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An overview of the issue
DKXTAL. SlGNAL PROCESSING 2, 121-122
(1992)
EDITORIAL
An Overview of the Issue Welcome to Issue 3 of this year’s volume. As usual, we have provided a diverse set of articles for your information and reading pleasure. I am especially pleased to recognize the increase in the number of contributors from outside the United States. Our first paper comes to us from Ben Friedlander and Anthony Weiss. The paper deals with what could be an extremely important “real-world” consideration-polarization diversity in an antenna array and the potential advantages to direction finding. The second paper is from Dwight Day at Kansas State and is concerned with acoustic well logs. (The Allan variance is, incidentally, named after Dave Allan at NIST in Boulder-a brilliant metrologist who has personally helped out this editor from time to time.) Next up is a paper by Sam Stearns et al. in which the authors develop a new encoding technique for broadband residue sequences. This should be of interest to those many folks who are concerned with compression of data-rich sources. The fourth paper is from Kuldip Paliwal and discusses some interesting work on dimensionality reduction for hidden Markov model-based speech recognition. Dimensionality reduction can be crucial to any sort of feature recognition approach as the data available grow extremely rapidly. Next is a contribution from Piero Zamperoni on adaptive filters for certain image processing tasks. The paper pays close attention to the computational complexity issues and should be of value to designers of real-time image processing architectures. The next paper from Gale et al. is really counterintuitive. I first ran into their work last fall and was both amazed and skeptical. I won’t summarize it for you but I will say that if what they
have found is indeed viable, it is possible that some very knotty preprocessing tasks in communications may have a highly interesting solution. I asked the authors for this paper because I believe that the results so far should be disseminated to as wide an audience in as short a time as possible. We have had good feedback on our Personal Glimpse section. This issue we’re going to try another twist-the Sideways Glance. Oh, all right, the title is showmanship, I suppose. But it is fun. Anyway, the paper is from some very clever people at Southwest Research Institute. What these folks have done is to devise a temperature telemetry system for a piston in a reciprocating engine. What on earth has this to do with digital signal processing!? The answer is NOTHING-and yet EVERYTHING. Two stories I will tell you. The first concerns my early professional career. My boss once told me something to the effect, “You know, John, you wouldn’t have any signal processing to do if it weren’t for a bevy of talented folks who got you the signals in the first place.” The remark, which was probably oilhand, was sobering. He was right. I later heard another individual in the government refer to the same issue by way of introducing the “Bit Fairy.” The Bit Fairy is the kind, invisible spirit that brings the analysts their bits to analyze. The Bit Fairy is very important. The second story is from a later time. I had become a bit more accomplished, or at least older, and had been invited to give a series of lectures on machine vision and image understanding at the University of Colorado in Boulder. I wanted to impress on the students a maxim I had taken to heart; which was to be sure you understood your sensors and what they did to your data BEFORE you did your magical processing. I asked them to take out a sheet of paper and describe an algorithm that would recognize a horse. They then handed in their work and it was inspiring
1051-2004/92 $4.00 Copyright 0 1992 by Academic Press, Inc. All rights of reproduction in any form reserved.
to read of the graceful equine shape that could be easily picked out. I then showed them the picture below, which was taken by a friend, Dar Miner, and is here
used by permission. Well, it’s a horse all right. Where does it fall on the ROC curve of your algorithm? Have fun!
John Hershey and Rao Yarlagadda
(1992)
EDITORIAL
An Overview of the Issue Welcome to Issue 3 of this year’s volume. As usual, we have provided a diverse set of articles for your information and reading pleasure. I am especially pleased to recognize the increase in the number of contributors from outside the United States. Our first paper comes to us from Ben Friedlander and Anthony Weiss. The paper deals with what could be an extremely important “real-world” consideration-polarization diversity in an antenna array and the potential advantages to direction finding. The second paper is from Dwight Day at Kansas State and is concerned with acoustic well logs. (The Allan variance is, incidentally, named after Dave Allan at NIST in Boulder-a brilliant metrologist who has personally helped out this editor from time to time.) Next up is a paper by Sam Stearns et al. in which the authors develop a new encoding technique for broadband residue sequences. This should be of interest to those many folks who are concerned with compression of data-rich sources. The fourth paper is from Kuldip Paliwal and discusses some interesting work on dimensionality reduction for hidden Markov model-based speech recognition. Dimensionality reduction can be crucial to any sort of feature recognition approach as the data available grow extremely rapidly. Next is a contribution from Piero Zamperoni on adaptive filters for certain image processing tasks. The paper pays close attention to the computational complexity issues and should be of value to designers of real-time image processing architectures. The next paper from Gale et al. is really counterintuitive. I first ran into their work last fall and was both amazed and skeptical. I won’t summarize it for you but I will say that if what they
have found is indeed viable, it is possible that some very knotty preprocessing tasks in communications may have a highly interesting solution. I asked the authors for this paper because I believe that the results so far should be disseminated to as wide an audience in as short a time as possible. We have had good feedback on our Personal Glimpse section. This issue we’re going to try another twist-the Sideways Glance. Oh, all right, the title is showmanship, I suppose. But it is fun. Anyway, the paper is from some very clever people at Southwest Research Institute. What these folks have done is to devise a temperature telemetry system for a piston in a reciprocating engine. What on earth has this to do with digital signal processing!? The answer is NOTHING-and yet EVERYTHING. Two stories I will tell you. The first concerns my early professional career. My boss once told me something to the effect, “You know, John, you wouldn’t have any signal processing to do if it weren’t for a bevy of talented folks who got you the signals in the first place.” The remark, which was probably oilhand, was sobering. He was right. I later heard another individual in the government refer to the same issue by way of introducing the “Bit Fairy.” The Bit Fairy is the kind, invisible spirit that brings the analysts their bits to analyze. The Bit Fairy is very important. The second story is from a later time. I had become a bit more accomplished, or at least older, and had been invited to give a series of lectures on machine vision and image understanding at the University of Colorado in Boulder. I wanted to impress on the students a maxim I had taken to heart; which was to be sure you understood your sensors and what they did to your data BEFORE you did your magical processing. I asked them to take out a sheet of paper and describe an algorithm that would recognize a horse. They then handed in their work and it was inspiring
1051-2004/92 $4.00 Copyright 0 1992 by Academic Press, Inc. All rights of reproduction in any form reserved.
to read of the graceful equine shape that could be easily picked out. I then showed them the picture below, which was taken by a friend, Dar Miner, and is here
used by permission. Well, it’s a horse all right. Where does it fall on the ROC curve of your algorithm? Have fun!
John Hershey and Rao Yarlagadda