Entropy and relative entropy are proposed as features extracted from symbol sequences. Firstly, a proper Iterated Function System is driven by the sequence, producing a fractaMike representation (CSR) with a low computational cost. Then, two entropic measures are applied to the CSR histogram of the CSR and theoretically justified. Examples are included.

We present a new pattern generation approach for deterministic built-in self testing (BIST) of sequential circuits. Our approach is based on precomputed test sequences, and is especially suited to sequential circuits that contain a large number of flip-flops but relatively few controllable primary inputs. Such circuits, often encountered as embedded cores and as filters for digital signal processing, are difficult to test and require long test sequences. We show that statistical encoding of precomputed test sequences can be combined with low-cost pattern decoding to provide deterministic BIST with practical levels of overhead. Optimal Huffman codes and near-optimal Comma codes are especially useful for test set encoding. This approach exploits recent advances in automatic test pattern generation for sequential circuits and, unlike other BIST schemes, does not require access to a gate-level model of the circuit under test. It can be easily automated and integrated with design automation tools. Experimental results for the ISCAS 89 benchmark circuits show that the proposed method provides higher fault coverage than pseudorandom testing with shorter test application time and low to moderate hardware overhead.

This paper proposes using computational learning theory (CLT) as a framework for analyzing the information processing behavior of firms; we argue that firms can be viewed as learning algorithms. The costs and benefits of processing information are linked to the structure of the firm and its relationship with the environment. Wemodel the firm as a type of artificial neural network (ANN).By a simulation experiment, we show which types of networks maximize the net return to computation given different environments. 2002 Elsevier Science B.V. All rights reserved.

We consider a fixed quantum measurement performed over n identical copies of quantum states. Using a rigorous notion of distinguishability based on Shannon’s 12th theorem, we show that in the case of a single qubit the number of distinguishable states is W(α1, α2, n) = |α1 − α2 | √ 2n, where (α1, α2) is the angle interval from which the states πe are chosen. In the general case of an N-dimensional Hilbert space and an area Ω of the domain on the unit sphere from which the states are chosen, the number of distinguishable states is W(N, n,Ω) = Ω ( 2n πe N −1