ADAPTIVE SIGNAL PROCESSING BERNARD WIDROW SOLUTION MANUAL PDF

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Bernard Widrow was born in in Norwich, CT. He finished his Ph. That same year he moved to Stanford, and has remained on Stanford's faculty since that time. His major research interests have been in the fields of pattern recognition, adaptive filters and adaptive controls, bioengineering, adaptive beam-forming, adaptive geophysical imaging, and particularly adaptive neural networks.

Widrow is the co-author of two major engineering texts, Adaptive Signal Processing with S. Stearns, , and Adaptive Inverse Control with E. He holds fifteen patents and is the author or co-author of over articles. The bulk of the interview concerns Widrow's explanations of his solutions to several research problems related to signal processing.

There is a lengthy discussion of his Ph. He also describes in depth his work on adaptive FIR filtering, which has applications in modem technology. Finally he discusses his development of an adaptive antenna in the late s. This manuscript is being made available for research purposes only.

It should include identification of the specific passages to be quoted, anticipated use of the passages, and identification of the user. Can we begin by talking about your early life, leading up to your education and how you became involved with signal processing? I grew up in the small town of Norwich, Connecticut.

I was born December 24th, Christmas Eve. Yes, a little Christmas present I suppose. Boy, what a surprise. As I grew up, my dream and hope was to be able to go to MIT and become an electrical engineer. As a kid, I played with electrical apparatus and radios , and I was completely hooked on electronics.

But I didn't think that I had much of a chance at getting into MIT, as it was well known to be very competitive, and here I was coming from nowhere in this little town. But a very distinguished gentleman whom I never met before and never saw again asked me, "Sonny, what do you want to be when you grow up?

I could never get in. You apply. You'll never forgive yourself if you don't. You can do it just as well as anybody else. You go ahead and do that. I went to high school close to our home, and I would very frequently come home for lunch. So one day in my senior year of high school, I came home for lunch and my father was waiting for me. He said that the medical doctor at MIT wanted to know about my health. I said to my father, "Well, why on earth would he care about that? They accepted you! I began in and got my undergraduate degree in When I first went there, I thought that a great life would be to learn all I could about electrical engineering and get a really good job with a big electronics company or a big electrical house like GE or Westinghouse.

That was my goal and ambition. But then as I went further, I began to hear people talking about graduate school. That sounded like a good idea. And so, I was able to get a research assistantship in the MIT Digital Computer Laboratory, which at that time was developing a machine called Whirlwind.

It was all hand-made, and pieces of it are now in the Smithsonian. I began graduate study as an electronic engineer in the magnetic core memory group.

This was a competition that everybody got their heart and soul into. I still have an array up on that shelf, right near the little robot. There was a time when I had the largest magnetic core memory on the planet on my workbench. It stored all of bits. It was just remarkable how well it worked.

Then other memories were built that were bigger, and pretty soon it became clear that the magnetic core memory was going to supplant all other forms of random access memory.

So RAM was magnetic core until some people working at Fairchild decided to leave the company to develop integrated circuit memory.

Nobody believed that you could afford to use a flip-flop to store 1 bit. But this group went off, and this led to the founding of Intel. So, my experience with computing started with work on memory.

I always had a perspective on computers which is sort of a memory's eye view. I look for the memory and see what you have to connect around it. In my work for a master's degree I wanted to improve the signal-to-noise ratio of the sensing signal coming out of core memory. In those days, the materials for making magnetic cores were very poor compared to what they finally evolved into.

What was needed was a very square hysteresis loop, and they couldn't get that exactly. The signals coming from the selected core in the memory plane containing the bit that the computer was trying to read became corrupted with noise, and sometimes the signal could be noisy enough to cause an error. I had the idea of driving the Cartesian x and y axis grid lines of the core with currents of two different frequencies, and I chose 10 mhz and The core at the intersection would produce a beat frequency signal at a half a megahertz.

I discovered that if the core was in the "zero" state, the beat frequency appeared at a certain phase. But if the core was flipped to the "one" state, then the phase of the beat frequency signal would be flipped degrees. So you have to sense whether the phase was zero or degrees, which would indicate a one or a zero. There were noise, interference at 10 mhz and its harmonics, and at But the signal of interest was at half a mhz.

With filtering, I was able to get a nice clean signal, and we were able, in principal, to make the memory planes much bigger. The bigger you made them, you see, the poorer was the signal-to-noise ratio. The more things you create, the more mischief you are going to find. So my master's thesis contained signal processing concepts. It proposed a completely different way to sense the information from a magnetic core.

I guess in retrospect you can call it signal processing , but you did you use that terminology at the time? The phrase "signal processing" was not used. But in fact, I was dealing with signals and determining what was happening with nonlinearity and mixing in the frequency domain. All these concepts were well understood. What was new was the way of putting this together to get information from a magnetic core in a memory plane.

Today the same course might have two names. The course taught both signals and control. Since the signals were sampled in time, he called this sample data systems. There was something about the course that fascinated me, and Professor Linvill was just about the most popular professor in electrical engineering because of his personality, the nature of what he did, and the way he thought about things.

Everybody wanted to do their theses with him. I could only dream about the possibility of joining his group and doing a thesis under his supervision. What happened is that I discovered the beat frequency principle for magnetic-core memory, presented it to him, and asked him whether he'd be willing to supervise my master's thesis. He said, "Absolutely, this is a wonderful idea.

The fact that I got the highest grade on the final in his course helped. Well here I was finishing a master's degree and the time came to think about going on for a doctorate I thought, "Why go on for a doctorate? Is it possible to do a thing like that, to do research that is really original. So I asked him, and it was my lucky day. Professor Linvill was interested in signal processing and control. Whatever he was interested in, I would be interested in. My research focus switched from hardware to algorithms and mathematics.

All my life up to that point I thought I was going to be a circuit guy. I loved vacuum tubes. They had sort of a glow about them. I just loved vacuum tubes , and I really understood vacuum tubes. The transistor was a mystery. The first experiments I did with transistors during my senior year were for a lab course to could choose your own experiments. Two transistors existed at MIT. They had somehow been secreted out of Bell Labs. Somebody told us that if you did something wrong and you burned out one of them, you would be burned out half the supply of transistors for the whole of New England.

So we were very careful.

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