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A novel design methodology is illustrated, along with the fabrication technique for a wrap-around antenna in the C band on a conical surface. The methodology illustrates an attempt to achieve broadband performance with a thin conformal antenna. An attempt is also made to fabricate such an antenna on the parent body by a progressive lamination process of the layers. Experimental results are shown for the antenna fabrication technique.

Since the invention of fast algorithms for the computation of the discrete Fourier transform (DFT) by Cooley and Tukey (1965), the DFT has been widely used for the frequency-domain analysis and design of signals and systems in communications, digital signal processing, and in many other areas of science and engineering. While the Cooley-Tukey algorithms are simple, regular, and efficient, they have the drawback of requiring a significant amount of overhead operations such as bit-reversal, data-swapping, etc. In this paper, we develop a generalized version of a new family of DFT algorithms by decomposing a form of the DFT relation in which the input data and transform quantities are represented as vectors. These algorithms have the features that eliminate or reduce the drawbacks of the Cooley-Tukey algorithms while improving the simplicity and regularity of their implementations. The generalized version makes it easier to deduce a large family of algorithms with different features. The relative merits of the various algorithms with different vector lengths are discussed and the optimum vector length for DFT computation is pointed out.

The DFT is of fundamental importance in many areas of science and engineering due to its efficient evaluation of frequently used operations such as signal analysis, convolution, and correlation. It has been shown that radix-2 DFT (discrete Fourier transform) algorithms can be designed based on vector representation of data providing several advantages. These algorithms provide more efficient solution for the problem of DFT computation. An index mapping approach is used to derive these algorithms. This approach makes the derivation simpler and provides a better insight into the functioning of the algorithms. The signal flow graph of the algorithm is also presented.

Fast algorithms for the computation of the discrete Fourier transform (DFT) of real signals are important since the signals in practical situations are mostly real. The more efficient algorithms for real data are those that are derived from the algorithms for complex data. So far, all such algorithms use a real array to store the data. However, as the data values are real and their transform values are mostly complex, two possible data structures can be used for these algorithms: real or complex. DFT algorithms for real data that use a complex array for storing both the real data and their transform values are derived from the Cooley-Tukey radix-2 algorithm for complex data. This approach reduces the number of bit-reversal and array-index updating operations, eliminates independent data-swapping operations, and yields a computational structure that is almost as regular as that of the algorithms for complex data. Detailed derivations of the proposed algorithms for the computation of both the DFT of real data and the inverse DFT of the transform of real data, as well as their computational complexities, are presented. A C-language program of one of the proposed algorithms is given, illustrating the use of all the features of the new approach in software implementation. Comparison results are included to show that the proposed algorithms are faster and simpler than the real-valued split-radix and other algorithms.

The necessity for an efficient bit-reversal routine in the implementation of fast discrete Fourier transform algorithms is well known. In this paper, we propose a bit-reversal algorithm that reduces the computational effort to an extent that it becomes negligible compared with the data swapping operation for which the bit-reversal is required. >

The median of a set of numbers is a number which partitions the given set, excluding that number, into two subsets with an equal number of elements such that the number is greater than or equal to the elements in one subset and less than or equal to in the other. In image processing, in order to compute the running median, the window is moved from one neighborhood to the next. In this paper, a fast two-dimensional median filtering algorithm is proposed. The algorithm is designed in such a way that in order to find the median of a window, the results obtained during the partitioning of the previous window are used. Test results obtained by running the algorithm on VAX 11/780 are presented and its performance is compared with the Huang's histogram algorithm for median filtering. It is shown that the proposed algorithm's execution time is faster and is independent of the number of bits used to represent the data values. The novel features in the algorithm design that contribute to fast execution are also presented.

An implementation of the two-dimensional convolution algorithm that is suitable for image-processing applications is presented. The execution time of the algorithm is shown to be reduced due to an efficient data organization and by taking into account the fact that the pixel values in an image assume a fixed number of discrete levels.