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Conventional adaptive array antenna processing must observe signals on all of the array antenna elements. However, because the low-cost electronically steerable parasitic array radiator (ESPAR) antenna has only a single-port output, none of the signals on the antenna's parasitic elements can be observed. A direct application of most of the algorithms for the conventional adaptive array antenna is impractical. In this paper, a new technique of estimation of direction-of-arrivals (DoAs) is proposed for the ESPAR antenna. This technique is based on the modified MUltiple SIgnal Classification (MUSIC) algorithm. The correlation matrix used in the MUSIC algorithm is estimated from the signal received through the single-port output of the ESPAR antenna as it switches over a set of antenna patterns. Simulation results show that DoAs can be estimated by the reactance domain MUSIC algorithm for ESPAR antennas. Furthermore, the statistical performance on estimation error variance of the reactance domain MUSIC estimator is analyzed and compared with the Cramér-Rao lower bound. Analytic and empirical results show that high-resolution DoAs estimation can be achieved by using the reactance domain MUSIC algorithm for ESPAR antennas.Index Terms-Direction finder, electronically steerable parasitic array radiator (ESPAR) antenna, reactance domain MUltiple SIgnal Classification (MUSIC) algorithm.

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We propose and implement a method to obtain all duplicated sequences (repeats) from a chromosome or whole genome. Unlike existing approaches our method makes it possible to simultaneously identify and classify repeats into super, local, and non-nested local maximal repeats. Computation veri¯cation demonstrates that maximal repeats for a genome of several gigabases can be identi¯ed in a reasonable time, enabling us to identi¯ed these maximal repeats for any sequenced genome. The algorithm used for the identi¯cation relies on enhanced su±x array data structure to achieve practical space and time e±ciency, to identify and classify the maximal repeats, and to perform further post-processing on the identi¯ed duplicated sequences. The simplicity and e®ectiveness of the implementation makes the method readily extendible to more sophisticated computations. Maxmers can be exhaustively accounted for in few minutes for genome sequences of dozen megabases in length and in less than a day or two for genome sequences of few gigabases in length. One application of duplicated sequence identi¯cation is to the study of duplicated sequence length distributions, which our found to exhibit for large lengths a persistent power-law behavior. Variation of estimated exponents of this power law are studied among di®erent species and successive assembly release versions of the same species. This makes the characterization of the power-law regime of sequenced genomes via maximal repeats identi¯cation and classi¯cation, an important task for the derivation of models that would help us to elucidate sequence duplication and genome evolution.