By Jeffrey R. Sampson
This publication begun as a sequence of lecture notes for a direction referred to as Introduc tion to Adaptive platforms which I built for undergraduate Computing technology majors on the collage of Alberta and primary taught in 1973. the target of the direction has been threefold: (l) to show undergraduate computing device scientists to a number of matters within the conception and alertness of computation, matters that are too frequently postponed to the graduate point or by no means taught in any respect; (2) to supply undergraduates with a history enough to cause them to powerful members in graduate point classes in Automata conception, organic details Processing, and synthetic Intelligence; and (3) to give a private standpoint which unifies the it seems that varied elements of the subject material lined. All of those pursuits observe both to this publication, that is essentially designed to be used in a one semester undergraduate computing device technological know-how direction. i guess the reader has a basic wisdom of desktops and programming, even though no longer of specific machines or languages. His mathematical historical past should still contain simple innovations of quantity platforms, set conception, user-friendly discrete chance, and logic.
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Extra resources for Adaptive Information Processing: An Introductory Survey
With a minimum distance of 2 between any pair of code words we have the computer parity bit scheme and we can detect single errors. Hamming has shown the following relation between minimum distance and a code's error detecting/correcting properties: Minimum distance 1 2 3 4 5 Code properties No error detection Single error detection Single error correction Double error detection Double error correction The last line of this table, for example, confirms our intuition that if 5 vertices must be traversed to get to the next nearest valid code word, two errors will lead to a vertex closer to the original code word than to any other valid one.
6 shows a program which Before: After: t o1 o1 101 1 1 0 000 0 0 0 0 t 101 1101111110 1 2 3 4 5 6 7 8 9 10 + 0 RTZ + 0 RTZ RTZ 1 LTZ LTZ 11 12 13 14 15 16 17 18 19 20 1 + t (5) LTZ LTZ 1 + . 6. (a) Example and (b) program for Wang machine multiplication. multiplies two numbers in unary form (and, unlike the addition program, preserves the original arguments). For arguments of m and n this program makes m consecutive copies of n digits. The routine in instructions 5-13 copies n digits and is used m times by the remaining instructions.
2. 3. 2 for Huffman binary and ternary codes. 4. 02. 5. 2 to correct (if necessary) the following received code words: (a) 1101001 (b) 0101100 (c) 1111111 (d) 1000000 24 3 Finite automata In this chapter we consider the kinds of information processing that can be carried out by machines built from rather simple components. A finite automaton (or finite state machine) is a member of a class of abstract machines whose behavior may always be described in terms of a series of states occurring at successive units of discrete time.
Adaptive Information Processing: An Introductory Survey by Jeffrey R. Sampson