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This report describes the various techniques and algorithms developed to enhance the accuracy of the measurements of very fast transient electromagnetic fields generated during EMP testing. Some fundamentals of digital signal processing are presented and the practical aspects are highlighted. The response of some sensors to incident fields is studied. Appropriates algorithms for reconstructing the incident field from the measurements are developed. The effect of low frequency cut-off of some sensors is also studied. A method to design a digital filter to arbitrary transfer function is presented. It is applied to compensate for losses occurring in long cables and also to fine tune other filters. The Hilbert transform is introduced to relate the magnitude and phase component of a transfer function. An implementation of the transform is given, allowing the design of digital filters when only the magnitude of the transfer function is known.

This report describes the various techniques and algorithms developed to enhance the accuracy of the measurements of very fast transient electromagnetic fields generated during EMP testing. Some fundamentals of digital signal processing are presented and the practical aspects are highlighted. The response of some sensors to incident fields is studied. Appropriates algorithms for reconstructing the incident field from the measurements are developed. The effect of low frequency cut-off of some sensors is also studied. A method to design a digital filter to arbitrary transfer function is presented. It is applied to compensate for losses occurring in long cables and also to fine tune other filters. The Hilbert transform is introduced to relate the magnitude and phase component of a transfer function. An implementation of the transform is given, allowing the design of digital filters when only the magnitude of the transfer function is known.

From the bitsavers.org collection, a scanned-in computer-related document. components :: ti :: TMS320xx :: 3202x :: 1986 TI Digital Signal Processing Applications with the TMS320 Family Theory Algorithms and Implementations 198605

The current digital signal analysis algorithms are investigated that are implemented in automatic voice recognition algorithms. Automatic voice recognition means, the capability of a computer to recognize and interact with verbal commands. The digital signal is focused on, rather than the linguistic, analysis of speech signal. Several digital signal processing algorithms are available for voice recognition. Some of these algorithms are: Linear Predictive Coding (LPC), Short-time Fourier Analysis, and Cepstrum Analysis. Among these algorithms, the LPC is the most widely used. This algorithm has short execution time and do not require large memory storage. However, it has several limitations due to the assumptions used to develop it. The other 2 algorithms are frequency domain algorithms with not many assumptions, but they are not widely implemented or investigated. However, with the recent advances in the digital technology, namely signal processors, these 2 frequency domain algorithms may be investigated in order to implement them in voice recognition. This research is concerned with real time, microprocessor based recognition algorithms.

The current digital signal analysis algorithms are investigated that are implemented in automatic voice recognition algorithms. Automatic voice recognition means, the capability of a computer to recognize and interact with verbal commands. The digital signal is focused on, rather than the linguistic, analysis of speech signal. Several digital signal processing algorithms are available for voice recognition. Some of these algorithms are: Linear Predictive Coding (LPC), Short-time Fourier Analysis, and Cepstrum Analysis. Among these algorithms, the LPC is the most widely used. This algorithm has short execution time and do not require large memory storage. However, it has several limitations due to the assumptions used to develop it. The other 2 algorithms are frequency domain algorithms with not many assumptions, but they are not widely implemented or investigated. However, with the recent advances in the digital technology, namely signal processors, these 2 frequency domain algorithms may be investigated in order to implement them in voice recognition. This research is concerned with real time, microprocessor based recognition algorithms.

During the last 3 years we have developed a mathematical theory of algorithms and implementation strategies for DSP computations on RISC and DSP chips and parallel architectures ranging from scalable multinode boards to massively parallel multinode computers as typified by the Intel's touchstone systems.

A new algorithm for the multiprocessor computation of the multidimensional discrete Fourier transform is presented. The algorithm eliminates the need for interprocessor communication and is highly scalable. The cost of eliminating interprocessor communication by this method is that one addition must be performed at each processing element for every input loaded into the machine. The algorithm is based on a new multidimensional discrete Fourier transform algorithm that computes a d-dimensional discrete Fourier transform by a set of independent k-dimensional discrete Fourier transforms (kd); it is a reduction algorithm in the sense that it has lowered the dimension of the Fourier transforms that are computed. The k-dimensional discrete Fourier transforms are performed on data derived from the input using only additions, and produce k-dimensional hyperplanes of the output array. Multidimensional discrete fourier transform algorithms, Parallel algorithms.

Hyperspectral imaging is an important tool in various fields, notably geosensing and astronomy, and with the development of new devices, it is now also being applied also in medicine. Concepts and tools from signal processing and data analysis need to be employed to analyze these large and complex data sets. In this paper, we present several techniques which generally apply to hyperspectral data, and we use them to analyze a particular data set. With light sources of increasingly broader ranges, spectral analysis of tissue sections has evolved from 2 wavelength image subtraction techniques to Raman near infra-red microspectroscopic analysis permitting discrimination of cell types and tissue patterns. We have developed and used a unique tuned light source based on micro-optoelectromechanical systems (MOEMS) and applied algorithms for spectral microscopic analysis of normal and malignant colon tissue. We compare the results to our previous studies which used a tunable liquid filter light source.

Hyperspectral imaging is an important tool in various fields, notably geosensing and astronomy, and with the development of new devices, it is now also being applied also in medicine. Concepts and tools from signal processing and data analysis need to be employed to analyze these large and complex data sets. In this paper, we present several techniques which generally apply to hyperspectral data, and we use them to analyze a particular data set. With light sources of increasingly broader ranges, spectral analysis of tissue sections has evolved from 2 wavelength image subtraction techniques to Raman near infra-red microspectroscopic analysis permitting discrimination of cell types and tissue patterns. We have developed and used a unique tuned light source based on micro-optoelectromechanical systems (MOEMS) and applied algorithms for spectral microscopic analysis of normal and malignant colon tissue. We compare the results to our previous studies which used a tunable liquid filter light source.