Vincent C. Vannicola

A Comparison of Two Techniques for the Interpolation/Extrapolation of Frequency Domain Responses

Abstract : In a host of applications in engineering, it is necessary to obtain information about a system over a broad frequency range. In most cases, it is not possible to evaluate the parameter of interest in a closed form. However, either theoretical or experimental data is available in a narrow band. In this paper, a comparison is made between two methods that are used for interpolation/extrapolation of frequency domain responses. The first method discussed is a direct approach, derived from model based parameter estimation, which utilizes the principle of analytic continuation to interpolate/extrapolate the data over a wide band. The second uses the property of the discrete Hilbert Transform, based on the principle of causality, which relates the real and imaginary components of the frequency domain to iteratively interpolate/extrapolate the frequency response. The paper discusses the princlples behind each approach, and the algorithm used to implement the interpolation/extrapolation of frequency domain data, and then presents some numerical examples to compare the two methods.

Interpolation/extrapolation of frequency domain responses using the Hilbert transform

The Hilbert transform relates the real and the imaginary parts of the transfer function of a causal system. The objective of this paper is to illustrate how the Hilbert transform relationship can be utilized to interpolate/extrapolate measured frequency domain responses of devices. Sample numerical examples are presented to illustrate the efficacy of this method.

Asymptotic performance of a class of multisensor memoryless detectors

The authors investigate detectors for multisensor distributed detection, considering memoryless detector structures which generalize nonparametric tests and exhibit nonparametric properties in special cases. In general, such structures are convenient to implement. The optimal design of nonlinear transformations and linear weighting functions is discussed, and some optimal solutions are obtained. Asymptotically optimal implementation of the maximum likelihood detector is derived, and the resulting error probabilities are derived or bounded. Asymptotic relative efficiencies are calculated in some cases. The robust design of multisensor detection systems for a large number of samples is also discussed and solved in one case.<<ETX>>

New statistical procedure for the segmentation of contiguous nonhomogeneous regions based on the Ozturk algorithm

Using thresholding techniques it is possible to separate between contiguous non-homogeneous patches with different power levels. When the power levels of the patches are similar if not equal, the global histogram of the patches is unimodal and the thresholding approach becomes very difficult if not impossible. In this paper, we propose to use a statistical procedure to separate between contiguous non-homogeneous patches with similar power levels but different data statistics. The procedure separates different regions by distinguishing between their data probability distributions. The procedure is based on the Ozturk algorithm which uses the sample order statistics for the approximation of univariate distributions.

Survey of state-of-the-art technology in remote concealed weapon detection

Recent advances in millimeter-wave (MMV), microwave, and infrared (IR) technologies provide the means to detect concealed weapons remotely through clothing and is some cases through walls. Since the developemnt of forward-looking infrared instruments, work has been ongoing in attempting to use these devices for concealed weapon detection based on temperatrue differences between metallic weapons and in the infrared has led to the development of techniques based on lower frequencies. Focal plane arrays operating MMW frequencies are becoming available which eliminate the need for a costly and slow mechanical scanner for generating images. These radiometric sensors also detect temperature differences between weapons and the human body background. Holographic imaging systems operating at both microwave and MMW frequencies have been developed which generate images of near photographic quality through clothing and through thin, nonmetallic walls. Finally, a real- aperture radar is useful for observing people and detecting weapons through walls and in the field under reduced visibility conditions. This paper will review all of these technologies and give examples of images generated by each type of sensor. An assessment of the future of this technology with regard to law enforcement applications will also be given.

New approach to the analysis of IR images

In signal processing applications it is common to assume a Gaussian problem in the design of optimal signal processors. However, non-Gaussian processes do arise in many situations. When the possibility of a non-Gaussian problem is encountered, the question as to which probability distributions should be utilized in a specific situation for modeling the data needs to be answered. In practice, the underlying probability distributions are not known a priori. Consequently, an assessment must be made by monitoring the environment to subdivide for each patch. In this paper, an automatic statistical characterization and partitioning of environments process, previously used on simulated data, is applied to real data of an IR image. Two separate procedures are used to determine all homogeneous patches and subpatches in the IR image. The first procedure, referred to as the mapping procedure, is used to separate contiguous homogeneous regions by segregating between their power levels. The second procedure, referred to as the statistical procedure, separates contiguous homogeneous regions by segregating between their probabilistic data distributions. The latter procedure makes use of Ozturk algorithm, a newly developed algorithm for analyzing random data. Furthermore, the statistical procedure identifies suitable approximations to the probability density function for each homogeneous patch and determines the location of outliers. Convergence of the procedures is controlled by an expert system shell.

Advances in signal detection for distributed multisensor data

The binary signal detection problem is considered, when a distributed system of sensors operates in a decentralized fashion, i.e. local processing is performed at each sensor. Chernoffs large deviation theorem is used, and the rate of convergence of the error probability to zero is taken as a criterion. It is shown that the optimum quantizer of blocks of data under the above criterion is the likelihood ratio quantizer. A lower bound to the error probability is also developed. The monotonicity of performance with refinement of quantization is proved. The question of how many coarsely quantized sensors can replace the infinitely quantized one is also answered.<<ETX>>