November 1962 - Vol. 5 No. 11

The use of programmed digital computers as general pattern classification and recognition devices is one phase of the current lively interest in artificial intelligence. It is important to choose a class of signals which is, at present, undergoing a good deal of visual inspection by trained people for the purpose of pattern recognition. In this way comparisons between machine and human performance may be obtained. A practical result also serves as additional motivation. Clinical electrocardiograms make up such a class of signals. The approach to the problem presented here centers upon the use of multiple adaptive matched filters that classify normalized signals. The present report gives some of the background for the application of this method.

Special difficulties are encountered in devising reliable systems for searching and updating any large files of documents that must be identified primarily on the basis of names and other personal particulars. The underlying problem is that of making nearly maximum use of items of identifying information that are individually unreliable but that may collectively be of considerable discriminating power. Rules that can be applied generally to name retrieval systems have been developed In a methodological study of the linkage of vital and health records into family groupings for demographic research purposes. These rules are described, and the ways in which information utilization for matching may be optimized are discussed.

Quantative measurements of the neurological control system for hand movement have shown results both of diagnostic value and of theoretical interest in understanding the underlying neural mechanisms and their system organization [1-4]. The requirement for dynamic analysis for a complex unpredictable input is necessary in order to distinguish between behavior attributable to the “neurological” system and that due to the powerful brain prediction apparatus. In order to meet this need, reduce the hidden errors in analyzing filtered data, permit shorter experiments (and thus less dependence upon assumptions of time invariance) and eliminate painstaking pencil-and-ruler analysis, we have inaugurated an online digital computer system for this and related experiments. The RW-300 computer is a magnetic-drum (8000 18-bit words), solid-state computer that is often used in process control applications.1 It has integral analog-to-digital and digital-to-analog conversion facilities.