Download Artech. Radio Frequency Integrated Circuit Design

Contents

01.Introduction to Communications Circuits
02.Issues in RFIC Design, Noise, Linearity, and Filtering
03.A Brief Review of Technology
04.Impedance Matching
05.The Use and Design of Passive Circuit Elements in IC Technologies
06.LNA Design
07.Mixers
08.Voltage-Controlled Oscillators
09.High-Frequency Filter Circuits
10.Power Amplifiers



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Download eWiley Mobile Fading Channels Modelling Analysis Simulation

CONTENTS

01. INTRODUCTION
02. RANDOM VARIABLES, STOCHASTIC PROCESSES, AND DETERMINISTIC SIGNALS
03. RAYLEIGH AND RICE PROCESSES AS REFERENCE MODELS
04. INTRODUCTION TO THE THEORY OF DETERMINISTIC PROCESSES
05. METHODS FOR THE COMPUTATION OF THE MODEL PARAMETERS OF DETERMINISTIC PROCESSES
06. FREQUENCY-NONSELECTIVE STOCHASTIC AND DETERMINISTIC CHANNEL MODELS
07. FREQUENCY-SELECTIVE STOCHASTIC AND DETERMINISTIC CHANNEL MODELS
08. FAST CHANNEL SIMULATORS


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Download 802.11 Security

Part I: 802.11 Security Basics
        Chapter 1. A Wireless World
                Section 1.1. What Is Wireless?
                Section 1.2. Radio Transmission
                Section 1.3. Inherent Insecurity
                Section 1.4. 802.11
                Section 1.5. Structure of 802.11 MAC
                Section 1.6. WEP
                Section 1.7. Problems with WEP
                Section 1.8. Is It Hopeless?
        Chapter 2. Attacks and Risks
                Section 2.1. An Example Network
                Section 2.2. Denialof-Service Attacks
                Section 2.3. Man-inthe-Middle Attacks
                Section 2.4. Illicit Use
                Section 2.5. Wireless Risks
                Section 2.6. Knowing Is Half the Battle
Part II: Station Security
        Chapter 3. Station Security
                Section 3.1. Client Security Goals
                Section 3.2. Audit Logging
                Section 3.3. Security Updates
        Chapter 4. FreeBSD Station Security
                Section 4.1. FreeBSD Client Setup
        Chapter 5. Linux Station Security
                Section 5.1. Linux Client Setup
                Section 5.2. Kernel Configuration
                Section 5.3. OS Protection
                Section 5.4. Audit Logging
                Section 5.5. Secure Communication
        Chapter 6. OpenBSD Station Security
                Section 6.1. OpenBSD Client Setup
                Section 6.2. Kernel Configuration
                Section 6.3. OS Protection
                Section 6.4. Audit Logging
        Chapter 7. Mac OS X Station Security
                Section 7.1. Mac OS X Setup
                Section 7.2. OS Protection
               Section 7.3. Audit Logging
        Chapter 8. Windows Station Security
                Section 8.1. Windows Client Setup
                Section 8.2. OS Protection
                Section 8.3. Audit Logging
                Section 8.4. Secure Communication
Part III: Access Point Security
        Chapter 9. Setting Up an Access Point
                Section 9.1. General Access Point Security
                Section 9.2. Setting Up a Linux Access Point
                Section 9.3. Setting Up a FreeBSD Access Point
                Section 9.4. Setting Up an OpenBSD Access Point
                Section 9.5. Taking It to the Gateway
Part IV: Gateway Security
        Chapter 10. Gateway Security
                Section 10.1. Gateway Architecture
                Section 10.2. Secure Installation
                Section 10.3. Firewall Rule Creation
                Section 10.4. Audit Logging
        Chapter 11. Building a Linux Gateway
                Section 11.1. Laying Out the Network
                Section 11.2. Building the Gateway
                Section 11.3. Configuring Network Interfaces
                Section 11.4. Building the Firewall Rules
                Section 11.5. MAC Address Filtering
                Section 11.6. DHCP
                Section 11.7. DNS
                Section 11.8. Static ARP
                Section 11.9. Audit Logging
                Section 11.10. Wrapping Up
        Chapter 12. Building a FreeBSD Gateway
                Section 12.1. Building the Gateway
                Section 12.2. Building the Firewall Rules
                Section 12.3. Rate Limiting
                Section 12.4. DHCP
                Section 12.5. DNS
                Section 12.6. Static ARP
                Section 12.7. Auditing
        Chapter 13. Building an OpenBSD Gateway
                Section 13.1. Building the Gateway
                Section 13.2. Building the Firewall Rules
                Section 13.3. Rate Limiting
                Section 13.4. DHCP
                Section 13.5. DNS
                Section 13.6. Static ARP
                Section 13.7. Auditing
        Chapter 14. Authentication and Encryption
                Section 14.1. Portals
                Section 14.2. IPsec VPN
                Section 14.3. 802.1x
        Chapter 15. Putting It All Together
                Section 15.1. Pieces of a Coherent System
                Section 15.2. User Knowledge
                Section 15.3. Looking Ahead Colophon Index

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Download Kluwer Reuse Methodology Manual for System on a Chip Designs 3rd Edition

TABLE OF CONTENTS
1. Introduction
    1.1 Goals of This Manual     
    1.2 Design for Reuse: The Challenge         
    1.3 The Emerging Business Model for Reuse
2. The System-on-Chip Design Process
    2.1 A Canonical SoC Design
    2.2 System Design Flow
    2.3 The Specification Problem         
    2.4 The System Design Process
3. System-Level Design Issues: Rules and Tools
    3.1 The Standard Model          
    3.2 Design for Timing Closure: Logic Design Issues        
    3.3 Design for Timing Closure: Physical Design Issues         
    3.4 Design for Verification: Verification Strategy
    3.5 System Interconnect and On-Chip Buses         
    3.6 Design for Bring-Up and Debug: On-Chip Debug Structures
    3.7 Design for Low Power          

    3.8 Design for Test: Manufacturing Test Strategies         

4. The Macro Design Process
    4.1 Overview of IP Design
    4.2 Key Features
    4.3 Planning and Specification          
    4.4 Macro Design and Verification         
    4.5 Soft Macro Productization         
5. RTL Coding Guidelines
    5.1 Overview of the Coding Guidelines
    5.2 Basic Coding Practices       
    5.3 Coding for Portability         
    5.4 Guidelines for Clocks and Resets
    5.5 Coding for Synthesis
    5.6 Partitioning for Synthesis
    5.7 Designing with Memories
    5.8 Code Profiling
6. Macro Synthesis Guidelines
    6.1 Overview of the Synthesis Problem
    6.2 Macro Synthesis Strategy
    6.3 Physical Synthesis
    6.4 RAM and Datapath Generators
    6.5 Coding Guidelines for Synthesis Scripts
7. Macro Verification Guidelines
    7.1 Overview of Macro Verification
    7.2 Inspection as Verification
    7.3 Adversarial Testing
    7.4 Testbench Design
    7.5 Design of Verification Components
    7.6 Getting to 100%
    7.7 Timing Verification
8. Developing Hard Macros
    8.1 Overview
    8.2 Design Issues for Hard Macros
    8.3 The Hard Macro Design Process
    8.4 Productization of Hard Macros
    8.5 Model Development for Hard Macros
    8.6 Porting Hard Macros
9. Macro Deployment: Packaging for Reuse
    9.1 Delivering the Complete Product
    9.2 Contents of the User Guide
10. System Integration with Reusable Macros
      10.1 Integration Overview
      10.2 Integrating Macros into an SoC Design
      10.3 Selecting IP
      10.4 Integrating Memories
      10.5 Physical Design
11. System-Level Verification Issues
      11.1 The Importance of Verification
      11.2 The Verification Strategy

      11.3 Interface Verification
      11.4 Functional Verification
      11.5 Random Testing

      11.6 Application-Based Verification
      11.7 Gate-Level Verification
      11.8 Specialized Hardware for System Verification
12. Data and Project Management
      12.1 Data Management
      12.2 Project Management
13. Implementing Reuse-Based SoC Designs
      13.1 Alcatel 
      13.2 Atmel 
      13.3 Infineon Technologies
      13.4 LSI Logic
      13.5 Philips Semiconductor
      13.6 STMicroelectronics

      13.7 Conclusion

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Download Schaum's Outlines of Signals And Systems

Contents
Chapter 1. Signals and Systems 
                   1.1 Introduction 
                   1.2 Signals and Classification of Signals 
                   1.3 Basic Continuous-Time Signals
                   1.4 Basic Discrete-Time Signals 
                   1.5 Systems and Classification of Systems 
                         Solved Problems 
Chapter 2. Linear Time-Invariant Systems 
                   2.1 Introduction 
                   2.2 Response of a Continuous-Time LTI System and the Convolution Integral 
                   2.3 Properties of Continuous-Time LTI Systems 
                   2.4 Eigen functions of Continuous-Time LTI Systems 
                   2.5 Systems Described by Differential Equations 
                   2.6 Response of a Discrete-Time LTI System and Convolution Sum 
                   2.7 Properties of Discrete-Time LTI Systems 
                   2.8 Eigenfunctions of Discrete-Time LTI Systems 
                   2.9 Systems Described by Difference Equations 
                         Solved Problems 
Chapter 3. Laplace Transform and Continuous-Time LTI Systems 
                   3.1 Introduction 
                   3.2 The Laplace Transform 
                   3.3 Laplace Transforms of Some Common Signals 
                   3.4 Properties of the Laplace Transform 
                   3.5 The Inverse Laplace Transform 
                   3.6 The System Function 
                   3.7 The Unilateral Laplace Transform 
                         Solved Problems 
Chapter 4. The z-Transform and Discrete-Time LTI Systems
                   4.1 Introduction 
                   4.2 The z-Transform 
                   4.3 z-Transforms of Some Common Sequences 
                   4.4 Properties of the z-Transform 
                   4.5 The Inverse z-Transform 
                   4.6 The System Function of Discrete-Time LTI Systems
                   4.7 The Unilateral z-Transform 
                          Solved Problems 
Chapter 5. Fourier Analysis of Continuous-Time Signals and Systems 
                   5.1 Introduction 
                   5.2 Fourier Series Representation of Periodic Signals 
                   5.3 The Fourier Transform 
                   5.4 Properties of the Continuous-Time Fourier Transform 
                   5.5 The Frequency Response of Continuous-Time LTI Systems
                   5.6 Filtering 
                   5.7 Bandwidth 
                         Solved Problems 
Chapter 6. Fourier Analysis of Discrete-Time Signals and Systems 
                   6.1 Introduction 
                   6.2 Discrete Fourier Series 
                   6.3 The Fourier Transform 
                   6.4 Properties of the Fourier Transform 
                   6.5 The Frequency Response of Discrete-Time LTI Systems 
                   6.6 System Response to Sampled Continuous-Time Sinusoids
                   6.7 Simulation 
                   6.8 The Discrete Fourier Transform 
                         Solved Problems 
Chapter 7. State Space Analysis 
                    7.1 Introduction 
                    7.2 The Concept of State 
                    7.3 State Space Representation of Discrete-Time LTI Systems
                    7.4 State Space Representation of Continuous-Time LTI Systems
                    7.5 Solutions of State Equations for Discrete-Time LTI Systems
                    7.6 Solutions of State Equations for Continuous-Time LTI Systems

                          Solved Problems

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Download Schaum's Outlines of Digital Signal Processing

Contents
Chapter 1. Signals and Systems 
                  1.1 Introduction 
                  1.2 Discrete-Time Signals 
                  1.3 Discrete-Time Systems 
                  1.4 Convolution
                  1.5 Difference Equations 
                        Solved Problems 
Chapter 2. Fourier Analysis 
                  2.1 Introduction 
                  2.2 Frequency Response 
                  2.3 Filters 
                  2.4 Interconnection of Systems 
                  2.5 The Discrete-Time Fourier Transform 
                  2.6 DTFT Properties 
                  2.7 Applications 
                        Solved Problems 
Chapter 3. Sampling 
                  3.1 Introduction 
                  3.2 Analog-to-Digital Conversion 
                  3.3 Digital-to-Analog Conversion 
                  3.4 Discrete-Time Processing of Analog Signals 
                  3.5 Sample Rate Conversion 
                        Solved Problems 
Chapter 4. The Z-Transform 
                   4.1 Introduction 
                   4.2 Definition of the z-Transform

                   4.3 Properties 
                   4.4 The Inverse z-Transform 
                   4.5 The One-Sided z-Transform 
                          Solved Problems 
Chapter 5. Transform Analysis of Systems 
                  5.1 Introduction 
                  5.2 System Function 
                  5.3 Systems with Linear Phase 
                  5.4 Allpass Filters 
                  5.5 Minimum Phase Systems 
                  5.6 Feedback Systems 
                        Solved Problems 
Chapter 6. The DFT 
                  6.1 Introduction 
                  6.2 Discrete Fourier Series 
                  6.3 Discrete Fourier Transform 
                  6.4 DFT Properties 
                  6.5 Sampling the DTFT 
                  6.6 Linear Convolution Using the DFT 
                        Solved Problems 
Chapter 7. The Fast Fourier Transform 
                  7.1 Introduction 
                  7.2 Radix-2 FFT Algorithms 
                  7.3 FFT Algorithms for Composite N 
                  7.4 Prime Factor FFT 
                        Solved Problems 
Chapter 8. Implementation of Discrete-Time Systems 
                  8.1 Introduction 
                  8.2 Digital Networks 
                  8.3 Structures for FIR Systems 
                  8.4 Structures for IIR Systems 
                  8.5 Lattice Filters 
                  8.6 Finite Word-Length Effects 
                        Solved Problems 
Chapter 9. Filter Design 
                  9.1 Introduction 
                  9.2 Filter Specifications
                  9.3 FIR Filter Design 
                  9.4 IIR Filter Design 
                  9.5 Filter Design Based on a Least Squares Approach 

                        Solved Problems 

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Cybernetics

Cybernetics, interdisciplinary science dealing with communication and control systems in living organisms, machines, and organizations. The term, derived from the Greek word kybernētēs (“steersman” or “governor”), was first applied in 1948 to the theory of control mechanisms by the mathematician Norbert Wiener.

Cybernetics developed as the investigation of the techniques by which information is transformed into desired performance. The science arose out of problems that were encountered during World War II in the development of so-called electronic brains and automatic-control mechanisms for military apparatuses such as bombsights.

Systems of communication and control in living organisms and those in machines are considered analogous in cybernetics. To achieve desired performance from human organs or from mechanical devices, information concerning the actual results of intended action must be made available as a guide for future action. In the human body, the brain and nervous system function to coordinate the information, which is then used to determine a future course of action; control mechanisms for self-correction in machines serve a similar purpose. The principle is known as feedback, which is the fundamental concept of automation.

According to information theory, one of the basic tenets of cybernetics is that information is statistical in nature and is measured in accordance with the laws of probability. In this sense, information is regarded as a measure of the freedom of choice involved in selection. As the freedom of choice increases, the probability that any particular message will be chosen decreases. The measure of probability is known as entropy. According to the second law of thermodynamics, in natural processes the tendency is towards a state of disorganization, or chaos, occurring without assistance or control. Thus, according to the principles of cybernetics, order (lowering of entropy) is least probable and chaos (increased entropy) is most probable. Purposive behaviour in humans or in machines requires control mechanisms that maintain order by counteracting the natural tendency towards disorganization.

Cybernetics has also been applied to the study of psychology, artificial intelligence, servomechanisms, economics, neurophysiology, systems engineering, and the study of social systems. The term cybernetics is no longer much used to describe a separate field of study, and most of the research activity in the field now focuses on the study and design of artificial neural networks.

Artificial Neural Network

                                               The neural networks that are increasingly being used in artificial intelligence research mimic those found in the nervous systems of vertebrates. The main characteristic of these (top) is that each neuron, or nerve cell, receives signals from many other neurons, through its branching dendrites. It produces an output signal that depends on the values of all the input signals, and passes this output on to many other neurons along a branching fibre called an axon. In an artificial neural network (bottom), input signals, such as signals from a television camera’s image, fall on a layer of input nodes, or computing units. Each of these is linked to several other nodes, which, being intermediate between the input and output nodes of the network, are called “hidden” nodes. Each hidden node performs a calculation on the signals reaching it, and sends a corresponding output signal to further nodes. The final output is a highly processed version of the input. Artificial neural networks can be rapid, and can “learn” to perform more and more accurately without needing to be explicitly programmed.

ARTIFICIAL INTELLIGENCE AND REAL INTELLIGENCE

ARTIFICIAL INTELLIGENCE AND REAL INTELLIGENCE

                        The question, “Is artificial intelligence possible?” is ambiguous. It may mean “Can AI programs actually produce results that resemble human behaviour?” This is a scientific question. The answer at present is yes, at least in some cases. Whether it would also be true to say that this is so in all cases is not yet known. Some things that most people assume computers could never do are already possible. AI programs can compose aesthetically appealing music, draw attractive pictures, and even play the piano “expressively”. Other things are more elusive: producing perfect translations of a wide range of texts; making fundamental, yet aesthetically acceptable, transformations of musical style; producing robots that move nimbly over rough ground, swim across rivers, or climb mountains. It is controversial whether these things are merely very difficult in practice, or impossible in principle.

Alternatively, “Is artificial intelligence possible?” may mean “Could any program (or robot), no matter how humanlike its performance, really be intelligent?” This question involves highly controversial issues in the philosophy of mind, including the importance of embodiment and the nature of intentionality and consciousness. Some philosophers and AI researchers argue that intelligence can arise only in bodily creatures sensing and acting in the real world. If this is correct, then robotics is essential to the attempt to construct truly intelligent artefacts. If not, then a mere AI program might be intelligent.

The celebrated mathematician and computer scientist Alan Turing proposed what is now called the Turing Test as a way of deciding whether a machine is intelligent. He imagined a person and a computer hidden behind a screen, communicating by electronic means. If we cannot tell which one is the human, we have no reason to deny that the machine is thinking. That is, a purely behavioural test is adequate for identifying intelligence (and consciousness). The philosopher John Searle has expressed a different view. He admits that a program might produce replies identical to those of a person, and that a programmed robot might behave exactly like a human. But he argues that a program cannot understand anything it “says”. It is not actually saying (asserting) anything at all, merely outputting meaningless symbols that it has manipulated according to purely formal rules. Lacking understanding (intentionality), it is all syntax and no semantics. But human beings can ascribe meaning to its empty symbols, because our brains can somehow (Searle does not say how) cause intentionality, whereas metal and silicon cannot. There is no consensus, in either AI or philosophy, as to which theory, that of Turing or that of Searle, is right.

Whether an AI system could be conscious is an especially controversial topic. The concept of consciousness itself is ill-understood, both scientifically and philosophically. Some people think it obvious that any robot, no matter how superficially humanlike, must be zombie-like. But others think it obvious that a robot whose functions matched the relevant functions of the brain (whatever those may be) would inevitably be conscious. The answer has moral implications: if an AI system were conscious, it would arguably be wrong to “kill” it, or even to use it as a “slave”.

PURPOSES OF ARTIFICIAL INTELLIGENCE

PURPOSES OF ARTIFICIAL INTELLIGENCE

AI developers have one or both of two motivations: technological and psychological. Some want to make their computers do a useful task, without caring just how they do it. These may include methods that people cannot match, such as sensitivity to ultraviolet light, or an exhaustive search ahead through all the legal chess moves for several steps. Others want to learn about human minds (or brains). They see their programs as psychological theories, and avoid methods that humans cannot use.

Psychologists can be helped by AI because they must state their theories very clearly to express them as programs. If the program fails to produce the intended results, then the theory must be mistaken, but the computer run may indicate where the mistake is. If the program succeeds, it does not follow that people think in the same way: only psychological (or neurophysiological) evidence can confirm that.

AI is used by financial institutions, scientists and medical practitioners, design engineers, public transport schedulers, planning authorities, government departments, and security services, among many others. AI techniques are also applied in systems used to browse the Internet and online news and wire services. In the home, AI systems can provide guidance on gardening, travel, car maintenance, and many other matters; and AI robots are being developed to assist the disabled.

Types of Artificial Intelligence

 TYPES OF ARTIFICIAL INTELLIGENCE 

There are three types of AI: symbolic, connectionist, and evolutionary. Each has characteristic strengths and weaknesses.

A  Symbolic AI 

Symbolic AI is based in logic. It uses sequences of rules to tell the computer what to do next. Expert systems consist of many so-called IF-THEN rules: IF this is the case, THEN do that. Since both sides of the rule can be defined in complex ways, rule-based programs can be very powerful. The performance of a logic-based program need not appear “logical”, since some rules may cause it to take apparently irrational actions. “Illogical” AI programs are not used for practical problem-solving, but are useful in modelling how humans think. Symbolic programs are good at dealing with set problems, and at representing hierarchies (in grammar, for example, or planning). But they are brittle: if part of the expected input data is missing or mistaken, they may give a bad answer, or no answer at all.

B  Connectionist AI 

Connectionism is inspired by the brain. It is closely related to computational neuroscience, which models actual brain cells and neural circuits. Connectionist AI uses artificial neural networks made of many units working in parallel. Each unit is connected to its neighbours by links that can raise or lower the likelihood that the neighbour unit will fire (excitatory and inhibitory connections respectively). Neural networks that are able to learn do so by changing the strengths of these links, depending on past experience. These simple units are much less complex than real neurons. Each can do only one thing: for instance, report a tiny vertical line at a particular place in an image. What matters is not what any individual unit is doing, but the overall activity-pattern of the whole network.

Consequently, connectionist systems are less brittle than symbolic AI programs: even if the input data is faulty, the network may give the right answer. They are therefore good at pattern recognition, where the input-patterns within a certain class need not be identical. But connectionism is weak at doing logic, following action sequences, or representing hierarchies of goals. What symbolic AI does well, connectionism does badly, and vice versa. “Hybrid” systems combine the two, switching between them as appropriate. And work on recurrent neural networks, where the output of one layer of units is fed back as input to some previous layer, aims to enable connectionist systems to deal with sequential action and hierarchy.

C  Evolutionary AI 

Evolutionary AI draws on biology. Its programs make random changes in their own rules, and select the best daughter programs to breed the next generation. This method develops problem-solving programs, and can evolve the “brains” and “eyes” of robots. It is often used in modelling artificial life (A-Life). A-Life studies self-organization: how order arises from something that is ordered to a lesser degree. Biological examples include the flocking patterns of birds and the development of embryos. Technological examples include the A-Life flocking algorithms used for computer animation.

Artificial Neural Network

                        The neural networks that are increasingly being used in artificial intelligence research mimic those found in the nervous systems of vertebrates. The main characteristic of these (top) is that each neuron, or nerve cell, receives signals from many other neurons, through its branching dendrites. It produces an output signal that depends on the values of all the input signals, and passes this output on to many other neurons along a branching fibre called an axon. In an artificial neural network (bottom), input signals, such as signals from a television camera’s image, fall on a layer of input nodes, or computing units. Each of these is linked to several other nodes, which, being intermediate between the input and output nodes of the network, are called “hidden” nodes. Each hidden node performs a calculation on the signals reaching it, and sends a corresponding output signal to further nodes. The final output is a highly processed version of the input. Artificial neural networks can be rapid, and can “learn” to perform more and more accurately without needing to be explicitly programmed.

Scope of Artificial Intelligence

                    AI programs can do many different things. They can play games, predict share values, interpret photographs, diagnose diseases, plan travel itineraries, translate languages, take dictation, draw analogies, help design complex machinery, teach logic, make jokes, compose music, do drawings, and learn to do tasks better. Some of these things they do well. Expert systems can make medical diagnoses as well as, or better than, most human doctors. The world chess champion Garry Kasparov was beaten by a program in 1997, computers often predict share prices better than humans, and some AI-generated music sounds like compositions by famous composers. Other things, they do rather badly. Their translations are imperfect, but good enough to be understood. Their dictation is reliable only if the vocabulary is predictable and the speech unusually clear. And their jokes are poor, although some are found funny by children. To match everything that people can do, they would need to model the richness and subtlety of human memory and common sense. Moreover, programs do only one thing, whereas people do many things.

AI robots, although more flexible than industrial robots, are similarly limited. Very few can avoid obstacles smoothly, or move across uneven surfaces without falling over. Robots that plan their actions beforehand are vulnerable to unexpected environmental changes. Even if a robot performs successfully, it cannot undertake a wide variety of tasks. And its success often requires simplification of the environment: floor-cleaning robots are useful only if the floor is uncluttered. Nevertheless, AI-robots can do boring, dirty, or dangerous jobs, sometimes in places that humans cannot reach.

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Artificial Intelligence

              

                      Artificial Intelligence (AI), the study of how to make computers do things that minds can do. These include many things not normally thought of as intelligent, such as moving without bumping into obstacles, or gaining information about an environment through vision. Humans share these capacities, and also the ability to learn from experience, with many other animals. Only humans, however, have language. The intellectual aspects of intelligence depend on language. Much work in AI models intellectual tasks, as opposed to the sensory, motor, and adaptive abilities possessed by all mammals. Most AI systems are programs, existing only inside the computer. Others are robots, controlled either by a program or (in “situated” robots) by engineered reflexes.

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Computer Languages

LANGUAGE	ORIGIN OF NAME	YEAR	USES/COMMENTS
ADA	Augusta ADA Byron (Lady Lovelace)	1979	Derived from Pascal, used primarily by the military
ALGOL	ALGOrithmic Language	1960	First structured procedural programming language, used mainly for solving mathematics problems.
APL	A Programming Language	1961	Interpreted language using a large set of special symbols and terse syntax. Used primarily by mathematicians.
BASIC	Beginners All-Purpose Symbolic Instruction Code	1965	Very popular high-level programming language, frequently used by beginning programmers.
C	Predecessor was Bell Laboratory's 1972 B Programming Language	1972	Compiled, structured programming language, commonly used in many workplaces because its programs are easy to transfer between different types of computers
C++	Advanced version of C. Developed by ATT Bell Labs.	1985	C++ is used in numerous fields, such as accounting and finance systems, and computer-aided design. Supports object-oriented programming.
C#	C Sharp	2000	Progression from C and C++, developed by Microsoft. Object-oriented language that is embedded in an Internet-friendly software environment and enables programmers to build a range of applications.
COBOL	COmmon Business-Oriented Language	1959	English-like programming language, emphasizes data structures. Widely used, especially in businesses
FORTH	FOuRTH generation language	1970	Interpreted, structured language, easily extended. Provides high functionality in limited space..
FORTRAN	FORmula TRANslation	1954	Initially designed for scientific and engineering uses, a high-level, compiled language now used in many fields. Precursor of several concepts, such as variables, conditional statements, and separately compiled subroutines.
JAVA	Developed by Sun Microsystems	1990	Originally developed for use in set-top boxes, transitioned to the World Wide Web in 1994.
LISP	LISt Programming	1960	A list-oriented programming language, mainly used to manipulate lists of data. Interpreted language, often used in research, generally considered the 'standard' language for Artificial Intelligence (AI) projects
LOGO	Derived from Greek logos ,  meaning word	1968	Programming language often used with children. Features a simple drawing environment and several higher-level features from LISP. Primarily educational
MODULA-2	MODULAr Language, designed as secondary phase of Pascal (Niklaus Wirth devised both).	1980	Language that emphasizes modular programming. High-level language based on Pascal, characterized by lack of standard functions and procedures
PASCAL	Blaise PASCAL, mathematician and inventor of first computing device	1971	Compiled, structured language, based on ALGOL. Adds data types and structures while simplifying syntax. Like C language, it is a standard development language for microcomputers.
PILOT	Programmed Inquiry, Learning Or Teaching	1969	Programming language used primarily to create applications for computer-aided instruction. Contains very little syntax.
PL/1	Programming Language 1	1964	Designed to combine the key features of FORTRAN, COBOL, and ALGOL, a complex programming language. Compiled, structured language capable of error handling and multitasking, used in some academic and research environments.
VB	Visual Basic	1990	Sometimes called the Rapid Applications Development system, is used to build applications quickly