Similarity retrieval from a pic torial data base can be made more efficient by encoding the original data base into certain convenient format. Generalized hyper cube (GH) encoding is one such technique. To optimize GH encoding, a heuristic approach has been formulated. Two optimization problems have been considered here: (1) given the handle length m, find optimal GHm encoding; (2) given the threshold density, find optimal GHm encoding such that each hypercube has den sity no less than threshold density.

Two finite automata are devised for modeling two classes of demand paging algorithms. The first one of one input and three outputs models the class of algorithms with a constant amount of allocated space. The second one of one input and six outputs models the class of algorithms with a variable amount of allocated space. Some evaluation techniques are developed following each model. The memory states of the first class algorithm with the Least Recently Used (LRU) replacement policy and the working set model of the second class are recursively defined by strings of the loaded pages. The adopted replacement policy and the state string updating procedure are imbedded in the recursive definition of memory states. Properties of some algorithms are developed to fit the finiteness assumption of a reference string.