5 files changed, 0 insertions, 316 deletions
diff --git a/cpukit/zlib/doc/rfc1950.txt b/cpukit/compression/zlib/doc/rfc1950.txt
index ce6428a0f2..ce6428a0f2 100644
--- a/cpukit/zlib/doc/rfc1950.txt
+++ b/cpukit/compression/zlib/doc/rfc1950.txt
diff --git a/cpukit/zlib/doc/rfc1951.txt b/cpukit/compression/zlib/doc/rfc1951.txt
index 403c8c722f..403c8c722f 100644
--- a/cpukit/zlib/doc/rfc1951.txt
+++ b/cpukit/compression/zlib/doc/rfc1951.txt
diff --git a/cpukit/zlib/doc/rfc1952.txt b/cpukit/compression/zlib/doc/rfc1952.txt
index a8e51b4567..a8e51b4567 100644
--- a/cpukit/zlib/doc/rfc1952.txt
+++ b/cpukit/compression/zlib/doc/rfc1952.txt
diff --git a/cpukit/zlib/doc/algorithm.txt b/cpukit/zlib/doc/algorithm.txt
deleted file mode 100644
index 34960bddac..0000000000
--- a/cpukit/zlib/doc/algorithm.txt
+++ /dev/null
@@ -1,209 +0,0 @@
-1. Compression algorithm (deflate)
-
-The deflation algorithm used by gzip (also zip and zlib) is a variation of
-LZ77 (Lempel-Ziv 1977, see reference below). It finds duplicated strings in
-the input data.  The second occurrence of a string is replaced by a
-pointer to the previous string, in the form of a pair (distance,
-length).  Distances are limited to 32K bytes, and lengths are limited
-to 258 bytes. When a string does not occur anywhere in the previous
-32K bytes, it is emitted as a sequence of literal bytes.  (In this
-description, `string' must be taken as an arbitrary sequence of bytes,
-and is not restricted to printable characters.)
-
-Literals or match lengths are compressed with one Huffman tree, and
-match distances are compressed with another tree. The trees are stored
-in a compact form at the start of each block. The blocks can have any
-size (except that the compressed data for one block must fit in
-available memory). A block is terminated when deflate() determines that
-it would be useful to start another block with fresh trees. (This is
-somewhat similar to the behavior of LZW-based _compress_.)
-
-Duplicated strings are found using a hash table. All input strings of
-length 3 are inserted in the hash table. A hash index is computed for
-the next 3 bytes. If the hash chain for this index is not empty, all
-strings in the chain are compared with the current input string, and
-the longest match is selected.
-
-The hash chains are searched starting with the most recent strings, to
-favor small distances and thus take advantage of the Huffman encoding.
-The hash chains are singly linked. There are no deletions from the
-hash chains, the algorithm simply discards matches that are too old.
-
-To avoid a worst-case situation, very long hash chains are arbitrarily
-truncated at a certain length, determined by a runtime option (level
-parameter of deflateInit). So deflate() does not always find the longest
-possible match but generally finds a match which is long enough.
-
-deflate() also defers the selection of matches with a lazy evaluation
-mechanism. After a match of length N has been found, deflate() searches for
-a longer match at the next input byte. If a longer match is found, the
-previous match is truncated to a length of one (thus producing a single
-literal byte) and the process of lazy evaluation begins again. Otherwise,
-the original match is kept, and the next match search is attempted only N
-steps later.
-
-The lazy match evaluation is also subject to a runtime parameter. If
-the current match is long enough, deflate() reduces the search for a longer
-match, thus speeding up the whole process. If compression ratio is more
-important than speed, deflate() attempts a complete second search even if
-the first match is already long enough.
-
-The lazy match evaluation is not performed for the fastest compression
-modes (level parameter 1 to 3). For these fast modes, new strings
-are inserted in the hash table only when no match was found, or
-when the match is not too long. This degrades the compression ratio
-but saves time since there are both fewer insertions and fewer searches.
-
-
-2. Decompression algorithm (inflate)
-
-2.1 Introduction
-
-The key question is how to represent a Huffman code (or any prefix code) so
-that you can decode fast.  The most important characteristic is that shorter
-codes are much more common than longer codes, so pay attention to decoding the
-short codes fast, and let the long codes take longer to decode.
-
-inflate() sets up a first level table that covers some number of bits of
-input less than the length of longest code.  It gets that many bits from the
-stream, and looks it up in the table.  The table will tell if the next
-code is that many bits or less and how many, and if it is, it will tell
-the value, else it will point to the next level table for which inflate()
-grabs more bits and tries to decode a longer code.
-
-How many bits to make the first lookup is a tradeoff between the time it
-takes to decode and the time it takes to build the table.  If building the
-table took no time (and if you had infinite memory), then there would only
-be a first level table to cover all the way to the longest code.  However,
-building the table ends up taking a lot longer for more bits since short
-codes are replicated many times in such a table.  What inflate() does is
-simply to make the number of bits in the first table a variable, and  then
-to set that variable for the maximum speed.
-
-For inflate, which has 286 possible codes for the literal/length tree, the size
-of the first table is nine bits.  Also the distance trees have 30 possible
-values, and the size of the first table is six bits.  Note that for each of
-those cases, the table ended up one bit longer than the ``average'' code
-length, i.e. the code length of an approximately flat code which would be a
-little more than eight bits for 286 symbols and a little less than five bits
-for 30 symbols.
-
-
-2.2 More details on the inflate table lookup
-
-Ok, you want to know what this cleverly obfuscated inflate tree actually
-looks like.  You are correct that it's not a Huffman tree.  It is simply a
-lookup table for the first, let's say, nine bits of a Huffman symbol.  The
-symbol could be as short as one bit or as long as 15 bits.  If a particular
-symbol is shorter than nine bits, then that symbol's translation is duplicated
-in all those entries that start with that symbol's bits.  For example, if the
-symbol is four bits, then it's duplicated 32 times in a nine-bit table.  If a
-symbol is nine bits long, it appears in the table once.
-
-If the symbol is longer than nine bits, then that entry in the table points
-to another similar table for the remaining bits.  Again, there are duplicated
-entries as needed.  The idea is that most of the time the symbol will be short
-and there will only be one table look up.  (That's whole idea behind data
-compression in the first place.)  For the less frequent long symbols, there
-will be two lookups.  If you had a compression method with really long
-symbols, you could have as many levels of lookups as is efficient.  For
-inflate, two is enough.
-
-So a table entry either points to another table (in which case nine bits in
-the above example are gobbled), or it contains the translation for the symbol
-and the number of bits to gobble.  Then you start again with the next
-ungobbled bit.
-
-You may wonder: why not just have one lookup table for how ever many bits the
-longest symbol is?  The reason is that if you do that, you end up spending
-more time filling in duplicate symbol entries than you do actually decoding.
-At least for deflate's output that generates new trees every several 10's of
-kbytes.  You can imagine that filling in a 2^15 entry table for a 15-bit code
-would take too long if you're only decoding several thousand symbols.  At the
-other extreme, you could make a new table for every bit in the code.  In fact,
-that's essentially a Huffman tree.  But then you spend too much time
-traversing the tree while decoding, even for short symbols.
-
-So the number of bits for the first lookup table is a trade of the time to
-fill out the table vs. the time spent looking at the second level and above of
-the table.
-
-Here is an example, scaled down:
-
-The code being decoded, with 10 symbols, from 1 to 6 bits long:
-
-A: 0
-B: 10
-C: 1100
-D: 11010
-E: 11011
-F: 11100
-G: 11101
-H: 11110
-I: 111110
-J: 111111
-
-Let's make the first table three bits long (eight entries):
-
-000: A,1
-001: A,1
-010: A,1
-011: A,1
-100: B,2
-101: B,2
-110: -> table X (gobble 3 bits)
-111: -> table Y (gobble 3 bits)
-
-Each entry is what the bits decode as and how many bits that is, i.e. how
-many bits to gobble.  Or the entry points to another table, with the number of
-bits to gobble implicit in the size of the table.
-
-Table X is two bits long since the longest code starting with 110 is five bits
-long:
-
-00: C,1
-01: C,1
-10: D,2
-11: E,2
-
-Table Y is three bits long since the longest code starting with 111 is six
-bits long:
-
-000: F,2
-001: F,2
-010: G,2
-011: G,2
-100: H,2
-101: H,2
-110: I,3
-111: J,3
-
-So what we have here are three tables with a total of 20 entries that had to
-be constructed.  That's compared to 64 entries for a single table.  Or
-compared to 16 entries for a Huffman tree (six two entry tables and one four
-entry table).  Assuming that the code ideally represents the probability of
-the symbols, it takes on the average 1.25 lookups per symbol.  That's compared
-to one lookup for the single table, or 1.66 lookups per symbol for the
-Huffman tree.
-
-There, I think that gives you a picture of what's going on.  For inflate, the
-meaning of a particular symbol is often more than just a letter.  It can be a
-byte (a "literal"), or it can be either a length or a distance which
-indicates a base value and a number of bits to fetch after the code that is
-added to the base value.  Or it might be the special end-of-block code.  The
-data structures created in inftrees.c try to encode all that information
-compactly in the tables.
-
-
-Jean-loup Gailly        Mark Adler
-jloup@gzip.org          madler@alumni.caltech.edu
-
-
-References:
-
-[LZ77] Ziv J., Lempel A., ``A Universal Algorithm for Sequential Data
-Compression,'' IEEE Transactions on Information Theory, Vol. 23, No. 3,
-pp. 337-343.
-
-``DEFLATE Compressed Data Format Specification'' available in
-http://www.ietf.org/rfc/rfc1951.txt
diff --git a/cpukit/zlib/doc/txtvsbin.txt b/cpukit/zlib/doc/txtvsbin.txt
deleted file mode 100644
index 3d0f0634f7..0000000000
--- a/cpukit/zlib/doc/txtvsbin.txt
+++ /dev/null
@@ -1,107 +0,0 @@
-A Fast Method for Identifying Plain Text Files
-==============================================
-
-
-Introduction
-------------
-
-Given a file coming from an unknown source, it is sometimes desirable
-to find out whether the format of that file is plain text.  Although
-this may appear like a simple task, a fully accurate detection of the
-file type requires heavy-duty semantic analysis on the file contents.
-It is, however, possible to obtain satisfactory results by employing
-various heuristics.
-
-Previous versions of PKZip and other zip-compatible compression tools
-were using a crude detection scheme: if more than 80% (4/5) of the bytes
-found in a certain buffer are within the range [7..127], the file is
-labeled as plain text, otherwise it is labeled as binary.  A prominent
-limitation of this scheme is the restriction to Latin-based alphabets.
-Other alphabets, like Greek, Cyrillic or Asian, make extensive use of
-the bytes within the range [128..255], and texts using these alphabets
-are most often misidentified by this scheme; in other words, the rate
-of false negatives is sometimes too high, which means that the recall
-is low.  Another weakness of this scheme is a reduced precision, due to
-the false positives that may occur when binary files containing large
-amounts of textual characters are misidentified as plain text.
-
-In this article we propose a new, simple detection scheme that features
-a much increased precision and a near-100% recall.  This scheme is
-designed to work on ASCII, Unicode and other ASCII-derived alphabets,
-and it handles single-byte encodings (ISO-8859, MacRoman, KOI8, etc.)
-and variable-sized encodings (ISO-2022, UTF-8, etc.).  Wider encodings
-(UCS-2/UTF-16 and UCS-4/UTF-32) are not handled, however.
-
-
-The Algorithm
--------------
-
-The algorithm works by dividing the set of bytecodes [0..255] into three
-categories:
-- The white list of textual bytecodes:
-  9 (TAB), 10 (LF), 13 (CR), 32 (SPACE) to 255.
-- The gray list of tolerated bytecodes:
-  7 (BEL), 8 (BS), 11 (VT), 12 (FF), 26 (SUB), 27 (ESC).
-- The black list of undesired, non-textual bytecodes:
-  0 (NUL) to 6, 14 to 31.
-
-If a file contains at least one byte that belongs to the white list and
-no byte that belongs to the black list, then the file is categorized as
-plain text; otherwise, it is categorized as binary.  (The boundary case,
-when the file is empty, automatically falls into the latter category.)
-
-
-Rationale
----------
-
-The idea behind this algorithm relies on two observations.
-
-The first observation is that, although the full range of 7-bit codes
-[0..127] is properly specified by the ASCII standard, most control
-characters in the range [0..31] are not used in practice.  The only
-widely-used, almost universally-portable control codes are 9 (TAB),
-10 (LF) and 13 (CR).  There are a few more control codes that are
-recognized on a reduced range of platforms and text viewers/editors:
-7 (BEL), 8 (BS), 11 (VT), 12 (FF), 26 (SUB) and 27 (ESC); but these
-codes are rarely (if ever) used alone, without being accompanied by
-some printable text.  Even the newer, portable text formats such as
-XML avoid using control characters outside the list mentioned here.
-
-The second observation is that most of the binary files tend to contain
-control characters, especially 0 (NUL).  Even though the older text
-detection schemes observe the presence of non-ASCII codes from the range
-[128..255], the precision rarely has to suffer if this upper range is
-labeled as textual, because the files that are genuinely binary tend to
-contain both control characters and codes from the upper range.  On the
-other hand, the upper range needs to be labeled as textual, because it
-is used by virtually all ASCII extensions.  In particular, this range is
-used for encoding non-Latin scripts.
-
-Since there is no counting involved, other than simply observing the
-presence or the absence of some byte values, the algorithm produces
-consistent results, regardless what alphabet encoding is being used.
-(If counting were involved, it could be possible to obtain different
-results on a text encoded, say, using ISO-8859-16 versus UTF-8.)
-
-There is an extra category of plain text files that are "polluted" with
-one or more black-listed codes, either by mistake or by peculiar design
-considerations.  In such cases, a scheme that tolerates a small fraction
-of black-listed codes would provide an increased recall (i.e. more true
-positives).  This, however, incurs a reduced precision overall, since
-false positives are more likely to appear in binary files that contain
-large chunks of textual data.  Furthermore, "polluted" plain text should
-be regarded as binary by general-purpose text detection schemes, because
-general-purpose text processing algorithms might not be applicable.
-Under this premise, it is safe to say that our detection method provides
-a near-100% recall.
-
-Experiments have been run on many files coming from various platforms
-and applications.  We tried plain text files, system logs, source code,
-formatted office documents, compiled object code, etc.  The results
-confirm the optimistic assumptions about the capabilities of this
-algorithm.
-
-
---
-Cosmin Truta
-Last updated: 2006-May-28