Currently, hardware intellectual property (IP) is delivered at three levels of abstraction: hard, firm, and soft. In order to further enhance performance, efficiency, and flexibility of IP design, we have developed a new approach for designing hardware and software IP called MetaCores. The new design approach starts at the algorithm level and leverages on the algorithms intrinsic optimization degrees of freedom. The approach has four main components: (i) problem formulation and identification of optimization degrees of freedom, (ii) objective functions and constraints, (iii) cost evaluation engine, and (iv) multiresolution design space search. From the algorithmic viewpoint, the main contribution is the introduction of multiresolution search in algorithm optimization and synthesis process. We have applied the approach to the development of Viterbi and IIR MetaCores. Experimental results demonstrate the effectiveness of the new approach.

A two-stage decoding procedure for pragmatic trellis-coded modulation (TCM) is introduced. It applies a transformation from the received I-channel and Q-channel samples onto points in a two-dimensional (2-D) signal space that contains a coset constellation. For pragmatic TCM over M-PSK signal sets with /spl nu/ coded bits per symbol, /spl nu/=1, 2, the signal points in the coset constellations represent cosets of a B/QPSK signal subset-associated with the coded bits-in the original M-PSK signal constellation. A conventional Viterbi decoder operates on the transformed symbols to estimate the coded bits. After reencoding these bits, the uncoded bits are estimated in a second stage, on a symbol-by-symbol basis, with decisions based on the location of the received symbols. In addition to requiring no changes in the Viterbi decoder core, it is shown that the proposed method results in savings of up to 40% in the memory required to store (or in the size of the logic required to compute) metrics and transformed symbols.

This paper studies the invariance of a given punctured convolutional code to an affine class of symbol transformations known to commonly occur in digital transceiver systems. A set of conditions to test the invariance of a code to these transformations has been derived, followed by two proposed methods to compensate for an invariant transformation. The viability of these methods has been examined by doing an invariant factor decomposition on the equivalent code generator matrix (obtained from the original code generator matrix and the given transform). The knowledge of the transformation and the nature of its occurrence greatly determines which method of compensation could be used. This study has much use in not only enabling a code designer to evaluate the invariance of an affine transformation to a given code, but also on the other hand to make the appropriate choice of code generators, puncturing schemes, and bit-to-constellation symbol mapping; so as to allow a channel coding scheme to be either sensitive or invariant to a given transformation, depending upon design objectives.

In image analysis, there have been few effective procedures that deal with a wide class of imagery acquired by different sensors under different environmental conditions. The success of a given classification algorithm is dependent upon pattern familiarity, background, and the image acquisition process. Thus, with the inaccuracies in the acquisition process, as well as incomplete or incorrect knowledge about the pattern classes, one cannot place complete confidence in the classifier outcome. There has been increasing success in making decisions under such uncertain conditions by using a rule-based approach with effective uncertainty management, which involves identifying the causes of uncertainty and developing mathematical models for the same. These are incorporated into the rule structure so that the result would be a set of choices or decisions, with a set of associated certainty values or confidences. This paper proposes a "unified" methodology to combine the uncertainties associated with evidence for a given proposition, which is then systematically propagated down the decision tree. The relative importance of propositions as well as the rules themselves have also been considered. Finally, the methodology has been applied to an ATR problem and the results, when compared to some existing methods, show the overall effectiveness of this approach.>

Any decision procedure that is applied to a given problem involves both the gathering of relevant information from different sources and, when possible, accounting for the reliability of each source or group of sources. Due to insufficient knowledge, it may not be possible to isolate an unreliable or poor source of information. However, if a set of sources provides information to a decision algorithm, the effect of these sources as a group could be analyzed in terms of the relative coherence of the sources within it. A group whose sources are in agreement regarding the same decision is more reliable than one in which the individual sources supported different decisions. A measure of the unreliability of a group is its 'disparity', which could compensate for disagreement within a group. Theory and results to support this group unreliability are presented.>