Ranking and selection theory is applied to the eigenvalue problem. Of concern is the development of a procedure for computing the number of signals in a measurement data vector. In the authors' approach, the multiplicity of the noise eigenvalue is computed, and used in calculating the number of non-noise (signal) eigenvalues.

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Wireless tomography, a novel approach to remote sensing, is proposed in Part I of this series. The methodology, literature review, related work, and system engineering are presented. Concrete algorithms and hardware platforms are implemented to demonstrate this concept. Self-cohering tomography is studied in depth. More research will be reported, following this initiative.

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SummaryBECEHOFER and TURNBULL (1978) considered the problems of selecting the best normal popUl8-tion, provided it is better than a standard for the case of known variances or equal but unknown variances. Wmcox (1984) considered the same selection goal for the case of unequal unknown variance and provided the appropriate probability equations and the neceasary table. Under the same selection formulation (which we will describe formally in the following sections), this article studies a class of composite inverse sampling procedures for selecting the best multinomial cell that is better than a control cell with unknowncell probability. The procedures guaraiitee that (a) with probability a t least Pg (specified), no cell is selected when the largest cell probability is sufficiently less than the control and (b), with probability a t least P f (specified), the cell with the largest probability is selected when its probability is sufficiently greater than the second largext cell probability and the control cell probability.