eword length is bit/codeword or trit/codeword, but, for simplicity and clarity, I shall use here the names bit/symbol or trit/symbol, it being understood that there is a one-to-one correspondence between codewords and symbols. 8.4 Mean code length and coding efficiency H3 = 2.640 trit/symbol. We, thus, find that n = 74.5%. As we shall see further in this chapter, such a ratio defines coding efficiency, a parameter that cannot exceed unity. Put simply: The mean codeword length (or effective code entropy) cannot exceed the source entropy. This is a fundamental property of codes, which was originally demonstrated by Shannon. We can thus conclude that with a 74% coding efficiency, the Morse code (analyzed as a ternary code) is a reasonably good choice. Yet, despite its popularity and usefulness in the past (and until as recently as 1999), the Morse code is quite far from optimal. The main reason for this is the use of the blank, which is first required for the code to be uniquely decodable, and second for being usable by human operators. Such a blank, however, does not carry any information whatsoever, and it takes precious codeword resources! This observation shows that we could obtain significantly greater coding efficiencies if the Morse code was uniquely decodable without making use of blanks. This improved code could be transmitted as uninterrupted bit or trit sequences. But it would be only intelligible to machines, because human beings would be too slow to recognize the unique symbol patterns in such sequences. Considering then both binary and ternary codings (with source entropies Hsource shown in Table 8.3), and either fixed or variable symbol or codeword sizes, there are four basic possibilities: (i) Fixed-length binary codewords with l = 5 bit (25=32), giving L = 5.000 bit/ symbol, or Hsource/L L=83.7%; (ii) Fixed-length ternary codewords with l = 3 trit (33=27), giving L = 3.000 trit/ symbol, or Hsource/L = 88.0%; (iii) Variable-length binary codewords with lengths between l = 1 and l = 10 bits, gi- ving 