computerscience

                              COMPUTER SCIENCE
computer science is a basics technology in  our life.computer is most need in our life.today in the world every persson use in computer . today ,s every person is used computer  and every work do a computer .

How Computers Work: Input and Output

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The central processing unit is the unseen part of a computer system, and users are only dimly aware of it. But users are very much aware of the input and output associated with the computer. They submit input data to the computer to get processed information, the output.

Sometimes the output is an instant reaction to the input. Consider these examples:

• Zebra-striped bar codes on supermarket items provide input that permits instant retrieval of outputs - price and item name - right at the checkout counter.
• A bank teller queries the computer through the small terminal at the window by giving a customer's account number as input. The same screen immediately provides the customer's account balance as output.
• A forklift operator speaks directly to a computer through a microphone. Words like left, right, and lift are the actual input data. The output is the computer's instant response, which causes the forklift to operate as requested.
• A medical student studies the human body on a computer screen, inputting changes to the program to show a close-up of the leg and then to remove layers of tissue to reveal the muscles and bone underneath. The screen outputs the changes, allowing the student (without donning a mask, sanitary gloves, or operating gown) to simulate surgery on the computer.
• A sales representative uses an instrument that looks like a pen to enter an order on a special pad. The handwritten characters are displayed as "typed" text and are stored in the pad, which is actually a small computer.

Input and output may sometimes be separated by time or distance or both. Here are some examples:

• Factory workers input data by punching in on a time clock as they go from task to task. The time clock is connected to a computer. The outputs are their weekly paychecks and reports for management that summarize hours per project on a quarterly basis.
• A college student writes checks. The data on the checks is used as input to the bank computer, which eventually processes the data to prepare a bank statement once a month.
• Charge-card transactions in a retail store provide input data that is processed monthly to produce customer bills.
• Water-sample data is collected at lake and river sites, keyed in at the environmental agency office, and used to produce reports that show patterns of water quality.

The examples in this section show the diversity of computer applications, but in all cases the process is the same: input-processing-output. We have already had an introduction to processing. Now, in this chapter we will examine input and output methods in detail.

Input: Getting Data from the User to the Computer

Some input data can go directly to the computer for processing. Input in this category includes bar codes, speech that enters the computer through a microphone, and data entered by means of a device that converts motions to on-screen action. Some input data, however, goes through a good deal of intermediate handling, such as when it is copied from a source document and translated to a medium that a machine can read, such as a magnetic disk. In either case the task is to gather data to be processed by the computer ‹sometimes called raw data and convert it into some form the computer can understand.

Keyboard
A keyboard is usually part of a personal computer or part of a terminal that is connected to a computer somewhere else. Not all keyboards are traditional, however. A fast-food franchise like McDonald's, for example, uses keyboards whose keys represent items such as large fries or a Big Mac. Even less traditional in the United States are keyboards that are used to enter Chinese characters.

Mouse
A mouse is an input device with a ball on its underside that is rolled on a flat surface, usually the desk on which the computer sits. The rolling movement causes a corresponding movement on the screen. Moving the mouse allows you to reposition the pointer, or cursor, an indicator on the screen that shows where the next interaction with the computer can take place. The cursor can also be moved by pressing various keyboard keys. You can communicate commands to the computer by pressing a button on top of the mouse. In particular, a mouse button is often used to click on an icon, a pictorial symbol on a screen; the icon represents a computer activity-a command to the computer-so clicking the icon invokes the command.

Trackball
A variation on the mouse is the trackball. You may have used a trackball to play a video game. The trackball is like an upside-down mouse-you roll the ball directly with your hand. The popularity of the trackball surged with the advent of laptop computers, when traveling users found them- selves without a flat surface on which to roll the traditional mouse.

Source Data Automation: Collecting Data Where It Starts
Efficient data input means reducing the number of intermediate steps required between the origination of data and its processing. This is best accomplished by source data automation ‹the use of special equipment to collect data at the source, as a by-product of the activity that generates the data, and send it directly to the computer. Recall, for example, the supermarket bar code, which can be used to send data about the product directly to the computer. Source data automation eliminates keying, thereby reducing costs and opportunities for human-introduced mistakes. Since data about a transaction is collected when and where the transaction takes place, source data automation also improves the speed of the input operation.

For convenience, we will divide this discussion into the primary areas related to source data automation: magnetic-ink character recognition, optical recognition, data collection devices, and even directly by your own voice, finger, or eye. Let us consider each of these in turn.

Magnetic-Ink Character Recognition
Abbreviated MICR, magnetic-ink character recognition is a method of machine-reading characters made of magnetized particles. The most common example of magnetic characters is the array of numbers across the bottom of your personal check.

Most magnetic-ink characters are preprinted on your check. If you compare a check you wrote that has been cashed and cleared by the bank with those that are still unused in your checkbook, you will note that the amount of the cashed check has been reproduced in magnetic characters in the lower-right corner. These characters were added by a person at the bank by using a MICR inscriber.

computer science

  

Computer Science Careers

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What exactly is Computer Science? 
Computer Science is the science of using computers to solve problems. Mostly, this involves designing software (computer programs) and addressing fundamental scientific questions about thenature of computation but also involves many aspects of hardware and architecting the large computer systems that form the infrastructure of commercial and government enterprises. Computer scientists work in many different ways: pen-and-paper theoretical work on the foundations and fundamentals, programming work at the computer and collaborative teamwork in doing research and solving problems. What Computer Science is not ... 
Computer Science is not about using software, such as spreadsheets (like Excel), word processors (like Word) or image tools (like Photoshop). Many software packages are complicated to master (such as Photoshop or Excel) and it is true that many jobs depend on expertise in using such tools, but computer science is not about using the tools. It is not about expertise in computer games, it is not about about writing content in websites, and it is not about not about assembling computers or knowing which computers are best buys. Edsger Dijkstra, a famous award-winning computer scientist once said, "Computer Science is no more about computers than Astronomy is about telescopes". Computer Science is about the principles behind building the above software packages, about the algorithms used in computer games, about the technology behind the internet and about the architecture of computing devices. What is Information Technology, and how is it different from Computer Science?
While computer science has become a somewhat precise term as a field of study (like geology), information technology (IT) is a somewhat more vague term. The commercial world uses the term IT in a variety of contexts, generally, to mean "anything to do with computers". Many business uses of this term refer specifically to the combination of databases, information processing systems and communication systems (email, web browsing) they have been installing in the 80's and 90's. Thus, an IT job could mean a sales job in a computer company, or a business manager overseeing the installation of software, or it could mean a network technician who installs fiber-optic cable, or of course a software engineer. However, computer science generally denotes a professional with computer science training, one who is involved in the creation of software and software systems. Most educational programs are in computer science, which has a long tradition of accredition, standardized testing (such as the GRE subject test in computer science), prestigious research journals and well-defined curricula. In contrast, while some schools offer IT curricula, these are less well-defined, and probably not as rigorous as computer science curricula and degrees. What is software?
Computer science is not about building keyboards or monitors or the cables that connect your PC to your printer. While these are important to the functioning of a computer, as is electricity, computer software consists of interacting programs each of which is a collection of instructions capable of being executed on a computer. So, first we need to think of a computer as a "dumb" machine that knows how to execute elementary "instructions" (add this, multiply that). Then, software programs are collections of instructions that achieve higher-level end objectives. In a sense, the "intelligence" lies in the software and it is the difficulty of creating reliable, intelligent software that has made the young discipline of computer science into the large, diverse field it is today. Software systems now pervade almost all aspects of life, including high-end entertainment (such as the computer-generated dinosaurs in Jurassic Park), mission-critical control systems (factories, robots, aircrafts, space-travel), information systems (banks, websites, medical databases, government systems) and research tools (earthquake simulators, drug-design software, astronomy databases). What is programming?
Programming is the intellectual endeavor of creating individual software programs. Part of it involves thinking (design, analysis), part of it involves coding (translating a design into instructions via a programming languages such as Java or C++) and part of it involves testing (subjecting software to a battery of tests to make sure it works). Programming has been likened to mathematics (analytic thinking) to writing (artfully telling a story), to engineering (building larger software out of smaller software units) and to art (exercising creativity). The part of programming that is most easily identified in Hollywood depictions is coding, the process of typing instructions in a programming language (such as Java or C++); this involves the stereotypical hunching over a monitor, pounding at the keyboard and watching the software execute. Is Computer Science mostly programming?
Far from it. Initially, it may seem that it is all about programming because it is the skill whose teaching we start with (because it's fun, it's challenging and it's a prerequisite to further computer science). However, most undergraduate curricula devote 3 to 4 courses exclusively to programming, leaving 10-15 other computer science courses. Some of these use a student's programming skills acquired earlier, but most concentrate on some aspect of computer science central to the discipline. So, what are these areas of computer science? You can: learn about how computers are built (architecture), the principles behind important "infrastructure" software systems (operating systems, databases), study classic algorithms and learn to design your own, learn how compilers and language translation is done, study specialized computer science areas such as artificial intelligence, parallel computing, networks, graphics, bioinformatics, robotics, education or multimedia. What's interesting about Computer Science?
Why do people find computer science interesting? Initially, interest usually begins with programming and mastering the many details and thought processes involved in programming. Later, once programming is "been there, done that", people get interested in designing large software systems, or in computer architecture, or in one of the many specialized areas of computer science. One of the best aspects of a young discipline is that there are many open problems awaiting the next generation of computer scientists. For example, one of the "holy grail" problems in computer science (the P=NP question) is still unsolved. Many people believe that the golden age of computing has just begun.

coputer science

    Copy your macros to a Personal Macro Workbook

Hide All

 

 

If you find yourself recreating the same macros, you can copy those macros to a special workbook called Personal.xlsb that is saved on your computer.

By default, when you create a macro in Excel, the macro works only in the workbook that contains it. This behavior is okay as long as you don’t need to use that macro in other workbooks. Any macros that you store in your personal workbook on a computer become available to you in any workbook whenever you start Excel on that same computer.

In this article

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• How does this work?
• Create and update the personal workbook
• Best practices for saving macros

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How does this work?

The personal workbook (Personal.xlsb) is opened as a hidden workbook every time you start Excel. Excel saves the personal workbook as Personal.xlsb, in the following folder on Windows 7 and Windows Vista computers:

C:\Users\<username>\AppData\Roaming\Microsoft\Excel\XLSTART
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Create and update the personal workbook

To create the personal workbook, you first need to create a macro and store it in the Personal Macro Workbook. Before you get started, make sure that the Developer tab is available in the ribbon in Excel.
Make the Developer tab visible in Excel 2010

1. On the File tab, click Options, and then click Customize Ribbon
2. Under Customize Ribbon, in the Main Tabs box, make sure that the Developer check box is selected.
3. Click OK.

Make the Developer tab visible in Excel 2007

1. Click the Microsoft Office Button , and then click Excel Options.
2. Click Popular, and then select the Show Developer tab in the Ribbon check box.
3. Click OK.

Now you’re ready to record a macro and save it in the Personal Macro Workbook. In this example, you will create a simple macro by using the Macro Recorder. This macro will format the text in the current cell as bold.

1. In cell A1, enter some text, such as “Some text.”
2. On the Developer tab, in the Code group, click Record Macro.
   
3. In the Record Macro dialog box, type a name for the macro in the Macro name box, such as BoldMe. Make sure you don’t use any spaces in the name.
4. In the Store macro in box, select Personal Macro Workbook.
   
5. Click OK.
6. Press CTRL+B to apply bold formatting.
   That is only step that is recorded in the macro.
7. On the Developer tab, in the Code group, click Stop Recording.
   
8. Close any open workbooks and then exit Excel.
   A message appears that prompts you to save the changes that you made to the Personal Macro Workbook.
9. Click Yes to save the personal workbook.

The next time you start Excel, the personal workbook is loaded but you can’t see it because it’s hidden by default. You can view Personal.xlsb by doing the following:

1. On the View tab, in the Window group, click Unhide.
   
   In the Unhide dialog box, you should see PERSONAL.XLSB.
2. Click OK to view the personal workbook.
   Any macros you save to the personal workbook can be edited only by first unhiding the personal workbook. This prevents you from accidentally deleting or making unwanted changes to those macros.
3. To hide the personal workbook, make sure you have Personal.xlsb selected, and then click Hide.

Any time you create a new macro and save it in your personal workbook or update any macros that it contains, you are prompted to save the personal workbook just as it did the first time you saved it.
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Best practices for saving macros

It’s not possible to share your Personal.xlsb between computers, but you can copy it to the XLSTART folder on other computers.
If you have one or just a few macros that you want to share with others, you can send them the workbook that contains it in an email message. You can also make the workbook available on a shared network drive or from a SharePoint Services library.
If you want to copy macros from the personal workbook to another workbook or vice versa, you can do so in the Project Explorer, which is in the Visual Basic Editor. You can start the Visual Basic Editor in Excel by pressing ALT+F11. For more information about copying a macro from one workbook to another, see Copy a macro module to another workbook.
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Introduction to the Web Design Course

Hello, and a very warm welcome to the Home and Learn computer course for Web Design. The software you need is set out below. We assume that you have absolutely no knowledge of designing web pages. Throughout the course of this book you will learn the fundamentals of Web Design. And, of course, you will start creating your own pages. By the end of the book, you will have acquired a good understanding of what web design is all about, and have the ability to take it further, if you so wish.

What you will learn

The aim of this course is to get you started designing web pages. It is assumed that you have little or no experience of the subject. During our time together, you'll learn some new concepts and ideas, most of which will not be difficult. After all, there are billions of pages on the internet, designed by the whole spectrum of humanity: children, adults, pensioners, people from all walks of life have sites out there. And if they can do it, so can you. In fact, follow the lessons carefully and you WILL do it. Before long, you'll have your own pages designed and ready to be uploaded, there for all the world to see.
The technologies you will learn are HTML, HTML5, and Cascading Style Sheets. (HTML5 is the newest version of the of the language.)
At the heart of every web page is something called HTML. You will learn what this is, and how to code it. You will also learn the newest version of HTML, which is called HTML5. Both versions are included in this course. As well as HTML, you will learn about Cascading Style Sheets, and will be able to improve the look of your web pages by adding CSS to enhance them.
At the end of most chapters in this book you will see a summary table of what you have learnt. The summary tables are also included in a separate document, called quick_reference.pdf. This is one of the files that come with this book. All the files you should have are detailed below.

What you need to do the course

The only thing you really need to do the course is a simple text editor. We explain all about this in the first chapter, Anatomy of a Web Page, in the section Software for Writing Web Pages. But the operating system you have is not important. So you can do this course on a PC or an Apple Mac.

Web Design Course Files

There are a number of files that you will need in order to complete certain sections. Whenever you need a file for a section of your course, it will be explained in the relevant section. The download location is here, under the heading Web Design - New Course (you don't need the downloads for the old course.):

Download the Extra Files needed for this course

Once you have all the necessary files, you can begin. The first part is all about the software you need for this course. Don't worry - you'll already have all you need, so there's nothing to buy. Click the link below for the first section.
Good luck!

COMPUTER SCIENCE

             REMOVABLE  HARD DISK                                                                            A type of disk drive system in which hard disks are enclosed in plastic or metal cartridges so that they can be removed like floppy disks. Removable disk drives combine the best aspects of hard and floppy disks. They are nearly as capacious and fast as hard disks and have the portability of floppy disks. Their biggest drawback is that they're relatively expensive.

                       HARD DISK
The term hard is used to describe anything that is permanent or physically exists. In contrast, the term soft refers to concepts, symbols and other intangible and changeable objects.https://www.blogger.com/blogger.g?blogID=7423979372168233121#editor/target=post;postID=4529639920865938934
                                                                                           
                                FLOPPY DISK

I)A soft magnetic disk. It is called floppy because it flops if you wave it (at least, the 5��-inch variety does). Unlike most hard disks, floppy disks (often called floppies or diskettes) are portable, because you can remove them from a disk drive. Disk drives for floppy disks are called floppy drives. Floppy disks are slower to access than hard disks and have less storagecapacity, but they are much less expensive. And most importantly, they are portable.
Floppies come in three basic sizes:

8-inch:The first floppy disk design, invented by IBM in the late 1960s and used in the early 1970s as first a read-only format and then as a read-write format. The typical desktop/laptop computer does not use the 8-inch floppy disk.

5��-inch: The common size for PCs made before 1987 and the predecessor to the 8-inch floppy disk. This type of floppy is generally capable of storing between 100K and 1.2MB (megabytes) of data. The most common sizes are 360K and 1.2MB.

3��-inch: Floppy is something of a misnomer for these disks, as they are encased in a rigid envelope. Despite their small size, microfloppies have a larger storage capacity than their cousins -- from 400K to 1.4MB of data. The most common sizes for PCs are 720K (double-density) and 1.44MB (high-density). Macintoshes support disks of 400K, 800K, and 1.2MB.                          SYSTEM DISK

 Another name for a bootable diskette, a floppy disk (or CD-ROM) that a computer can use to boot the operating system (OS) if the hard drive fails to boot the OS. Typically, PCs will read the floppy disk drive before the hard drive when booting, so if the user leaves a floppy disk in the drive that does not contain the operating system or another control program, the computer will display a system disk error. The user can change the system BIOS so that the hard drive is read before the floppy drive.

computer materials in use

                                                

               COMPUTER SCIENCE   

        Computer

 

From Wikipedia, the free encyclopedia

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"Computer technology" and "Computer system" redirect here. For the company, see Computer Technology Limited. For other uses, see Computer (disambiguation) and Computer system (disambiguation).

Computer

A computer is a general purpose device that can be programmed to carry out a set of arithmetic or logical operations automatically. Since a sequence of operations can be readily changed, the computer can solve more than one kind of problem.
Conventionally, a computer consists of at least one processing element, typically a central processing unit (CPU), and some form of memory. The processing element carries out arithmetic and logic operations, and a sequencing and control unit can change the order of operations in response to stored information. Peripheral devices allow information to be retrieved from an external source, and the result of operations saved and retrieved.
In World War II, mechanical analog computers were used for specialized military applications. During this time the first electronic digital computers were developed. Originally they were the size of a large room, consuming as much power as several hundred modern personal computers (PCs).^[1]
Modern computers based on integrated circuits are millions to billions of times more capable than the early machines, and occupy a fraction of the space.^[2] Simple computers are small enough to fit into mobile devices, and mobile computers can be powered by small batteries. Personal computers in their various forms are icons of the Information Age and are what most people think of as “computers.” However, the embedded computers found in many devices from MP3 players to fighter aircraft and from toys to industrial robots

are the most numerous

Etymology

The first use of the word “computer” was recorded in 1613 in a book called “The yong mans gleanings” by English writer Richard Braithwait I haue read the truest computer of Times, and the best Arithmetician that euer breathed, and he reduceth thy dayes into a short number. It referred to a person who carried out calculations, or computations, and the word continued with the same meaning until the middle of the 20th century. From the end of the 19th century the word began to take on its more familiar meaning, a machine that carries out computations.^[3]

History

Main article: History of computing hardware

Rudimentary calculating devices first appeared in antiquity and mechanical calculating aids were invented in the 17th century. The first recorded use of the word "computer" is also from the 17th century, applied to human computers, people who performed calculations, often as employment. The first computer devices were conceived of in the 19th century, and only emerged in their modern form in the 1940s.

First general-purpose computing device

A portion of Babbage's Difference engine.

Charles Babbage, an English mechanical engineer and polymath, originated the concept of a programmable computer. Considered the "father of the computer",^[4] he conceptualized and invented the first mechanical computer in the early 19th century. After working on his revolutionary difference engine, designed to aid in navigational calculations, in 1833 he realized that a much more general design, an Analytical Engine, was possible. The input of programs and data was to be provided to the machine via punched cards, a method being used at the time to direct mechanical looms such as the Jacquard loom. For output, the machine would have a printer, a curve plotter and a bell. The machine would also be able to punch numbers onto cards to be read in later. The Engine incorporated an arithmetic logic unit, control flow in the form of conditional branching and loops, and integrated memory, making it the first design for a general-purpose computer that could be described in modern terms as Turing-complete.^[5][6]
The machine was about a century ahead of its time. All the parts for his machine had to be made by hand - this was a major problem for a device with thousands of parts. Eventually, the project was dissolved with the decision of the British Government to cease funding. Babbage's failure to complete the analytical engine can be chiefly attributed to difficulties not only of politics and financing, but also to his desire to develop an increasingly sophisticated computer and to move ahead faster than anyone else could follow. Nevertheless his son, Henry Babbage, completed a simplified version of the analytical engine's computing unit (the mill) in 1888. He gave a successful demonstration of its use in computing tables in 1906.

Early analog computers

Sir William Thomson's third tide-predicting machine design, 1879-81

During the first half of the 20th century, many scientific computing needs were met by increasingly sophisticated analog computers, which used a direct mechanical or electrical model of the problem as a basis for computation. However, these were not programmable and generally lacked the versatility and accuracy of modern digital computers.^[7]
The first modern analog computer was a tide-predicting machine, invented by Sir William Thomson in 1872. The differential analyser, a mechanical analog computer designed to solve differential equations by integration using wheel-and-disc mechanisms, was conceptualized in 1876 by James Thomson, the brother of the more famous Lord Kelvin.^[8]
The art of mechanical analog computing reached its zenith with the differential analyzer, built by H. L. Hazen and Vannevar Bush at MIT starting in 1927. This built on the mechanical integrators of James Thomson and the torque amplifiers invented by H. W. Nieman. A dozen of these devices were built before their obsolescence became obvious.

The modern computer age begins

The principle of the modern computer was first described by computer scientist Alan Turing, who set out the idea in his seminal 1936 paper,^[9] On Computable Numbers. Turing reformulated Kurt Gödel's 1931 results on the limits of proof and computation, replacing Gödel's universal arithmetic-based formal language with the formal and simple hypothetical devices that became known as Turing machines. He proved that some such machine would be capable of performing any conceivable mathematical computation if it were representable as an algorithm. He went on to prove that there was no solution to the Entscheidungsproblem by first showing that the halting problem for Turing machines is undecidable: in general, it is not possible to decide algorithmically whether a given Turing machine will ever halt.
He also introduced the notion of a 'Universal Machine' (now known as a Universal Turing machine), with the idea that such a machine could perform the tasks of any other machine, or in other words, it is provably capable of computing anything that is computable by executing a program stored on tape, allowing the machine to be programmable. Von Neumann acknowledged that the central concept of the modern computer was due to this paper.^[10] Turing machines are to this day a central object of study in theory of computation. Except for the limitations imposed by their finite memory stores, modern computers are said to be Turing-complete, which is to say, they have algorithm execution capability equivalent to a universal Turing machine.

The first electromechanical computers

Replica of Zuse's Z3, the first fully automatic, digital (electromechanical) computer.

Early digital computers were electromechanical; electric switches drove mechanical relays to perform the calculation. These devices had a low operating speed and were eventually superseded by much faster all-electric computers, originally using vacuum tubes. The Z2, created by German engineer Konrad Zuse in 1939, was one of the earliest examples of an electromechanical relay computer.^[11]
In 1941, Zuse followed his earlier machine up with the Z3, the world's first working electromechanical programmable, fully automatic digital computer.^[12][13] The Z3 was built with 2000 relays, implementing a 22 bit word length that operated at a clock frequency of about 5–10 Hz.^[14] Program code and data were stored on punched film. It was quite similar to modern machines in some respects, pioneering numerous advances such as floating point numbers. Replacement of the hard-to-implement decimal system (used in Charles Babbage's earlier design) by the simpler binary system meant that Zuse's machines were easier to build and potentially more reliable, given the technologies available at that time.^[15] The Z3 was probably a complete Turing machine.

The introduction of electronic programmable computers with vacuum tubes

Purely electronic circuit elements soon replaced their mechanical and electromechanical equivalents, at the same time that digital calculation replaced analog. The engineer Tommy Flowers, working at the Post Office Research Station in London in the 1930s, began to explore the possible use of electronics for the telephone exchange. Experimental equipment that he built in 1934 went into operation 5 years later, converting a portion of the telephone exchange network into an electronic data processing system, using thousands of vacuum tubes.^[7] In the US, John Vincent Atanasoff and Clifford E. Berry of Iowa State University developed and tested the Atanasoff–Berry Computer (ABC) in 1942,^[16] the first "automatic electronic digital computer".^[17] This design was also all-electronic and used about 300 vacuum tubes, with capacitors fixed in a mechanically rotating drum for memory.^[18]

Colossus was the first electronic digital programmable computing device, and was used to break German ciphers during World War II.

During World War II, the British at Bletchley Park achieved a number of successes at breaking encrypted German military communications. The German encryption machine, Enigma, was first attacked with the help of the electro-mechanical bombes. To crack the more sophisticated German Lorenz SZ 40/42 machine, used for high-level Army communications, Max Newman and his colleagues commissioned Flowers to build the Colossus.^[18] He spent eleven months from early February 1943 designing and building the first Colossus.^[19] After a functional test in December 1943, Colossus was shipped to Bletchley Park, where it was delivered on 18 January 1944^[20] and attacked its first message on 5 February.^[18]
Colossus was the world's first electronic digital programmable computer.^[7] It used a large number of valves (vacuum tubes). It had paper-tape input and was capable of being configured to perform a variety of boolean logical operations on its data, but it was not Turing-complete. Nine Mk II Colossi were built (The Mk I was converted to a Mk II making ten machines in total). Colossus Mark I contained 1500 thermionic valves (tubes), but Mark II with 2400 valves, was both 5 times faster and simpler to operate than Mark 1, greatly speeding the decoding process.^[21][22]

ENIAC was the first Turing-complete device,and performed ballistics trajectory calculations for the United States Army.

The US-built ENIAC^[23] (Electronic Numerical Integrator and Computer) was the first electronic programmable computer built in the US. Although the ENIAC was similar to the Colossus it was much faster and more flexible. It was unambiguously a Turing-complete device and could compute any problem that would fit into its memory. Like the Colossus, a "program" on the ENIAC was defined by the states of its patch cables and switches, a far cry from the stored program electronic machines that came later. Once a program was written, it had to be mechanically set into the machine with manual resetting of plugs and switches.
It combined the high speed of electronics with the ability to be programmed for many complex problems. It could add or subtract 5000 times a second, a thousand times faster than any other machine. It also had modules to multiply, divide, and square root. High speed memory was limited to 20 words (about 80 bytes). Built under the direction of John Mauchly and J. Presper Eckert at the University of Pennsylvania, ENIAC's development and construction lasted from 1943 to full operation at the end of 1945. The machine was huge, weighing 30 tons, using 200 kilowatts of electric power and contained over 18,000 vacuum tubes, 1,500 relays, and hundreds of thousands of resistors, capacitors, and inductors.^[24]

Stored program computers eliminate the need for re-wiring

A section of the Manchester Small-Scale Experimental Machine, the first stored-program computer.

Early computing machines had fixed programs. Changing its function required the re-wiring and re-structuring of the machine.^[18] With the proposal of the stored-program computer this changed. A stored-program computer includes by design an instruction set and can store in memory a set of instructions (a program) that details the computation. The theoretical basis for the stored-program computer was laid by Alan Turing in his 1936 paper. In 1945 Turing joined the National Physical Laboratory and began work on developing an electronic stored-program digital computer. His 1945 report ‘Proposed Electronic Calculator’ was the first specification for such a device. John von Neumann at the University of Pennsylvania, also circulated his First Draft of a Report on the EDVAC in 1945.^[7]

Ferranti Mark 1, c. 1951.

The Manchester Small-Scale Experimental Machine, nicknamed Baby, was the world's first stored-program computer. It was built at the Victoria University of Manchester by Frederic C. Williams, Tom Kilburn and Geoff Tootill, and ran its first program on 21 June 1948.^[25] It was designed as a testbed for the Williams tube the first random-access digital storage device.^[26] Although the computer was considered "small and primitive" by the standards of its time, it was the first working machine to contain all of the elements essential to a modern electronic computer.^[27] As soon as the SSEM had demonstrated the feasibility of its design, a project was initiated at the university to develop it into a more usable computer, the Manchester Mark 1.
The Mark 1 in turn quickly became the prototype for the Ferranti Mark 1, the world's first commercially available general-purpose computer.^[28] Built by Ferranti, it was delivered to the University of Manchester in February 1951. At least seven of these later machines were delivered between 1953 and 1957, one of them to Shell labs in Amsterdam.^[29] In October 1947, the directors of British catering company J. Lyons & Company decided to take an active role in promoting the commercial development of computers. The LEO I computer became operational in April 1951 ^[30] and ran the world's first regular routine office computer job

 

 

 

computer science.

Computer Science Complete Course List

CS103 Exploring Computer Science (Staff)

A survey of the many sub-fields of computer science, which will provide an introduction to what computer science is and what computer scientists do. Topics will range from the theoretical (mathematical foundations of computing, design of algorithms) to the practical (components of the computer, how the Internet works). No credit will be given for this coruse if a student has previous credit for CS110 or any computer science course with a higher number than 110.

CS110 Introductory CS and Programming (Staff)

An introduction to the fundamental concepts and abstractions of computer science, using a study of algorithms and computer programming as a vehicle. Topics include: the design, implementation, and application of algorithms; the uses of abstraction; the modeling and representation of values and entities; control flow and modularity. A high-level programming language is introduced and used. F, S.

CS210 Intermediate CS and Data Structures (Zaring)

A continuation of the study of abstraction, algorithms, and computer programs. Concepts related to the design, analysis, and implementation of more advanced data types (lists, stacks, queues, trees, graphs, etc.) are covered in detail. Prerequisite: CS110. S.

CS255 Computer Organization (McCulloch)

The organization and inner-workings of computers are covered in some detail, with an emphasis on the relationships among the various levels of hardware and software found in such systems. Attention is focused on both general concepts and on case studies of specific systems. Assembly language programming is introduced. Prerequisite: CS110 and CS210 or consent of instructor. F.

CS270 Paradigms of Computation (Zaring)

An introduction to the fundamental and emergent paradigms, both formal and pragmatic, of algorithms and computation. Topics include basic automata theory, functional programming, object-oriented design, and concurrent/parallel programming. The Scheme programming language is introduced. Prerequisite: CS210, CS255, MATH250. S.

CS310 Database Systems (Zaring)

A study of the foundations, design, and implementation of database systems. Topics include data models, database design, query languages, database architectures, implementation issues, and case studies. Projects involving implementations of or use of database systems are required. Prerequisite: CS210, CS270 or consent of instructor, MATH250.

CS320 Computer Systems and Architecture (McCulloch)

A detailed discussion of computer architecture of computer systems, including studies of data- and control-paths, memory systems, and parallel/distributed systems. Programming and digital-circuit design projects may be required. Prerequisite: CS255, CS210, MATH250.

CS340 Artificial Intelligence (McCulloch)

An introduction to issues surrounding machine intelligence. General topics include knowledge representation, searching, reasoning, learning, and planning. Specific problems drawn from areas including computer game playing, theorem proving, natural language processing, expert systems, and robotics will be addressed. Programming projects in LISP, Scheme, Prolog, or related languages are required. Prerequisite: CS210, CS270, MATH250.

CS350 Operating Systems (Wiebe)

The fundamental concepts of resource allocation in operating systems. This includes process management, storage management, device management, and networking issues. Case studies of actual operating systems will be presented. Prerequisite: CS255.

CS360 Algorithm Analysis and Design (McCulloch)

A continuation of the study of data structures begun in CS210 with emphasis on the design and analysis of algorithms. Also an introduction to questions of efficiency and NP completeness. Prerequisite: CS210, MATH111, MATH250.

CS370 Programming Languages (Zaring)

A systematic study of programming language design, analysis, and implementation. Relationships among languages, language properties and features, and formal notions of language semantics are considered. Major language paradigms (imperative, functional, object-oriented, logic programming, and others) are studied. Prerequisite: CS210, CS270,MATH250.

CS380 Theory of Computation (Zaring)

A study of the formal theories underlying computer science. Topics include Turing machines, automata theory, recursive functions, computability, and formal languages. Prerequisite:CS270 or consent of instructor, MATH250. F.

CS390 Special Topics in Computer Science (Staff)

A course of varying content reflecting the needs and interests of students.

CS410 Compiler Theory and Design (Zaring)

An investigation of compiler theory, design, and construction. Formal and practical issues in lexical analysis, syntactic analysis, semantic analysis (including type-checking and optimization), and code generation are covered. Substantial projects concerning implementations of working compilers are required. Prerequisite: CS210, CS255, CS270,CS380. S.

CS490 Independent Study (Staff)

Independent study of a topic in advanced computer science under the guidance of a faculty member. Individually arranged.

CS491 Directed Readings (Staff)

Reading in advanced computer science under the guidance of a faculty member. Individually arranged.

CS499 Seminar (Staff)

Intensive study of a topic selected by the faculty member in charge with presentations by students..

Computer Science Pre-Engineering Option

Computer science students interested in pursuing an undergraduate degree in engineering may choose to participate in Ohio Wesleyan's 3-2 engineering program. Under this option, a student completes a specially-designed computer science major in three years and then transfers to one of several participating engineering schools for two additional years of study. Upon successfully completing this five-year course of study, the student receives a computer science degree from Ohio Wesleyan and an engineering degree from the chosen engineering school. Participating engineering schools currently include (among others) Case Western Reserve University, Rensselaer Polytechnic University, and Washington University (St. Louis). Interested students are urged to check the course catalog for complete information on this program.

Facilities

Mathematics and computer science students and faculty have access to a variety of computing equipment and environments, some administered by the university for general use and some administered by the Dept. of Mathematics and Computer Science for use primarily in mathematics and computer science.

The Dept. of Mathematics and Computer Science operates the X-Lab, a growing laboratory of networked Linux workstations. The X-Lab provides computer science and mathematics students and faculty with a modern UNIX workstation computing environment in which to carry out projects connected with courses, independent studies, and advanced topics. Students with X-Lab accounts have 24-hour access to the lab, and experienced students regularly help administer and maintain the lab, helping test and install software, determine lab policies, suggest lab upgrades, etc.

The department has recently acquired an eight-processor, Linux-based parallel/distributed computing cluster. Plans are underway to develop uses of the cluster in courses and research. In addition, access to high-performance computing facilities for instructional and research purposes is readily available through the Ohio Supercomputer Center.)

The Ohio Wesleyan's Office of Information Systems operates a number of microcomputer laboratories. Collectively, these labs contain hundreds on Windows-based PC's, all connected to the university's internal computing network. A wide variety of application software is available. These labs are regularly used in introductory and intermediate computer science and mathematics courses.

In addition, the Office of Information Systems operates a variety of servers for student and faculty use. Any Ohio Wesleyan student or faculty member may obtain an account on these machines. (The Office of Information Systems also provides technical support to those students wishing to have a computer in their dormitory rooms and offers a variety of PC-clone systems for sale through the university. Please contact them directly for more information.)

All users on all departmental and university computers have full access to the Internet for email, news, remote login, and World-Wide Web use.

Advanced Biometric Techniques This project is investigating various biometric sources (face, iris, hand, fingerprint, and gait) and sensors (2D, 3D, infra-red, ...) with the goal of developing more accurate biometric techniques. Our research group is supporting the government programs on the Gait Challenge Problem, the Face Recognition Grand Challenge, and the Iris Challenge Evaluation. Faculty: Bowyer, Flynn Algorithms and Software for Problems in Radiosurgery, Radiation Therapy, and Other Medical Applications This project is on the design, analysis, implementation, and experimentation of new algorithms and software for solving geometric optimization problems arising in radiosurgery, radiation therapy, and other related medical applications. A key step in radiotherapy and radiosurgery is to develop a treatment plan that defines the best radiation beam arrangements and time settings to destroy the target tumor without harming the surrounding healthy tissues. At the core of the planning process is a set of substantially challenging geometric optimization problems. We have been investigating a number of such geometric optimization problems, such as beam selection, beam shaping, surgical navigation and routing, sphere packing, shape approximation, leaf sequencing, field covering and partitioning, image segmentation, and beam source path planning. This is a joint project with the Department of Radiation Oncology, University of Maryland School of Medicine. Our goal is to incorporate our new algorithms and software into clinical radiation treatment planning systems for treating cancer patients. Faculty: Chen Anopheles Mosquito Comparative Genomics In September of 2008, the National Human Genome Research Institute (NHGRI) and the National Institute of Allergy and Infectious Diseases (NIAID) of the U.S. National Institutes of Health approved funding for the sequencing of the genomes and transcriptomes of thirteen Anopheles species, as described in the white paper. Since the initial white paper was approved, two additional species have been added to the project. This project, coordinated by ND biologist Nora Besansky, was inspired by very ambitious goals: improved understanding of vectorial capacity, and the application of that understanding toward reducing malaria disease burden. The Notre Dame Bioinformatics Lab, in collaboration with Prof. Besansky, will help uncover the evolutionary genomics of the An. gambiae species group and the larger set of species in relation to malaria. Faculty: Emrich Automatic Emotion Detection The goal of this project is to develop computer systems that automatically sense when a user is bored, confused, frustrated, etc., by monitoring facial features, speech contours, body movements, interaction patterns, and physiological responses. The emotion detection systems will be integrated into computer interfaces in an attempt to provide more effective, user-friendly, and naturalistic interactions. Several projects focusing on either adapting existing emotion detection systems or investigating new modalities for emotion detection are available. Faculty: D'Mello Compucell: Computational Methods for Simulation of Biological Development We are creating a model that includes how genetics at the subcellular level interacts with biophysics at the cellular level to orchestrate the development of organisms. This model is implemented in a software package called CompuCell. Users define the model to simulate using BioLogo, a domain specific language that generates a simulation package for the desired model. We also work with biologists, physicists, and mathematicians in the development and validation of simulations of chicken limb development as part of a National Science Foundation Biocomplexity project. Faculty: Izaguirre DARTS - Design and Analysis of Real-Time Systems Real-time embedded systems can be found in many applications such as communication devices, transportation machines, entertainment appliances, and medical instruments. This research targets at two important problems in real-time system design: performance analysis and scheduling algorithm design. Our current focus is on dealing with uncertainty and flexibility presented in many real-time control applications. Faculty: Poellabauer Data Intensive Abstractions for High End Biometric Applications Research in biometrics depends upon the effective management and processing of many terabytes of digital data. Because these workloads are so data intensive, they are very challenging to scale up to large clusters and grids. To address this, we are designing a data repository and web-enabled tools that simplify browsing and processing large data sets. Our work has produced some of the largest data analysis results to date in the field, reducing the execution time of some problems from years into days. Faculty: Flynn, Thain Debugging Grids with Machine Learning Techniques Debugging large computing grids is notoriously hard. What can an end user do when a workload of millions of jobs experiences thousands of failures? We propose that data mining techniques are an effective way of explaining what happens to large workloads in grids. We are building and deploying tools that explain failures in computing grids of thousands of processors. Faculty: Chawla, Thain Designing Ultra-Dense Computers with QCAs Problem: Most projections of CMOS technologies perceive an ultimate limit of about 0.05 micron feature sized devices in about 10 years." The QCA Solution: Utilize a new technology termed Quantum Cellular Automata (QCA) to build real computers orders of magnitude denser than the limits of CMOS from molecularly sized devices where information is moved by Coulombic interactions rather than current flow. Faculty: Kogge Dynamic Data-Driven Applications Simulation (WIPER) This project is developing an integrated Wireless Phone Based Emergency Response System (WIPER) that is capable of real-time monitoring of normal social and geographical communication and activity patterns of millions of wireless phone users, recognizing unusual human agglomerations, potential emergencies and traffic jams. WIPER will select from these massive data streams high-resolution information in the physical vicinity of a communication or traffic anomaly, and dynamically inject it into an agent-based simulation system to classify and predict the unfolding of the emergency in real time. The agent-based simulation system will dynamically steer local data collection in the vicinity of the anomaly. Multiple distributed data collection, monitoring, analysis, simulation and decision support modules will be integrated using a Service Oriented Architecture (SOA) to generate traffic forecasts and emergency alerts for engineering, public safety and emergency response personnel. Faculty: Madey Environmental Simulation (NOM) This project consists of an interdisciplinary team of environmental (biology, chemistry, geology) and IT scientists that is developing a stochastic model for the time-dependent evolution of NOM in the environment. The scientific objectives are to produce both a new methodology and a specific program for predicting the properties of NOM over time as it evolves from precursor molecules to eventual mineralization. The methodology being developed is a mechanistic, stochastic simulation of NOM transformations, including biological and non-biological reactions, as well as adsorption, aggregation and physical transport. It employs recent advances in agent-based simulation, web-based deployment of scientific applications, a collaboratory for sharing simulations and data, and scalable web-based database management systems to improve the reliability of the stochastic simulations and to facilitate analysis of the resulting large datasets using datamining techniques. Faculty: Madey ExPERTS - Energy/Power Efficient, Real-Time system Scheduling A collaborative research that aims at developing scheduling algorithms to minimize the energy/power consumption of real-time embedded systems. Some techniques being considered include Dynamic Voltage Scaling (DVS), Dynamic Frequency Scaling (DFS), Sleep Mode Control (SMC), etc. Faculty: Chen, Hu From Computational Discovery to Privacy Preservation in Social, Product, and Health Networks The fast emergence of interaction networks brings a rich source of information not only about the nodes in such networks, but also about the relationships between them. This notion of collective intelligence opens up a new tremendous source of data which can vastly improve decision making in many domains, be it advertising, the effectiveness of offering related products, or the ability to diagnose medical conditions. Such networks, however, carry enough information about the entities participating in such networks to compromise their privacy. Now a node can expose not only its own information, but information about its neighborhood as well. The goal of this project is to extract rich information available in product, social, and medical networks while preserving the privacy of their users. We allow users to have control over what information about them can be released and secure the data through cryptographic means. Faculty: Blanton, Chawla Intelligent Edge Devices In this research, we design and implement prototypes of intelligent network edge devices such as routers, modems, or wireless base stations. The goal is to build sophisticated network and system management stations which exploit their location at the edge of a network and their ability to communicate directly with their end devices to provide efficient and centralized resource and network management functionalities. Faculty: Chawla, Poellabauer, Striegel Judicious Resource Management This project studies the complex relationships between resources and the effect resource adaptation has on application performance. This work is driven by the insight that careless (non-cooperative) adaptation of multiple resource or multiple communicating devices can lead to sub-optimal savings in resource utilization or degraded application performance; effects which are often difficult to capture with theoretical models alone, thereby requiring extensive experimental studies. Faculty: Poellabauer Languages and Systems for Data Intensive Scientific Computing Many problems in science and engineering can only be solved by harnessing large collections of computers called cluster, clouds, or grids. Unfortunately, these systems are very challenging to use, particularly for data intensive applications. To address this, our lab is designing new languages and systems that allow end users to easily specify and execute workloads that run on hundreds of processors. Faculty: Thain Morph: Morphable Computer Architectures for Highly Energy-Aware Systems Adding an Energy Gear to High Performance Embedded Systems. Faculty: Brockman, Kogge PIM: Processing in Memory With the current trend of rapidly increasing CPU speeds and ballooning RAM capacities, the bottleneck between the processor and main memory is becoming more and more costly. PIM is an attempt to solve this problem by combining processor and memory macros on a single chip. The benefits of such an architectural shift include very high bandwidth and multi-processor scaling capabilities. These possibilities and more are being enthusiastically explored by our group. Faculty: Brockman, Kogge Protomol: Computational Methods for Simulation of Proteins We are developing multiscale methods for simulation of proteins and other biological molecules. These methods are useful for understanding important post-human-genome biological questions, such as the folding pathways of proteins or the relationship between structure and function. Our goal is to provide algorithms that scale with system size and simulation length. We also provide efficient and extendible implementations of these algorithms using a high performance object-oriented framework called ProtoMol. This project is funded by the National Science Foundation. Faculty: Izaguirre SPIRIT: Spontaneous Information and Resource Sharing The objective of this project is to overcome the limitations of mobile wireless devices by allowing them to spontaneously request access to resources (such as storage, CPUs, network bandwidths) and information (e.g., obtained from sensors) residing on wireless peers in their proximity. The project addresses the resource-efficient and reliable discovery and access of such resources and information. Faculty: Poellabauer Scalable Bioinformatics We are creating a series of cloud-enabled bioinformatics tools through Biocompute, which is a web-based tool that leverages grid-computing resources for solving large bioinformatics problems. Biocompute divides large bioinformatics jobs into hundreds of smaller jobs that are sent to either a large collection of personal computers or an external cloud. This drastically reduces the time required to complete desired tasks. Further, each Biocompute user has a personal workspace where he or she can store and share files and results with others. Users can create custom databases to run jobs on or they can use public databases. Biocompute is maintained by the Cooperative Computing Lab and is supported by the Bioinformatics Core Facility at the University of Notre Dame. Faculty: Emrich, Thain Secure and Reliable Computation Outsourcing This project allows resource-constrained devices (limited in their battery life or computational capabilities) to use external computing resources such as supercomputers or grids to carry out their extensive computational tasks in a secure and reliable way. Secure computation means that powerful helper machines do not learn any information about the data being processed yet help a weak device to compute its task; and reliable computation means that the device is able to verify that the computation was indeed performed correctly without the need to recompute the task itself. We develop cryptographic techniques to securely outsource different types of computation such as biometric and DNA comparisons and others. We also use the fact that helper servers do not obtain access to the data they are processing to allow the device to detect deviations from the prescribed computation more effectively. Faculty: Blanton Sensor Networks The objective of this project is to develop techniques for the flexible and efficient collaboration among sensors in a wireless sensor network, including techniques for energy-efficient real-time routing of sensor data or resource-efficient in-network data aggregation and fusion. Faculty: Chawla, Poellabauer Software Engineering of Scientific Software We try to develop tools that simplify the design and implementation of high-performance software, utilizing object-oriented and generic programming in languages such as C++ and Eiffel. Examples are tools to automatically generate tests from semi-formal specifications of programs that are part of the code itself. We use Eiffel and design by contract. We also are trying to build self-adaptive programs that can choose the best algorithms and parameters for particular problems at run time. Faculty: Izaguirre Spanids The SPANIDS project is developing a scalable architecture for real-time intrusion detection on high-speed networks. We are developing techniques that exploit concurrency in network traffic to distribute high-bandwidth network streams across several sensors to significantly improve the capabilities of existing NIDS systems. We implemented a prototype system built from scalable inexpensive hardware and open source software that demonstrates the scalability and flexbility of our approach. Faculty: Freeland