ssible. We can now say quantitatively that 'Last winter it snowed in Adelaide, Australia' has more information than 'Last winter it snowed in Pittsburgh, USA'. The quantised information content as defined earlier allows for: If the probability of an event is 1, then its information content is 0 ('the sun rose from the east today' conveys no information). The information content is never negative: if there is a non-zero probability of an event occurring, there exists positive information content. If the probability of an event is 0, then it has no information content, or its information content is not defined ('the sun rose from the west today' conveys no information as it is impossible). The lower the likelihood of an event, the higher the information content. The joint probability of two independent events occurring, for example 'it rained in Adelaide today' and 'temperature on Mars was -60 °C yesterday' is the product of their individual probabilities. The total information content is the sum of individual information contents. Usage of base-2 logarithm fits with the binary character of digital communication of information. In general the occurrence probability of a '1' is equal to probability of '0' and equal to 50%. The information content is one bit, and can be represented by one binary digit or 'bit'. Example 4.1 A source outputs one of four possible messages, m1, m2 m3 and m4, with the following probabilities: p = :1/2;p2=1/4;p3=1/8;p4=1/8 Broadband Telecommunications Technologies and Management The information content of each message is calculated as: (m) - log (1/2)=1bit (m2)-10g2(1/4)=2 (M3)I(m4)=-log2(1/8)=3bits An important application of information quantisation is that it can be used to calculate the length of a code needed to represent a message. For example, if a message is generated frequently it should be assigned a shorter code and if it is infrequent then a longer code. This is an intuitive principle and was used in the Morse code design: for example 'E' a frequently occurring letter is repres