Turing machine An artistic representation of a Turing machine (Rules table not represented) A Turing machine is a hypothetical device that manipulates symbols on a strip of tape according to a table of rules. Despite its simplicity, a Turing machine can be adapted to simulate the logic of any computer algorithm, and is particularly useful in explaining the functions of a CPU inside a computer. The "Turing" machine was invented in 1936 by Alan Turing[1] who called it an "a-machine" (automatic machine). The Turing machine is not intended as practical computing technology, but rather as a hypothetical device representing a computing machine. Turing machines help computer scientists understand the limits of mechanical computation. Turing gave a succinct definition of the experiment in his 1948 essay, "Intelligent Machinery". ...an unlimited memory capacity obtained in the form of an infinite tape marked out into squares, on each of which a symbol could be printed. Informal description[edit] where to
Quantification In logic, quantification is the binding of a variable ranging over a domain of discourse. The variable thereby becomes bound by an operator called a quantifier. Academic discussion of quantification refers more often to this meaning of the term than the preceding one. Natural language[edit] All known human languages make use of quantification (Wiese 2004). Every glass in my recent order was chipped.Some of the people standing across the river have white armbands.Most of the people I talked to didn't have a clue who the candidates were.A lot of people are smart. The words in italics are quantifiers. The study of quantification in natural languages is much more difficult than the corresponding problem for formal languages. Montague grammar gives a novel formal semantics of natural languages. Logic[edit] In language and logic, quantification is a construct that specifies the quantity of specimens in the domain of discourse that apply to (or satisfy) an open formula. Mathematics[edit] . means
Floating point A diagram showing a representation of a decimal floating-point number using a mantissa and an exponent. In computing, floating point describes a method of representing an approximation of a real number in a way that can support a wide range of values. The numbers are, in general, represented approximately to a fixed number of significant digits (the significand) and scaled using an exponent. Significant digits × baseexponent The term floating point refers to the fact that a number's radix point (decimal point, or, more commonly in computers, binary point) can "float"; that is, it can be placed anywhere relative to the significant digits of the number. Over the years, a variety of floating-point representations have been used in computers. The speed of floating-point operations, commonly referred to in performance measurements as FLOPS, is an important characteristic of a computer system, especially in software that performs large-scale mathematical calculations. Overview[edit]
Notation Standard notations refer to general agreements in the way things are written or denoted. The term is generally used in technical and scientific areas of study like mathematics, physics, chemistry and biology, but can also be seen in areas like business, economics and music. Written communication[edit] Phonographic writing systems, by definition, use symbols to represent components of auditory language, i.e. speech, which in turn refers to things or ideas. The five main kinds of phonographic notational system are the alphabet and syllabary. Some written languages are more consistent in their correlation of written symbol or grapheme and sound or phoneme, and are therefore considered to have better phonemic orography.Ideographic writing, by definition, refers to things or ideas independently of their pronunciation in any language. Biology and Medicine[edit] Chemistry[edit] Computing[edit] Logic[edit] A variety of symbols are used to express logical ideas; see the List of logic symbols
IEEE 754-2008 The IEEE Standard for Floating-Point Arithmetic (IEEE 754) is a technical standard for floating-point computation established in 1985 by the Institute of Electrical and Electronics Engineers (IEEE). Many hardware floating point units use the IEEE 754 standard. The current version, IEEE 754-2008 published in August 2008, includes nearly all of the original IEEE 754-1985 standard and the IEEE Standard for Radix-Independent Floating-Point Arithmetic (IEEE 854-1987). The international standard ISO/IEC/IEEE 60559:2011 (with identical content to IEEE 754) has been approved for adoption through JTC1/SC 25 under the ISO/IEEE PSDO Agreement[1] and published.[2] The standard defines The standard also includes extensive recommendations for advanced exception handling, additional operations (such as trigonometric functions), expression evaluation, and for achieving reproducible results. Formats[edit] An IEEE 754 format is a "set of representations of numerical values and symbols". A format comprises:
Signature (logic) In logic, especially mathematical logic, a signature lists and describes the non-logical symbols of a formal language. In universal algebra, a signature lists the operations that characterize an algebraic structure. In model theory, signatures are used for both purposes. Signatures play the same role in mathematics as type signatures in computer programming. They are rarely made explicit in more philosophical treatments of logic. Formally, a (single-sorted) signature can be defined as a triple σ = (Sfunc, Srel, ar), where Sfunc and Srel are disjoint sets not containing any other basic logical symbols, called respectively function symbols (examples: +, ×, 0, 1) andrelation symbols or predicates (examples: ≤, ∈), and a function ar: Sfunc Srel → which assigns a non-negative integer called arity to every function or relation symbol. A signature with no function symbols is called a relational signature, and a signature with no relation symbols is called an algebraic signature. Symbol types S.
Reduced instruction set computing Reduced instruction set computing, or RISC , is a CPU design strategy based on the insight that simplified (as opposed to complex) instructions can provide higher performance if this simplicity enables much faster execution of each instruction. A computer based on this strategy is a reduced instruction set computer, also called RISC. The opposing architecture is called complex instruction set computing, i.e. Various suggestions have been made regarding a precise definition of RISC, but the general concept is that of a system that uses a small, highly-optimized set of instructions, rather than a more specialized set of instructions often found in other types of architectures. History and development[edit] An IBM PowerPC 601 RISC microprocessor. Michael J. Co-designer Yunsup Lee holding RISC-V prototype chip in 2013. Characteristics and design philosophy[edit] Instruction set[edit] Hardware utilization[edit] Other features that are typically found in RISC architectures are:
First-order logic A theory about some topic is usually first-order logic together with a specified domain of discourse over which the quantified variables range, finitely many functions which map from that domain into it, finitely many predicates defined on that domain, and a recursive set of axioms which are believed to hold for those things. Sometimes "theory" is understood in a more formal sense, which is just a set of sentences in first-order logic. The adjective "first-order" distinguishes first-order logic from higher-order logic in which there are predicates having predicates or functions as arguments, or in which one or both of predicate quantifiers or function quantifiers are permitted.[1] In first-order theories, predicates are often associated with sets. In interpreted higher-order theories, predicates may be interpreted as sets of sets. First-order logic is the standard for the formalization of mathematics into axioms and is studied in the foundations of mathematics. Introduction[edit] . x in .
Complex instruction set computing Examples of CISC instruction set architectures are System/360 through z/Architecture, PDP-11, VAX, Motorola 68k, and x86. Historical design context[edit] Incitements and benefits[edit] Before the RISC philosophy became prominent, many computer architects tried to bridge the so-called semantic gap, i.e. to design instruction sets that directly supported high-level programming constructs such as procedure calls, loop control, and complex addressing modes, allowing data structure and array accesses to be combined into single instructions. Instructions are also typically highly encoded in order to further enhance the code density. The compact nature of such instruction sets results in smaller program sizes and fewer (slow) main memory accesses, which at the time (early 1960s and onwards) resulted in a tremendous savings on the cost of computer memory and disc storage, as well as faster execution. New instructions[edit] Design issues[edit] The RISC idea[edit] Superscalar[edit] See also[edit]
Hydrogen Chemical element with symbol H and atomic number 1; lightest and most abundant chemical substance in the universe Chemical element, symbol H and atomic number 1 Hydrogen is nonmetallic, except at extremely high pressures, and readily forms a single covalent bond with most nonmetallic elements, forming compounds such as water and nearly all organic compounds. Hydrogen plays a particularly important role in acid–base reactions because these reactions usually involve the exchange of protons between soluble molecules. Hydrogen gas was first artificially produced in the early 16th century by the reaction of acids on metals. Industrial production is mainly from steam reforming natural gas, and less often from more energy-intensive methods such as the electrolysis of water.[12] Most hydrogen is used near the site of its production, the two largest uses being fossil fuel processing (e.g., hydrocracking) and ammonia production, mostly for the fertilizer market. Properties Combustion Flame Reactants
Von Neumann architecture Von Neumann architecture scheme The design of a Von Neumann architecture is simpler than the more modern Harvard architecture which is also a stored-program system but has one dedicated set of address and data buses for reading data from and writing data to memory, and another set of address and data buses for fetching instructions. A stored-program digital computer is one that keeps its programmed instructions, as well as its data, in read-write, random-access memory (RAM). Stored-program computers were an advancement over the program-controlled computers of the 1940s, such as the Colossus and the ENIAC, which were programmed by setting switches and inserting patch leads to route data and to control signals between various functional units. In the vast majority of modern computers, the same memory is used for both data and program instructions, and the Von Neumann vs. History[edit] The earliest computing machines had fixed programs. Development of the stored-program concept[edit]
What is a homunculus and what does it tell scientists A homunculus is a sensory map of your body, so it looks like an oddly proportioned human. The reason it's oddly proportioned is that a homunculus represents each part of the body in proportion to its number of sensory neural connections and not its actual size. The layout of the sensory neural connections throughout your body determines the level of sensitivity each area of your body has, so the hands on a sensory homunculus are its largest body parts, exaggerated to an almost comical degree, while the arms are quite skinny. The homunculus, then, gives a vivid picture of where our sensory system gets the most bang for its buck. Chemoreceptors, which sense chemicals.