1201_Lesson1_DataFill_rls15

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ANSYSWorkbench15.0学习必备

第1章初识ANSYSWorkbench项目视图系统使用起来非常简单：直接从左边的工具箱（Toolbox）中将所需的分析系统拖曳到右边的项目视图窗口中或双击即可。

工具箱（Toolbox）中的分析系统（AnalysisSystems）部分，包含了各种已预置好的分析类型（如显式动力分析、FLUENT流体分析、结构模态分析、随机振动分析等），每一种分析类型都包含完成该分析所需的完整过程（如材料定义、几何建模、网格生成、求解设置、求解、后处理等过程），按其顺序一步步往下执行即可完成相关的分析任务。

当然也可从工具箱中的ComponentSystems里选取各个独立的程序系统，自己组装成一个分析流程。

一旦选择或定制好分析流程后，Workbench平台将能自动管理流程中任何步骤发生的变化（如几何尺寸变化、载荷变化等），自动执行流程中所需的应用程序，从而自动更新整个仿真项目，极大缩短了更改设计所需的时间。

Workbench仿真流程具有良好的可定制性，只须通过鼠标拖曳操作，即可非常容易地创建复杂的、包含多个物理场的耦合分析流程，在各物理场之间所需的数据传输也能自动定义。

ANSYSWorkbench平台在流体和结构分析之间自动创建数据连接以共享几何模型，使数据保存更轻量化，并更容易分析几何改变对流体和结构两者产生的影响。

同时，从流体分析中将压力载荷传递到结构分析中的过程也是完全自动的。

工具栏中预置的分析系统（AnalysisSystems）使用起来非常方便，因为它包含了所选分析类型所需的所有任务节点及相关应用程序。

Workbench项目视图的设计是非常柔性的，用户可以非常方便地对分析流程进行自定义，把ComponentSystems中的各工具当成砖块，按照任务需要进行装配。

ANSYSWorkbench环境中的应用程序都是支持参数变量的，包括CAD几何尺寸参数、材料特性参数、边界条件参数以及计算结果参数等。

在仿真流程各环节中定义的参数都是直接在项目窗口中进行管理的，因而非常容易研究多个参数变量的变化。

TSPL中文文档

目录文件字体规则 (1)系统设定指令 (2)SIZE (2)GAP (3)BLINE (4)OFFSET (5)SPEED (6)DENSITY (7)DIRECTION (8)REFERENCE (9)COUNTRY (10)CODEPAGE (11)CLS (12)FEED (13)FORMFEED (14)HOME (15)PRINT (16)SOUND (17)CUT (18)LIMITFEED (19)卷标内容设计指令 (20)BAR (20)BARCODE (21)BITMAP (25)BOX (26)ERASE (27)DMATRIX (28)MAXICODE (29)PDF417 (32)PUTPCX (34)REVERSE (35)TEXT (36)询问打印机状态指令 (38)<ESC>!? (38)~!A (40)~!T (41)~!C (42)~!I (43)~!F (44)~!@ (45)信息传递协议 (46)<ESC>! (46)<ESC>& (46)~# (47)WINDOWS DRIVER驱动程序指令 (48)!B (48)!J (49)!N (50)档案管理指令 (51)DOWNLOAD (51)REDRAW (59)EOP (60)FILES (61)KILL (62)MOVE (63)UPDATBIOS (64)BASIC 指令及函式 (65)ABS( ) (65)ASC( ) (66)CHR$( ) (67)END (68)EOF( ) (69)OPEN (71)READ (73)SEEK (75)LOF( ) (77)FREAD$( ) (77)FOR...NEXT.. (79)IF...THEN...ELSE (81)GOSUB...RETURN. (83)GOTO (85)INPUT (88)REM (90)OUT (91)GETKEY( ) (92)INT( ) (93)LEFT$( ) (94)LEN( ) (95)MID$( ) (96)RIGHT$( ) (97)STR$( ) (98)VAL( ) (99)BEEP (100)打印机外围功能设定指令 (101)SET COUNTER (101)SET CUTTER (103)SET KEY1 (105)SET KEY2 (105)SET LED1, LED2, LED3 (107)SET PEEL (108)SET DEBUG (109)SET GAP (110)SET RIBBON (111)SET COM1 (112)@LABEL (114)PEEL (115)LED1, LED2, LED3 (116)KEY1, KEY2 (117)YEAR (118)MONTH (119)DATE (120)WEEK (121)HOUR (122)MINUTE (123)SECOND (125)文件字体规则本文件使用以下字体规则文件规则描述[表示内容] 在`中括号内表示该参数为选项<ESC> <ESC>代表ASCII 27 字符，当打印机收到以该控制字符为启始之指令将立即响应(即使打印机在错误状态时也将实时回应)~ (ASCII 126), 该字符启始的指令用于询问打印机的状态注: 200 DPI: 1 mm = 8 dots 粗斜体Arial,字型，用于表300 DPI: 1 mm = 12 dots 示批注DOWNLOAD “TEST.BAS”当所列出的内容为程序SET COUNTER @1 1 时以Curier 字型表示@1=”0001”TEXT 10,10,”3”,0,1,1,@1PRINT 3,2EOP系统设定指令SIZE说明该指令用于设定卷标纸的宽度及长度指令语法(1) 英制系统(英寸)SIZE m, n(2) 公制系统(公厘)SIZE m mm, n mm参数说明m 标签纸的宽度 (不含背纸)n 标签纸的长度 (不含背纸)Note: 200 DPI: 1 mm = 8 dots*300 DPI: 1 mm = 12 dots范例(1) 英制系统 (英寸)SIZE 3.5, 3.00(2) 公制系统 (公厘)SIZE 100 mm, 100 mmGAP说明该指令定义两张卷标纸间的垂直间距距离指令语法(1) 英制系统 (英寸)GAP m, n(2) 公制系统 (公厘)GAP m mm, n mm参数说明m 两标签纸中间的垂直距离0 ≤ m ≤ 1 (英寸), 0 ≤ m ≤ 25.4 (公厘)n 垂直间距的偏移[-]n ≤标签纸张长度 (英寸或公厘)Note: 200 DPI : 1 mm = 8 dots300 DPI : 1 mm = 12 dots范例一般垂直间距设定(1) 英制系统 (英寸)GAP 0.12,0(2) 公制系统 (公厘)GAP 3 mm,0特殊垂直间距设定(1) 英制系统 (英寸)GAP 0.30,-0.10(2) 公制系统 (公厘)GAP 7.62 mm, -2.54 mmBLINE说明该指令用于设定黑标的高度及偏移位置指令语法(1) 英制系统 (英寸)BLINE m, n(2) 公制系统 (公厘)BLINE m mm, n mm参数说明m 黑标的高度，以英寸或公厘表示0.1 ≤m ≤1 (英寸), 2.54 ≤m ≤ 25.4 (公厘)n 黑标偏移量 0 ≤n ≤标签纸张高度范例(1) 英制系统 (英寸)BLINE 0.20,0.50(2) 公制系统 (公厘)BLINE 5.08 mm,12.7 mmOFFSET说明该指令用于控制在剥离模式时(pee-off mode)每张卷标停止的位置，该指令仅适用于剥离模式。

1IDL入门(下)

小括号() 小括号() 中括号[] 中括号[] 条件表达式?: 条件表达式?: 对象方法调用符对象方法调用符-> 指针引用符* 指针引用符*
运行教材示例 P72
IDL语法基础变量数组字符串结构体指针运算符其他符号
IDL语法基础-其他符号换行符$ 换行符$ 同行符& 同行符& 注释符; 注释符;
阶乘
Factorial()
平均值
Mean()
方差计算
Variance()
标准差计算
Stddev()
平均值、方差、平均值、方差、倾斜度
Moment()
运行教材示例 P58
IDL语法基础-数组矩阵运算
矩阵间
A M ×N # B L ×M AM×N ## BL×M
矩阵函数
运行教材示例 P59
IDL语法基础变量数组字符串结构体指针运算符其他符号
IDL语法基础变量数组字符串结构体指针运算符其他符号
IDL语法基础数组定义
程序设计中，为了处理方便，把具有相同类型的若干变量按有序的形式组织起来。这些按序排列的同类数据元素的集合称为数组。 IDL支持0→8维数组数组的下标是★先列后行先列后行★ 先列后行
IDL语法基础-数组数组创建
运行测试 chapter4\test_*.pro
IDL程序基础程序控制位置参数与关键字参数错误处理程序调用方式编译规则高效编程
IDL程序基础-位置与关键字参数位置参数关键字参数参数继承参数传递机理
值传递地址传递
相关函数
运行教材示例 P79
IDL程序基础程序控制位置参数与关键字参数错误处理程序调用方式编译规则高效编程

lesson

{
}
finally 的应用：无论如何都会执行的语句。只有在System.exit(0);
一个方法被覆盖时，覆盖它的方法必须扔出相同的异常或是异常的子类；
如果父类扔出多个异常，那么重写方法必须扔出异常的子集，不能扔出新的异常；
线程的创建：一段代码在一个新的线程上运行该代码应该在一个类的润run()方法中，run()函数所在的类是Thread类的子类。star();使线程启动并调用run()函数：
java的文档注释：
public de 类的使用说明，在类定义的前面。标题：Title<br>.Description<br>.Copyright<br>,Company<br>;@author,@version是固定格式的
方法文档注释：@param sum 参数的说明@return
类的继承：通过继承可以简化类的定义，可以减少出错
使用：如设置选项将对象的关键字和值对应的的存入文件中；
Syste类都是静态的
File类是
节点流:
流是字节序列的抽象概念；
支持单继承允许多层继承；
子类不继承父类的构造方法；可以在子类的构造方法中调用父类中的构造方法
子类对象的实例化过程：
1，new创建对象，对实例变量初始化。绑定构造函数。如果有this()构造，执行，绑定构造函数。，显示或隐示的执行父类的构造方法先
，父类中是否有this执行this然后对实例变量显示初始化，然后再构造函数的初始化。对实例变量的显示初始化。再执行子类的构造方法中的代码；
}
在构造方法中调用构造方法；可以简化构造方法的编写代码
垃圾回收。每个类中finalize方法，是从object类中继承的。对象释放时，自动调用

________________________________________________ Session S3C MINORITY ENGINEERING PROGRAM C

________________________________________________ 1Joseph E. Urban, Arizona State University, Department of Computer Science and Engineering, P.O. Box 875406, Tempe, Arizona, 85287-5406, joseph.urban@ 2Maria A. Reyes, Arizona State University, College of Engineering and Applied Sciences, Po Box 874521, Tempe, Arizona 852189-955, maria@ 3Mary R. Anderson-Rowland, Arizona State University, College of Engineering and Applied Sciences, P.O. Box 875506, Tempe, Arizona 85287-5506, mary.Anderson@MINORITY ENGINEERING PROGRAM COMPUTER BASICS WITH AVISIONJoseph E. Urban 1, Maria A. Reyes 2, and Mary R. Anderson-Rowland 3Abstract - Basic computer skills are necessary for success in an undergraduate engineering degree program. Students who lack basic computer skills are immediately at risk when entering the university campus. This paper describes a one semester, one unit course that provided basic computer skills to minority engineering students during the Fall semester of 2001. Computer applications and software development were the primary topics covered in the course that are discussed in this paper. In addition, there is a description of the manner in which the course was conducted. The paper concludes with an evaluation of the effort and future directions.Index Terms - Minority, Freshmen, Computer SkillsI NTRODUCTIONEntering engineering freshmen are assumed to have basic computer skills. These skills include, at a minimum, word processing, sending and receiving emails, using spreadsheets, and accessing and searching the Internet. Some entering freshmen, however, have had little or no experience with computers. Their home did not have a computer and access to a computer at their school may have been very limited. Many of these students are underrepresented minority students. This situation provided the basis for the development of a unique course for minority engineering students. The pilot course described here represents a work in progress that helped enough of the students that there is a basis to continue to improve the course.It is well known that, in general, enrollment, retention, and graduation rates for underrepresented minority engineering students are lower than for others in engineering, computer science, and construction management. For this reason the Office of Minority Engineering Programs (OMEP, which includes the Minority Engineering Program (MEP) and the outreach program Mathematics, Engineering, Science Achievement (MESA)) in the College of Engineering and Applied Sciences (CEAS) at Arizona State University (ASU) was reestablished in 1993to increase the enrollment, retention, and graduation of these underrepresented minority students. Undergraduate underrepresented minority enrollment has increased from 400 students in Fall 1992 to 752 students in Fall 2001 [1]. Retention has also increased during this time, largely due to a highly successful Minority Engineering Bridge Program conducted for two weeks during the summer before matriculation to the college [2] - [4]. These Bridge students were further supported with a two-unit Academic Success class during their first semester. This class included study skills, time management, and concept building for their mathematics class [5]. The underrepresented minority students in the CEAS were also supported through student chapters of the American Indian Science and Engineering Society (AISES), the National Society of Black Engineers (NSBE), and the Society of Hispanic Professional Engineers (SHPE). The students received additional support from a model collaboration within the minority engineering student societies (CEMS) and later expanded to CEMS/SWE with the addition of the student chapter of the Society of Women Engineers (SWE) [6]. However, one problem still persisted: many of these same students found that they were lacking in the basic computer skills expected of them in the Introduction to Engineering course, as well as introductory computer science courses.Therefore, during the Fall 2001 Semester an MEP Computer Basics pilot course was offered. Nineteen underrepresented students took this one-unit course conducted weekly. Most of the students were also in the two-unit Academic Success class. The students, taught by a Computer Science professor, learned computer basics, including the sending and receiving of email, word processing, spreadsheets, sending files, algorithm development, design reviews, group communication, and web page development. The students were also given a vision of advanced computer science courses and engineering and of computing careers.An evaluation of the course was conducted through a short evaluation done by each of five teams at the end of each class, as well as the end of semester student evaluations of the course and the instructor. This paper describes theclass, the students, the course activities, and an assessment of the short-term overall success of the effort.M INORITY E NGINEERING P ROGRAMSThe OMEP works actively to recruit, to retain, and to graduate historically underrepresented students in the college. This is done through targeted programs in the K-12 system and at the university level [7], [8]. The retention aspects of the program are delivered through the Minority Engineering Program (MEP), which has a dedicated program coordinator. Although the focus of the retention initiatives is centered on the disciplines in engineering, the MEP works with retention initiatives and programs campus wide.The student’s efforts to work across disciplines and collaborate with other culturally based organizations give them the opportunity to work with their peers. At ASU the result was the creation of culturally based coalitions. Some of these coalitions include the American Indian Council, El Concilio – a coalition of Hispanic student organizations, and the Black & African Coalition. The students’ efforts are significant because they are mirrored at the program/staff level. As a result, significant collaboration of programs that serve minority students occurs bringing continuity to the students.It is through a collaboration effort that the MEP works closely with other campus programs that serve minority students such as: Math/Science Honors Program, Hispanic Mother/Daughter Program, Native American Achievement Program, Phoenix Union High School District Partnership Program, and the American Indian Institute. In particular, the MEP office had a focus on the retention and success of the Native American students in the College. This was due in large part to the outreach efforts of the OMEP, which are channeled through the MESA Program. The ASU MESA Program works very closely with constituents on the Navajo Nation and the San Carlos Apache Indian Reservation. It was through the MESA Program and working with the other campus support programs that the CEAS began investigating the success of the Native American students in the College. It was a discovery process that was not very positive. Through a cohort investigation that was initiated by the Associate Dean of Student Affairs, it was found that the retention rate of the Native American students in the CEAS was significantly lower than the rate of other minority populations within the College.In the spring of 2000, the OMEP and the CEAS Associate Dean of Student Affairs called a meeting with other Native American support programs from across the campus. In attendance were representatives from the American Indian Institute, the Native American Achievement Program, the Math/Science Honors Program, the Assistant Dean of Student Life, who works with the student coalitions, and the Counselor to the ASU President on American Indian Affairs, Peterson Zah. It was throughthis dialogue that many issues surrounding student success and retention were discussed. Although the issues andconcerns of each participant were very serious, the positiveeffect of the collaboration should be mentioned and noted. One of the many issues discussed was a general reality that ahigh number of Native American students were c oming to the university with minimal exposure to technology. Even through the efforts in the MESA program to expose studentsto technology and related careers, in most cases the schoolsin their local areas either lacked connectivity or basic hardware. In other cases, where students had availability to technology, they lacked teachers with the skills to help them in their endeavors to learn about it. Some students were entering the university with the intention to purse degrees in the Science, Technology, Engineering, and Mathematics (STEM) areas, but were ill prepared in the skills to utilize technology as a tool. This was particularly disturbing in the areas of Computer Science and Computer Systems Engineering where the basic entry-level course expected students to have a general knowledge of computers and applications. The result was evident in the cohort study. Students were failing the entry-level courses of CSE 100 (Principals of Programming with C++) or CSE 110 (Principals of Programming with Java) and CSE 200 (Concepts of Computer Science) that has the equivalent of CSE 100 or CSE 110 as a prerequisite. The students were also reporting difficulty with ECE 100, (Introduction to Engineering Design) due to a lack of assumed computer skills. During the discussion, it became evident that assistance in the area of technology skill development would be of significance to some students in CEAS.The MEP had been offering a seminar course inAcademic Success – ASE 194. This two-credit coursecovered topics in study skills, personal development, academic culture issues and professional development. The course was targeted to historically underrepresented minority students who were in the CEAS [3]. It was proposed by the MEP and the Associate Dean of Student Affairs to add a one-credit option to the ASE 194 course that would focus entirely on preparing students in the use of technology.A C OMPUTERB ASICSC OURSEThe course, ASE 194 – MEP Computer Basics, was offered during the Fall 2001 semester as a one-unit class that met on Friday afternoons from 3:40 pm to 4:30 pm. The course was originally intended for entering computer science students who had little or no background using computer applications or developing computer programs. However, enrollment was open to non-computer science students who subsequently took advantage of the opportunity. The course was offered in a computer-mediated classroom, which meantthat lectures, in- class activities, and examinations could all be administered on comp uters.During course development prior to the start of the semester, the faculty member did some analysis of existing courses at other universities that are used by students to assimilate computing technology. In addition, he did a review of the comp uter applications that were expected of the students in the courses found in most freshman engineering programs.The weekly class meetings consisted of lectures, group quizzes, accessing computer applications, and group activities. The lectures covered hardware, software, and system topics with an emphasis on software development [9]. The primary goals of the course were twofold. Firstly, the students needed to achieve a familiarity with using the computer applications that would be expected in the freshman engineering courses. Secondly, the students were to get a vision of the type of activities that would be expected during the upper division courses in computer science and computer systems engineering and later in the computer industry.Initially, there were twenty-two students in the course, which consisted of sixteen freshmen, five sophomores, and one junior. One student, a nursing freshman, withdrew early on and never attended the course. Of the remaining twenty-one students, there were seven students who had no degree program preference; of which six students now are declared in engineering degree programs and the seventh student remains undecided. The degree programs of the twenty-one students after completion of the course are ten in the computing degree programs with four in computer science and six in computer systems engineering. The remaining nine students includes one student in social work, one student is not decided, and the rest are widely distributed over the College with two students in the civil engineering program and one student each in bioengineering, electrical engineering, industrial engineering, material science & engineering, and mechanical engineering.These student degree program demographics presented a challenge to maintain interest for the non-computing degree program students when covering the software development topics. Conversely, the computer science and computer systems engineering students needed motivation when covering applications. This balance was maintained for the most part by developing an understanding that each could help the other in the long run by working together.The computer applications covered during the semester included e-mail, word processing, web searching, and spreadsheets. The original plan included the use of databases, but that was not possible due to the time limitation of one hour per week. The software development aspects included discussion of software requirements through specification, design, coding, and testing. The emphasis was on algorithm development and design review. The course grade was composed of twenty-five percent each for homework, class participation, midterm examination, and final examination. An example of a homework assignment involved searching the web in a manner that was more complex than a simple search. In order to submit the assignment, each student just had to send an email message to the faculty member with the information requested below. The email message must be sent from a student email address so that a reply can be sent by email. Included in the body of the email message was to be an answer for each item below and the URLs that were used for determining each answer: expected high temperature in Centigrade on September 6, 2001 for Lafayette, LA; conversion of one US Dollar to Peruvian Nuevo Sols and then those converted Peruvian Nuevo Sols to Polish Zlotys and then those converted Polish Zlotys to US Dollars; birth date and birth place of the current US Secretary of State; between now and Thursday, September 6, 2001 at 5:00 pm the expected and actual arrival times for any US domestic flight that is not departing or arriving to Phoenix, AZ; and your favorite web site and why the web site is your favorite. With the exception of the favorite web site, each item required either multiple sites or multiple levels to search. The identification of the favorite web site was introduced for comparison purposes later in the semester.The midterm and final examinations were composed of problems that built on the in-class and homework activities. Both examinations required the use of computers in the classroom. The submission of a completed examination was much like the homework assignments as an e-mail message with attachments. This approach of electronic submission worked well for reinforcing the use of computers for course deliverables, date / time stamping of completed activities, and a means for delivering graded results. The current technology leaves much to be desired for marking up a document in the traditional sense of hand grading an assignment or examination. However, the students and faculty member worked well with this form of response. More importantly, a major problem occurred after the completion of the final examination. One of the students, through an accident, submitted the executable part of a browser as an attachment, which brought the e-mail system to such a degraded state that grading was impossible until the problem was corrected. An ftp drop box would be simple solution in order to avoid this type of accident in the future until another solution is found for the e-mail system.In order to get students to work together on various aspects of the course, there was a group quiz and assignment component that was added about midway through the course. The group activities did not count towards the final grade, however the students were promised an award for the group that scored the highest number of points.There were two group quizzes on algorithm development and one out-of-class group assignment. The assignment was a group effort in website development. This assignment involved the development of a website that instructs. The conceptual functionality the group selected for theassignment was to be described in a one-page typed double spaced written report by November 9, 2001. During the November 30, 2001 class, each group presented to the rest of the class a prototype of what the website would look like to the end user. The reports and prototypes were subject to approval and/or refinement. Group members were expected to perform at approximately an equal amount of effort. There were five groups with four members in four groups and three members in one group that were randomly determined in class. Each group had one or more students in the computer science or computer systems engineering degree programs.The three group activities were graded on a basis of one million points. This amount of points was interesting from the standpoint of understanding relative value. There was one group elated over earning 600,000 points on the first quiz until the group found out that was the lowest score. In searching for the group award, the faculty member sought a computer circuit board in order to retrieve chips for each member of the best group. During the search, a staff member pointed out another staff member who salvages computers for the College. This second staff member obtained defective parts for each student in the class. The result was that each m ember of the highest scoring group received a motherboard, in other words, most of the internals that form a complete PC. All the other students received central processing units. Although these “awards” were defective parts, the students viewed these items as display artifacts that could be kept throughout their careers.C OURSE E VALUATIONOn a weekly basis, there were small assessments that were made about the progress of the course. One student was selected from each team to answer three questions about the activities of the day: “What was the most important topic covered today?”, “What topic covered was the ‘muddiest’?”, and “About what topic would you like to know more?”, as well as the opportunity to provide “Other comments.” Typically, the muddiest topic was the one introduced at the end of a class period and to be later elaborated on in the next class. By collecting these evaluation each class period, the instructor was able to keep a pulse on the class, to answer questions, to elaborate on areas considered “muddy” by the students, and to discuss, as time allowed, topics about which the students wished to know more.The overall course evaluation was quite good. Nineteen of the 21 students completed a course evaluation. A five-point scale w as used to evaluate aspects of the course and the instructor. An A was “very good,” a B was “good,” a C was “fair,” a D was “poor,” and an E was “not applicable.” The mean ranking was 4.35 on the course. An average ranking of 4.57, the highest for the s even criteria on the course in general, was for “Testbook/ supplementary material in support of the course.” The “Definition and application of criteria for grading” received the next highest marks in the course category with an average of 4.44. The lowest evaluation of the seven criteria for the course was a 4.17 for “Value of assigned homework in support of the course topics.”The mean student ranking of the instructor was 4.47. Of the nine criteria for the instructor, the highest ranking of 4.89 was “The instructor exhibited enthusiasm for and interest in the subject.” Given the nature and purpose of this course, this is a very meaningful measure of the success of the course. “The instructor was well prepared” was also judged high with a mean rank of 4.67. Two other important aspects of this course, “The instructor’s approach stimulated student thinking” and “The instructor related course material to its application” were ranked at 4.56 and 4.50, respectively. The lowest average rank of 4.11 was for “The instructor or assistants were available for outside assistance.” The instructor keep posted office hours, but there was not an assistant for the course.The “Overall quality of the course and instruction” received an average rank of 4.39 and “How do you rate yourself as a student in this course?” received an average rank of 4.35. Only a few of the students responded to the number of hours per week that they studies for the course. All of the students reported attending at least 70% of the time and 75% of the students said that they attended over 90% of the time. The students’ estimate seemed to be accurate.A common comment from the student evaluations was that “the professor was a fun teacher, made class fun, and explained everything well.” A common complaint was that the class was taught late (3:40 to 4:30) on a Friday. Some students judged the class to be an easy class that taught some basics about computers; other students did not think that there was enough time to cover all o f the topics. These opposite reactions make sense when we recall that the students were a broad mix of degree programs and of basic computer abilities. Similarly, some students liked that the class projects “were not overwhelming,” while other students thought that there was too little time to learn too much and too much work was required for a one credit class. Several students expressed that they wished the course could have been longer because they wanted to learn more about the general topics in the course. The instructor was judged to be a good role model by the students. This matched the pleasure that the instructor had with this class. He thoroughly enjoyed working with the students.A SSESSMENTS A ND C ONCLUSIONSNear the end of the Spring 2002 semester, a follow-up survey that consisted of three questions was sent to the students from the Fall 2001 semester computer basics course. These questions were: “Which CSE course(s) wereyou enrolled in this semester?; How did ASE 194 - Computer Basi cs help you in your coursework this semester?; and What else should be covered that we did not cover in the course?”. There were eight students who responded to the follow-up survey. Only one of these eight students had enrolled in a CSE course. There was consistency that the computer basics course helped in terms of being able to use computer applications in courses, as well as understanding concepts of computing. Many of the students asked for shortcuts in using the word processing and spreadsheet applications. A more detailed analysis of the survey results will be used for enhancements to the next offering of the computer basics course. During the Spring 2002 semester, there was another set of eight students from the Fall 2001 semester computer basi cs course who enrolled in one on the next possible computer science courses mentioned earlier, CSE 110 or CSE 200. The grade distribution among these students was one grade of A, four grades of B, two withdrawals, and one grade of D. The two withdrawals appear to be consistent with concerns in the other courses. The one grade of D was unique in that the student was enrolled in a CSE course concurrently with the computer basics course, contrary to the advice of the MEP program. Those students who were not enrolled in a computer science course during the Spring 2002 semester will be tracked through the future semesters. The results of the follow-up survey and computer science course grade analysis will provide a foundation for enhancements to the computer basics course that is planned to be offered again during the Fall 2002 semester.S UMMARY A ND F UTURE D IRECTIONSThis paper described a computer basics course. In general, the course was considered to be a success. The true evaluation of this course will be measured as we do follow-up studies of these students to determine how they fare in subsequent courses that require basic computer skills. Future offerings of the course are expected to address non-standard computing devices, such as robots as a means to inspire the students to excel in the computing field.R EFERENCES[1] Office of Institutional Analysis, Arizona State UniversityEnro llment Summary, Fall Semester , 1992-2001, Tempe,Arizona.[2] Reyes, Maria A., Gotes, Maria Amparo, McNeill, Barry,Anderson-Rowland, Mary R., “MEP Summer Bridge Program: A Model Curriculum Project,” 1999 Proceedings, American Society for Engineering Education, Charlotte, North Carolina, June 1999, CD-ROM, 8 pages.[3] Reyes, Maria A., Anderson-Rowland, Mary R., andMcCartney, Mary Ann, “Learning from our MinorityEngineering Students: Improving Retention,” 2000Proceedings, American Society for Engineering Education,St. Louis, Missouri, June 2000, Session 2470, CD-ROM, 10pages.[4] Adair, Jennifer K,, Reyes, Maria A., Anderson-Rowland,Mary R., McNeill, Barry W., “An Education/BusinessPartnership: ASU’s Minority Engineering Program and theTempe Chamber of Commerce,” 2001 Proceeding, AmericanSociety for Engineering Education, Albuquerque, NewMexico, June 2001, CD-ROM, 9 pages.[5] Adair, Jennifer K., Reyes, Maria A., Anderson-Rowland,Mary R., Kouris, Demitris A., “Workshops vs. Tutoring:How ASU’s Minority Engineering Program is Changing theWay Engineering Students Learn, “ Frontiers in Education’01 Conference Proceedings, Reno, Nevada, October 2001,CD-ROM, pp. T4G-7 – T4G-11.[6] Reyes, Maria A., Anderson-Rowland, Mary R., Fletcher,Shawna L., and McCartney, Mary Ann, “ModelCollaboration within Minority Engineering StudentSocieties,” 2000 Proceedings, American Society forEngineering Education, St. Louis, Missouri, June 2000, CD-ROM, 8 pages.[7] Anderson-Rowland, Mary R., Blaisdell, Stephanie L.,Fletcher, Shawna, Fussell, Peggy A., Jordan, Cathryne,McCartney, Mary Ann, Reyes, Maria A., and White, Mary,“A Comprehensive Programmatic Approach to Recruitmentand Retention in the College of Engineering and AppliedSciences,” Frontiers in Education ’99 ConferenceProceedings, San Juan, Puerto Rico, November 1999, CD-ROM, pp. 12a7-6 – 12a7-13.[8] Anderson-Rowland, Mary R., Blaisdell, Stephanie L.,Fletcher, Shawna L., Fussell, Peggy A., McCartney, MaryAnn, Reyes, Maria A., and White, Mary Aleta, “ACollaborative Effort to Recruit and Retain UnderrepresentedEngineering Students,” Journal of Women and Minorities inScience and Engineering, vol.5, pp. 323-349, 1999.[9] Pfleeger, S. L., Software Engineering: Theory and Practice,Prentice-Hall, Inc., Upper Saddle River, NJ, 1998.。

01_基本管理命令

目录第1章交换机基本配置命令.......................................................1-11.1 基本配置命令...................................................................................1-11.1.1 clock set................................................................................................1-11.1.2 config.....................................................................................................1-11.1.3 debug ssh-server..................................................................................1-11.1.4 enable....................................................................................................1-11.1.5 enable password...................................................................................1-21.1.6 exec-timeout..........................................................................................1-21.1.7 exit..........................................................................................................1-31.1.8 help........................................................................................................1-31.1.9 hostname...............................................................................................1-31.1.10 ip host..................................................................................................1-41.1.11 ipv6 host..............................................................................................1-41.1.12 ip http server.......................................................................................1-41.1.13 language..............................................................................................1-51.1.14 login.....................................................................................................1-51.1.15 login local............................................................................................1-51.1.16 password.............................................................................................1-61.1.17 reload...................................................................................................1-61.1.18 service password-encryption............................................................1-61.1.19 service terminal-length.......................................................................1-71.1.20 set default............................................................................................1-71.1.21 setup....................................................................................................1-71.1.22 show clock..........................................................................................1-81.1.23 show temperature...............................................................................1-81.1.24 show tech-support..............................................................................1-81.1.25 show version.......................................................................................1-81.1.26 username.............................................................................................1-81.1.27 web language......................................................................................1-91.1.28 web-user..............................................................................................1-91.1.29 write...................................................................................................1-101.2 远程管理........................................................................................1-101.2.1 authentication login............................................................................1-101.2.2 terminal length....................................................................................1-101.2.3 terminal monitor.................................................................................1-111.2.4 telnet....................................................................................................1-111.2.5 telnet-server enable............................................................................1-121.2.6 telnet-server securityip......................................................................1-121.2.7 telnet-server securityipv6..................................................................1-121.2.8 telnet-user...........................................................................................1-131.2.9 ssh-server authentication-retries......................................................1-131.2.10 ssh-server enable.............................................................................1-131.2.11 ssh-server host-key create rsa........................................................1-141.2.12 ssh-server timeout............................................................................1-141.2.13 ssh-user.............................................................................................1-141.2.14 show ssh-server................................................................................1-151.2.15 show ssh-user...................................................................................1-151.2.16 show telnet login...............................................................................1-151.2.17 show telnet user................................................................................1-16 1.3 配置交换机的IP地址.......................................................................1-161.3.1 interface vlan.......................................................................................1-161.3.2 interface ethernet................................................................................1-161.3.3 ip address............................................................................................1-161.3.4 ipv6 address........................................................................................1-171.3.5 ip bootp-client enable.........................................................................1-171.3.6 ip dhcp-client enable..........................................................................1-18 1.4 SNMP命令......................................................................................1-181.4.1 debug snmp mib.................................................................................1-181.4.2 debug snmp kernel.............................................................................1-191.4.3 rmon enable........................................................................................1-191.4.4 show snmp..........................................................................................1-191.4.5 show snmp engineid..........................................................................1-201.4.6 show snmp group...............................................................................1-211.4.7 show snmp mib...................................................................................1-211.4.8 show snmp status...............................................................................1-211.4.9 show snmp user.................................................................................1-221.4.10 show snmp view...............................................................................1-221.4.11 snmp-server community..................................................................1-231.4.12 snmp-server enable..........................................................................1-231.4.13 snmp-server enable traps................................................................1-241.4.14 snmp-server engineid.......................................................................1-241.4.15 snmp-server group...........................................................................1-241.4.16 snmp-server host..............................................................................1-251.4.17 snmp-server securityip....................................................................1-261.4.18 snmp-server securityip enable........................................................1-261.4.19 snmp-server view..............................................................................1-261.4.20 snmp-server user..............................................................................1-27 1.5 交换机升级命令..............................................................................1-281.5.1 copy（FTP）.......................................................................................1-281.5.2 copy（TFTP）.....................................................................................1-291.5.3 dir.........................................................................................................1-301.5.4 ftp-server enable.................................................................................1-311.5.5 ftp-server timeout...............................................................................1-311.5.6 ip ftp.....................................................................................................1-311.5.7 show ftp...............................................................................................1-321.5.8 show tftp..............................................................................................1-321.5.9 tftp-server enable................................................................................1-321.5.10 tftp-server retransmission-number.................................................1-331.5.11 tftp-server transmission-timeout.....................................................1-33第2章集群配置命令..................................................................2-12.1 clear cluster candidate table..........................................................2-12.2 cluser auto-add enable...................................................................2-12.3 cluster commander.........................................................................2-12.4 cluster heartbeat.............................................................................2-22.5 cluster holdtime..............................................................................2-22.6 cluster ip-pool.................................................................................2-32.7 cluster member...............................................................................2-32.8 cluser register timer.......................................................................2-42.9 cluster reset member.....................................................................2-42.10 cluster run.....................................................................................2-42.11 cluster update member.................................................................2-52.12 debug cluster application............................................................2-52.13 debug cluster packets..................................................................2-52.14 debug cluster statemach..............................................................2-62.15 rcommand commander................................................................2-62.16 rcommand member......................................................................2-62.17 show cluster..................................................................................2-72.18 show cluster candidates..............................................................2-72.19 show cluster members.................................................................2-7第1章交换机基本配置命令1.1基本配置命令1.1.1 clock set命令：clock set <HH:MM:SS> <YYYY.MM.DD>功能：设置系统日期和时钟。

LabWindows CVI 2015 Release Notes说明书

RELEASE NOTESLabWindows /CVI Version 2015These release notes introduce LabWindows ™/CVI ™ 2015. Refer to this document for system requirements, installation and activation instructions, and information about new features in LabWindows/CVI.ContentsLabWindows/CVI System Requirements (1)Installing LabWindows/CVI (2)Before Installation (2)Running the Installation (2)Activating LabWindows/CVI (4)What’s New in LabWindows/CVI? (4)Upgraded Version of Clang (4)Improved Source Code Browsing (4)Include Runtime Installers in Distributions (5)Include Driver and Component Files in Patch Distributions (5)Updated Windows SDK (5)Improved Installer Messages and Errors (6)Bug Fixes (6)LabWindows/CVI Resources...................................................................................................6LabWindows/CVI System Requirements To run LabWindows/CVI, you must have the following:•Personal computer using a Pentium 4/M or equivalent processor •Microsoft operating systems:–Windows 8.1 (32-bit and 64-bit)–Windows 8.0 (32-bit and 64-bit)–Windows 7 (32-bit and 64-bit), including Starter Edition –Windows Server 2012 R2 (64-bit)–Windows Server 2008 R2 Service Pack 2 (64-bit)Note LabWindows/CVI supports only R2 editions of Windows Server.•1024 × 768 resolution (or higher) video adapter •Minimum of 512 MB of RAM, 2 GB recommended™™•7 GB free hard disk space for full installation, which includes the Windows SDK 8.1 and the Microsoft .NET Framework 4.5.2; additional space needed for National Instruments Device Drivers•Microsoft-compatible mouseInstalling LabWindows/CVIThe LabWindows/CVI Platform DVD includes LabWindows/CVI and the following modules and toolkits:Modules•Real-Time Module•Vision Development ModuleToolkits•Real-Time Execution Trace Toolkit•SQL Toolkit•Signal Processing Toolkit•PID Toolkit•Execution Profiler Toolkit•ECU Measurement and Calibration Toolkit•Automotive Diagnostic Command SetIf you purchased any of these modules or toolkits, you can install them using the LabWindows/CVI Platform DVD. If you want to evaluate any of these modules or toolkits before purchasing them, you can install these add-ons from the LabWindows/CVIPlatform DVD.Before InstallationKeep the following points in mind before you install LabWindows/CVI:•If you already have a different version of LabWindows/CVI installed on your computer, be sure to install version 2015 in a different directory. If you want to install to an existing directory, uninstall the other version before installing LabWindows/CVI 2015.•You must have administrator privileges to install LabWindows/CVI.•If your software is part of a V olume License Agreement (VLA), contact your VLA administrator for installation instructions.Running the InstallationComplete the following steps to install LabWindows/CVI:LabWindows/CVI Runtime with the LabWindows/CVI 2015 Runtime. To restore theprevious runtime, uninstall LabWindows/CVI 2015, the LabWindows/CVI 20152||LabWindows/CVI Release NotesLabWindows/CVI Release Notes |© National Instruments |3Runtime, and any previous versions of LabWindows/CVI and LabWindows/CVIRuntimes on the computer. Then reinstall the LabWindows/CVI version you want to use, along with any additional National Instruments software you might haveinstalled.1.Insert the LabWindows/CVI media into the disk drive. If the media does not runautomatically, open Windows Explorer, right-click the disk drive icon, and selectAutoPlay .2.On installation startup, the National Instruments LabWindows/CVI 2015 screen appears. Click Install LabWindows/CVI, Modules, and Toolkits .3.Continue to follow the instructions on the screen.Note If you have a serial number for the product you want to install, enter thenumber during installation when you are prompted. You also can activate the product after installation. For more information about finding serial numbers, refer to/info and enter SerialNumbers_en as the Info Code.Each product on the LabWindows/CVI Platform DVD has a different serial number, with the possible exception of the LabWindows/CVI Execution Profiler Toolkit.The Execution Profiler Toolkit does not require a separate license if youhave a LabWindows/CVI Full Development System license. If you have theLabWindows/CVI Base Package, you can install the Execution Profiler Toolkit for evaluation.4.If you select Device Drivers in the Features panel, the LabWindows/CVI installer promptsyou to insert the National Instruments Device Drivers media, which is available on . The NI Device Drivers media is required only if you want to upgrade existing driver software to the latest version. Otherwise, you can ignore this prompt.5.If you have an active Internet connection, the installer prompts you to select Windows SDK components to install. The components you select are downloaded and installed from the Microsoft website. For more information about the components, refer to /info and enter the Info Code CVI2015_WindowsSDK .If you do not have an active Internet connection, LabWindows/CVI installs all Windows SDK components, which might not be the latest components available on the Microsoft website.Note If you cancel the Windows SDK installation, LabWindows/CVI will notfunction properly. You can download the Windows SDK from one of the following places:•The Microsoft website•—Visit /info and enter the Info CodeDownloadMSDTWindowsSDK4| |LabWindows/CVI Release Notes6.Install hardware. Refer to your device documentation, such as printed manuals or PDFs, for information about installing your NI hardware.7.To activate a National Instruments product, refer to the What’s New in LabWindows/CVI? section of this document.Activating LabWindows/CVIIf you did not enter a serial number during installation, click Activate Products in the License Status dialog box to launch the NI Activation Wizard.Once you choose your activation method and launch the NI Activation Wizard, follow the instructions on the screen to activate LabWindows/CVI. For more information about activation, refer to the Activating Your Software topic in the LabWindows/CVI Help .Note If you are unable to activate LabWindows/CVI, refer to the web page at/activate .What’s New in LabWindows/CVI?This section includes information about changes and enhancements in LabWindows/CVI 2015.Upgraded Version of ClangLabWindows/CVI has updated the Clang 2.9 compiler to Clang 3.3. This upgrade provides the following features:•New warning flags and warnings messages •Improved detection of unintialized local variables •Improved stability when building large files •Up to 21% faster execution speed for 64-bit binariesNote The compiler backend is particularly suited for optimizing resources used in mathematical calculations, so you will see the highest performance gains if youperform complex computation, mathematics, or analysis.Improved Source Code BrowsingIn addition to the updated compiler, source code browsing also has been improved. These improvements include the following features:•Improved array support for the function prototype tooltip, Select Variable dialog box, and documentation generation from source code •Improved preprocessor support with macros •Improved stability due to various fixesLabWindows/CVI Release Notes |© National Instruments |5Include Runtime Installers in DistributionsSelect the Only display runtime installers option in the Drivers & Components tab of the Edit Installer dialog box to show which runtime installers are available for deployment. This option makes it easy to distinguish between full installers and runtime installers. Runtime installers are typically smaller in size, allowing you more control over the size of your distribution.Include Driver and Component Files in Patch DistributionsYou now can include NI components and driver files in your patch distributions. You also can choose to include in your patch all products with upgrades or patches by selecting the Include driver updates option in the Drivers & Components tab of the Edit Installer dialog box.Updated Windows SDKThis version of LabWindows/CVI installs the Windows SDK 8.1. Refer to MSDN for a complete list of enhancements. Some of the features provided by the Windows SDK include the following items:•Handle processes and threads—You can use functions such asSetProcessInformation to lower the priority of processes that perform background operations, GetProcessInformation to get the memory priority of a process, SetThreadInformation to lower the priority of a thread that does not need to run immediately, and GetThreadInformation to get the priority of a thread.•Get the firmware type—Call GetFirmwareType to find the firmware type of your users’ computers.•Speed up operations that access the same file data repeatedly—Call OperationStart and OperationEnd .•Take advantage of better virtual memory handling—Call functions such asPrefetchVirtualMemory , OfferVirtualMemory , ReclaimVirtualMemory , and DiscardVirtualMemory .•Take advantage of better physical memory handling—Call functions such as GetMemoryErrorHandlingCapabilities ,RegisterBadMemoryNotification , andUnregisterBadMemoryNotification .•Call helpers for National Language Support functions—For example, you can call IsValidNLSVersion to determine whether a version is valid for a National Language Support function.To use the Windows SDK 8.1, include the following in the Compiler Defines dialog box: _WIN32_WINNT=_WIN32_WINNT_WIN8 or WINVER=_WIN32_WINNT_WIN8.NoteThe Windows SDK 8.1 requires Windows 7 (minimum).Improved Installer Messages and ErrorsErrors and warning messages you receive when you create installers provide more useful information.Bug FixesFor a list of bugs fixed in LabWindows/CVI 2015, refer to the NI web page at /info and enter the Info Code exmvwx.LabWindows/CVI ResourcesHow do I get started?Read the Getting Started with LabWindows/CVI manual, which provides a tutorial for learning basic LabWindows/CVI program development techniques.Are there known issues or late-breaking information?Refer to the LabWindows/CVI Readme, which you can access from Start»All Programs»National Instruments»LabWindows CVI 2015»LabWindows CVI 2015 Documentation. The readme file contains information about known issues.Where can I find reference information?The LabWindows/CVI Help contains complete reference information. Use the Search tab in the LabWindows/CVI Help to quickly locate specific information.Where can I find examples?Find examples with the NI Example Finder, which you can access by selecting Help»Find Examples.LabWindows/CVI example programs are located in the following location:C:\Users\Public\Documents\National Instruments\CVI2015\samples.Is there a list of LabWindows/CVI documentation?The Guide to LabWindows/CVI Documentation topic describes documentation available for new users and upgrade users. In addition, this topic provides links to LabWindows/CVI documentation, including manuals and web resources. You can access the Guide to LabWindows/CVI Documentation topic through the LabWindows/CVI Help.Where else can I go for LabWindows/CVI information?Visit the LabWindows/CVI w ebsite at for the most up-to-date information about LabWindows/CVI.6||LabWindows/CVI Release NotesRefer to the NI Trademarks and Logo Guidelines at /trademarks for more information on National Instruments trademarks. Other product and company names mentioned herein are trademarks or trade names of their respective companies. For patents covering National Instruments products/technology, refer to the appropriate location: Help»Patents in your software, the patents.txt file on your media, or the National Instruments Patents Notice at /patents. You can find information about end-user license agreements (EULAs) and third-party legal notices in the readme file for your NI product. Refer to the Export Compliance Information at /legal/export-compliance for the National Instruments global trade compliance policy and how to obtain relevant HTS codes, ECCNs, and other import/export data. NI MAKES NO EXPRESS OR IMPLIED WARRANTIES AS TO THE ACCURACY OF THE INFORMATION CONTAINED HEREIN AND SHALL NOT BE LIABLE FOR ANY ERRORS. U.S. Government Customers: The data contained in this manual was developed at private expense and is subject to the applicable limited rights and restricted data rights as set forth in FAR 52.227-14, DFAR 252.227-7014, and DFAR 252.227-7015.© 2003–2015 National Instruments. All rights reserved.373607N-01Aug15。

小学高年级课后服务scratch3.0编程教学设计一阶第27课植物大战僵尸-僵尸来袭教学设计

6. 辅助材料：
- 课程教材
- 学生编程作品集
- 教学评价表
- 学习任务单
- 知识点总结手册
7. 网络资源：
- 教育资源库（不含网址）
- 编程社区交流平台（不含网址）
教学流程
（一）课前准备（预计用时：5分钟）
学生预习：
发放预习材料，引导学生提前了解循环结构和条件语句的学习内容，标记出有疑问或不懂的地方。
核心素养目标
本节课旨在培养学生以下学科核心素养：
1. 信息素养：通过学习scratch3.0编程，使学生掌握基本的编程知识和技能，提高解决问题的能力，形成良好的信息处理和运用能力。
2. 逻辑思维能力：在编程过程中，学生需要运用逻辑思维，设计合理的程序结构，培养分析问题和解决问题的能力。
3. 创新能力：鼓励学生在编程实践中勇于尝试，发挥创意，培养创新精神和实践能力。
5. 教师需关注学生的行为习惯，加强对学生的监督和引导，培养学生良好的时间管理能力和自律意识，提高课堂学习效果。
学具准备
Xxx
课型
新授课
教法学法
讲授法
课时
第一课时
步骤
师生互动设计
二次备课
教学资源
1. 硬件资源：
- 教室计算机
- 学生平板电脑
- 投影仪
- 网络连接设备
2. 软件资源：
- scratch3.0编程软件
然而，在教学过程中，我也发现了一些不足之处。在讲解循环结构和条件语句时，可能讲得有些快，导致部分学生跟不上我的思路。在今后的教学中，我需要更加关注学生的接受程度，适时调整教学节奏，确保每个学生都能听懂、学会。
此外，小组合作环节中学生们的参与度有待提高。有些学生在讨论过程中显得不够积极，可能是因为他们对编程知识的掌握还不够扎实，或者是对团队合作不够重视。针对这个问题，我计划在接下来的课程中加强学生团队合作能力的培养，让他们在合作中共同进步。

I_Deas训练教程讲义_入门篇

I-DEAS训练课程讲义第壹章启动 I-DEAS启动 I-DEAS，必须定义事先给定 4组数据设定Project (专案)：在新工作来临时，就需定义一个项目名称，其中内含储存标准零件库储存标准零件库(Catalog) ，公用数据库(Library) 与模型文件案的区域。

Model File (工程图文件)：是建立在项目底下的一个个人工作档案，可用来指定档案为存放路径与文件名设定的位置。

Application (应用)：是在I-DEAS内选定所使用的模块。

包含设计、分析、测试、制造与数据管理等。

Task (工作)：在所选定的其中某项模块中，再选取其中的工作项目，如在设计模块中，又含实体、组装、机构…等。

1 - 1I-DEAS 训练课程讲义I-DEAS使用者接口◎图示面板 (Icon Panel)分为四个主要区域：下拉式菜单、工作图标区、应用图标区及共享图标区。

◎使用绘图窗口产生选取与修改图素：当需要显示重迭或与突出式菜单时，将会显示在此窗口内。

◎提示窗口如何完成一程序或工作的信息，您也可以在命令行中输入资料以回复提示。

I-DEAS 训练课程讲义鼠标按钮操作表按钮操作用途■□□左键 (左键) 快按选择图像，窗体及选格项，选取图样Shift键及按此钮多重选取图素或取消已选择图素按着并拖动跳出更多选择图像选取屏幕上方块区域的项目(不用于选取图型)按多下〝避走〞零件树状建构阶层，如：第一选取边或面，第二选取整个零件，第三选取特征。

选择时出现反白，特征边缘出现黄框或零件出现白边框Control键及按此钮关闭动态图形指针□■□中键(中键) 按一下作用如(Return)键，用于采纳默认值或终止操作□□■右键(右键) 接着，拖动选择其它的辅助菜单单＊通常如果您能看到某个图景，您就可以选到它。

只要将光标放在图素上并按下左鼠标键极可，当该图素被选取后，它会以反白显示。

＊若是一次选取多个图素时，按下〝Shift〞键，或拉伸出一方框以包含若干临近图素，重复挑选一图案，即可除该选择。

IDL_total

基础篇－编程基础
IDLDE是IDL的集成开发环境，可以使用是的集成开发环境，命令进行交互式命令运行，的集成开发环境可以使用IDL命令进行交互式命令运行，编写、调试、运行命令进行交互式命令运行编写、调试、运行IDL程程使用GUI Builer开发用户界面，使用项目管理器管理工程项目等。开发用户界面，序，使用开发用户界面使用项目管理器管理工程项目等。 1. IDL程序程序批处理：由一系列IDL命令组成，以IDL->@batchfile方式运行。批处理文件运行时并不编译，因批处理此使用控制结构时必须大量使用续行符（$）,给书写、理解造成困难。主程序：与批处理相似，但以end结束，以IDL->.run profile方式运行。主程序运行时先编译，因主程序此可以正常使用控制结构。过程：过程：与主程序相似，但以pro proname开始，以end结束。以IDL->proname方式运行（也可以先运行IDL->.compile proname，编译但不运行）。函数：函数：与过程相似，但以function fnname开始，以end结束，并以return语句返回一个IDL变量。以IDL->ret=fnname(para_list)方式运行。在IDL系统中，一个过程或函数即为一个新的IDL命令。变量作用范围：变量作用范围：批处理和主程序方式的变量为全局变量，可以在IDL开发环境中使用。过程和函数的变量为局部变量，只在过程和函数运行过程中有效。
基础篇－编程基础
2. 参数传递位置参数：在参数列表中按位置列出参数名，严格的顺序限制。通常用于必选参数。位置参数定义：pro batch ,para1 ,para2 ,... 调用：IDL->batch ,para1 ,para2 ,… 关键字参数：关键字参数与位置无关，且可以与位置参数混合位置。通常放在位置参数之后，用关键字参数于可选参数。定义：pro batch ,keywordname=keywordsymbol ,... 调用：IDL->batch ,keywordname=keywordsymbol ,… IDL->batch ,/keywordname 注意：keywordname用于定义，keywordsymbol用于调用。引用传递和值传递：引用传递和值传递：所有变量为引用传递，其值会被修改。系统变量、下标变量、表达式和常量均为值传递，原变量的值不被修改。参数传递了吗？传递了什么？参数传递了吗？传递了什么？ n_params()：返回位置参数的个数 keyword_set()：关键字参数为不为0常量或已定义的引用传递时返回1，否则返回0 arg_present()：关键字参数为引用传递时返回1（无论是否定义），否则返回0 n_elements()：关键字参数未传递或未定义返回0，否则返回非0数

ACM-GIS%202006-A%20Peer-to-Peer%20Spatial%20Cloaking%20Algorithm%20for%20Anonymous%20Location-based%

A Peer-to-Peer Spatial Cloaking Algorithm for AnonymousLocation-based Services∗Chi-Yin Chow Department of Computer Science and Engineering University of Minnesota Minneapolis,MN cchow@ Mohamed F.MokbelDepartment of ComputerScience and EngineeringUniversity of MinnesotaMinneapolis,MNmokbel@Xuan LiuIBM Thomas J.WatsonResearch CenterHawthorne,NYxuanliu@ABSTRACTThis paper tackles a major privacy threat in current location-based services where users have to report their ex-act locations to the database server in order to obtain their desired services.For example,a mobile user asking about her nearest restaurant has to report her exact location.With untrusted service providers,reporting private location in-formation may lead to several privacy threats.In this pa-per,we present a peer-to-peer(P2P)spatial cloaking algo-rithm in which mobile and stationary users can entertain location-based services without revealing their exact loca-tion information.The main idea is that before requesting any location-based service,the mobile user will form a group from her peers via single-hop communication and/or multi-hop routing.Then,the spatial cloaked area is computed as the region that covers the entire group of peers.Two modes of operations are supported within the proposed P2P spa-tial cloaking algorithm,namely,the on-demand mode and the proactive mode.Experimental results show that the P2P spatial cloaking algorithm operated in the on-demand mode has lower communication cost and better quality of services than the proactive mode,but the on-demand incurs longer response time.Categories and Subject Descriptors:H.2.8[Database Applications]:Spatial databases and GISGeneral Terms:Algorithms and Experimentation. Keywords:Mobile computing,location-based services,lo-cation privacy and spatial cloaking.1.INTRODUCTIONThe emergence of state-of-the-art location-detection de-vices,e.g.,cellular phones,global positioning system(GPS) devices,and radio-frequency identiﬁcation(RFID)chips re-sults in a location-dependent information access paradigm,∗This work is supported in part by the Grants-in-Aid of Re-search,Artistry,and Scholarship,University of Minnesota. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for proﬁt or commercial advantage and that copies bear this notice and the full citation on theﬁrst page.To copy otherwise,to republish,to post on servers or to redistribute to lists,requires prior speciﬁc permission and/or a fee.ACM-GIS’06,November10-11,2006,Arlington,Virginia,USA. Copyright2006ACM1-59593-529-0/06/0011...$5.00.known as location-based services(LBS)[30].In LBS,mobile users have the ability to issue location-based queries to the location-based database server.Examples of such queries include“where is my nearest gas station”,“what are the restaurants within one mile of my location”,and“what is the traﬃc condition within ten minutes of my route”.To get the precise answer of these queries,the user has to pro-vide her exact location information to the database server. With untrustworthy servers,adversaries may access sensi-tive information about speciﬁc individuals based on their location information and issued queries.For example,an adversary may check a user’s habit and interest by knowing the places she visits and the time of each visit,or someone can track the locations of his ex-friends.In fact,in many cases,GPS devices have been used in stalking personal lo-cations[12,39].To tackle this major privacy concern,three centralized privacy-preserving frameworks are proposed for LBS[13,14,31],in which a trusted third party is used as a middleware to blur user locations into spatial regions to achieve k-anonymity,i.e.,a user is indistinguishable among other k−1users.The centralized privacy-preserving frame-work possesses the following shortcomings:1)The central-ized trusted third party could be the system bottleneck or single point of failure.2)Since the centralized third party has the complete knowledge of the location information and queries of all users,it may pose a serious privacy threat when the third party is attacked by adversaries.In this paper,we propose a peer-to-peer(P2P)spatial cloaking algorithm.Mobile users adopting the P2P spatial cloaking algorithm can protect their privacy without seeking help from any centralized third party.Other than the short-comings of the centralized approach,our work is also moti-vated by the following facts:1)The computation power and storage capacity of most mobile devices have been improv-ing at a fast pace.2)P2P communication technologies,such as IEEE802.11and Bluetooth,have been widely deployed.3)Many new applications based on P2P information shar-ing have rapidly taken shape,e.g.,cooperative information access[9,32]and P2P spatio-temporal query processing[20, 24].Figure1gives an illustrative example of P2P spatial cloak-ing.The mobile user A wants toﬁnd her nearest gas station while beingﬁve anonymous,i.e.,the user is indistinguish-able amongﬁve users.Thus,the mobile user A has to look around andﬁnd other four peers to collaborate as a group. In this example,the four peers are B,C,D,and E.Then, the mobile user A cloaks her exact location into a spatialA B CDEBase Stationregion that covers the entire group of mobile users A ,B ,C ,D ,and E .The mobile user A randomly selects one of the mobile users within the group as an agent .In the ex-ample given in Figure 1,the mobile user D is selected as an agent.Then,the mobile user A sends her query (i.e.,what is the nearest gas station)along with her cloaked spa-tial region to the agent.The agent forwards the query to the location-based database server through a base station.Since the location-based database server processes the query based on the cloaked spatial region,it can only give a list of candidate answers that includes the actual answers and some false positives.After the agent receives the candidate answers,it forwards the candidate answers to the mobile user A .Finally,the mobile user A gets the actual answer by ﬁltering out all the false positives.The proposed P2P spatial cloaking algorithm can operate in two modes:on-demand and proactive .In the on-demand mode,mobile clients execute the cloaking algorithm when they need to access information from the location-based database server.On the other side,in the proactive mode,mobile clients periodically look around to ﬁnd the desired number of peers.Thus,they can cloak their exact locations into spatial regions whenever they want to retrieve informa-tion from the location-based database server.In general,the contributions of this paper can be summarized as follows:1.We introduce a distributed system architecture for pro-viding anonymous location-based services (LBS)for mobile users.2.We propose the ﬁrst P2P spatial cloaking algorithm for mobile users to entertain high quality location-based services without compromising their privacy.3.We provide experimental evidence that our proposed algorithm is eﬃcient in terms of the response time,is scalable to large numbers of mobile clients,and is eﬀective as it provides high-quality services for mobile clients without the need of exact location information.The rest of this paper is organized as follows.Section 2highlights the related work.The system model of the P2P spatial cloaking algorithm is presented in Section 3.The P2P spatial cloaking algorithm is described in Section 4.Section 5discusses the integration of the P2P spatial cloak-ing algorithm with privacy-aware location-based database servers.Section 6depicts the experimental evaluation of the P2P spatial cloaking algorithm.Finally,Section 7con-cludes this paper.2.RELATED WORKThe k -anonymity model [37,38]has been widely used in maintaining privacy in databases [5,26,27,28].The main idea is to have each tuple in the table as k -anonymous,i.e.,indistinguishable among other k −1tuples.Although we aim for the similar k -anonymity model for the P2P spatial cloaking algorithm,none of these techniques can be applied to protect user privacy for LBS,mainly for the following four reasons:1)These techniques preserve the privacy of the stored data.In our model,we aim not to store the data at all.Instead,we store perturbed versions of the data.Thus,data privacy is managed before storing the data.2)These approaches protect the data not the queries.In anonymous LBS,we aim to protect the user who issues the query to the location-based database server.For example,a mobile user who wants to ask about her nearest gas station needs to pro-tect her location while the location information of the gas station is not protected.3)These approaches guarantee the k -anonymity for a snapshot of the database.In LBS,the user location is continuously changing.Such dynamic be-havior calls for continuous maintenance of the k -anonymity model.(4)These approaches assume a uniﬁed k -anonymity requirement for all the stored records.In our P2P spatial cloaking algorithm,k -anonymity is a user-speciﬁed privacy requirement which may have a diﬀerent value for each user.Motivated by the privacy threats of location-detection de-vices [1,4,6,40],several research eﬀorts are dedicated to protect the locations of mobile users (e.g.,false dummies [23],landmark objects [18],and location perturbation [10,13,14]).The most closed approaches to ours are two centralized spatial cloaking algorithms,namely,the spatio-temporal cloaking [14]and the CliqueCloak algorithm [13],and one decentralized privacy-preserving algorithm [23].The spatio-temporal cloaking algorithm [14]assumes that all users have the same k -anonymity requirements.Furthermore,it lacks the scalability because it deals with each single request of each user individually.The CliqueCloak algorithm [13]as-sumes a diﬀerent k -anonymity requirement for each user.However,since it has large computation overhead,it is lim-ited to a small k -anonymity requirement,i.e.,k is from 5to 10.A decentralized privacy-preserving algorithm is proposed for LBS [23].The main idea is that the mobile client sends a set of false locations,called dummies ,along with its true location to the location-based database server.However,the disadvantages of using dummies are threefold.First,the user has to generate realistic dummies to pre-vent the adversary from guessing its true location.Second,the location-based database server wastes a lot of resources to process the dummies.Finally,the adversary may esti-mate the user location by using cellular positioning tech-niques [34],e.g.,the time-of-arrival (TOA),the time diﬀer-ence of arrival (TDOA)and the direction of arrival (DOA).Although several existing distributed group formation al-gorithms can be used to ﬁnd peers in a mobile environment,they are not designed for privacy preserving in LBS.Some algorithms are limited to only ﬁnding the neighboring peers,e.g.,lowest-ID [11],largest-connectivity (degree)[33]and mobility-based clustering algorithms [2,25].When a mo-bile user with a strict privacy requirement,i.e.,the value of k −1is larger than the number of neighboring peers,it has to enlist other peers for help via multi-hop routing.Other algorithms do not have this limitation,but they are designed for grouping stable mobile clients together to facil-Location-based Database ServerDatabase ServerDatabase ServerFigure 2:The system architectureitate eﬃcient data replica allocation,e.g.,dynamic connec-tivity based group algorithm [16]and mobility-based clus-tering algorithm,called DRAM [19].Our work is diﬀerent from these approaches in that we propose a P2P spatial cloaking algorithm that is dedicated for mobile users to dis-cover other k −1peers via single-hop communication and/or via multi-hop routing,in order to preserve user privacy in LBS.3.SYSTEM MODELFigure 2depicts the system architecture for the pro-posed P2P spatial cloaking algorithm which contains two main components:mobile clients and location-based data-base server .Each mobile client has its own privacy proﬁle that speciﬁes its desired level of privacy.A privacy proﬁle includes two parameters,k and A min ,k indicates that the user wants to be k -anonymous,i.e.,indistinguishable among k users,while A min speciﬁes the minimum resolution of the cloaked spatial region.The larger the value of k and A min ,the more strict privacy requirements a user needs.Mobile users have the ability to change their privacy proﬁle at any time.Our employed privacy proﬁle matches the privacy re-quirements of mobiles users as depicted by several social science studies (e.g.,see [4,15,17,22,29]).In this architecture,each mobile user is equipped with two wireless network interface cards;one of them is dedicated to communicate with the location-based database server through the base station,while the other one is devoted to the communication with other peers.A similar multi-interface technique has been used to implement IP multi-homing for stream control transmission protocol (SCTP),in which a machine is installed with multiple network in-terface cards,and each assigned a diﬀerent IP address [36].Similarly,in mobile P2P cooperation environment,mobile users have a network connection to access information from the server,e.g.,through a wireless modem or a base station,and the mobile users also have the ability to communicate with other peers via a wireless LAN,e.g.,IEEE 802.11or Bluetooth [9,24,32].Furthermore,each mobile client is equipped with a positioning device, e.g.,GPS or sensor-based local positioning systems,to determine its current lo-cation information.4.P2P SPATIAL CLOAKINGIn this section,we present the data structure and the P2P spatial cloaking algorithm.Then,we describe two operation modes of the algorithm:on-demand and proactive .4.1Data StructureThe entire system area is divided into grid.The mobile client communicates with each other to discover other k −1peers,in order to achieve the k -anonymity requirement.TheAlgorithm 1P2P Spatial Cloaking:Request Originator m 1:Function P2PCloaking-Originator (h ,k )2://Phase 1:Peer searching phase 3:The hop distance h is set to h4:The set of discovered peers T is set to {∅},and the number ofdiscovered peers k =|T |=05:while k <k −1do6:Broadcast a FORM GROUP request with the parameter h (Al-gorithm 2gives the response of each peer p that receives this request)7:T is the set of peers that respond back to m by executingAlgorithm 28:k =|T |;9:if k <k −1then 10:if T =T then 11:Suspend the request 12:end if 13:h ←h +1;14:T ←T ;15:end if 16:end while17://Phase 2:Location adjustment phase 18:for all T i ∈T do19:|mT i .p |←the greatest possible distance between m and T i .pby considering the timestamp of T i .p ’s reply and maximum speed20:end for21://Phase 3:Spatial cloaking phase22:Form a group with k −1peers having the smallest |mp |23:h ←the largest hop distance h p of the selected k −1peers 24:Determine a grid area A that covers the entire group 25:if A <A min then26:Extend the area of A till it covers A min 27:end if28:Randomly select a mobile client of the group as an agent 29:Forward the query and A to the agentmobile client can thus blur its exact location into a cloaked spatial region that is the minimum grid area covering the k −1peers and itself,and satisﬁes A min as well.The grid area is represented by the ID of the left-bottom and right-top cells,i.e.,(l,b )and (r,t ).In addition,each mobile client maintains a parameter h that is the required hop distance of the last peer searching.The initial value of h is equal to one.4.2AlgorithmFigure 3gives a running example for the P2P spatial cloaking algorithm.There are 15mobile clients,m 1to m 15,represented as solid circles.m 8is the request originator,other black circles represent the mobile clients received the request from m 8.The dotted circles represent the commu-nication range of the mobile client,and the arrow represents the movement direction.Algorithms 1and 2give the pseudo code for the request originator (denoted as m )and the re-quest receivers (denoted as p ),respectively.In general,the algorithm consists of the following three phases:Phase 1:Peer searching phase .The request origina-tor m wants to retrieve information from the location-based database server.m ﬁrst sets h to h ,a set of discovered peers T to {∅}and the number of discovered peers k to zero,i.e.,|T |.(Lines 3to 4in Algorithm 1).Then,m broadcasts a FORM GROUP request along with a message sequence ID and the hop distance h to its neighboring peers (Line 6in Algorithm 1).m listens to the network and waits for the reply from its neighboring peers.Algorithm 2describes how a peer p responds to the FORM GROUP request along with a hop distance h and aFigure3:P2P spatial cloaking algorithm.Algorithm2P2P Spatial Cloaking:Request Receiver p1:Function P2PCloaking-Receiver(h)2://Let r be the request forwarder3:if the request is duplicate then4:Reply r with an ACK message5:return;6:end if7:h p←1;8:if h=1then9:Send the tuple T=<p,(x p,y p),v maxp ,t p,h p>to r10:else11:h←h−1;12:Broadcast a FORM GROUP request with the parameter h 13:T p is the set of peers that respond back to p14:for all T i∈T p do15:T i.h p←T i.h p+1;16:end for17:T p←T p∪{<p,(x p,y p),v maxp ,t p,h p>};18:Send T p back to r19:end ifmessage sequence ID from another peer(denoted as r)that is either the request originator or the forwarder of the re-quest.First,p checks if it is a duplicate request based on the message sequence ID.If it is a duplicate request,it sim-ply replies r with an ACK message without processing the request.Otherwise,p processes the request based on the value of h:Case1:h= 1.p turns in a tuple that contains its ID,current location,maximum movement speed,a timestamp and a hop distance(it is set to one),i.e.,< p,(x p,y p),v max p,t p,h p>,to r(Line9in Algorithm2). Case2:h> 1.p decrements h and broadcasts the FORM GROUP request with the updated h and the origi-nal message sequence ID to its neighboring peers.p keeps listening to the network,until it collects the replies from all its neighboring peers.After that,p increments the h p of each collected tuple,and then it appends its own tuple to the collected tuples T p.Finally,it sends T p back to r (Lines11to18in Algorithm2).After m collects the tuples T from its neighboring peers, if m cannotﬁnd other k−1peers with a hop distance of h,it increments h and re-broadcasts the FORM GROUP request along with a new message sequence ID and h.m repeatedly increments h till itﬁnds other k−1peers(Lines6to14in Algorithm1).However,if mﬁnds the same set of peers in two consecutive broadcasts,i.e.,with hop distances h and h+1,there are not enough connected peers for m.Thus, m has to relax its privacy proﬁle,i.e.,use a smaller value of k,or to be suspended for a period of time(Line11in Algorithm1).Figures3(a)and3(b)depict single-hop and multi-hop peer searching in our running example,respectively.In Fig-ure3(a),the request originator,m8,(e.g.,k=5)canﬁnd k−1peers via single-hop communication,so m8sets h=1. Since h=1,its neighboring peers,m5,m6,m7,m9,m10, and m11,will not further broadcast the FORM GROUP re-quest.On the other hand,in Figure3(b),m8does not connect to k−1peers directly,so it has to set h>1.Thus, its neighboring peers,m7,m10,and m11,will broadcast the FORM GROUP request along with a decremented hop dis-tance,i.e.,h=h−1,and the original message sequence ID to their neighboring peers.Phase2:Location adjustment phase.Since the peer keeps moving,we have to capture the movement between the time when the peer sends its tuple and the current time. For each received tuple from a peer p,the request originator, m,determines the greatest possible distance between them by an equation,|mp |=|mp|+(t c−t p)×v max p,where |mp|is the Euclidean distance between m and p at time t p,i.e.,|mp|=(x m−x p)2+(y m−y p)2,t c is the currenttime,t p is the timestamp of the tuple and v maxpis the maximum speed of p(Lines18to20in Algorithm1).In this paper,a conservative approach is used to determine the distance,because we assume that the peer will move with the maximum speed in any direction.If p gives its movement direction,m has the ability to determine a more precise distance between them.Figure3(c)illustrates that,for each discovered peer,the circle represents the largest region where the peer can lo-cate at time t c.The greatest possible distance between the request originator m8and its discovered peer,m5,m6,m7, m9,m10,or m11is represented by a dotted line.For exam-ple,the distance of the line m8m 11is the greatest possible distance between m8and m11at time t c,i.e.,|m8m 11|. Phase3:Spatial cloaking phase.In this phase,the request originator,m,forms a virtual group with the k−1 nearest peers,based on the greatest possible distance be-tween them(Line22in Algorithm1).To adapt to the dynamic network topology and k-anonymity requirement, m sets h to the largest value of h p of the selected k−1 peers(Line15in Algorithm1).Then,m determines the minimum grid area A covering the entire group(Line24in Algorithm1).If the area of A is less than A min,m extends A,until it satisﬁes A min(Lines25to27in Algorithm1). Figure3(c)gives the k−1nearest peers,m6,m7,m10,and m11to the request originator,m8.For example,the privacy proﬁle of m8is(k=5,A min=20cells),and the required cloaked spatial region of m8is represented by a bold rectan-gle,as depicted in Figure3(d).To issue the query to the location-based database server anonymously,m randomly selects a mobile client in the group as an agent(Line28in Algorithm1).Then,m sendsthe query along with the cloaked spatial region,i.e.,A,to the agent(Line29in Algorithm1).The agent forwards thequery to the location-based database server.After the serverprocesses the query with respect to the cloaked spatial re-gion,it sends a list of candidate answers back to the agent.The agent forwards the candidate answer to m,and then mﬁlters out the false positives from the candidate answers. 4.3Modes of OperationsThe P2P spatial cloaking algorithm can operate in twomodes,on-demand and proactive.The on-demand mode:The mobile client only executesthe algorithm when it needs to retrieve information from the location-based database server.The algorithm operatedin the on-demand mode generally incurs less communica-tion overhead than the proactive mode,because the mobileclient only executes the algorithm when necessary.However,it suﬀers from a longer response time than the algorithm op-erated in the proactive mode.The proactive mode:The mobile client adopting theproactive mode periodically executes the algorithm in back-ground.The mobile client can cloak its location into a spa-tial region immediately,once it wants to communicate withthe location-based database server.The proactive mode pro-vides a better response time than the on-demand mode,but it generally incurs higher communication overhead and giveslower quality of service than the on-demand mode.5.ANONYMOUS LOCATION-BASEDSERVICESHaving the spatial cloaked region as an output form Algo-rithm1,the mobile user m sends her request to the location-based server through an agent p that is randomly selected.Existing location-based database servers can support onlyexact point locations rather than cloaked regions.In or-der to be able to work with a spatial region,location-basedservers need to be equipped with a privacy-aware queryprocessor(e.g.,see[29,31]).The main idea of the privacy-aware query processor is to return a list of candidate answerrather than the exact query answer.Then,the mobile user m willﬁlter the candidate list to eliminate its false positives andﬁnd its exact answer.The tighter the spatial cloaked re-gion,the lower is the size of the candidate answer,and hencethe better is the performance of the privacy-aware query processor.However,tight cloaked regions may represent re-laxed privacy constrained.Thus,a trade-oﬀbetween the user privacy and the quality of service can be achieved[31]. Figure4(a)depicts such scenario by showing the data stored at the server side.There are32target objects,i.e., gas stations,T1to T32represented as black circles,the shaded area represents the spatial cloaked area of the mo-bile client who issued the query.For clariﬁcation,the actual mobile client location is plotted in Figure4(a)as a black square inside the cloaked area.However,such information is neither stored at the server side nor revealed to the server. The privacy-aware query processor determines a range that includes all target objects that are possibly contributing to the answer given that the actual location of the mobile client could be anywhere within the shaded area.The range is rep-resented as a bold rectangle,as depicted in Figure4(b).The server sends a list of candidate answers,i.e.,T8,T12,T13, T16,T17,T21,and T22,back to the agent.The agent next for-(a)Server Side(b)Client SideFigure4:Anonymous location-based services wards the candidate answers to the requesting mobile client either through single-hop communication or through multi-hop routing.Finally,the mobile client can get the actualanswer,i.e.,T13,byﬁltering out the false positives from thecandidate answers.The algorithmic details of the privacy-aware query proces-sor is beyond the scope of this paper.Interested readers are referred to[31]for more details.6.EXPERIMENTAL RESULTSIn this section,we evaluate and compare the scalabilityand eﬃciency of the P2P spatial cloaking algorithm in boththe on-demand and proactive modes with respect to the av-erage response time per query,the average number of mes-sages per query,and the size of the returned candidate an-swers from the location-based database server.The queryresponse time in the on-demand mode is deﬁned as the timeelapsed between a mobile client starting to search k−1peersand receiving the candidate answers from the agent.On theother hand,the query response time in the proactive mode is deﬁned as the time elapsed between a mobile client startingto forward its query along with the cloaked spatial regionto the agent and receiving the candidate answers from theagent.The simulation model is implemented in C++usingCSIM[35].In all the experiments in this section,we consider an in-dividual random walk model that is based on“random way-point”model[7,8].At the beginning,the mobile clientsare randomly distributed in a spatial space of1,000×1,000square meters,in which a uniform grid structure of100×100cells is constructed.Each mobile client randomly chooses itsown destination in the space with a randomly determined speed s from a uniform distribution U(v min,v max).When the mobile client reaches the destination,it comes to a stand-still for one second to determine its next destination.Afterthat,the mobile client moves towards its new destinationwith another speed.All the mobile clients repeat this move-ment behavior during the simulation.The time interval be-tween two consecutive queries generated by a mobile client follows an exponential distribution with a mean of ten sec-onds.All the experiments consider one half-duplex wirelesschannel for a mobile client to communicate with its peers with a total bandwidth of2Mbps and a transmission range of250meters.When a mobile client wants to communicate with other peers or the location-based database server,it has to wait if the requested channel is busy.In the simulated mobile environment,there is a centralized location-based database server,and one wireless communication channel between the location-based database server and the mobile。

codeforeces cf15acottage village题解

Codeforces CF15A Cottage Village解决思路：
题目大意：
在Codeforces Round #15的A题Cottage Village中，给定一个由组成的字符串，你需要将所有的小屋移动到字符串的开头，每次操作可以选择一个非首字符的小屋，并将其与首字符交换位置。

要求计算出完成此任务所需的最小操作次数。

解题思路：
这是一道模拟类型的题目，核心思想是优先将离开始位置最近的小屋交换到最前面。

具体做法是从前往后遍历字符串，遇到第一个小屋（#）时，就将其与第一个位置进行交换，然后继续遍历，再遇到小屋时重复此操作。

这样，每找到一个小屋并进行一次交换，就确保了当前这个小屋被移动到了正确的位置上。

最后，统计总共进行了多少次交换操作即可。

代码示例（Python）：
在这段代码中，是为了确保当前字符已经是# 并且不是字符串的第一个字符，这样才能执行交换操作。

注意，由于Python字符串是不可变的，因此在实际交换字符时，需要重新构建新的字符串。

Advances in Minimum Description Length

Advances in Minimum Description Length: Theory and ApplicationsMIT:series name??MIT:series editors name??TODO,Editor MIT??TODO,Associate EditorsAdvances in Minimum Description Length: Theory and Applicationsedited byPeter D.Gr¨u nwaldIn Jae MyungMark A.PittThe MIT PressCambridge,MassachusettsLondon,Englandc 2003Massachusetts Institute of TechnologyAll rights reserved.No part of this book may be reproduced in any form by any electronic or mechanical means(including photocopying,recording,or information storage and retrieval) without permission in writing from the publisher.Typeset by the authors using L A T E X2εLibrary of Congress Control No.2001095750Printed and bound in the United States of AmericaLibrary of Congress Cataloging-in-Publication DataAdvances in Minimum Description Length:Theory and Applications/edited by Peter D.Gr¨u nwald,In Jae Myung,and Mark A.Pitt.p.cm.Includes bibliographical references and index.ISBN TODO(alk.paper)1.Statistics.2.Machine learning.rmation TheoryI.Gr¨u nwald,Peter D.II.Myung,In Jae III.Pitt,Mark TODO.Contents1Algorithmic Statistics and Kolmogorov’s Structure Functions1 Paul Vit´a nyiIndex271Algorithmic Statistics and Kolmogorov’s Structure FunctionsPaul Vit´a nyiCWI,Kruislaan4131098SJ Amsterdam,The Netherlandspaulv@cwi.nl,http://www.cwi.nl/paulvA non-probabilistic foundation for model selection and prediction can be based onKolmogorov complexity(algorithmic information theory)and Kolmogorov’s Struc-ture functions(representing all stochastic properties of the data).A distinguishingfeature is the analysis of goodness-of-ﬁt of an individual model for an individualdata string.Among other things it presents a new viewpoint on the foundationsof‘maximum likelihood’and‘minumum description length’.We provide a leasureintroduction to the central notions and results.“To each constructive object corresponds a functionΦx(k)of a natural numberk—the log of minimal cardinality of x-containing sets that allow deﬁnitions ofcomplexity at most k.If the element x itself allows a simple deﬁnition,then thefunctionΦdrops to1even for small cking such deﬁnition,the element is“random”in a negative sense.But it is positively“probabilistically random”onlywhen functionΦhaving taken the valueΦ0at a relatively small k=k0,thenchanges approximately asΦ(k)=Φ0−(k−k0).”[A.N.Kolmogorov[6]]1.1IntroductionNaively speaking,Statistics deals with gathering data,ordering and representingdata,and using the data to determine the process that causes the data.That thisviewpoint is a little too simplistic is immediately clear:suppose that the true causeof a sequence of outcomes of coinﬂips is a“fair”coin,where both sides come upwith equal probability.It is possible that the sequence consists of“heads”only.Suppose that our statistical inference method succeeds in identifying the true cause2Algorithmic Statistics and Kolmogorov’s Structure Functions(fair coinﬂips)from these data.Such a method is clearly at fault:from an all-headssequence a good inference should conclude that the cause is a coin with a heavy biastoward“heads”,irrespective of what the true cause is.That is,a good inferencemethod must assume that the data is“typical”for the cause—that is,we don’t aimatﬁnding the“true”cause,but we aim atﬁnding a cause for which the data is as“typical”as possible.Such a cause is called a model for the data.But what if thedata consists of a sequence of precise alternations“head–tail”?This is as unlikelyan outcome for a fair coinﬂip as the all-heads sequence.Yet,within the coin-typemodels we have no alternative than to choose a fair coin.But we know very wellthat the true cause must be diﬀerent.For some data it may not even make senseto ask for a“true cause”.This suggests that truth is not our goal;but within givenconstraints on the model class we try toﬁnd the model for which the data is most“typical”in an appropriate sense,the model that best“ﬁts”the data.Consideringthe available model class as a a magnifying glass,ﬁnding the bestﬁtting modelfor the data corresponds toﬁnding the position of the magnifying glass that bestbrings the object into focus.In the coin-ﬂipping example,it is possible that thedata have no sharply focused model,but within the allowed resolution—ignoringthe order of the outcomes but only counting the number of“heads”—weﬁnd thebest model.Classically,the setting of statistical inference is as follows:We carry out aprobabilistic experiment of which the outcomes are governed by an unknownprobability distribution P.Suppose we obtain as outcome the data sample x.Givenx,we want to recover the distribution P.For certain reasons we can choose adistribution from a set of acceptable distributions only(which may or may notcontain P).Intuitively,our selection criteria are that(i)x should be a“typical”outcome of the distribution selected,and(ii)the selected distribution has a“simple”description.We need to make the meaning of“typical”and“simple”rigorous andbalance the requirements(i)and(ii).In probabilistic statistics one analyzes theaverage-case performance of the selection process.For traditional problems,dealingwith frequencies over small sample spaces,this approach is appropriate.But forcurrent novel applications,average relations are often irrelevant,since the part ofthe support of the probability density function that will ever be observed has aboutzero measure.This is the case in,for example,complex video and sound analysis.There arises the problem that for individual cases the selection performance maybe bad although the performance is good on average.or vice versa.There is alsothe problem of what probability means,whether it is subjective,objective,or existsat all.Kolmogorov’s proposal outlined strives for theﬁrmer and less contentiousground expressed inﬁnite combinatorics and eﬀective computation.We embark on a systematic study of model selection where the performance isrelated to the individual data sample and the individual model selected.It turnsout to be more straightforward to investigate models that areﬁnite setsﬁrst,andthen generalize the results to models that are probability distributions.To simplifymatters,and because all discrete data can be binary coded,we consider only datasamples that areﬁnite binary strings.Classical statistics has diﬃculty to express1.2Algorithmic Statistics3the notion of an individual“best”model for an individual data sample.But thelucky conﬂuence of information theory,theory of algorithms,and probability,leadsto the notion of Kolmogorov complexity—the notion of information content inan individual object,and it allows us to express and analyze the novel notion ofthe information in one individual object(for example,a model)about anotherindividual object(for example,the data).Development of this theory allows us toprecisely formulate and quantify how well a particular modelﬁts a particular pieceof data,a matter which formerly was judged impossible.1.2Algorithmic StatisticsIn1965A.N.Kolmogorov[5]combined the theory of computation and a combina-torial approach to information theory into a proposal for an objective and absolutedeﬁnition of the information contained by an individualﬁnite object,commonlyrepresented by aﬁnite binary string.This is to be contrasted with the averagenotion of the entropy of a random source as proposed by C.Shannon.The the-ory of“Kolmogorov complexity”has turned out to be ubiquitously applicable[4].Continuing this train of thought,as perhaps the last mathematical innovation ofan extraordinary scientiﬁc career,Kolmogorov in1974[6]proposed a reﬁnement,which,in Jorma Rissanen phrasing,“permits extraction of a desired amount ofproperties from the data,leaving the remainder as something like noise.The prop-erties are modeled by aﬁnite set that includes the data string,which amounts tomodeling data by uniform distribution,and the amount of properties is measuredby the Kolmogorov complexity of the description of theﬁnite set involved.”Thisproposal can be viewed as one to found statistical theory onﬁnite combinatorialprinciples independent of probabilistic assumptions,as the relation between theindividual data and its explanation(model),expressed by Kolmogorov’s structurefunction.While these notions have been intermittently studied over the years,andhave been described in articles and in Cover and Thomas’s inﬂuential textbook[1],as well as our own[4],they have been previously but poorly understood.Recently,however,the situation has been changed through a sequence of results concerningthe“algorithmic”suﬃcient statistic,and its relation with the corresponding proba-bilistic notion in[3],and a comprehensive body of results concerning Kolmogorov’sso-called“structure function”in[12].The purpose of this paper is to brieﬂy outlinethe basic notions involved and the signiﬁcance of the main results obtained.Basic Notions of the Theory:We want to describe every individualﬁnitebinary sequence x in two parts,one part being the model description(properties,the meaning of the data)and one part being the data-to-model code(the remainingrandom‘noise’).It is convenient to consider models that areﬁnite sets ofﬁnitebinary strings,and a contemplated model for x contains x as one of its elements.Itturns out that the results are true for more sophisticated models like computableprobability density functions.An example of the data-to-model code of x withrespect to the model S is the index of x in the lexicographical enumeration of the4Algorithmic Statistics and Kolmogorov’s Structure Functionselements of S.The description of the model,typically a program that generates themodel,is called a‘statistic’.The following properties of how a model S relates tosequence x are crucial:Typicality:Is x a typical or random element of S?Optimality:We call S optimal for x if the two-part description of x based onmodel S is minimal(among all potential two-part descriptions).A shortest description(or program)for an optimal set S is called an‘algorithmicstatistic’for x.The developments in the paper are based on imposing a constraint onthe number of bits allowed to describe the model(the amount of desired properties).Letαindicate the maximum number of bits allowed to describe the model.Forﬁxedα,we consider selection of a model for x in three ways,characterized by threediﬀerent functions:1.Selection based on the minimum randomness deﬁciency function(what we shallargue is the modelﬁtness estimator)βx(α);2.Selection based on using Kolmogorov’s structure function(what we shall argueis the maximum likelihood estimator)h x(α);and3.Selection based on the shortest two-part code length(what we shall argue is the)MDL estimatorλx(α).Method1is based on the notion of‘typicality’and basically selects the modelS for which the data x looks most typical.In a precise mathematical sense,statedbelow in terms of Kolmogorov complexity,this implies that S is a model of‘best-ﬁt’for x.So method1gives us the proper model for x.Unfortunately,it turns outthat method1is too diﬃcult to apply.But we can obtain our goal in a round-about manner:Method2selects a model S—containing data x—that minimizesthe data-to-model code length that maximally can occur for a string in S.It isuseful to explain the notion of data-to-model code length by example.For datastring x=00...0of length n and the large-cardinality but small-complexity model{0,1}n,the data-to-model code length is at about n bits since there are elements inS that require n bits to be singled ing the small-cardinality but potentiallyhigh-complexity model{x},the data-sto-model code length is O(1).This data-to-model code length may be very diﬀerent from the shortest way to describe xin a model like{0,1}n,which is O(1)bits,since x is the lexicographicallyﬁrstelement in{0,1}n.Method3selects the model S such that the total two-partdescription length,consisting of one part describing S containing x,and the secondpart describing the maximal data-to-model code of a string in S,is minimized.Wewill establish,in a mathematically rigorous manner,that the minimax procedurein Methods2and3result in correct selection according to the criterium of Method1.The methods are not equivalent,since selection according to Method1doesn’timply a correct choice according to the criteria of either Method2or Method3;and Method3doesn’t imply a correct choice according to the criterium of Method2.1.2Algorithmic Statistics5Outline of the Results:Kolmogorov’s structure function,its variations andits relation to model selection,have obtained some notoriety,but no previouscomprehension.Before,it has always been questioned why Kolmogorov chose tofocus on the mysterious function h x,rather than on a more evident function denotedasβx.The main result,in[12],with the beauty of truth,justiﬁes Kolmogorov’sintuition.One way to phrase it is this:The structure function determines allstochastic properties of the data in the sense of determining the best-ﬁtting modelat every model-complexity level.One easily stated consequence is:For all data x,both method2(which below is interpreted as the maximum likelihood estimator)and method3(that can be viewed as a minimum description length estimator)select a model that satisﬁes the best-ﬁt criterion of method1(the best-explanationestimator),in every case(and not only with high probability).In particular,whenthe model that generated the data is not in the model class considered,thenthe ML or MDL estimator still give a model that“bestﬁts”the data,among all themodels in the contemplated class.This notion of“best explanation”and“bestﬁt”is understood in the sense that the data is“most typical”for the selected model ina rigorous mathematical sense that is discussed below.A practical consequence is asfollows:While the bestﬁt(a model that witnessesβx(α))cannot be computationallymonotonically approximated up to any signiﬁcant precision,we can monotonicallyminimize the two-part code(ﬁnd a model witnessingλx(α)),or the one-part code(ﬁnd a model witnessing h x(α))and thus monotonically approximate implicitlythe bestﬁtting model,[12].But this should be suﬃcient:we want the best modelrather than a number that measures its goodness.We show that—within the obviousconstraints—every graph is realized by the structure function of some data.Thismeans that there are data of each conceivable combination of stochastic properties.All these results are not completely precise:they hold up to a logarithmic additiveerror.They usher in an era of statistical inference that is always(almost)bestrather than expected.Reach of Results:In Kolmogorov’s initial proposal,as in this work,models areﬁnite sets ofﬁnite binary strings,and the data is one of the strings(all discretedata can be binary encoded).The restriction toﬁnite set models is just a matter ofconvenience:the main results generalize to the case where the models are arbitrarycomputable probability density functions,and in fact other model classes.Sinceour results hold only within additive logarithmic precision,and the equivalencesof the relevant notions and results between the model classes hold up to the sameprecision,the results hold equally for the more general model classes.The generality of the results are at the same time a restriction.In classical statis-tics one is commonly interested in model classes that are partially poorer and par-tially richer than the ones we consider.For example,the class of Bernoulli processes,or k-state Markov chains,is poorer than the class of computable probability densityfunctions of moderate maximal Kolmogorov complexityα,in that the latter maycontain functions that require far more complex computations than the rigid syn-tax of the former classes allows.Indeed,the class of computable probability densityfunctions of even moderate complexity allows implementation of a function mim-6Algorithmic Statistics and Kolmogorov’s Structure Functionsicking a universal Turing machine computation.On the other hand,even the lowlyBernoulli process can be equipped with a non-computable real bias in(0,1),andhence the generated probability density function over n trials is not a computablefunction.This incomparability of the here studied algorithmic model classes,andthe traditionally studied statistical model classes,means that the current resultscannot be directly transplanted to the traditional setting.Indeed,they should beregarded as pristine truths that hold in a platonic world that can be used as guide-line to develop analogues in model classes that are of more traditional concern,asin[8].See also the later Remark1.9.1.3PreliminariesLet x,y,z∈N,where N denotes the natural numbers and we identify N and{0,1}∗according to the correspondence(0, ),(1,0),(2,1),(3,00),(4,01),...Here denotes the empty word.The length|x|of x is the number of bits in thebinary string x,not to be confused with the cardinality|S|of aﬁnite set S.Forexample,|010|=3and| |=0,while|{0,1}n|=2n and|∅|=0.The emphasis ison binary sequences only for convenience;observations in any alphabet can be soencoded in a way that is‘theory neutral’.A binary string y is a proper preﬁx of a binary string x if we can write x=yzfor z= .A set{x,y,...}⊆{0,1}∗is preﬁx-free if for any pair of distinct elementsin the set neither is a proper preﬁx of the other.A preﬁx-free set is also calleda preﬁx code.There is a special type of preﬁx code,the self-delimiting code,thathas the added property of being eﬀective in the sense that there is an algorithmthat,starting at the beginning of the code word,scanning from left to right,candetermine where the code word ends.A simple example of this is the code thatencodes the source word x=x1x2...x n by the code word¯x=1n0x.Using this code we deﬁne the standard self-delimiting code for x to be x =|x|x.It is easy to check that|¯x|=2n+1and|x |=n+2log n+1.We can extend thiscode to pairs of strings:Let · be a standard invertible eﬀective one-one encodingfrom N×N to a subset of N.For example,we can set x,y =x y or x,y =¯x y.We can iterate this process to deﬁne x, y,z ,and so on.Kolmogorov Complexity:For precise deﬁnitions,notation,and results seethe text[4].Informally,the Kolmogorov complexity,or algorithmic entropy,K(x)of a string x is the length(number of bits)of a shortest binary program(string)tocompute x on aﬁxed reference universal computer(such as a particular universalTuring machine).Intuitively,K(x)represents the minimal amount of informationrequired to generate x by any eﬀective process.The conditional Kolmogorov com-1.3Preliminaries 7plexity K (x |y )of x relative to y is deﬁned similarly as the length of a shortestprogram to compute x ,if y is furnished as an auxiliary input to the computation.For technical reasons we use a variant of complexity,so-called preﬁx complexity,which is associated with Turing machines for which the set of programs resultingin a halting computation is preﬁx free.We realize preﬁx complexity by consider-ing a special type of Turing machine with a one-way input tape,a separate worktape,and a one-way output tape.Such Turing machines are called preﬁx Turing machines.If a machine T halts with output x after having scanned all of p on theinput tape,but not further,then T (p )=x and we call p a program for T .It iseasy to see that {p :T (p )=x,x ∈{0,1}∗}is a preﬁx code .In fact,because the algorithm (in this case a Turing machine T )determines the end of the code word forthe source word x (that is,the program p such that T (p )=x ),this code is in factself-delimiting .Let T 1,T 2,...be a standard enumeration of all preﬁx Turing ma-chines with a binary input tape,for example the lexicographical length-increasingordered syntactic preﬁx Turing machine descriptions,[4],and let φ1,φ2,...be theenumeration of corresponding functions that are computed by the respective Tur-ing machines (T i computes φi ).These functions are the partial recursive functionsor computable functions (of eﬀectively preﬁx-free encoded arguments).The Kol-mogorov of x is the length of the shortest binary program from whichx is computed.Deﬁnition 1.1The preﬁx Kolmogorov complexity of x isK (x )=min p,i {|¯i |+|p |:T i (p )=x },(1.1)where the minimum is taken over p ∈{0,1}∗and i ∈{1,2,...}.For the developmentof the theory we actually require the Turing machines to use auxiliary (alsocalled conditional )information,by equipping the machine with a special read-only auxiliary tape containing this information at the outset.Then,the conditionalversion K (x |y )of the preﬁx Kolmogorov complexity of x given y (as auxiliaryinformation)is is deﬁned similarly as before,and the unconditional version is setto K (x )=K (x | ).One of the main achievements of the theory of computation is that the enumer-ation T 1,T 2,...contains a machine,say U =T u ,that is computationally universalin that it can simulate the computation of every machine in the enumeration whenprovided with its index.Expressing an index i by the shortest self-delimiting codei ∗(if there is more then one such code then the notation is disambiguated in astandard manner that does not need to concern us here)for i usable by U we have:U ( y,i ∗p )=T i ( y,p )for all i,p,y .We ﬁx one such machine and designate it as thereference universal preﬁx Turing machine .Using this universal machine it is easy8Algorithmic Statistics and Kolmogorov’s Structure Functionsto show[12]K(x|y)=minq{|q|:U( y,q )=x}+O(1)(1.2)K(x)=minq{|q|:U(q)=x}+O(1).Remark1.2A prominent property of the preﬁx-freeness of the set of programsfor the reference preﬁx Turing machine U is that we can interpret2−K(x)as aprobability distribution.By the fundamental Kraft’s inequality,see for example[1;4],we know that if l1,l2,...are the code-word lengths of a preﬁx code,thenx2−l x≤1.Hence,x2−K(x)≤1.(1.3)This leads to the notion of universal distribution—a rigorous form of Occam’srazor—which implicitly plays an important part in the present exposition.Thefunctions K(·)and K(·|·),though deﬁned in terms of a particular machine model,are machine-independent up to an additive constant and acquire an asymptoticallyuniversal and absolute character through Church’s thesis,from the ability ofuniversal machines to simulate one another and execute any eﬀective process.TheKolmogorov complexity of an individual object was introduced by Kolmogorov[5]as an absolute and objective quantiﬁcation of the amount of information init.The information theory of Shannon[9],on the other hand,deals with averageinformation to communicate objects produced by a random source.Since the formertheory is much more precise,it is surprising that analogs of theorems in informationtheory hold for Kolmogorov complexity,be it in somewhat weaker form,see[4].Precision:It is customary in this area to use“additive constant c”or equiva-lently“additive O(1)term”to mean a constant,accounting for the length of aﬁxedbinary program,independent from every variable or parameter in the expression inwhich it occurs.In this paper we use the preﬁx complexity variant of Kolmogorovcomplexity for convenience.Actually some results are easier to prove for plain com-plexity.Most results presented here are precise up to an additive logarithmic term,which means that they are valid for plain complexity as well—preﬁx complexityexceeds plain complexity by at most a logarithmic additve term.Thus,our use ofpreﬁx complexity is important for“ﬁne details”only.Meaningful Information:The information contained in an individualﬁniteobject(like aﬁnite binary string)is measured by its Kolmogorov complexity—thelength of the shortest binary program that computes the object.Such a shortestprogram contains no redundancy:every bit is information;but is it meaningfulinformation?If weﬂip a fair coin to obtain aﬁnite binary string,then with over-whelming probability that string constitutes its own shortest program.However,also with overwhelming probability all the bits in the string are meaningless in-formation,random noise.On the other hand,let an object x be a sequence of1.3Preliminaries 9observations of heavenly bodies.Then x can be described by the binary string pd ,where p is the description of the laws of gravity,and d the observational parame-ter setting:we can divide the information in x into meaningful information p andaccidental information d .The main task for statistical inference and learning the-ory is to distil the meaningful information present in the data.The question arises whether it is possible to separate meaningful information from accidental informa-tion,and if so,how.The essence of the solution to this problem is revealed whenwe rewrite (1.1)via (1.2)as follows:K (x )=min p,i {|¯i |+|p |:T i (p )=x }(1.4)=min p,i{2|i |+|p |+1:T i (p )=x },=min p,i{K (i )+|p |:T i (p )=x }+O (1),where the minimum is taken over p ∈{0,1}∗and i ∈{1,2,...}.In the last step weuse ﬁrst the equality according to (1.2),then that the ﬁxed reference universal preﬁxTuring machine U =T u with |u |=O (1),and ﬁnally that U (i ∗p )=T i (p )for all iand p .Here i ∗denotes the shortest self-delimiting program for i ;therefore |i ∗|=K (i ).The expression (1.4)emphasizes the two-part code nature of Kolmogorovcomplexity.In the examplex =10101010101010101010101010we can encode x by a small Turing machine printing a speciﬁed number of copiesof the pattern “01”which computes x from the program “13.”This way,K (x )isviewed as the shortest length of a two-part code for x ,one part describing a Turingmachine T ,or model ,for the regular aspects of x ,and the second part describingthe irregular aspects of x in the form of a program p to be interpreted by T .Theregular,or “valuable,”information in x is constituted by the bits in the “model”while the random or “useless”information of x constitutes the remainder.Thisleaves open the crucial question:How to choose T and p that together describe x ?In general,many combinations of T and p are possible,but we want to ﬁnd a Tthat describes the meaningful aspects of x .Data and Model:We consider only ﬁnite binary data strings x .Our modelclass consists of Turing machines T that enumerate a ﬁnite set,say S ,such that oninput p ≤|S |we have T (p )=x with x the p th element of T ’s enumeration of S ,and T (p )is a special undeﬁned value if p >|S |.The “best ﬁtting”model for x isa Turing machine T that reaches the minimum description length in (1.4).Such amachine T embodies the amount of useful information contained in x ,and we havedivided a shortest program x ∗for x into parts x ∗=T ∗p such that T ∗is a shortestself-delimiting program for T .Now we consider only low complexity ﬁnite-set models,and under these constraints the shortest two-part description happensto be longer than the shortest one-part description.For example,this can happenif the data is generated by a model that is too complex to be in the contemplatedmodel class.Does the model minimizing the two-part description still capture all。

Lisp教程(下册 DCL编程)彩版

DCL(D i alog Control Languag e)语言教程课程简介课程主要介绍AutoLISP程序的编写,配合使用DC L(Dialog Control Language)语言,作对话方框的开发及应用.本课程只适合对AutoLISP程序设计有相当认识之人仕.全课共分为12篇,每篇一个主题;而每篇再分为若干小节,每天顺序显示.内文若有错漏,敬祁见谅,欢迎来函赐教,多谢!!课程大纲第一篇(0 - 4 课) AutoLISP及Dialog程序设计基本知识第二篇(5 - 8 课)开启对话方框第三篇(9 - 13 课)设定及取得对话框内组件的值第四篇(14 - 19 课)按钮(button)及核取框(切换钮,toggle)第五篇(20 - 25 课)横列(row),直行(colum n),空白(spacer)及文字组件(text)第六篇(26 - 30 课)滑杆(滚动条)组件(sli d er)第七篇(31 - 36 课)其它按钮及影像按钮组件(image_button)第八篇(37 - 43 课)列表选框组件(list_bo x)第九篇(44 - 48 课)下拉式(弹出式)列表选框组件(popup_list)第十篇(49 - 52 课)选台钮(radio_button)第十一篇(53- 57 课)影像组件(image)第十二篇(58- 60 课)其它组件注意事项在各篇课程所介绍的语法结构中,包含在方括号[]中的数据,表示为选择性项目;可因应情况,自行决定是否须要加上.字型为紫色的项目,表示可自行设定其名称或内容定义文件为设定对话框组件的纯文本文件(扩展名为DCL),程序文件则是编写AuotLISP程序的纯文本文件(扩展名LSP)在定义档中,批注以双除号//开始,批注符号及其右边的文字可以不输入;但在程序文件中,批注以分号;开始,批注符号及其右边的文字可以不输入.第一篇基本知识基本知识对话框是现今最流行的人机互动面接口;在早期的AutoCAD版本中巳经使用,但只有在R12版本开始,才提供给用户自行编程的对话框开发功能.对话框的描述定义在一纯文本檔(扩展名为DC L)内,无须特别的开发环境定义文件中的对话框描述,均以对话控制语言(D ialog Control Language,简称DCL语言) 所建立在AutoLISP程序中,配合可编程对话框(Porg r ammable Dialog Box,简称PDB)函数,激活及操控对话框可以在一个定义档(扩展名DCL)中定义多个对话框对话框由方块本身,及包含于其内的组件(或称为控件,构件)所组成每个组件的属性定义均包含在一对大括号{}中在定义文件中,各组件及其属性均为小写;只有在赋值给属性,或设定对话框名称时才可使用大写字母属性以等号=赋值,并以分号;结束(注意: 在A u toLISP程序中,分号是作为批注) 一般要在组件名称前加上一个冒号:,但若组件并不设定属性,则不需要在组件名称前加冒号,但却要以分号结束空行会被忽若定义档发生严重的错误,会在当前的目录下,产生ACAD.DCE文件,以指明所发生的错误.定义档BASE.DCL默认在SUPPORT的目录中,它包含了各组件的原型定义,及各类型巳定义的其它基本组件;而所有自行开发的对话框定义文件,都可以使用在BASE.DCL定义文件中的各个组件ACAD.DCL默认在SUPPORT的目录中,它包含了AutoCAD中所有标准对话框的定义参考定义档可在自行开发的对话框定义文件中,经由inclu de指令,取得指定DCL文件中所定义的组件;其语法如下:@include "对话框定义档"e.g. @include "david.dcl"定义档名称要包含在双引号"中,可以在檔名前加上定的目录路径会先在当前的目录中搜寻该定义文件,然后是定义文件所在的目录;若有指定档案的路径,则只会在指定的目录内寻找不可以参考ACAD.DCL所定义的对话框,即不可以使用@include"acad.dcl"预视对话框若在Visual LISP中开启一对话框定义文件,即可使用:工具-- 接口工具-- 预览编辑器DCL在下拉式列示框中,选取该定义文件中所设定的对话框名称,即可预视对话框亦可直接输入该对话框的名称(要注意大小写)批注///**/表示其右边(至该行末端)的文字视为注释包括在两个批注符号中间的所有文字(可跨越多行)均视为注释语义检核提供4个等级的检核,该些检核会在对话框载入时完成;可以将下列语句放在定义档中的任何位置,但不能在任何对话框的定义内.dcl_settings : default_dcl_settin g s {audit_level = 3;}等级0 不检查: 只有在该定义档巳被检核且不会再作修改时使用等级1 错误: 找出可能造成AutoCAD终止的错误,此等级也是默认值等级2 警告: 找出配置或行为模式的错误,所有定义在修改后,最少应执行此等级的检查一次等级3 提示: 找出多余的属性定义对话框的设计美观性颜色配比,组件整齐排列等方便性相同功能的项目要编排在一起,或使用线框包围使用频繁的项目应设置在最显眼及方便的位置适当设置快捷键及默认值(默认值,省缺值)除非对话框中的项目过多,否则应避免使用巢状(嵌套)式对话方框互锁或互换项目选取(或不选取)时,会否引致其它项目功能的改变(enable或disable...等)对话框定义文件内容模式对话框名称:dialog{label="对话框标题";:组件名称{属性名称=属性值;}//组件定义结束}//对话框结束定义说明1 对话框名称就是由AutoLISP程序,启动对话框时所呼叫的名称;可以自行设定,要区分大小写字母dialog为对话框中,最外层的组件(即整个对话框),其它的组件均包含在其中一般组件要以冒号:开始,并为小写字母;因为不是属性,所以不须使用分号;结束2 开大括号{紧随组件名称之后,以包含该组件的属性或其它组件label为属性名称,并以等号=赋以其右边的属性值,最后以分号;结束该行叙述3 包含在对话框中的其它组件组件要以冒号:开始,并为小写字母;因为不是属性,所以不须使用分号;结束4 开大括号{紧随组件名称之后,以包含组件的属性或其它组件包含在组件中的属性,要以等号=赋以其右边的属性值;最后以分号;结束该行叙述5 以关大括号}与第4行的开大括号}对应,以结束该组件(第3行)之定义双除号//右边的文字即为批注6 以关大括号}与第2行的开大括号}对应,以结束该组件之定义(在本例中为整个对话框的定义,即第1行的dialog组件注意: 上例中缩排只为方便观察及维护,不作缩排亦可包含在对话框中的组件及组件中的属性可以不只一个练习(Exercises)请输入以下的对话框定义,并命名为RECT.DCL(纯文本格式),存在C盘的根目录中RECT:dialog{label="Rectangle" ;:text//文字组件{label="Rectangle width :" ; //组件的属性}ok_only;}定义说明:1 设定对话框的名称为RECT,紧接一个以冒号开始的组件dialog2 最左边为dialog组件的开大括号,右边是其属性label,并以等号设定其属性值为"Rectangle",以分号结束此行叙述3 text为"字符串"组件,其作用是在对话框中显示文字,组件名称前要加冒号4 最左边为text组件的开大括号,右边是其属性l a bel,并以等号设定其属性值为"Rectanglewidth",最后以分号结束此行叙述5 关大括号是对应第4行的开大括号,这对大括号同属于第3行的text组件6 ok_only为一"按钮组件",它的作用是在对话框中显示一个"确定(OK)"按钮;在一个对话框中,必需提供最少一个"确定"按钮,或一个"取消"按钮(组件名称为cancel_button),以作为结束对话框之用;本例中不设定按钮之属性,固不需以冒号放在组件名称的前面,但却需要以分号结束7 关大括号是对应第2行的开大括号,这对大括号同属于第1行的dialog组件注意:虽然定义档巳经完作,但仍要待学习完第2篇的课程后,才能开启及显示该对话框第二篇开启对话框AutoLISP函数加载DCL定义文件函数(LOAD_DIALOG "对话框定义档名称")e.g. (LOAD_DIALOG "C:/RECT.DCL")自变量是要加载的对话框定义文件名称字符串(所以要在前后加双引号);若不设定扩展名则预设为.DCL 函数会依资源搜寻路径找寻该定义文件;不在搜寻路径中的档案,要在文件名前加上指定的路径函数若成功加载该定义档,会传回一正整数值的档案处理码,供其它函数呼叫使用;无法载入时则传回负整数值一般会将传回的档案处理码以SETQ函数存入变量中e.g. (SETQ DCL_ID (LOAD_DIALO G"C:/R E CT.DCL"))开启并显示对话框(NEW_DIALOG "对话框名称"档案处理码["预设动作"[对话框位置]])e.g. (NEW_DIALOG "RECT" DCL_ID)对话框名称为字符串,要在前后加双引号;要注意一个定义檔中,可包含多个对话框名称及其定义档案处理码是经由LOAD_DIALOG函数取得默认动作为字符串表示式;函数可以不加默认动作,或以空字符串""表示如果要设定对话框显示时的位置,则必须同时设定默认动作对话框位置为2D的点串行,指定对话框左上角的X,Y坐标如果以'(-1 -1)为对话框位置,则会在绘图屏幕的中心点开启对话框如果成功开启对话框,函数会传回T,否则传回N IL开始接受使用者输入(START_DIALOG)函数不须提供任何自变量使前一个用NEW_DIALOG函数开启的对话框开始作用,直至操作表示式或回复函数呼叫DONE_DIALOG函数为止一般DONE_DIALOG与关键词accept(一般是按下确定按钮)或关键词cancel(一般是按下取消按钮)相关若传回值为1,表示使用者按下确定钮结束对话框;0表示按下取消钮;-1表示全部对话框都以TERM_DIALOG函数终止;大于1的整数,其意义由应用程序决定释放(卸除)对话框(UNLOAD_DIALOG "档案处理码")e.g. (UNLOAD_DIALOG DCL_ID)从内存释放档案处理码(经由LOAD_DIALOG函数取得)指定的对话框定义档函数传回值一定是NIL对话框开启方式(DEFUN C:函数名称()(SETQ 变量(LOAD_DIALOG "对话框定义档"))(NEW_DIALOG "对话框名称"变量)(START_DIALOG) (UNLOAD_DIALOG 变数))程序说明:1 以DEFUN函数定义程序的名称,使用LOAD_DIALOG函数加载指定的对话框定义档使用SETQ函数,将LOAD_DIALOG传回的档案处理码存入变量中,以方便其它函数使用2 使用NEW_DIALOG,开启及显示定义文件中(档案处理码),指定的对话框3 使用START_DIALOG函数,使对话框开始作用并接受使用者输入使用UNLOAD_DIALOG函数以结束对话框4 关括号是对应第1行DEFUN函数左边的开括号,结束整个程序练习(Exercises)编写一AutoLISP程序,以开启上一课所定义的对话框;并将该程序命名为RECT.LSP,存在C盘的根目录中,程序代码如下:(DEFUN C:RECT() (SETQ DCL_ID (LOAD_DIALOG "C:/RECT.DCL"))(NEW_DIALOG "RECT" DCL_ID)(START_DIALOG) (UNLOAD_DIALOG DCL_ID))程序说明:1 以DEFUN函数定义程序的名称为RECT,使用LOAD_DIALOG函数将对话框定义文件RECT.DCL载入;使用SETQ函数,将LOAD_DIALOG传回的档案处理码存入变量DCL_ID中,以方便其它函数使用2 使用NEW_DIALOG,开启及显示定义文件中(档案处理码)名称为RECT的对话框3 使用START_DIALOG函数,使对话框开始作用并接受使用者输入(在本例中只有确定钮可按);使用UNLOAD_DIALOG函数以结束对话框4 关括号是对应第1行DEFUN函数左边的开括号,结束整个程序编辑框组件(edit_box)可供用户直接输入字符串的矩形方框;注意在编辑框中所显示及输入的数据均为字符串型态,而数字则要在取得编辑框的字符串内容后(在下一篇中介绍),使用AutoLISP函数将其转为数值型态的数据:edit_box//组件名称{属性="属性值";}属性label显示在编辑框前面(左边)的提示文字key 其属性值即为使用该组件的关键词,使在AutoL I SP程序中操控此组件;在同一个对话框定义中,不能有相同名称的关键词定义edit_limit指定在编辑框中,可输入字符串的最大字符长度edit_width指定编辑框显示时的阔度(通常与fixed_width属性一拼使用)value 指定编辑框显示时,当中的默认值(注意显示数值数据时,要先用AutoLISP 函数将数据(或变量)转为字符串型态)fixed_width通常与width属性一拼使用,以固定编辑框显示时的阔度(其属性值可设定为true或false)is_enable 可设定编辑框是否可以使用(其属性值可设定为true或false,默认值为true)alignment 如果组件成水平方向排列,属性值可设定为lef t,right或centere d如果组件成垂直方向排列,属性值可设定为top,bottom或centere d(水平方向的默认值为left,垂直方向则为cent e red)其它属性action allow_accept fixed_height heightis_tab_stop mnemonic width练习(Exercises)将上一课的RECT.DCL对话框定义档复制至RECT1.DCL,开启新的定义檔并作如下的修改,使其可以输入四边形的阔度及高度;将AutoLISP程序文件RECT.LSP复制至RECT1.LSP,开启新的程序文件,并将要加载的对话框定义档改为RECT1(程序第1行);完成后即可加载及执行新的程序文件RECT:dialog{label="Rectangle" ;:edit_box//编辑框组件{label="Rectangle width : " ;key="RECT_W" ;edit_limit=16;edit_width=10;fixed_width=true;}:edit_box//编辑框组件{label="Rectangle heigth :" ;key="RECT_H" ;edit_limit=16;edit_width=10;fixed_width=true;}ok_only;}定义说明:1 设定对话框的名称为RECT,紧接一个以冒号开始的组件dialog2 最左边为dialog组件的开大括号,右边是其属性label,并以等号设定其属性值为"Rectangle",以分号结束此行叙述3 edit_box为"编辑框"组件,组件名称前要加冒号4 最左边为edit_box组件的开大括号,右边是其属性label,并以等号设定其属性值为"Rectangle width : ",最后以分号结束此行叙述5 设定属性key的属性值为RECT_W(即设定此编辑框的名称)6 设定属性edit_limit的属性值为16(即最多可以在编辑框中输入16个数字)7 设定属性edit_width的属性值为10(编辑框只会显示10个字的长度)8 设定属性fixed_width的属性值为true,表示表将编辑框以固定的阔度显示9 关大括号是对应第4行的开大括号,这一对大括号同属于第3行的edit_box组件10至16 与3至9行相似,设定另一个编辑框以输入高度的数据;只有属性label及key的属性值不同17 以ok_only组件,在对话框中显示一个确定(OK)按钮,作为结束对话框之用18 关大括号是对应第2行的开大括号,这一对大括号同属于第1行的dialog组件注意1 在第一个编辑框(输入四边形的阔度)的label属性值中,在字符串最后的冒号后面,加多了一个空格,目的是方更与下一个编辑框对齐第三篇设定及取得对话框组件内的值AutoLISP函数设定组件的值(SET_TILE "组件关键词"设定值)e.g. (SET_TILE "RECT_W" "50.0")组件的初始值可在定义文件中以属性value设定,但在程序文件中则以此函数设定组件关键词即是在对话框定义文件中,以组件属性key所定义的属性值,以赋予该组件一个操作名称组件关键词是有区分大小写的此函数要在START_DIALOG函数之后才能使用取得组件的值(GET_TILE "组件关键词")e.g. (GET_TILE "RECT_W")作用是在AutoLISP程序文件中取得组件(以关键词指定)的设定值组件关键词的说明与SET_TILE函数相同此函数要在DONE_DIALOG函数之前使用(即要在对话框结束前使用)设定组件的状态(MODE_TILE "组件关键词"状态模式)e.g. (MODE_TILE "RECT_W" 0)作用是在AutoLISP程序文件中设定组件(以关键词指定)的使用状态,其状态模式可以设定为下列任一整数值:0 使指定的组件成为使用状态1 使指定的组件成为禁用状态(组件以灰色显示)2 使指定的组件成为焦点3 选取指定编辑框组件的内容4 图像高亮度显示的开关组件关键词的说明与SET_TILE函数相同练习(Exercises)以修改对话框定义档的方式,设定输入阔度的编辑框其初始值为50,输入高度的编辑框其初始值为25定义档将RECT1.DCL复制至RECT2.DCL,在关键词为REC T_W的组件中,加入value属性并设定其初始值为50;在关键词为RECT_H的组件中,加入value属性并设定其初始值为25程序文件将RECT1.LSP复制至RECT2.LSP,只须修改其加载的定义档名称为RECT2.DCL即可;完成后存盘,进入AutoCAD并载入RECT2.LSP程序文件,执行RECT2程序RECT:dialog{label="Rectangle" ;:edit_box{"Rectangle width : " ;key="RECT_W" ;edit_limit=16;edit_width=10;fixed_width=true;value="50.0"//注意设定值为字符串型态,要在前后加双引号}:edit_box{"Rectangle height :" ;key="RECT_H" ;edit_limit=16;edit_width=10;fixed_width=true;value="25.0"//注意设定值为字符串型态,要在前后加双引号}}以修改AutoLISP程序文件的方式,使输入阔度的编辑框其初始值为50,输入高度的编辑框其初始值为25定义档将RECT1.DCL(不是RECT2.DCL)复制至RECT3.DC L,不作任何修改程序文件将RECT1.LSP复制至RECT3.LSP,程序代码如下:(DEFUN C:RECT() (SETQ DCL_ID (LOAD_DIALOG "C:/RECT3.DCL"))(NEW_DIALOG "RECT" DCL_ID)(SET_TILE "RECT_W" "50.0") (SET_TILE"RECT_H" "25.0")(START_DIALOG)(UNLOAD_DIALOG DCL_ID))程序说明:1 以DEFUN函数定义程序的名称RECT,使用LOAD_D I ALOG函数将对话框定义文件RECT3.DCL载入;使用SETQ函数,将LOAD_DIALOG传回的档案处理码存入变量DCL_ID中,以方便其它函数使用2 使用NEW_DIALOG,开启及显示定义文件中(档案处理码)名称为RECT的对话框3,4 使用SET_TILE函数,设定输入阔度的编辑框组件RECT_W(组件关键词,在定义文件中key设定的属性值)为50,设定输入高度的编辑框组件RE C T_H为255 使用START_DIALOG函数,使对话框开始作用并接受使用者输入(在本例中只有确定钮可按);使用UNLOAD_DIALOG函数以结束对话框6 关括号是对应第1行DEFUN函数左边的开括号,结束整个程序AutoLISP函数组件指定动作(ACTION_TILE "组件关键词""指定动作")e.g. (ACTION_TILE "RECT_W" "(S ETQ TEMP 1)")当焦点在指定的组件(关键词)上并按下接受键时,即会执行函数所指定的动作指定动作会取代定义文件中,该组件的action属性之默认动作组件关键词"accept"在默认情况下是与确定按钮组件关连(即是确定按钮的默认关键字),"cancel"则与取消按钮组件关连(即是取消按钮的默认关键词)终止对话框(DONE_DIALOG [指定传回值])e.g. (DONE_DIALOG)此函数的传回值为一个2D点串行坐标,为结束对话框时,对话框的所在位置;可作为下次启动同一个对话框的位置自变量,使对话框在之前结束时的位置再次显现练习(Exercises)将定义档RECT3.DCL复制至RECT4.DCL,但不作任何修改.将程序文件RECT3.LSP复制至RECT4.LSP,并修改为: 使用ACTION_TILE函数,指定在按下确定钮时,执行一辅助程序;在该辅助程序中,使用GET_TILE函数最得对话框中四边形的阔度及高度,并在用户选取的位置上绘画出四边形.(DEFUN C:RECT() (SETQ DCL_ID (LOAD_DIALOG "C:/RECT4.DCL"))(NEW_DIALOG "RECT" DCL_ID)(SET_TILE "RECT_W" "50.0")(SET_TILE "RECT_H" "25.0")(ACTION_TILE "accept" "(S_RECT)(DONE_DIA L OG)")(START_DIALOG)(UNLOAD_DIALOG)(SETQ PT_0 (GETPOINT "\nSelect rectangle lower left point :"))(SETQ PT_1 (POLAR PT_0 0 TMP_W)PT_2 (POLAR PT_1 (/ PI 2) TMP_H)PT_3 (POLAR PT_2 PI TMP_W))(COMMAND "PLINE" PT_0 PT_1 PT_2 PT_3 "C")(PRINC))(DEFUN S_RECT() (SETQ TMP_W (ABS (ATOF (GET_TILE "RECT_W")))TMP_H (ABS (ATOF (GET_TILE "RECT_H")))))主程序(RECT)说明:1 注意将LOAD_DIALOG函数所加载的对话框定义档改为RECT4.DCL2- 4参阅RECT3程序的说明5 使用ACTION_TILE函数设定关键词为accept的组件(默认为确定按钮)的指定动作: 先执行辅助程序S_RECT,取得输入的数据,再使用DO N E_DIALOG函数结束对话框6 参阅RECT3程序,第5行的说明7 使用GETPOINT函数让用户选取四边形的左下角位置坐标,存入变量PT_08使用POLAR函数取得新坐标点: 从选取点PT_0开始,向右(角度为0),距离为四边形的阔度(变量TMP_W,在辅助程序中取得),存入变量PT_19 使用POLAR函数取得新坐标点: 从上一点PT_1开始,向上(角度90,等于PI除2),距离为四边形的高度(变量TMP_H,在辅助程序中取得),存入变量PT_210 使用POLAR函数取得新坐标点: 从上一点PT_2开始,向左(角度1800,等于PI),距离为四边形的阔度(变量TMP_W,在辅助程序中取得),存入变量PT_3;注意最右边的关括号,是对应第8行SETQ函数左边的开括号11 使用COMMAND函数执行PLINE指令,并分别设定四个坐标变量,最后以"C"选项封闭聚合线12 以一个不带参数的PRINC函数,抑制多余的显示及传回值;最右边的关括号是对应第1行DEFUN函数左边的开括号辅助程序(S_RECT)说明:1 设定函数名称;使用GET_TILE函数取得指定关键词(RECT_W,代表输入阔度的编辑框组件)的值,并将该值使用ATOF函数由字符串型态转为实数型态(有小数);再使用ABS函数取得实数的绝对值,以防止使用者输入负数;最后将数据存入变量TMP_W2 使用GET_TILE函数取得指定关键词(RECT_H,代表输入高度的编辑框组件)的值,转型及取得绝对值后,存入变数TMP_H3 用一个关括号,对应第1行DEFUN函数左的开括号第四篇按钮及核取框(切换钮)按钮组件(button)一个矩形的按钮,可显示指定的讯息在按钮上:button//组件名称{属性="属性值";}属性label显示在按钮中的提示文字key 其属性值即为使用该按钮组件的关键词width 指定按钮的显示阔度height 指定按钮的显示高度fixed_width通常与width属性一并使用,以固定按钮的显示阔度fixed_height通常与height属性一并使用,以固定按钮的显示高度is_enable 可设定按钮是否可以使用,其属性值可设定为t r ue(可使用)或false(禁用,按钮中的消息正文变为灰色显示),默认值为tr ueis_default属性值可设定为true或false;当使用者按下接受键(大部份的情况,ENTER 被用作接受键)时,本属性设定为true的组件会自动被选取.当使用者在allow_accept属性设为true的编辑框,列表框或图像按钮中.按下接受键或双击鼠标键(只对列表框及图像按钮有效)时,本属性设定为true的组件亦会自动被选取alignment 如果组件成水平方向排列,属性值可设定为lef t,right或centered(若不设定则预设为left);如果组件成垂直方向排列,属性值可设定为top,bottom 或centered(若不设定则预设为centered)其它属性action is_cancel is_tab_top mnemonic核取框(切换钮)组件(toggle)是一个启用或关闭的切换方框,当方框中没有剔勾符号时,表示该功能为关闭状态,其组件的值为0;当方框中有剔勾符号时,表示该功能为启用状态,其组件的值为1:toggle//组件名称{属性="属性值";}属性label显示在切换钮后面(右边)的提示文字key 其属性值即为使用该按钮组件的关键词is_enable 可设定切换钮是否可以使用,其属性值可设定为true(可使用)或false(禁用),默认值为truevalue 方框中没有剔勾符号时,表示该功能为关闭状态,其组件的值为0(亦是预设值);相反则为启用状态,组件的值为1alignment 如果组件成水平方向排列,属性值可设定为lef t,right或centered(若不设定则预设为left);如果组件成垂直方向排列,属性值可设定为top,bottom或centered(若不设定则预设为centered)其它属性action is_tab_stop width heigthfixed_width fixed_heigth练习(Exercises)在四边形的对话框中,增加一核取框,以确定是否在四边形中加上交叉的对角线;另在对话框的底部增加两个按钮,按下时可使核取框作用或禁用定义档将RECT4.DCL复制至RECT5.DCL,在新的定义档中增加核取框及按钮定义RECT:dialog{label="Rectangle" ;:edit_box{"Rectangle width : " ;key="RECT_W" ;edit_limit=16;edit_width=10;fixed_width=true;value="50.0"}:edit_box{"Rectangle heigh t:" ;key="RECT_H" ;edit_limit=16;edit_width=10;fixed_width=true;value="25.0"}:toggle//新增的核取框(切换钮)组件{label="X line in rectangle" ;key="RECT_X" ;}:button//新增的按钮组件{label="&Enable" ;key="RECT_E" ;width=10;fixed_width=true;}:button//新增的按钮组件{label="&Disable" ;key="RECT_D" ;width=10;fixed_width=true;}ok_only;}定义说明:第1至第18行的定义,与上一课练习RECT4.DCL定义相同,不再叙述19 以冒号开始,定义一个切换钮组件20 切换钮的开大括号,以label属性定义切换钮右边的消息正文21 以key属性设定切换钮的操作关键词22 关大括号是对应第20行的开大括号,此对大括号同属于第19行的切换钮组件23 以冒号开始,定义一个按钮组件24 按钮的开大括号,以label属性定义按钮上显示的消息正文.注意: 字符串的&符号,表示设定在该符号右边的一个字母为快捷键;在显示时,&符号不会出现,但&符号右边的一个字符会以加底线显示25 以key属性设定按钮的操作关键词26 以width属性设定按钮的阔度,否则按钮会自动加长并占用整列的长度27 fixed_width属性设定为true,使按钮以固定的阔度显示28 关大括号是对应第24行的开大括号,此对大括号同属于第23行的按钮组件29至34 与上个按钮的定义类似(23至28行),但其label及key的属性值不同35 定义一个确定按钮,以结束对话框36 关大括号是对应第2行的开大括号,此对大括号同属于第1行的dialog组件程序文件将RECT4.LSP复制至RECT5.LSP,其程序代码如下:(DEFUN C:RECT() (SETQ DCL_ID (LOAD_D I ALOG "C:/RECT5.DCL"))(NEW_DIALOG "RECT" DCL_ID)(SET_TILE "RECT_W" "50.0")(SET_TILE "RECT_H" "25.0")(ACTION_TILE "accept" "(S_RECT)(DONE_DIALOG)")(ACTION_TILE "RECT_E" "(MODE_TILE \"RECT_X\" 0)");设定使用按钮的动作(ACTION_TILE "RECT_D" "(MODE_TILE \"RECT_X\" 0)");设定禁用按钮的动作(START_DIALOG)(UNLOAD_DIALOG)(SETQ PT_0 (GETPOINT "\nSelec t rectangle lower left point : "))(SETQ PT_1 (POLAR PT_0 0 TMP_W)PT_2 (POLAR PT_1 (/ PI 2)TMP_H)PT_3 (POLAR PT_2 P I TMP_W))(COMMAND "PLINE" PT_0 PT_1 PT_2 PT_3 "C")(IF (= TMP_X 1) (COMMAND"LINE" PT_0 PT_2 "" "LINE" PT_1 PT_3 ""))(PRINC))(DEFUN S_RECT() (SETQ TMP_W (ABS (AT O F(GET_TILE "RECT_W")))TMP_H (ABS (ATOF (GET_TILE "RECT_H")))TMP_X (ATOI (GET_TILE "RECT_X"))) ;取得切换钮的状态)主程序(RECT)说明:1至5 与程序RECT4.LSP相同,主要是加载及显示对话框,并设定其中的组件6 使用ACTION_TILE函数,设定当按下ENABLE按钮(关键词为RECT_E)时,所执行的动作: 使用MODE_TILE函数,设定切换钮(关键词为RECT_X)的状态为0,使其处于作用状态.注意指定的动作为字符串型态,前后要加上双引号";而包含在字符串中的双引号,则必须以反斜杠加双引号\"表示7 与第6行相似,设定按下DISABLE按钮(关键词为R ECT_D)时,将切换钮的状态设为1,使其处于禁用状态8至13 与程序RECT4.LSP相同,主要是用作绘画出四边形14 使用IF函数检查变量TMP_X(在辅助程序中取得,表示切换钮的状态)是否为1;若是则以COMMAND函数,执行两个LINE指令,绘画出四边形的对角线15 以一个不带参数的PRINC函数,抑制多余的显示及传回值;最右边的关括号是对应第1行DEFUN函数左边的开括号辅助程序(S_RECT)说明:1,2 大致与RECT4.LSP的辅助程序相同,都是用以取得编辑框内的数据并存入变量中3 使用GET_TILE函数取得切换钮(关键词为RECT_X)的状态(传回值是字符串,"0"是使用,"1"是禁用),再用ATOI函数将传回的字符串变为整数,存入变量TMP_X,供主程序第14行的IF函数,判断是否需要加上对角线.注意: 传回值转型后,结果只会是1或0,固不须使用ABS函数取其绝对值4 关括号与1行DEFUN函数左边的开括号对应第五篇横列,直行,空白及文字组件横列(row)及直行(column)组件其实这两个都不算是实体的组件,只是用作指定后续的其它组件的排列格式指定为横列时,则包含在此组件一对大括号中的所有组件,均作水平左至右排列;直行时则呈垂直上至下排列。

完整版)python教案

完整版)python教案Unit 5 Advanced Programming (total of 10 class hours)Lesson 1 Choosing a Programming Language (1 class hour)I。

Teaching Objectives1.Understand programming languages and two XXX;2.Understand Python background。

ns。

n。

and familiarize with Python programming environment;3.Experience the whole process of building。

inputting。

debugging。

running。

and saving a small program。

Master the call of the Turtle module and try to modify the XXX.XXX DifficultiesTeaching focus: Familiarize with Python programming environment。

Experience programming for the first time Teaching difficulties: Experience programming for the first time。

Writing ns for programs and using debugging windows.III。

Teaching ProcessI。

n to Python Language1.Python LanguagePython means "python" in English。

and the logo of this language is two XXX.Python language is widely used。

Clist的基本操作和使用

Lists‎将元素按顺‎序储存在链‎表中. 与向量(vecto‎r s)相比, 它允许快速‎的插入和删‎除，但是随机访‎问却比较慢‎.assig‎n() 给list‎赋值back() 返回最后一‎个元素begin‎() 返回指向第‎一个元素的‎迭代器clear‎() 删除所有元‎素empty‎() 如果lis‎t是空的则‎返回tru‎eend() 返回末尾的‎迭代器erase‎() 删除一个元‎素front‎() 返回第一个‎元素get_a‎l loca‎t or() 返回lis‎t的配置器‎inser‎t() 插入一个元‎素到lis‎t中max_s‎i ze() 返回lis‎t能容纳的‎最大元素数‎量merge‎() 合并两个l‎i stpop_b‎a ck() 删除最后一‎个元素pop_f‎r ont() 删除第一个‎元素push_‎b ack() 在list‎的末尾添加‎一个元素push_‎f ront‎() 在list‎的头部添加‎一个元素rbegi‎n() 返回指向第‎一个元素的‎逆向迭代器‎remov‎e() 从list‎删除元素remov‎e_if() 按指定条件‎删除元素rend() 指向lis‎t末尾的逆‎向迭代器resiz‎e() 改变lis‎t的大小rever‎s e() 把list‎的元素倒转‎size() 返回lis‎t中的元素‎个数sort() 给list‎排序splic‎e() 合并两个l‎i stswap() 交换两个l‎i stuniqu‎e() 删除lis‎t中重复的‎元素附List‎用法实例：#inclu‎d e <iostr‎e am>#inclu‎d e <list>#inclu‎d e <numer‎i c>#inclu‎d e <algor‎i thm>using‎names‎p ace std;//创建一个l‎i st容器‎的实例LI‎S TINT‎typed‎e f list<int> LISTI‎N T;//创建一个l‎i st容器‎的实例LI‎S TCHA‎Rtyped‎e f list<char> LISTC‎H AR;void main(void){//--------------------------//用list‎容器处理整‎型数据//--------------------------//用LIST‎I NT创建‎一个名为l‎i stOn‎e的lis‎t对象LISTI‎N T listO‎n e;//声明i为迭‎代器LISTI‎N T::itera‎t or i;//从前面向l‎i stOn‎e容器中添‎加数据listO‎n e.push_‎f ront‎(2);listO‎n e.push_‎f ront‎(1);//从后面向l‎i stOn‎e容器中添‎加数据listO‎n e.push_‎b ack (3);listO‎n e.push_‎b ack (4);//从前向后显‎示list‎O ne中的‎数据cout<<"listO‎n e.begin‎()--- listO‎n e.end():"<<endl;for (i = listO‎n e.begin‎(); i != listO‎n e.end(); ++i)cout << *i << " ";cout << endl;//从后向后显‎示list‎O ne中的‎数据LISTI‎N T::rever‎s e_it‎e rato‎r ir;cout<<"listO‎n e.rbegi‎n()---listO‎n e.rend():"<<endl;for (ir =listO‎n e.rbegi‎n(); ir!=listO‎n e.rend();ir++) {cout << *ir << " ";}cout << endl;//使用STL‎的accu‎m ulat‎e(累加)算法int resul‎t = accum‎u late‎(listO‎n e.begin‎(), listO‎n e.end(),0); cout<<"Sum="<<resul‎t<<endl;cout<<"------------------"<<endl;//--------------------------//用list‎容器处理字‎符型数据//--------------------------//用LIST‎C HAR创‎建一个名为‎l istO‎n e的li‎s t对象LISTC‎H AR listT‎w o;//声明i为迭‎代器LISTC‎H AR::itera‎t or j;//从前面向l‎i stTw‎o容器中添‎加数据listT‎w o.push_‎f ront‎('A');listT‎w o.push_‎f ront‎('B');//从后面向l‎i stTw‎o容器中添‎加数据listT‎w o.push_‎b ack ('x');listT‎w o.push_‎b ack ('y');//从前向后显‎示list‎T wo中的‎数据cout<<"listT‎w o.begin‎()---listT‎w o.end():"<<endl;for (j = listT‎w o.begin‎(); j != listT‎w o.end(); ++j)cout << char(*j) << " ";cout << endl;//使用STL‎的max_‎e leme‎n t算法求‎l istT‎w o中的最‎大元素并显‎示 j=max_e‎l emen‎t(listT‎w o.begin‎(),listT‎w o.end());cout << "The maxim‎u m eleme‎n t in listT‎w o is: "<<char(*j)<<endl; }#inclu‎d e <iostr‎e am>#inclu‎d e <list>using‎names‎p ace std;typed‎e f list<int> INTLI‎S T;//从前向后显‎示list‎队列的全部‎元素void put_l‎i st(INTLI‎S T list, char *name){INTLI‎S T::itera‎t or plist‎;cout << "The conte‎n ts of " << name << " : ";for(plist‎= list.begin‎(); plist‎!= list.end(); plist‎++)cout << *plist‎<< " ";cout<<endl;}//测试lis‎t容器的功‎能void main(void){//list1‎对象初始为‎空INTLI‎S T list1‎;//list2‎对象最初有‎10个值为‎6的元素INTLI‎S T list2‎(10,6);//list3‎对象最初有‎3个值为6‎的元素INTLI‎S T list3‎(list2‎.begin‎(),--list2‎.end());//声明一个名‎为i的双向‎迭代器INTLI‎S T::itera‎t or i;//从前向后显‎示各lis‎t对象的元‎素put_l‎i st(list1‎,"list1‎");put_l‎i st(list2‎,"list2‎");put_l‎i st(list3‎,"list3‎");//从list‎1序列后面‎添加两个元‎素list1‎.push_‎b ack(2);list1‎.push_‎b ack(4);cout<<"list1‎.push_‎b ack(2) and list1‎.push_‎b ack(4):"<<endl; put_l‎i st(list1‎,"list1‎");//从list‎1序列前面‎添加两个元‎素list1‎.push_‎f ront‎(5);list1‎.push_‎f ront‎(7);cout<<"list1‎.push_‎f ront‎(5) and list1‎.push_‎f ront‎(7):"<<endl; put_l‎i st(list1‎,"list1‎");//在list‎1序列中间‎插入数据list1‎.inser‎t(++list1‎.begin‎(),3,9);cout<<"list1‎.inser‎t(list1‎.begin‎()+1,3,9):"<<endl;put_l‎i st(list1‎,"list1‎");//测试引用类‎函数cout<<"list1‎.front‎()="<<list1‎.front‎()<<endl;cout<<"list1‎.back()="<<list1‎.back()<<endl;//从list‎1序列的前‎后各移去一‎个元素list1‎.pop_f‎r ont();list1‎.pop_b‎a ck();cout<<"list1‎.pop_f‎r ont() and list1‎.pop_b‎a ck():"<<endl;put_l‎i st(list1‎,"list1‎");//清除lis‎t1中的第‎2个元素list1‎.erase‎(++list1‎.begin‎());cout<<"list1‎.erase‎(++list1‎.begin‎()):"<<endl;put_l‎i st(list1‎,"list1‎");//对list‎2赋值并显‎示list2‎.assig‎n(8,1);cout<<"list2‎.assig‎n(8,1):"<<endl;put_l‎i st(list2‎,"list2‎");//显示序列的‎状态信息cout<<"list1‎.max_s‎i ze(): "<<list1‎.max_s‎i ze()<<endl; cout<<"list1‎.size(): "<<list1‎.size()<<endl;cout<<"list1‎.empty‎(): "<<list1‎.empty‎()<<endl;//list序‎列容器的运‎算put_l‎i st(list1‎,"list1‎");put_l‎i st(list3‎,"list3‎");cout<<"list1‎>list3‎: "<<(list1‎>list3‎)<<endl;cout<<"list1‎<list3‎: "<<(list1‎<list3‎)<<endl;//对list‎1容器排序‎list1‎.sort();put_l‎i st(list1‎,"list1‎");//结合处理list1‎.splic‎e(++list1‎.begin‎(), list3‎);put_l‎i st(list1‎,"list1‎");put_l‎i st(list3‎,"list3‎");}补充：STL标准‎函数fin‎d进行ve‎c tor 、list链‎表查找#inclu‎d e <vecto‎r>#inclu‎d e <algor‎i thm>#inclu‎d e <iostr‎e am>class‎examp‎l e{publi‎c:examp‎l e(int val){i = val;}bool opera‎t or==(examp‎l e const‎& rhs){retur‎n (i == rhs.i) ? true : false‎;}priva‎t e:int i;};using‎names‎p ace std;int main(void){vecto‎r<examp‎l e> ve;ve.push_‎b ack(1);vecto‎r<examp‎l e>::itera‎t or it; examp‎l e elem(1);it = find(ve.begin‎(), ve.end(), elem); cout<<boola‎l pha<<(*it == elem); }。

试题Python等级考试——第一课(1)

试题Python等级考试——第一课(1)一、选择题1．在Python中，关于变量的说法，正确的是（）A．变量必须以字母开头命名B．变量只能用来存储数字，不能存储汉字C．在python中变量类型一旦定义就不能再改变D．变量被第二次赋值后，新值会取代旧的值2．Python语言源代码程序编译后的文件扩展名为（）A．.py B．.c C．.java D．.c++3．在编写python程序时缩进的作用是（）。

A．让程序更美观B．只在for循环中使用C．只在if语句中使用D．用来界定代码块4．Python程序中第一行：a=int(input( ))，第二行：print(a+5)，运行程序后键盘输入3，输出结果是（）。

A．5 B．3 C．8 D．其他5．在Python中要生成随机数，应该使用（）。

A．math 模块B．random模块C．numpy 模块D．pygame 模块6．下列与数学表达式对应的python表达式，正确的是（）。

A．( - b + math. sqrt (b * b – 4 * a * c)) /a * aB．- b + math. sqrt (b * b – 4 * a * c) /2*aC．( -b + math. sqrt (b * 2 – 4 * a * c)) /(2 * a)D．( -b + math. sqrt ( b * b – 4 * a * c)) / (2 * a)7．下列选项中，可作为Python变量名的是（）A．int B．Abc C．float D．Complex8．在Python中运行后，b的值是（）a=6If a>=0：b=a+2Else：b=a-2print（b）A．6 B．8 C．10 D．129．下列选项中，不属于Python合法变量名的是（）A．int32 B．40xl C．self D．_name_10．下图是python34安装后目录文件的磁盘文件存储结构，下列说法错误的是( )A．图中文件存储结构为树结构，python34为树的根结点(父节点)B．图中python34根结点有4个子结点C．图中python34根结点下有5个子树(子结点)D．图中Tools是python34的子树(子结点)11．在Python中，表达式（21%4）+3的值是（）A．2 B．4 C．6 D．812．python文件的扩展名是（）A．py B．pye C．vbp D．pyr13．Python文件的后缀名是（）。

2022年12月Python 一级等级考试真题(附答案,解析)

青少年软件编程（Python）等级考试试卷（一级）分数：100 题数：37一、单选题(共25题，共50分)1.关于Python语言的注释，以下选项中描述错误的是？（）A.Python语言有两种注释方式：单行注释和多行注释B.Python语言的单行注释以#开头C.Python多行注释使用###来做为标记D.注释用于解释代码原理或者用途2.下列代码执行后最有可能绘制出的图形是？（）import turtleturtle.forward(200)turtle.left(144)turtle.forward(200)turtle.left(144)turtle.forward(200)turtle.left(144)turtle.forward(200)turtle.left(144)turtle.forward(200)turtle.hideturtle()A. B. C. D.3.下列关于Python中IDLE中基本操作表示正确的是？（）A. B.C. D.4.在turtle库中，turtle.speed(a)用于设定画笔的运动速度，关于该指令说法错误的是？（）A.在turtle.speed(a)指令中，当参数a的值为0时，画笔的运动速度最慢。

B.在turtle.speed(a)指令中，turtle.speed(9)使画笔的运动速度比turtle.speed(10)慢。

C. 在turtle.speed(a)指令中，a的数值最小是0，最大是10。

D. 在turtle.speed(a)指令中，除了0之外，数字越大，速度越快。

5.运行如下代码，在输入数值6后，输出的结果是？（）a=int(input("请输入你的年龄"))print(type(a))A. 6B. 6.0C. <class"int">D. <class "str">6.如程序所示，绘制完一个半圆圆弧，画笔最终坐标位置是？（）import turtleturtle.pendown()turtle.circle(50,180)turtle.penup()turtle.done()A.(0,50)B.(0,100)C.(50,0)D.(100,0)7.程序print(5+2)的运行结果是？（）A.7B.('5+2')C.5+2D.528.运行语句turtle.goto(-400,-300)，画笔将到？（）A.画布左上角B.画布右上角C.画布右下角D.画布左下角9.运行指令print(3*5>2 and 6>=6.0)，请问根据运算的优先级，首先进行哪一部分的运算？（）A.5>2B.6>=6.0C.andD.3*510.下列程序运行的结果是？（）a=10b=15b+=aprint(b)A.10B.25C.15D.3011. Python程序保存后的文件扩展名是？（）A. .sb3B. .pyC. .pnD. .cpp12.下列不是Python保留字的是？（）A.andB.falseC.TrueD.import13.根据Python变量的命名规则，下列不可以作为变量名的是？（）A.a2bB.2abC.ab2D._ab214.下面的turtle命令，不可能改变画笔的颜色的是？（）A.turtle.fillcolor()B.turtle.color()C.turtle.pencolor()D.turtle.color(255,255,255)15.turtle画图的功能中，能够实现隐藏画笔的是？（）A.turtle.undo()B.turtle.shape()C.turtle.penup()D.turtle.hideturtle()16.print(34 * 2.0)的输出结果是？（）A.34B.68C.68.0D.34*2.017.涛涛家一共有爸爸、妈妈、涛涛三个人，涛涛爸爸比涛涛妈妈大2岁，比涛涛大25岁，今年全家年龄加在一起是54岁，那么涛涛爸爸、涛涛妈妈、涛涛今年的年龄分别是？（）A.28、26、1B.25、27、2C.27、25、2D.30、28、518.print(9//2)的结果是？（）A.5.0B.4.0C.4D.519.下列代码的运行结果是？（）a=1A=Trueprint(a==A)A.FalseB.TrueC.1D.程序运行错误20.下列代码运行的结果是？（）num='5'*'5'print(num)A.25B.5, 5, 5, 5, 5C.‘5’ * ‘5’D.报错，无法运行。

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> Sector Specific Parameters
These parameters apply only to a specific sector(s) within a network. Sector specific can be BTS datafill (eg: PilotGain, MctaThreshold) or Pilot Database (eg: Cell Type, HHO Target Lists etc)
BTS
3
FOR TRAINING PURPOSES ONLY
Types of RF Optimization Parameters
- Over the Air (OTA) vs Configuration Parameters > IS2000 OTA Parameters
Defined in the CDMA standards documents.
BSC Datafill
BTS Datafill
PDB and BTS Sector Datafill for a Carrier Sector presented in CACP as one Row
14
FOR TRAINING PURPOSES ONLY
CACP Client and Server Interfaces
7
FOR TRAINING PURPOSES ONLY
Nortel Default Datafill Spreadsheet
System Determination and Acquisition Access Parameters
Request/Response Parms Registration Parms
Power Control (OTA Sent to MS)
Open-Loop RS1 Forward
Handoff Parameters
Pilot Search ARP Extended Neighbor List Pilot Strength
PilotDatabase (PDB) Record
9
FOR TRAINING PURPOSES ONLY
BSSM Navigator or GUI
Parameters Contained in Advanced Sector MO
10
FOR TRAINING PURPOSES ONLY
BSMCI Table-based Datafill Tool
5
FOR TRAINING PURPOSES ONLY
Types of RF Optimization Parameters
> BTS OTA Datafilled Parameters
Values are communicated to the mobile on the sync and paging channels during idle state. If a call is started, the mobile uses the parameter settings it received from the last sector where it was idle until they are overwritten.
Selector SubSystem Common/Global Flexible Power Control Arrays
RS1 (RC1) Fwd/Rvs RS2 (RC2) Fwd/Rvs RC3 FCH Fwd/Rvs RC3 SCH Fwd 19.2 Fwd 38.4 Fwd 76.8 Fwd 153.6 RC4 FCH Fwd/Rvs RC4 SCH Fwd 19.2 Fwd 38.4 Fwd 76.8 Fwd 153.6 RC5 FCH Fwd/Rvs RC3 FCH Rvs 19.2 Rvs 38.4 Rvs 76.8 Rvs 153.6
Legacy BSC
15
FOR TRAINING PURPOSES ONLY
Lesson Summary
In this lesson, you learned how to: > Identify what are the Nortel Configuration Management systems > Be able to identify and describe RF parameter classifications > Describe the how the Nortel configuration systems interface each other
16
FOR TRAINING PURPOSES ONLY
�
These parameters apply to an entire network.
Any changes made to these parameters at the BSC willl have an overall effect to the whole BSC. For example; PILOT_INC in the System Selector Subsystem is global to the BSC
Frequency-Sector Record Hard Handoff Triggers/Targets
Traffic Management
MCTA Cell Capability RadioResourceManagement(RRM) GlobalServiceRedirection (GSRM) Extended GSRM
> BSC OTA Datafilled Parameters
Values are communicated to the mobile on the forward traffic channel. The SBS constantly updates the mobile with new parameter values as the mobile acquires new sectors.
SCH SCH SCH SCH
Power Management Parameters (BTS)
Receive Transmit (cont over)
8
FOR TRAINING PURPOSES ONLY
Nortel Default Datafill Spreadsheet (cont)
BTS Receiver Acquisition Windows Access Channel Traffic Channel Extended Search Rings Page Zone Table (IZP) Alarms OCM Throttling Packet Data Parameters
OTA and Configuration parms appear in BTS, PDB and Global areas of datafill
4
FOR TRAINING PURPOSES ONLY
Types of RF Optimization Parameters
- Global vs Sector Specific > Global Parameters
> Nortel Networks Configuration Specific Parameters
Outlined in the Network Management Interface System (NMIS) documents.
Designated by UpperCaseOfEachWord with no underscores. For example – QuickRepeat, PilotGain, TxMaxGain Always used directly by Nortel Networks infrastructure.
Introduction
Configuration management of wireless involves a vast number of parameters. In the Nortel CDMA system the RF parameters are configured both during the design stage and during optimization. This lesson endeavors to go over the configuration strategy and tools at a high level.
Designated by ALL_UPPER_CASE with underscores. For example - T_ADD, SRCH_WIN_A Usually sent to the mobile to control the mobile's actions (but not always), for example CDMA Neighbor List direct MS idle-mode neighbor search
12
FOR TRAINING PURPOSES ONLY
CACP Datafill Tool
13
FOR TRAINING PURPOSES ONLY
Datafill for one Carrier Sector in CACP Row View
PDB Records
F1
Sector Records
When a mobile is in soft handoff with more than one sector, the "safer" search parameter value is sent from the SBS to the mobile. Safer meaning widest Search Windows, and lowest T_ADD (-15 would be picked over -14) and lowest T_DROP, etc