Picture of Brad Lucier

Bradley J. Lucier

Professor Emeritus of Mathematics and Computer Science
Purdue University
Math G138
150 North University Street
W. Lafayette, IN 47907-2067


ORCID iD iconorcid.org/0000-0003-3808-939X

A professor can do as he pleases, but a professor emeritus can do as he damn well pleases. Nicolaas Bloembergen

Kipling was a Conservative, a thing that does not exist nowadays. Those who now call themselves Conservatives are either Liberals, Fascists or the accomplices of Fascists. George Orwell, September 1941

You're an evil man. Linus Torvalds
Thank you.


A complete list of my papers can be found on my "bio" page . You can download various papers by me and my previous students on image processing and wavelets and numerical methods for partial differential equations and related topics.

A publication that doesn't quite fit in with those two lists is How I First Heard About Calculus which appears in the Journal of Humanistic Mathematics. I heard about calculus from a daily science cartoon strip, and I say in the article that I was interested enough in science to search out that strip in my local paper, but that isn't quite right---I was interested enough in cartoons to search for all of them in the daily newspaper, and because a group in Australia syndicated a daily science cartoon that my local paper decided to reproduce, I happened to learn about science. Not only is that version more correct, but it's more interesting.

The paper The Nature of Numbers: Real Computing also appeared in the Journal of Humanistic Mathematics. The abstract is:

While studying the computable real numbers as a professional mathematician, I came to see the computable reals, and not the real numbers as usually presented in undergraduate real analysis classes, as the natural culmination of my evolving understanding of numbers as a schoolchild. This paper attempts to trace and explain that evolution. The first part recounts the nature of numbers as they were presented to us grade-school children. In particular, the introduction of square roots induced a step change in my understanding of numbers. Another incident gave me insight into the brilliance of Alan Turing in his paper introducing both the computable real numbers and his famous ``Turing machine''. The final part of this paper describes the computable real numbers in enough detail to supplement the usual undergraduate real analysis class. An appendix presents programs that implement the examples in the text.

The page of code at the end is written in the Scheme programming language.

Errata (some are thanks to Bill Richter):

Conjecture on the norm of "Discrete White Noise" in the dual of BV

In Pointwise Besov Space Smoothing of Images we consider the following problem: Let an image $\epsilon$ be defined on $I=[0,1]^2$, the unit square, divided into $N\times N$ subsquares $I_i$ indexed by the multi-index $i=(i_1,i_2)$, $0\leq i_1,i_2<N$. The value of the image $\epsilon$ on $I_i$ is given by $\epsilon_i$, where the $\epsilon_i$ are i.i.d. $N(0,1)$ random variables. For various reasons it's interesting to estimate the expected value of the norm of $\epsilon$ in the dual space of $\text{BV}(I)$, the space of functions of bounded variation on $I$. Specifically, we want to estimate $$E\left(|\epsilon|_{\text{BV(I)}^*}\right)=E\left(\sup_{\|f\|_{\text{BV}(I)}\leq 1}\left|\int_I f\epsilon\right|\right).\tag{1}$$ We note in Section 8 of the previously mentioned paper that because of the strict norm inclusions $B^1_1(L_1(I))\subset\text{BV}(I)\subset B^1_\infty(L_1(I))$ between $\text{BV}(I)$ and the Besov spaces $B^1_1(L_1(I))$ and $ B^1_\infty(L_1(I))$, we can exploit wavelet atomic decompositions of the Besov spaces to show that there exist two constants $C_1$ and $C_2$ such that $$ C_1\frac{|\log N|^{1/2}}N\leq E\left(|\epsilon|_{\text{BV(I)}^*}\right)\leq C_2\frac{|\log N|^{3/2}}N. $$ I conjecture the tighter bounds $$ C_1\frac{\log N}N\leq E\left(|\epsilon|_{\text{BV(I)}^*}\right)\leq C_2\frac{\log N}N. $$ Update: We updated the supremeum in Equation (1) to replace $|f|_{\text{BV}(I)}$ by $\|f\|_{\text{BV}(I)}=|f|_{\text{BV}(I)}+\|f\|_{L^1(I)}$. Alternately, one could keep $|f|_{\text{BV}(I)}$ and require $\int_I f=0$.


Test Images

High quality, grey-scale, test images for purposes of testing algorithms on natural images. Some results from my compression program are given.

The original Kodak Photo CD Photo Sampler color images from which the greyscale images above were derived, and the copyright terms under which the Kodak Photo CD Photo Sampler images were released. If anyone can tell me a standard way to convert these to YUV, YCrCb, or RGB, I'd be interested. Later note: it seems that various versions of Photoshop have good color conversion between Kodak Photo CD format and RGB, so that is what I used for the color versions. I'm still not happy with Photoshop's conversion to grey-scale. You can find a lot of useful information about working with Photo CD images at Ted's Unofficial Kodak Photo CD Homepage.





My wife Maureen is an ATA-certified translator from French into English.

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