Introduction

Young tableaux have had a long history since their introduction by A. Young a century ago. It is only in the 1960s that there came to the fore a monoid structure on them, a structure taking into account most of their combinatorial properties, and having applications to the different fields in which Young tableaux were used.

Summarizing what had been his motivation to spend so much time on the plactic monoid, M.R Schützenberger drew out three reasons: (1) it allows us to embed the ring of symmetric polynomials into a noncommutative ring; (2) it is the syntactic monoid of a function on words generalizing the maximal length of a nonincreasing subword; (3) it is a natural generalization to alphabets with more than two letters of the monoid of parentheses.

The starting point of the theory is an algorithm, due to C. Schensted, for the determination of the maximal length of a nondecreasing subword of a given word. The output of this algorithm is a tableau, and if one decides to identify the words leading to the same tableau, one arrives at the plactic monoid, whose defining relations were determined by D. Knuth.

The first significant application of the plactic monoid was to provide a complete proof of the Littlewood-Richardson rule, a combinatorial algorithm for multiplying Schur functions (or equivalently, to decompose tensor products of representations of unitary groups, a fundamental issue in many applications, e.g., in particle physics), which had been in use for almost 50 years before being fully understood.

When we hear the word signal we may first think of a phenomenon that occurs continuously over time that carries some information. Most phenomena observed in nature are continuous in time, as for instance, our speech or heart beating, the temperature of the city where we live, the car speed during a given trip, the altitude of the airplane we are traveling in – these are typical continuous-time signals. Engineers are always devising ways to design systems, which are in principle continuous time, for measuring and interfering with these and other phenomena.

One should note that, although continuous-time signals pervade our daily life, there are also several signals which are originally discrete time, as, for example, the stock-market weekly financial indicators, the maximum and minimum daily temperatures in our cities, and the average lap speed of a racing car.

If an electrical or computer engineer has the task of designing systems to interact with natural phenomena, his/her first impulse is to convert some quantities from nature into electric signals through a transducer. Electric signals, which are represented by voltages or currents, have a continuous-time representation. Since digital technology constitutes an extremely powerful tool for information processing, it is natural to think of processing the electric signals generated using it. However, continuous-time signals cannot be processed using computer technology (digital machines), which are especially suited to deal with sequential computation involving numbers.

A large body of mathematics consists of facts that can be presented and described much like any other natural phenomenon. These facts, at times explicitly brought out as theorems, at other times concealed within a proof, make up most of the applications of mathematics, and are the most likely to survive changes of style and of interest.

This ENCYCLOPEDIA will attempt to present the factual body of all mathematics. Clarity of exposition, accessibility to the non-specialist, and a thorough bibliography are required of each author. Volumes will appear in no particular order, but will be organized into sections, each one comprising a recognizable branch of present-day mathematics. Numbers of volumes and sections will be reconsidered as times and needs change.

It is hoped that this enterprise will make mathematics more widely used where it is needed, and more accessible in fields in which it can be applied but where it has not yet penetrated because of insufficient information.

Information theory is a success story in contemporary mathematics. Born out of very real engineering problems, it has left its imprint on such far-flung endeavors as the approximation of functions and the central limit theorem of probability. It is an idea whose time has come.

Most mathematicians cannot afford to ignore the basic results in this field. Yet, because of the enormous outpouring of research, it is difficult for anyone who is not a specialist to single out the basic results and the relevant material.

Introduction

In Chapter 1, avoidable and unavoidable sets of words have been defined. The focus was then on the case of finite sets of words. In the present chapter, we turn to particular infinite sets of words, defined as pattern languages. A pattern is a word that contains special symbols called variables, and the associated pattern language is obtained by replacing the variables with arbitrary nonempty words, with the condition that two occurrences of the same variable have to be replaced with the same word.

The archetype of a pattern is the square, αα. The associated pattern language is L = {uu|u ∈ A+}, and it is now a classical result that L is an avoidable set of words if A has at least three elements, whereas it is an unavoidable set of words if A has only one or two elements. Indeed, an infinite square-free word on three letters can be constructed, and it is easy to check that every binary word of length 4 contains a square. For short, we will say that the pattern αα is 3-avoidable and 2-unavoidable.

General patterns can contain more than just one variable. For instance, αβα represents words of the form uvu, with u, v ∈ A+ (this pattern is unavoidable whatever the size of the alphabet; see Proposition 3.1.2). They could also be allowed to contain constant letters, which unlike variables are never replaced with arbitrary words, but this is not very useful in the context of avoidability, so we will consider here only “pure” patterns, constituted only of variables.

Introduction

This chapter deals with the design methods in which a desired frequency response is approximated by a transfer function consisting of a ratio of polynomials. In general, this type of transfer function yields an impulse response of infinite duration. Therefore, the systems approximated in this chapter are commonly referred to as infinite-duration impulse-response (IIR) filters.

In general, IIR filters are able to approximate a prescribed frequency response with fewer multiplications than FIR filters. For that matter, IIR filters can be more suitable for some practical applications, especially those ones involving real-time signal processing.

In Section 6.2, we study the classical methods of analog filter approximation, namely the Butterworth, Chebyshev, and elliptic approximations. These methods are the most widely used for approximations meeting prescribed magnitude specifications. They originated in the continuous-time domain and their use in the discrete-time domain requires an appropriate transformation.

We then address, in Section 6.3, two approaches that transform a continuous-time transfer function into a discrete-time transfer function, the impulse-invariance and bilinear transformation methods.

Section 6.4 deals with frequency transformation methods in the discrete-time domain. These methods allow the mapping of a given filter type to another, for example the transformation of a given lowpass into a desired bandpass filter.

In applications where magnitude and phase specifications are imposed, we can approximate the desired magnitude specifications by one of the classical transfer functions and design a phase equalizer to meet the phase specifications.

Introduction

This chapter serves the same function for Part two as Chapter 6 served for Part one, that is, it summarizes some of the most important results in coding theory which have not been treated in Chapters 7–11. In Sections 12.2, 12.3, and 12.4 we treat channel coding (block codes, convolutional codes, and a comparison of the two). Finally in Section 12.5 we discuss source coding.

Block codes

The theory of block codes is older and richer than the theory of convolutional codes, and so this section is much longer than Section 12.3. (This imbalance does not apply to practical applications, however; see Section 12.4.) In order to give this section some semblance of organization, we shall classify the results to be cited according to Berlekamp's [15] list of the three major problems of coding theory:

1. How good are the best codes?

2. How can we design good codes?

3. How can we decode such codes?

•How good are the best codes? One of the earliest problems which arose in coding theory was that of finding perfect codes. If we view a code of length n over the finite field Fq as a subset {x1; x2, …, xM} of the vector space Vn(Fq), the code is said to be perfect (or close packed) if for some integer e, the Hamming spheres of radius e around the M codewords completely fill Vn(Fq) without overlap.

Introduction

Periodicity is an important property of words that has applications in various domains. The first significant results on periodicity are the theorem of Fine and Wilf and the critical factorization theorem. These two results refer to two kinds of phenomena concerning periodicity: the theorem of Fine and Wilf considers the simultaneous occurrence of different periods in one finite word, whereas the critical factorization theorem relates local and global periodicity of words. Starting from these basic results the study of periodicity has grown along both directions. This chapter contains a systematic and self-contained exposition of this theory, including very recent results.

In Section 8.1 we analyze the structure of the set of periods of one finite word. This section includes a proof of the theorem of Fine and Wilf and also a generalization of this result to words having three periods. We next give the characterization of Guibas and Odlyzko concerning those sets of integers that yield the periods that can simultaneously occur in a single finite word. Another property is further investigated (similar to the one stated by the theorem of Fine and Wilf) in which the occurrence of two periods in a word of a certain length forces the word to have a shorter period only in a prefix (or suffix) of the word. The golden ratio appears in such a result as an extremal value of a parameter involved in the property. This section also contains some results concerning the squares that can appear as factors in a word. This is a prelude to the next section, since squares describe a special kind of local periodicity.

Introduction

It is a well-known and not too difficult result of combinatorics on words that if two words commute under the concatenation product, then they are both powers of the same word: they have a common root. This fact is essentially equivalent to the following one: the centralizer of a nonempty word, that is, the set of words commuting with it, is the set of powers of the shortest root of the given word.

The main results of this chapter are an extension of this latter result to noncommutative series and polynomials: Cohn's and Bergman's centralizer theorems. The first asserts that the centralizer of an element of the algebra of noncommutative formal series is isomorphic to an algebra of formal series in one variable. The second is the similar result for non-commutative polynomials. Note that these theorems admit the following consequences: if two noncommutative series (resp. polynomials) commute, then they may both be expressed as a series (resp. a polynomial) in a third one. This formulation stresses the similarity with the result on words given above.

We begin with Cohn's theorem, since it is needed for Bergman's theorem. Its proof requires mainly a divisibility property of noncommutative series. The proof of Bergman's theorem is rather indirect: it uses the noncommutative Euclidean division of Cohn, the difficult result that the centralizer of a noncommutative polynomial is integrally closed in its field of fractions, its embeddability in a one-variable polynomial ring, which uses a pretty argument of combinatorics on words, and finally another result of Cohn characterizing free subalgebras of a one-variable polynomial algebra.

Introduction

In many applications of digital signal processing, it is necessary for different sampling rates to coexist within a given system. One common example is when two subsystems working at different sampling rates have to communicate and the sampling rates must be made compatible. Another case is when a wideband digital signal is decomposed into several non-overlapping narrowband channels in order to be transmitted. In such a case, each narrowband channel may have its sampling rate decreased until its Nyquist limit is reached, thereby saving transmission bandwidth.

Here, we describe such systems which are generally referred to as multirate systems. Multirate systems are used in several applications, ranging from digital filter design to signal coding and compression, and have been increasingly present in modern digital systems.

First, we study the basic operations of decimation and interpolation, and show how arbitrary rational sampling-rate changes can be implemented with them. The design of decimation and interpolation filters is also addressed. Then, we deal with filter design techniques which use decimation and interpolation in order to achieve a prescribed set of filter specifications. Finally, MATLAB functions which aid in the design and implementation of multirate systems are briefly described.

Basic principles

Intuitively, any sampling-rate change can be effected by recovering the band-limited analog signal xa(t) from its samples x(m), and then resampling it with a different sampling rate, thus generating a different discrete version of the signal, x′(n).