Wayne Burdick wrote: Humans use lexicographical and semantic clues to fill in dropped CW characters, and computers can do the same. But this goes way beyond the simple signal processing used in, say, the K3's present CW decoder or the one used in HRD. (I studied natural language recognition in college and was anxious to play with either neural networks or traditional AI methods as the foundation for CW decoding, but my other classes got in the way :)
One idea from the early days of AI is the so-called "blackboard" model. Imagine a garbled sentence on a blackboard, with various experts offering their opinions about what each letter and word is based on their specialized knowledge of word morphology, letter frequency, syntax, semantics, etc. You weigh these opinions based on degree of confidence, and once there's enough evidence for a letter or word, you fill it in, which in turn offers additional information to the highest-level expert, who might be considering the actual meaning of a phrase. His predictions can then strengthen the evidence for lower level symbols, and so on. Such methods are very algorithm-intensive, but might be useful for some aspects of CW stream parsing. A neural network could handle this, too, and has the advantage of self-organization. This is how I'd approach it (assuming unlimited free time--not!). You could use any of several different types of networks that have been proven successful at NLP (natural language processing). For example, you might take the incoming CW, break it into samples (say a few samples per bit at the highest code speed to be processed), shift the serial data representing 5 to 20 letters into a serial-to-parallel shift register, then feed the parallel data to the network's inputs. Or you could use a network with internal feedback (memory), with just one input, which itself could be "fuzzy" (the analog voltage from an envelope detector) or digital (0 or 1 depending on the output of a comparator, looking at the CW stream). The output might be a parallel binary word, perhaps ASCII, or a single output with multiple levels, where the voltage itself represents a symbol. To make this work, you need at least three things: an input representation that provides adequate context (e.g., if you want to decode a letter, the input should contain at least a few letters on either side of the target); a sufficiently complex network; and a large corpus of clean text with which to train the network (probably thousands of words, drawn from actual on-air content). One classic method of training the network involves placing known-good signals at the input, then comparing the desired outputs to the actual outputs, and "back-propagating" the resulting error through the network--from outputs to hidden layers to inputs--so that the network's nodes gradually acquire the proper "weights." Once the network has been trained to the point that it perfectly copies clean CW, you can then present it with a noisy signal stream. A well-designed network would be able to correct dropped CW elements or even letters if its internal representation is highly evolved. The network will have learned language-specific rules, and you don't have to know how it works, anymore than you know how your own brain does it. The actual implementation is left as an exercise for the reader. If you come up with an algorithm written in 'C', let me know and I'll try to port it to the K3's PIC. Wayne N6KR Sounds good, Wayne. When can you have it done? Upper right hand button would be my choice. 73 de Terry, W0FM _______________________________________________ Elecraft mailing list Post to: Elecraft@mailman.qth.net You must be a subscriber to post to the list. Subscriber Info (Addr. Change, sub, unsub etc.): http://mailman.qth.net/mailman/listinfo/elecraft Help: http://mailman.qth.net/subscribers.htm Elecraft web page: http://www.elecraft.com
