Se June Hong

MINI: a heuristic approach for logic minimization

MINI is a heuristic logic minimization technique for many-variable problems. It accepts as input a Boolean logic specification expressed as an input-output table, thus avoiding a long list of minterms. It seeks a minimal implicant solution, without generating all prime implicants, which can be converted to prime implicants if desired. New and effective subprocesses, such as expanding, reshaping, and removing redundancy from cubes, are iterated until there is no further reduction in the solution. The process is general in that it can minimize both conventional logic and logic functions of multi-valued variables.

A General Class of Maximal Codes ror Computer Applications

The error-correcting codes for symbols from GF (2b) are often used for correction of byte-errors in binary data. In these byte-error-correcting codes each check symbol in GF (2b) is expressed as b binary check digits and each information symbol in GF (2b), likewise, is expressed by b binary information digits. A new class of codes for single-byte-error correction is presented. The code is general in that the code structure does not depend on symbols from GF (2b). In particular, the number of check bits are not restricted to the multiples of b as in the case of the codes derived from GF (2b) codes. The new codes are either perfect or maximal and are easily implementable using shift registers.

Data-intensive analytics for predictive modeling

The Data Abstraction Research Group was formed in the early 1990s, to bring focus to the work of the Mathematical Sciences Department in the emerging area of knowledge discovery and data mining (KD & DM). Most activities in this group have been performed in the technical area of predictive modeling, roughly at the intersection of machine learning, statistical modeling, and database technology. There has been a major emphasis on using business and industrial problems to motivate the research agenda. Major accomplishments include advances in methods for feature analysis, rule-based pattern discovery, and probabilistic modeling, and novel solutions for insurance risk management, targeted marketing, and text mining. This paper presents an overview of the groups major technical accomplishments.

Codes for self-clocking, AC-coupled transmission: aspects of synthesis and analysis

We consider NRZI waveform codes thats atisfy a given set of run-length constraints and the upper bound on the accumulated dc charge of the waveform. These constraints enable the codeword to be self-clocking, ac-coupled, and suitable for data processing tape and communication applications. Various aspects of synthesis and analysis of such codes, called (d, k, C) codes, are illustrated by means of several examples. The choice of the initial state of the encoder is shown to influence the length of the data sequence over which the encoder must look-ahead.

Generating test examples for heuristic Boolean minimization

This article describes simple methods of generating many-variable test-case problems for heuristic logic minimization studies. Covering problems and coloring problems are converted into Boolean functions that are useful test cases for minimization.

On Complementation of Boolean Functions

A theorem is presented that simplifies the computations necessary for complementing a Boolean function.

A Simple Procedure to Generate Optimum Test Patterns for Parity Logic Networks

A simple procedure to produce a minimum length test set for a parity network is presented. If M is the largest fan in of any EX-OR gate element in the tree, 2M test patterns are chosen by considering only 2M test sequences, of length 2M, assigned to each signal line.

AI at IBM Research

IBM has played an active role in AI research since the fields inception more than 50 years ago. In a trend that reflects the increasing demand for applications that behave intelligently, IBM today carries out most AI research in an interdisciplinary fashion by combining AI technology with other computing techniques to solve difficult technical problems. This article reports on the range of AI activities within IBM Research and discusses emerging issues. AI at IBM computer science research takes place in four broad areas: knowledge representation and reasoning; statistical AI; vision; and game playing.