. Lec9: Channel coding; Linear block codes. Diff: 3. Imp:7. Lec10: Convolutional codes. Diff: 2. Imp:8. Lec11: State and Trellis diagrams; Viterbi algorithm. Diff: 2. Imp: 9. Lec12: Properties of convolutional codes; interleaving; concatenated codes. Diff: 2. Imp: 5. Lec13:
signals and systems_introduction
I(x,y)
What is a Signal (信号)?
Definitions A Signal is formally defined as a function of one or more variables that conveys information on the nature of a physical phenomenon. 信号是一个或多个变量的函数,携带着某个物 理现象的信息。
3. Four different forms of Fourier Transform:
Continuous and Periodic Discrete and Periodic Continuous and Aperiodic Discrete and Aperiodic
4. Use the right one !!!
第三章 信号与线性非时变系统的傅里叶分析 (Fourier Representations for signals and Linear TimeInvariant Systems ) 离散时间周期与非周期信号、连续时间周期与非周期信号的傅里叶分析; 傅里叶分析的性质;LTI系统的频域分析。
Contents
Signals and Systems
--- One of the most important courses for us!
陈布雨 技术中心B区217室 图书馆720
Contents
第一章 信号与系统简介 (Introduction) 介绍信号与系统的基本概念; 信号分类及基本信号;系统分类和特性。 第二章 线性非时变系统的时域分析 (Time-Domain Representations for Linear TimeInvariant Systems) 单位冲激响应和单位脉冲响应,卷积积分和卷积和;系统的互联;系统 的响应求解;系统的方框图表示, 系统的状态空间分析。
1信号与系统英语课件
| 信
号 与
系
统
电 子
与
信
息
罗
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院
洪 薛 洋
What is a System
| 信
A System is formally defined as an entity that manipulates 号 one or more signals to accomplish a function, thereby 与 yielding new signals.
号
1 xo (t ) [ x(t ) x(t )] 2
与
系
统
电 子
与
信
息
罗
学
劲
院
洪 薛 洋
EX3:
x(t )
2 1 -2 -1 0 1 2
| 信
t
-2
xe (t )
1 0 2
xo (t )
1 -1
-1 1
号
t
t与
系
统
电 子
与
信
息
罗
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| 信 1.continuous-time complex exponential and sinusoidal signals
t2
| 信
号 与
t1
| x(t ) |2 dt
n2
Over the time interval [n1,n2], the total energy of x[n] is:
E=
n n1 2 | x [ n ] |
系
2
Definitions 能量信号
E lim x(t ) dt E lim n
通信原理Lecture&2Signals andSpectra
R x (τ ) ↔ ψ x (f)
∞
R x (0) =
−∞
∫
x 2 (t) dt
2. Autocorrelation of a Power Signal
Autocorrelation function of a real-valued power signal x(t) is defined as:
1. Autocorrelation of an Energy Signal
The autocorrelation function of a real-valued energy signal has the following properties:
R x (τ ) =R x (-τ )
R x (τ ) ≤ R x (0) for all τ
Px =
lim
T →∞
1 2 x (t) dt ∫ T −T / 2
T/2
(1.8)
Power signal has finite average power but infinite energy. As a general rule, (1) periodic signals and (2) random signals are classified as power signals
-∞
∫ψ
∞
x
(f) df
E x = 2 ∫ψ x (f) df
0
(1.16)
2. Power Spectral Density (PSD)
The power spectral density (PSD) function Gx(f ) of the periodic signal x(t) is a real, even, and nonnegative function of frequency that gives the distribution of the power of x(t) in the frequency domain. PSD is represented as: ∞ G x (f ) = |C n |2δ ( f − nf 0 ) (1.18) Whereas the average power of a periodic signal x(t) is represented as:
第六讲-遗传病和人类基因组计划
2. Physiological Origins of Biosignals
(1)Bioelectric Signals
Nerve and muscle cells generate bioelectric signals that are the result of electrochemical changes within and between cells. If a nerve or muscle cell is stimulated by a stimulus that is strong enough to reach a necessary threshold, the cell will generate an action potential. The action potential represents the flow of ions across the cell membrane and can be transmitted from one cell to adjacent cells. When many cells become excited, an electric field is generated and propagates through the biological medium. Changes in extracellular potential can be measured on the surface of the organ by using surface electrodes. (ECG, EEG, EMG)
MEG没有侵害性和危险性,具有毫秒级的时 间分辨率,对电活动源的定位可达到2mm的精度。
MEG对脑生理活动研究具有较好空间灵敏度 和时间灵敏度,操作简单,易于掌握。
信号与系统-课件-郑君里
2. Real Exponential Signal
f(t)Ce t (C αa, rre evaal lue)
f (t) >0
a1
= 0
< 0
o
t
School of Computer Science and Information
Notice: When α>0, f (t) is a growing function with t. When α<0, f (t) is a decaying function with t. When α=0, f (t) is a constant function with t.
Example
1 f(t) 0
(o 0 tth1 e) rs P E 0 1(Finite
Eneite Power Signal)
f
(t)
t
PE
(Signals with neither finite nor finite average power)
Vertical Wind Profile
School of Computer Science and Information
1.2 Systems
For the most part, our view of systems will be from an input-output perspective. A system responds to applied input signals, and its response is described in terms of one or more output signals.
Continuous-time Signal — The independent variable is continuous, and thus these signals are defined for a continuum of values of the independent variable.
通信原理(第二版)第2章确知信号与随机信号分析
信号分析
目录
• 确知信号分析 • 随机信号分析 • 确知信号与随机信号的应用 • 信号分析的现代方法
01
确知信号分析
定义与分类
定义
确知信号是指在任何时刻都已知 其全部信息的信号,如正弦波、 方波等。
分类
连续信号和离散信号,周期信号 和非周期信号,实信号和复信号 等。
小波变换具有多分辨率分析的 特点,能够适应不同频率的信 号处理需求。
小波变换在信号降噪、特征提 取、模式识别等领域有着广泛 的应用。
神经网络在信号分析中的应用
神经网络能够通过学习自动提取信号 中的特征,具有很强的自适应性。
神经网络在语音识别、图像处理、雷 达信号处理等领域有着广泛的应用。
神经网络可以处理非线性信号,对于 一些难以用传统方法处理的复杂信号 非常有效。
随机信号的时域分析
自相关函数
描述随机信号取值在时间上的相关性。
互相关函数
描述两个随机信号在时间上的相关性。
谱估计
通过时域数据估计随机信ห้องสมุดไป่ตู้的功率谱密度的方法。
03
确知信号与随机信号的应 用
确知信号在通信中的应用
载波信号
用于调制信息信号,实现信息的 传输。
脉冲信号
用于数字通信中表示二进制状态, 如脉冲编码调制(PCM)。
确知信号的频域分析
01
02
03
傅里叶级数
将确知信号表示为无穷多 个正弦波的叠加,每个正 弦波具有不同的幅度、频 率和相位。
频谱密度函数
描述信号中各频率分量的 强度,通常用图形表示, 即频谱图。
频谱分析
通过频谱图分析信号中各 频率分量的特性,如频率 范围、幅度和相位等。
数字信号处理第一章(南理工)
23
4. Examples of Signals
B. Musical Signals (1-D)(CD,MP3,MP4)
8
2. Characterization & Classification of Signals
● A signal is called continuous time signal (CTS) if the indep. var. is continuous. ● A signal is called discrete time signal (DTS) if the indep. var. is discrete. ● A CTS with continuous amplitude is called an analog signal (AS). ● A DTS with Quantized amplitude is called an Digital signal (DS).
15
3. Typical Signals Operations A. Simple Time-domain operations
Scaling(amplification, attenuation): y (t ) = α x (t ) Delay: y (t ) = x (t − t 0 ) Addition:y (t ) = x1 (t ) + x2 (t )
4
1. Signals & Signal Processing ● e.g.: Speech and music signals represent air pressure as a function of time at a point in space.
Decision Making in the Presence of Noise
1
by compact, closed-form formulas of low computational complexity. From these formulas, we gain qualitative insights into the Bayesian optimal decision. We observe, for example, that no algorithm that bases its decision solely on the information contained at the level-k nodes of the tree (as the Shannon algorithm does) is Bayesian optimal. We also give an example to show that the optimal decision sometimes depends not only on the information contained at the nodes, but also on knowledge of the expected amount of noise present in the data. Thus, an algorithm that is aware of the imperfection of its data can do better than one that is not. It is tempting to apply these insights to chess in order to obtain improved algorithms, and we are hopeful that workers on chess will nd our results enlightening. Nevertheless, we should point out a number of important di erences between our games and the problem of playing chess. In our games, uncertainty arises from two underlying sources of randomness. The instance of the game to be played is chosen at random (as in card games such as bridge or poker), and the information about the chosen game that is given to the player is also chosen at random. Thus, a player is presented with probabilistic, partial information about the true underlying game. The di culty the player faces is in knowing which game is being played, not how to play the game once it is known. In chess, on the other hand, the underlying game tree is xed, and the player's information is computed by a deterministic procedure of low computational complexity. The barrier to optimal play is not the lack of accurate information but the apparent intractability of the computational problem of making good use of that information. An important research problem is to clarify the relationship between probabilistic and computational sources of uncertainty in decision problems.
数字信号处理 随机信号分析
具有基础意义的离散信号的表示法(方程和图形),如delta (δ)函数、 单位跃阶序列、指数序列、周期序列等,可参见《信号与系统》、 《信号处理》等著作。 计算机只能处理离散的、量化的信息—数字序列。处理的结果也是离 散的。一个数字序列可用穷举法表示,如 x = {x0,x1,x2,x3,x4,…,xn} (2-5) 也可用集合记号表示,如 x = {x(n)} n = 0,1,2,3,4,…,N-1 (2-6) 本书多采用(2-6)式的表示方法。如无特别说明,时域量用小写字 母表示,如y(t)。频域量用大写表示,如Y(K)。 另一种观点认为,客观世界本质是连续的。也就是说,宇宙是物质的, 物质是连续的。离散是对连续的抽样的结果,也只是一种近似。抽样 要满足抽样定理,才能完全确定原始信号。理论上讲,或抽象地讲, 抽样可在时域完成,也可在频域完成。但是,在计算机数字信号处理 领域,很少有连续的频域信号存在,故频域抽样大都只有理论意义。
信号可分为确定性信号和随机信号。在数字 信号处理中,随机信号的处理有重要的意义,因 为随机信号的普遍存在的,如信号的任何实际测 量都会带来随机干扰。在很多实际应用领域,消 除随机信号的干扰,提取被掩埋于其中的确定成 分是根本的任务。还因为随机信号处理技术在信 号处理领域作为一种强有力的工具使用。在实际 应用中要区别的是随机性与非线性,随机信号与 非线性信号。应该注意的是,在信号处理中作为 一种工具使用的伪随机数或伪随机信号,是由计 算机用非线性算法产生的非线性信号,“伪”的 真实意义即在于此(貌似随机实为确定)。
二、随机过程的普遍存在性 随机信号或随机过程(random process) 是普遍存在的。一方面,任何确定性信号 经过测量后往往就会引入随机性误差而使 该信号随机化;另一方面,任何信号本身 都存在随机干扰,通常把对信号或系统功 能起干扰作用的随机信号称之为噪声。噪 声按功率谱密度划分可以分为白噪声 (white noise)和色噪声(color noise), 我们把均值为0的白噪声叫纯随机信号 (pure random signal)。
旅行商问题外文文献翻译
旅行商问题外文文献翻译(含:英文原文及中文译文)文献出处:Mask Dorigo. Traveling salesman problem [C]// IEEE International Conference on Evolutionary Computation. IEEE, 2013,3(1), PP:30-41.英文原文Traveling salesman problemMask Dorigo1 IntroductionIn operational research and theoretical computer science, the Traveling Salesman Problem (TSP) is a NP-difficult combinatorial optimization problem. By giving pairs of city-to-city distances, find each city exactly one shortest trip. It is a special case of buyer travel problems.The problem was first elaborated in 1930 as one of the most in-depth research questions in mathematics problems and optimization. It becomes a benchmark for many optimization methods. Although the problem is difficult to calculate, a large number of heuristic detections and exact methods are known to solve certain situations that contain tens of thousands of cities.TSP has many applications, even based on its most essential concept itself, such as planning, logistics, and manufacturing microchips. With minor changes, it has emerged as a sub-problem in many areas, such asDNA sequencing. In these applications, the cities in the TSP represent the customers, welding points, or DNA fragments. The distance in the TSP represents the travel time or cost, or similarity measure between DNA fragments. In many applications, additional constraints, such as limited resources or time windows, make the problem quite difficult. In computational complexity theory, the decision version of the TSP (given a length L, the goal is to judge whether there is any travel shorter than L) belongs to the class of np complete problems. Therefore, it is likely that in the worst case scenario, the operating time required to solve any of the TSP's algorithms increases exponentially with the number of cities.2 HistoryThe origin of the traveling salesman problem is still unclear. A manual of 1832 referred to the problem of travel salesmen, including examples from Germany and Switzerland. However, there is no mathematical treatment in the book. The traveling salesman problem was elaborated in the 19th century by the Irish mathematician W.R. and the English mathematician Thomas Kirkman. Hamilton's Icosian game is a casual game based on finding the Hamilton Circle. The general form of TSP, first studied by mathematicians and especially Karl Menger at the Vienna and Harvard universities in 1930, Karl Menger defined the problem, considered the obvious brute force algorithm, and examined the heuristics of non-nearest neighbors:We express the messenger problem (because in practice, every postman must solve this problem, and many tourists do the same), and its task is to know the limited number of points and their paired distances and find the shortest connection route. Of course, this problem is solvable for a limited number of trials. The rule allows the number of trials to be less than the number of species at a given point, but it is not known. First from the starting point to the nearest point, then from that point to the next point from its nearest point, this rule does not generally constitute the shortest possible line.After Hassler Whitney introduced the TSP at Princeton University, this issue quickly became popular in the European and American scientific communities in the 1950s and 1960s. In Dan Monica, the RAND Corporation's George Dantzig, Delbert Ray Fulkerson, and Selmer M. Johnson contributed to this and they solved TSP as an integer linear programming and an improved cutting plane problem. With these new solution methods, they built an optimal tour that solved an instance with 49 cities, and at the same time proved that no other tour can be shorter. In the following decades, the problem was studied by many researchers in mathematics, computer science, chemistry, physics, and other sciences.Richard M. Karp's research in 1972 showed that the Hamiltonian problem is NP-complete, which means that the TSP is NP-hard. Thisprovides a mathematical explanation as to why it is difficult to find the best travel.In the late 1970s and 1980s, there was a major breakthrough in the problem. Together with others, Gröötschel, Padberg, and Rinaldi used cut-plane methods and branch-and-bound methods to successfully solve instances of up to 2,392 cities.In the 1990s, Applegate, Bixby, Chvátal, and Cook developed the "Concordance" program that was used in many recent solutions. In 1991, Gerhard Reinelt published TSPLIB, which collected examples of different difficulties and was used by many research groups to compare results. In 2005, Cook and others found the best travel through 33,810 cities from a chip layout problem. This is the largest example of solving problems in TSPLIB. For many other examples with millions of cities, problem solving can be found and 1% is guaranteed to be the best one.3 Description3.1 As a Graphic ProblemTSP can be transformed into an undirected weighted graph. For example, the city is the vertex of the graph, the path is the edge of the graph, and the path distance is the length of the edge. This is a minimization problem that starts and ends at a specified vertex, and other vertices have exactly one access. A Hamiltonian circle is one of the best travels of the TSP and is proportional to the distance on each side.Normally, the model is a complete graph (ie each pair of vertices is connected by edges). If there is no path between the two cities, adding an edge of any length that does not affect the best travel becomes a complete picture.3.2 Asymmetry and symmetryIn a symmetrical TSP, the distance between two cities in each opposite direction is the same, forming an undirected graph. This symmetry splits the possible solutions in half. In an asymmetric TSP, there may be no two-way paths or two-way paths different to form a directed graph. Traffic accidents, one-way flights, and tickets of different times and prices are examples of disruptions to this symmetry.3.3 Related issuesAn equivalent proposition in graph theory is to give a complete weighted graph (where the vertices represent cities, the paths represented by the edges, and the weights represent costs or distances) and find the Hamiltonian ring with the smallest weight. Returning to the requirements of the departure city does not change the computational complexity of the problem. Look at the Hamilton route problem.Another related problem is the Bottleneck Traveling Salesman Problem (bottlenecks TSP): Find a Hamiltonian ring with the lowest critical edge weight in the weighted graph. The problem is of considerable practical significance, except that in the obvious areas oftransportation and logistics, a typical example is the drilling of drilling holes in PCBs for the manufacture of printed circuit dispatches. In machining or drilling applications, the “city” is the part or drill hole (different in size), and the “overhead of traverse” contains the time for replacement parts (stand-alone job scheduling problem). The general traveling salesman problem involves the “state,” “one or more” “city,” where the salesman visits each “city” from each “state,” and is also referred to as the “travel politician problem.” Surprisingly, Behzad and Modarres found that the general traveling salesman problem can be transformed into a standard traveling salesman problem with the same number of cities as the modified distance matrix.The problem of sequential ordering involves accessing a series of issues that have a city of priority relations with each other.The traveling salesman problem solves the buyer's purchase of a set of products. He can buy these products in several cities, but at different prices, while not all cities offer the same products. The goal is to find a path in all cities to minimize total expenses (travel expenses + purchase expenses).4 Calculation SolutionsThe traditional ideas for solving NP-hard problems are the following:1) Design the algorithm to find the exact solution (only applicable tosmall problems, which will be completed soon).2) Develop a "sub-optimal" or heuristic algorithm, ie the algorithm seems or may provide a good solution, but it cannot be proven to be optimal.3) It is possible to find solutions or heuristics in special cases of problems (sub-problems).4.1 Computational ComplexityThe problem has been proved to be an NP-difficult problem (more precisely, it is a complex class FP NP ), and the decision problem version (given the cost and a number x to determine whether there is a cheaper path than X) is a NP-complete problem. The bottleneck traveling salesman problem is also an NP-hard problem. Cancelling the "visit only once" condition for each city does not eliminate Np-difficulty, because it is easy to see that the best travel in the flat case must be visited once per city (otherwise, as seen by the triangle inequality, A short cut to skip repeat visits will not increase the length of the tour.)4.2 Approximate ComplexityIn general, finding the shortest traveling salesman problem is a NPO-complete. If the distance is measurable and symmetrical, the problem becomes APX-complete. Christofides's algorithm is within about 1.5.If the limits are 1 and 2 (but still a metric), the approximate ratio is7/6. In the case of asymmetry and metering, only the logarithmic performance can be guaranteed. The best performance of the current algorithm is 0.814log n. If there is a constant factor approximation, it is an open problem.The corresponding maximization problem found the longest traveling salesman to travel around 63/38. If the distance function is symmetric, the longest tour can be approximated by 4/3 with a deterministic algorithm and a random algorithm.4.3 Accurate AlgorithmThe most straightforward approach is to try all permutations (ordered combinations) to see which one is the least expensive (use brute force search). The time complexity of this method is O (n !), the factorial of the number of cities, so this solution, even if only 20 cities are unrealistic. One of the earliest applications of dynamic programming was the Held-Karp algorithm. The time complexity of problem solving was O (n 22n ).Dynamic programming solutions require the time complexity of the index. Use inclusion-exclusion to solve problems in 2n time and space.It seems difficult to improve these times. For example, it is not known whether there is an accurate algorithm for TSP and the time complexity is O (1.9999n).4.4 Other methods include1) Different branch and bound algorithms can be used for TSP in 40-60 cities.2) An improved linear programming algorithm that handles TSPs in 200 cities.3) Branch and bounds and specific cuts are the preferred method of solving a large number of instances. The current method has a record of solving 85,900 city examples (2006).A solution for 15,112 German towns was discovered in TSPLIB in 2001 using the cutting plane method proposed by George Dantzig, Ray Fulkerson, and Selmer M. Johnson in 1954 based on linear programming.Rice University and Princeton University have performed calculations on a network of 110 processors. The total computation time is equivalent to a 2.5 MHz processor working 22.6 years. In 2004, the problem of traveling salesman visited all 24,978 towns in Sweden and was about 72,500 kilometers in length. At the same time, it proved that there is no shorter travel.In March 2005, accessing all 33,810 point of travel salesman problems on a circuit board was solved by using the Concord TSP solver: a tour with a length of 66,048,945 units was found, which at the same time proved that there was no shorter tour. Calculated for approximately 15.7 CPU years (Cook et al., 2006). In April 2006, an instance of 85,900 points using the Concord TSP solver solved the CPU time of more than136 years (2006).4) Heuristic approximation algorithmA variety of heuristic approximation algorithms that can quickly produce good solutions have been developed. The current method can solve very large problems (with millions of cities) in a reasonable amount of time, and only 2–3% of the probability is far from the optimal solution.Constructive heuristics The nearest neighbor (neural network) algorithm (or so-called greedy algorithm) lets the salesman choose the nearest city that has not been visited as his next action goal. The algorithm quickly produces a valid short path. For N cities randomly distributed on one plane, the average path generated by the algorithm is 25% longer than the shortest path. However, there are many cities with special distributions that make the neural network algorithm give the worst path (Gutin, Y eo, and Zverovich, 2002). This is a real problem with symmetric and asymmetric traveling salesman problems (Gutin and Y eo, 2007). Rosenkrantz et al. showed that the neural network algorithm satisfies the triangle inequality when the approximation factor Θ(log| V | ).The approximate ratio of the construction based on the minimum spanning tree is 2 . The Christofides algorithm achieved a ratio of 1.5.Bitonic travel is a monotonic polygon made up of the smallest perimeter of a set of points, which can be calculated efficiently throughdynamic planning.Another constructive heuristic, the twice comparison merge (MTS) (Kahng, Reda 2004), performs two consecutive matches, and the second match is executed after all the first matching edges have been removed. Then merge to produce the final travel.Iterative refinements, pairwise exchanges, or Lin-Kernighan heuristics, pairwise exchanges, or 2-technologies involve the repeated deletion of two edges and the replacement of edges that are not needed to create a new and shorter tour. This is a special case of a K-OPT method. Please note that Lin –Kernighan is often a misname of 2-OPT. Lin –Kernighan is actually a more general approach.K-opt heuristics, a given tour, removes k-disjoint edges. Regroup the remaining parts into one tour, leaving the disjoint sub-tours (ie, the end of a disjointed part together). This actually simplifies the TSP under consideration into a much simpler problem. There are 2K-2 other connection possibilities for each part of the endpoint: 2 K total destination can be connected, so this part cannot be considered. This constrained 2K-TSP can then be resolved using brute force methods to find the lowest partial reorganization of the original part. K-opt is a special case of V-opt or variable-opt technology. The most popular K-opt is 3-opt, which was introduced by Shen Lin of Bell Labs in 1965. There is a 3 - OPT special case where the edges do not intersect (adjacent to bothsides). The v-opt heuristic, the variable-opt method is related to the k-opt method, and is a generalization of the K-opt method. While K -opt removes a fixed number (K) from the original tour, the variable-opt method does not delete fixed-size edge sets. Instead, they continue to grow during the search process. The best method known here is Lin-Kernighan's method (mentioned above as a 2-OPT error). Shen Lin and Brian Kernighan first published their method in 1972, which was the most reliable heuristic for solving traveling salesman problems for nearly two decades. The more advanced variable-opt approach was developed at Bell Labs in the late 1980s by David Johnson and his research team. These methods (sometimes referred to as) add methods from tabu search and evolutionary computation to the Lin-Kernighan method. The basic Lin-Kernighan technique gives results that guarantee at least 3 -opt. The Lin–Kernighan–Johnson method calculates Lin–Kernighan's weekly tour, then disrupts the tour through so-called mutations that move at least four sides and connect them in different ways, and then establishes a new tour with the V-opt method. The v-opt method is widely considered to be the most powerful heuristic to solve a problem, and can solve problems under special circumstances, such as the Hamilton ring problem and other non-decimal TSPs, but other heuristics cannot. Over the years, Lin-Kernighan-Johnson has been shown to be the best solution to all TSP solutions that have been tried.中文译文旅行商问题Mask Dorigo1 引言在运筹学和理论计算机科学中,旅行商问题(TSP )是一个NP-困难的组合优化问题。
测试信号分析与处理-第1章(浏览版)
N −1
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三. 方差 (二阶中心矩)
2 定义: σ x (t1 ) = E[[ X (t1 ) − μ x (t1 )]2 ]
σ x2 (t1 ) = Ψ x2 (t1 ) − μ x2 (t1 ) σ x2 = E[( X − μ x ) 2 ] = Ψ x2 − μ x2
1 σ = lim T →∞ T
对平稳随机过程: F ( x1 ) = p[ x ≤ x1 ]
-6-
概率密度函数
随机过程 x(t)在 t1时刻落入 [x1 , x1 + Δx ]区间的概率。
p[ x1 ≤ x(t1 ) ≤ x1 + Δx] ∂ F ( x1 , t1 ) p( x1 , t1 ) = lim = Δx →0 Δx ∂ x1
(Ergodic Process)
平稳随机过程集合的数字特征(均值, 均方值, 方差, 相关函数, 功率谱密度函数等)可以用任 何一个样本全部时间历程的数字特征来代替。
-4-
各态历经(遍历)随机过程的特点
1 lim ● 一个样本的时间平均 T → ∞ T
N k =1
∫
T
0
x ( t ) d t 等同于
−∞
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小 结
遍历随机过程的数字特征:
1 T lim 一. 均值: μ x = T →∞ ∫0 x(t )dt T 1 T 2 2 二. 均方值:ψ x = lim ∫0 x (t )dt T →∞ T 1 T 2 2 lim 三. 方差: σ x = T →∞ ∫0 [ x(t ) − μ x ] dt T 1 T lim 四. 自相关函数:Rxx (τ ) = T →∞ ∫0 x(t ) x(t + τ )dt T
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The spectrum of a bandpass signal is Hermitian symmetric
X(f)
Since the spectrum is Hermitian
It is generally written as
x l(t) x(t)
带通信号
解析信号
X+(f)
x+(t)
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From x (t ) to x l (t )带通信号低通表示法
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