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The Red McKenna series chronicles the adventures of a six-foot, three-inch redhead with an athlete's body, a mathematical-genius mind, and an independent streak a mile wide.

Months ago, I promised to alert readers of this blog when my first full-length novel Red appeared. Well, it's out. Actually, it's been out for a while in hard cover, paperback, and e-book formats. It is available through online and brick-and-mortar booksellers. Published by iUniverse, the novel introduces a unique heroine whom I think readers of this blog could relate to. She's a six-foot, three-inch redhead with a mathematical genius mind, as well as a crack-athelete's body and an independent streak a mile wide. Her soul mate is a biker who's even bigger, smarter, and more independent. Together, they harness science and advanced technology to solve riddles that life throws at them.

The idea for the story started back in early 2001 on a bitterly cold January night in Woonsocket, Rhode Island. I'd just flown in from Arizona to spend a week emptying out and closing up the house of my recently deceased father, who'd finally succumbed to cancer at age 87.

When I say it was bitterly cold, I ain't just kiddin'. The high for the day was about zero Fahrenheit, which is cold even for New England in January. For a desert rat living in Arizona, it was unbelievable!

Then, the sun set, and it got colder.

I curled up in a lotus position with the thickest quilt I could find wrapped around me, hoping the furnace would soon drive away the chill that had seeped into the walls during two weeks of the house being empty. Since the house had been empty, there was no TV. I'd been cooped up on an airplane for hours with nothing to do but read, so I was read out. My body was still on Mountain Standard Time, and I'm a night owl, anyway, so sleep was many hours away.

I just sat, and thought.

What I thought was the beginning of this story. It was going to be the adventures of two young people who made a transcontinental journey by motorcycle, visiting all the places I liked to go by motorcycle, doing the things I like to do when touring by motorcycle, and meeting the kinds of people I meet when wandering around by motorcycle.

To make it interesting, I'd have the lady be a newbie biker, who'd never been on a motorcycle tour before. Everything would be new to her, and a surprise.

What would she look like? Well, I like tall redheads who are really, really smart. My mother was tall, had auburn hair, and was one of the smartest people I've ever met. My wife is tall, has red hair, and is no slouch between the ears, either. In fact, I'm a sucker for tall redheads with lots of brains. So, my heroine would be tall, have red hair, and be really, really smart.

Since everything in an exciting fiction story must be bigger than life, she'd have to be extremely tall - like six-foot, three-inches tall - have lots of flaming red hair, and be a genius with a full scholarship in an Ivy League college studying something that gives most people phobias: mathematics.

The guy would be a veteran biker, who knew all the right places to go, and could introduce her to the most interesting people. To be able to match her, he'd have to be really tall - like six-foot, six-inches tall - more athletic, and even smarter.

They'd visit motorcycle races, camp out at biker rallies, spend hours shopping at motorcycle flea markets, and spend evenings getting plastered at biker bars. Being really, really smart would give them the wherewithal to thumb their noses at convention whenever they wanted to. They could get into stuff the rest of us only fantasize about.

It'd be a lot of fun for them, and, maybe, for readers.

In that form, however, it'd be lucky to make fifty pages long. That's a longish short story, not a novel. A novel needs a lot more. It needs character development. It needs suspense. It needs mystery.

It needed a lot of work.

Over the next nine years, the story grew. The young lady got a name, Judith McKenna (nicknamed "Red" for obvious reasons), as well as a troubled past. Her troubles, however, were not her fault, and not the fault of any character flaw. The troubles stemmed from a singular event that made building relationships difficult at best, especially building relationships with guys. That event was the untimely and mysterious disappearance of her father just at the time an adolescent girl needs a father figure most.

So, the father figure would be supplied by the mysterious biker, who takes her on a journey, which is no longer a touristy vacation, but a journey of self-discovery. Who was she, inside? How could she relate to other people? What was she going to do with her life?

One of the ambiguities she'd have to resolve could be a bit of sexual confusion. That could be fun!

The mystery, of course, is what happened to her father. Why'd he leave? Why'd he not come back?

Now, my favorite fiction genres over the past lots-and-lots-of-decades have been mystery and science fiction. And, my favorite stories have always combined both. And, my favorite author has been Rober Heinlein, who generally combined those two genres and used them to weave epic tales that explored basic human values. That's what I'd try to do.

Judith's story had a mystery, and had some serious character-development potential. It also had two young people off on their own, providing plenty of opportunities for fooling around between sheets, which will seriously spice up any story. In fact, giving her a chance to peel back layers to slowly discover who this biker was would add a second mystery, which might be fun to develop as well.

What she would find is a scientific genius who could provide technology that would make solving her other mystery - what happened to her father - possible, where it hadn't been before. He'd have built his own company in very short time, capitalizing on his inventions in aerospace technology. I know about aerospace technology. I can do that.

With all that additional content packed in, the space needed to tell the story expanded tenfold. When I finally sat down to type it out, it took a year instead of the three-to-six months I envisioned. From a simple little story about a motorcycle trip, it grew to an epic adventure.

By the way, it's still growing, with new titles coming soon. My wife says she likes the sequel even better.

I think you'll like it, too.

Graphing historic DJIA prices on a semi-log plot shows that our financial markets experience a price wave with a 20 year period superposed on a steady long-term growth trend.

Yesterday was the first day that the Dow Jones Industrial Average (DJIA) managed to close above 11,000 in a long time. It had been flirting with that level for almost a week, now, and had crossed that level several times intraday, but never held it through the close of trading.

The media, of course, made a significant bit of noise about it - enough that my wife asked me, after reading the headlines in the local newspaper, whether it really was a good thing. Now, my lady is quite bright (she's working on her second Master's degree), but, as a humanities major, her long suit is not the kind of quantitative analysis necessary to interpret what moves in various economic metrics, such as the DJIA, mean to actual people trying to get by.

"Is the Dow over 11,000 a good thing?" she asked.

"Yes, but it doesn't really mean much," I replied. "I predicted it'd spike over 11,000 a month or so ago, then slide back. But, things are pretty much on track."

Analysis I did last fall (see image above) indicates that the DJIA is just about exactly on its long-term track. It should be just peeking above 10,000 right about now. Since we've just experienced a short spike down (You do remember we've experienced a recession over the last year and a half, don't you?). We can expect an overshoot on the recovery, then settling back to the long term trend modified by a chaotic wave with a period of about 20 years.

In the future, we can expect to see a slow rise with a long term trend of zero to a few percent for about the next five years. The trend should steepen thereafter, reaching a maximum about 2020. In the meantime, expect the DJIA to be in a trading range between 9,000 and 11,000.

The important thing to understand is that the huge price swings that many of us capitalized on over the past 18 months are unlikely to repeat, barring exigencies. Since stock traders make money by cleverly exploiting stock volatility, they won't do quite as well as they have over the past 20 years. Expect the real money to be made by investing in dividend-paying stocks. Expect portfolio returns in the 5-15% per annum range to be the norm. A good model for this investing environment would be the rather boring period from about 1965 through 1980, when the DJIA stayed essentially flat, with only short term ups, and downs.

Sorry, folks.

Business cycles are driven by a macroeconomic feedback mechanism that has a multi-year cycle time. Employment is one of the last economic metrics to show recovery because the process starts with unmet demand for goods and services, and only ends with jobs.

In every economic downturn, Chicken-Little pundits squawk about how we can't have a sustainable recovery until employment figures show improvement. Any investor, and here I use the word "investor" in its broadest sense to include those who put resources to work, not just those who invest in stocks and bonds, who listens to this drivel is destined to fail, and fail disasterously.

Macroeconomics - the study of large-scale economic trends affecting an economy as a whole - is based feedback loops that drive business activity. These loops describe causal relationships between economic factors affecting business. For example, an increase in production levels generally pushes employment up. Each of these causal relationships involve a time delay. So, when production levels increase, especially from a depressed level, employment does not rise until production levels exceed capacity at the current employment level. This takes time, as does the process of hiring new employees.

These delays are what cause business cycles in the first place. If we use, say, buggywhip manufacture as a hypothetical example, we might say that it takes 18 months for the buggywhip business to respond to a sudden change in the overall demand for buggywhips. So, if New York City should pass a law banning motorized vehicles, so all the Yellow Cabs in the city had to be replaced by horse-drawn surries overnight, that would ratchet up demand for buggywhips. Because it takes 18 months for buggywhip manufacturers to respond, actual sales of buggywhips would not stabilize at a level reflecting the new demand until a year and a half later.

Business cycles occur because it is not possible for businesses to precisely meet demand. In the buggywhip example, assume that there are two buggywhip manufacturers in business at the time the New York law passes. They will both attempt to grab more than their fair share of the enormous new market. Part of driving sales is assuring customers that you can actually deliver the goods ordered. So, both manufacturers will expand production faster than necessary to just meet demand. In addition, during that first 18 months, it will be clear that the established manufacturers won't be able to meet demand. Outside entrepreneurs will see this as an opportunity to jump in to the expanding market, by starting rival buggywhip manufacturing operations.

The result is that some 18 months after the new law passes, worldwide buggywhip manufacturing capacity will greatly exceed demand. Inventories of unsold buggywhips will expand. Buggywhip prices will fall. Marginal buggywhip manufacturers will fail. Buggywhip production capacity will drop. By three years into the process, we'd be back to having inadequate production capacity to meet demand, and the whole thing would start over again.

Boom and bust cycles like that are not some aberration, or the result of faulty business strategies, or some market inefficiency that politicians can erase by passing laws, it's how things inevitably work. In fact, most complex systems, such as economies, consist of multiple such cycles that operate on multiple time scales. Basically, they're all chaotic systems, which is why long term charts of practically every economic indicator - from long-term jobs trends to prices for individual stocks - look like profiles of the Andes Mountains. They're all fractals, which is the pattern most often associated with chaotic systems.

Economic expansions, recessions, depressions, and recoveries are actually just business-cycle components. As any Taoist sage could tell you, whenever the economy is expanding, you know that a contraction is on its way. Similarly, a depression always presages a recovery. It's inevitable. The Great Depression of the 1930s was, when looked at from a longer perspective, just a particularly deep bottom of the overall business cycle. The huge expansion we experienced during the 1990s was, conversely, a particularly robust phase of the overall business cycle.

This latest contraction, which started about 2005, and will probably not completely play out until 2015, was another particularly nasty dip in the more or less regular cycle. It's as inevitable as the tide.

So, getting back to jobs data, and the usual panicky predictions of a so-called "jobless recovery," the reason employment data have not significantly improved is that it's just too early in the process for it to show up. Those who ask: "How can sales recover when employment is down?" simply don't understand how the business cycle works. Sales aren't driven by jobs, it's the other way around, with a significant time lag between.

Jobs are driven by production requirements. As any industrial engineer could tell you, production is driven by inventories, not by demand. Demand is an intangible that is very difficult to predict or measure. Inventory levels, on the other hand, are easily measured and better reflect a company's ability to sell the products it makes.

In the real business world, the first thing to recover after a recession is demand. It begins to recover when end users have had their belts cinched so tight for so long, that they have no choice but to by new stuff. Demand for food starts to rise, for example, when pantries start to look bare. It makes no difference whether the family bread-winner has a job or not, when there's nothing for dinner, somebody makes a run to the store. Even if you have to beg a cup of sugar from the neighbors, that sends the neighbors off to the store for more sugar, increasing the demand for sugar. Therein lies the disconnect between jobs and demand.

Demand seems to have hit bottom about six months ago. Since then, we've been working off inventory that built up at the start of the downturn, when production still exceeded demand. Next, production has to rise (pulled by further increases in demand) until it exceeds capacity at the present depressed employment levels. Only then will employment figures begin to rise.

Don't look for employment metrics to turn up until at least the end of the first quarter 2010. The reason it hasn't happened yet is that it's just too darn early.

With the global economy generally in recovery mode, nearly half of respondents to a survey conducted by Control Engineering magazine in partnership with Morgan Stanley expect sales of industrial automation equipment to increase in 2010 Source: Control Engineering.

Over the next week or so, I hope to share with you results of studies pointing the directions we can expect technology trends likely will take next year, and in the decade ahead. The good news for Americans, and for many national economies around the world, is that the recovery is exactly on track. Yammering about "jobless recovery" and doubts over the U.S. economy's ability to expand until full employment returns simply demonstrate the commentators' ignorance of how economies work.

Garden variety depressions, which is what we've experienced over the past five years, take many years to play out. Calendar year 2008 saw the acute contraction phase, but things had been unraveling since late 2005. After a contraction, comes a bottoming, followed by an expansion phase.

Economic recoveries - that is the bottoming and expansion phases of a dip in economic activity - start with stock markets, which anticipate the turn around in general economic conditions by some months. The reason stock markets anticipate recoveries is that investment professionals, unlike media commentators, do understand economics, and recognize harbingers of business improvement long before the improvement happens. Just as meteorologists know that when days start getting longer, Spring is just a few months away, investors know that economic harbingers, such as inventory levels stabilizing at high levels, pre-announce changes in economic trends by several months, and stock prices rise as these investors put themselves in a position to capitalize on the new trend.

After stock prices hit bottom and begin to rise, we start seeing signs that the downward pressure on business activity begins to ease off. High inventory levels, for example, begin to drop. Productivity begins to rise as businesses streamline to cut costs. Later, these more efficient businesses begin reporting better than anticipated earnings on still-falling revenue. Still months later, revenues begin to rise as individuals and businesses can no longer put off purchases that have been delayed since the beginning of the downturn. More months later, employment figures, which conventional wisdom seems to think should lead the recovery despite the fact that it never happens, begin to recover as the productivity gains of a few months ago prove insufficient to meet the growing demand for goods and services. Finally, very late in the recovery, large capital investments, such as in real estate, reach their bottoms and start to recover.

At present, the U.S. economy, as well as that of most of the world, is recovering nicely. Trends in measures like corporate earnings are showing the correct patterns in the correct order and with the anticipated timing. Even the jobless numbers are tracking exactly as they're supposed to. Back at the end of 2008, when the depth of the dip became apparent, knowledgeable pundits were able to predict that the unemployment rate would reach just above 10%, which is just what it did, and begin to recover in late 2009, which it also has done.

By the way, don't listen to all that emotional drivel about some fictional "real" unemployment rate being something like 18% instead of the published 10% level. "The unemployment rate" is a real, clearly defined metric that we use to compare one time period with another. The "real unemployment rate" that Chicken-Little types yammer on about is poorly defined and very difficult to measure, so it's useless as an economic metric. It's only use is to give fear merchants something to shoot their mouths off about to their poorly educated audiences.

One extremely useful metric that can provide prescience about general industrial trends is expectations among industrial automation buyers and sellers about their purchases and sales (respectively) in the coming year.

To determine whether the market for industrial automation equipment was beginning to ascend from the depths of this latest downturn, or were destined to remain mired in the muck at the bottom of the pit for awhile longer, our friends at Control Engineering magazine in partnership with analysts at financial services leader Morgan Stanley surveyed participants in the industrial automation market. The reason to look especially at sentiment in this market is that factory automation is arguably the most important trend in industrial technology of the late 20th and early 21st Centuries.

Early in the 20th Century, factory automation was generally non-existent. We (or more accurately, our ancestors) simply did not have the tools available to automate production facilities in any meaningful way.

By the middle of the 21st Century, on the other hand, we anticipate that factories will run essentially fully automatically. That is, there will be no production tasks that are not done by automated machinery. Humans will generally hold supervisory positions. There will be CEOs, managers, engineers, maintenance technicians, and such like, but the population of assembly line workers, for example, will drop to more or less nil.

So, unlike the situation a few decades ago, perhaps the best measure of industrial activity available at the start of the second decade of this century is the level of activity in the industrial automation sector. That is what the survey set out to study, and that is why it's the first thing we looking at as we peer into our crystal ball.

"I'm happy to report that the survey does, indeed, offer more than few rays of hope," wrote David Greenfield, Control Engineering's editorial director, when reporting the survey findings in his article entitled 2010 Global Automation Industry Outlook. "Overall, the findings appear to indicate that a bottom in the market has been reached, pricing is holding firm, and that customers remain loyal - all positive signs for global automation players."

Greenfield cited four key findings of the survey:

1. The automation market has already bottomed; modest growth will return in 2010;

2. There is no evidence of a price war in automation equipment;

3. There is limited differentiation between the spending outlooks for process versus discrete industries;

4. While highly cyclical, automation is a good business to invest in over the long term.

It is important to note that the second finding belies the fear that inflation might be a an immediate threat. Despite concerns over accommodative monetary policies around the world, this survey shows no sign of inflation's return in the immediate future. It's axiomatic that for inflation to appear, prices must rise. This survey of a significant sector of the economy shows no hint of rapidly rising prices.

Greenfield pointed out that the near-term trend in demand for automation equipment appears brighter than it did in early in 2009 because of the percentage of respondents expecting demand to increase, more budgets going up or staying level versus retreating, and increasing demand to replace aging equipment. In addition, pricing appears to be stabilizing in the near term. Few respondents expect to see prices fall, but neither are they expecting out-of-the-ordinary upward price moves by suppliers to help offset losses in the past year.

These results are exactly what we would expect at this stage of the present economic recovery. Pundits prophesying a double dip, an L-shaped recovery, or any similar pattern find no support for their views in this important economic indicator.

The Mandelbrot set is the most famous illustration of a fractal in two dimensions. The same mathematics can be used in one dimension to describe financial market price activity. Source: Wikipedia

The other day I caught part of an interview with a spokesperson for Elliott Wave International on Bloomberg TV. Elliott Wave International is an organization devoted to promoting the use of a market-analysis technique developed in the 1930s by Ralph Nelson Elliott, called the wave principle. The most important thing to come out of the interview, to my mind, was an off-hand comment by the spokesperson that stock-price movement "is a fractal."

A fractal is a mathematical waveform with a texture that is of the same form at all spatial-frequency scales. For example, we've all seen those neat illustrations hung in hotel lobbies and meeting rooms that include what appear to be strikingly realistic silhouettes of mountain ranges. Artists make those illustrations by simply tearing a piece of construction paper, and airbrushing past the torn edge. Since construction-paper tears and mountain-range silhouettes are both fractals, the torn paper edge bears an uncanny resemblance to a generic mountain range.

The most famous fractal illustration, and the one mathematicians use most often to illustrate the properties of fractals, is the Mandelbrot set. I think people like to use it as an illustration because it's amazingly beautiful. It also has the advantage of clearly illustrating fractal properties. Unfortunately, it has no practical value, so using it to illustrate fractals leaves the impression that fractals have no practical value.

Nothing could be further from the truth.

Like all fractals, the Mandelbrot set repeats itself on all length scales. It thus displays the property of self similarity. Similarly, if you take a typical stock-price chart covering a time span of, say, one month, and compare it to an intraday chart, the two will look remarkably similar, and will also bear a striking resemblance to the mountain-range silhouette, and the torn paper. They're all fractals.

Self-similar charts, such as stock price charts, all have two spatial frequency properties: they're combinations of "tones" at a wide range of spatial frequencies, and the amplitude of any one tone is inversely proportional to its spatial frequency. Tones at lower spatial frequencies - so they take up more of the chart horizontally - also have proportionally higher amplitudes - so they take up more of the chart vertically.

Comparison of stock-price charts covering a wide range of time spans shows that they also show self similarity, and are therefore fractals. Financial markets exhibit this behavior because they are chaotic systems. That is, there are a large number of independent actors making buy/sell decisions based on different interpretations of the same enormous, but incomplete, data set. Each actor moves the price of an individual financial instrument slightly, but none has enough pricing power to exercise significant control alone. Everyone has an effect, but nobody has a controlling effect.

That is what drives fundamental stock analysts bonkers. Each property of the underlying company, each news item about the company, each pronouncement by a politician, has an affect - often a noticeable effect - but none of them has a determining effect. There's just too much else going on that also has an effect.

It is important to recognize that there are varying degrees of self-similarity. The Mandelbrot set displays the strongest type of self-similarity (exact self-similarity), meaning that it appears exactly the same at all spatial scales. Stock prices, on the other hand, display the weakest type of self-similarity (statistical self-similarity), meaning that the figure has numerical or statistical measures that are preserved over different spatial scales. The fact that stock charts have only statistical self-similarity is what makes stock-price movements inherently unpredictable.

Because Elliott wave analysis starts from the observation that charts of financial-market prices are fractals, the wave principle, out of all technical analysis tools, has the best hope of making sense of financial-market price movements. Elliott's original work was hampered by the fact that understanding of fractals was limited in the 1930s, and understanding of chaotic systems was effectively nil until the 1960s. Some misunderstandings, such as the representation of waves as triangular, rather than sinusoidal, still persist, but anyone interested in learning technical analysis could do a lot worse than beginning with Elliott wave analysis.

Figure 1: Financial markets exhibit variations at all time scales with amplitude proportional to period. Based on data from stockcharts.com.

Yesterday kicked off the first day of the NIWeek 2009. This is the annual National Instruments user group meeting. NI to followers of this company, makes hardware and software for data acquisition and control applications. For general engineering and research laboratory software, the company's LabVIEW product enjoys a dominant position. The way the company has built and maintains this position is by working hard to anticipate major technology trends that will affect their users, and what their executives learn from this ongoing exercise comes out in comments during keynote speeches at NIWeek.

During NI Chairman James Truchard's opening comments this year, he expressed the opinion that some of the economic difficulties we have experienced globally over the past year can be traced to inappropriate use of mathematics in finance

"... Mathematics, the way it has been taught in business schools the last few years," he said, "has a serious problem ..."

Dr T, as he is known to his friends in the industry, also trotted out the oft-quoted comment by Warren Buffet: "It seems like the higher mathematics, with more false precision, should help you, but it doesn't."

This reminded me of a couple of themes I've been studying over the past few years chaos and fuzzy logic as they apply to things in general, and financial markets in particular. These are surprisingly easy to understand, easy to explain, and universal in application.

Chaos has bad connotations in western philosophy, which guides most of what we do for better or worse. In western thought, chaos is the enemy of order, and one of western civilization's stated goals is maintaining order. Eastern philosophy, on the other hand, seeks to balance the "forces" of yin and yang. These are complex concepts, but the important point for this discussion is that, among other things, yin is an order-seeking force, and yang is a chaos-seeking force. The theory is that both yin and yang are necessary for good health, and specifically must be maintained in an dynamic balance.

Mathematically, chaotic systems can most easily recognized by being in constant motion with cycles represented at all time scales, and the amplitudes of these motions are roughly proportional to their periods. Thus, weekly fluctuations tend to have an amplitude 5-10 times larger than daily fluctuations, with the largest amplitude movements appearing on decades-long time scales.

What makes chaos important is that it can be shown to govern the operation of virtually every complex system, from ocean waves to all human history. If it's a large dynamic system with lots of independently moving parts, its behavior most likely is chaotic.

Fuzzy logic, on the other hand, seems unmathematical for a different reason. The term sounds like a synonym for "fuzzy thinking," which people associate with foolishness and ignorance. Mathematically, however, fuzzy logic is a way of dealing logically with things that cannot be precisely quantified.

Humans, in fact, make nearly all personal decisions - especially life-and-death decisions - using fuzzy logic.

When you merge your car into a busy traffic lane, for example, you don't get out a radar gun to precisely measure the speeds of vehicles already in the lane, then analyze video images of the spacings between cars, then do a calculation based on the measured data plus known performance of your vehicle at various settings of throttle and steering controls. You don't have the equipment available to make the measurements, and you wouldn't have time available to make the calculations if you did.

Instead, your brain converts sense impressions and memories from previous encounters into more-or-less broad categories. Spaces between other cars, for example, are "tight," "okay," or "really big." Their speeds are "slow," "medium," or "fast." Your car "accelerates fast," "moves out okay," or "handles like a dead whale." Instead of a dozens of variables with 12-bit precision, your brain reduces the data set to a handful of variables with at most two-bit precision. It also doesn't use arithmetic to work out the answer, but a fuzzy logic algorithm that resembles a decision tree. The answer comes back as "it's safe - go for it;" "gee, it's borderline," or "that looks like a trip to the emergency ward." The final fuzzy calculation says "no" in the last instance, causes you to look further in the middle instance, and spurs you into action in the first instance.

That's fuzzy logic. It's the fastest and most reliable way to make difficult decisions when time is limited (as it always is). It's also virtually the only way to deal with chaotic systems, which, as I said above, almost all real systems are.

Probably (in a fuzzy-logic sense) the way to solve the problem Truchard and Buffet identified, is by mentally taking chaos and fuzzy logic out of the "advanced mathematics" cubbyhole, and teach it to high-school freshman. If we did that, we'd quickly have a generation with the mental tools needed to analyze and deal with the real world, rather than a hypothetical world of mathematical abstractions.

Some media analysts have admitted to being confused by the fact that companies engaged in the personal computer business, such as Dell and Microsoft, have recently published less-than-stellar financial results and gloomy guidance for the future, while other companies, such as Intel and Apple, are fairly jumping with glee over future prospects. This seeming paradox evaporates, however, as soon as one realizes that the vast majority of computers aren't PCs, anymore.

I talked about one aspect of this phenomenon in this blog's last entry ("The PC as Dodo"). In today's entry, I'll talk about a second trend: embedded systems technology. I've mentioned embedded systems before in this blog, but today I want to get a little deeper into the guts of the things to show how this trend affects so many technology companies so differently.

Embedded systems, as Figure 1 shows, generally embody a control loop where a microcontroller reads signals from sensors attached to some equipment out in the real world (IRL). Based on those sensor readings, the microcontroller calculates some changes it wants to make IRL to control the equipment. The equipment responds to these changing signals, which changes the sensor readings.

Figure 1: Embedded systems include a control loop governed by a microcontroller.

What makes the system a control loop, rather than the proverbial snake swallowing its tail, is the fact that there is a control input, called a set point to which the controller compares the sensor inputs. The controller bases its output signals on how the actual readings from the sensors compare to the set point. In actual fact, there may be several sensors and several set points, and the controller likely will take into account how the sensor inputs are changing with time as well as their instantaneous values. People can select how they want the system to behave by changing the set points.

The classic embedded system that everyone uses as an example is a digital thermostat. This system has one sensor (a temperature sensor sampling the room air), one IRL equipment unit (a heater or air conditioner), and one controller (the digital thermostat). You control the temperature you want to have in the room by changing the temperature set point. Almost any digital thermostat worth its price will also include a time sensor (a clock) that allows you to program different temperature set points depending on the time of day.

What makes this technology important is the fact that embedded systems are now used to control just about every device we have. In the past, I've commented that microcontrollers now run just about every device more complicated than a lead pencil. That may be an exaggeration, but not much of one. To paraphrase the announcer from the old "Chickenman" radio show: "They're everywhere! They're everywhere!"

(If you don't know about Chickenman, you missed one of the great campy entertainment experiences of the mid-1960s. Episodes from the original series and two resurrections are still available for purchase on the Internet.)

Figure 2: Microcontrollers include a microprocessor, memory and I/O circuits on a single chip.

The heart of an embedded system is that little microcontroller. Figure 2 shows what's inside a typical microcontroller. It's a monolithic integrated circuit (IC) that has a microprocessor, multiple types of memory, including read-only memory (ROM), random-access memory (RAM) similar to what you see in a PC, along with a programmable read-only memory that holds the software that the microprocessor needs to run, along with several types of input/output circuits to take care of reading sensors, driving actuators, and communicating with the outside world. Many microcontrollers even have microscopic radio sets to communicate wirelessly with other systems.

What sets these things apart is that, unlike the components of a personal computer, all of this circuitry is crammed into one tiny chip. As anyone who's seen a PC with the covers off knows, the PC architecture has its circuitry spread around on a number of ICs. That takes up a lot of space, adds weight, and makes the whole thing bulky. One characteristic that embedded systems, from experimental nanobots to cellphones to television set-top boxes, share is the need to have their controllers as tiny and as light as possible.

Now, the semiconductor companies that make chips for PCs also make chips for embedded systems. The companies that use these chips in their products are more-or-less traditional industrial companies that make dishwashers, microwave ovens, cars, cellphones, etc.

The software these microcontrollers run is not the same as the software PCs run, either. Instead of operating systems like Windows Vista, or Apple Mac OS, they run things like LynxOS, QNX, and VxWorks that most people have never heard of.

In the world of computer technology, embedded systems are where the action is. PCs, for all their historical significance and public share of mind, are a small part of the market with lackluster (at best) growth prospects.

So, companies involved in the embedded system business, such as Intel and Apple, report spectacular profits and predict stellar growth prospects. Companies whose businesses depend on the PC industry complain of shrinking markets and poor future prospects.

The media is painting it as a wrestling match between giants: Google vs. Microsoft. Operating system king Microsoft recently introduced a new Bing! browser, followed last night by search engine titan Google's announcement that it's working on an operating system for netbooks.

As usual, the mass media have somewhat missed the mark. What's actually happening is the whole landscape of computing is changing, and a race is on to see who's going to plant their flag on the new territory first.

The change in computing is the steady migration of computer technology from a thick client model to a thin client model for most routine computing needs. If you haven't yet heard about this, yet, let me explain:

Thick Clients are powerful stand-alone computers with network access. To do something useful, you download the file you want to do it to from a server; do it; then upload the file to the server again, keeping (or not) a copy of the updated file on your local computer.

Thin Clients are computers with powerful communications and display capabilities, but which are otherwise pretty anemic by conventional computer-performance standards. To do something useful, you visit an extremely powerful server, which is actually a supercomputer based on cloud-computing architecture (see "Computing With Your Head in the Clouds"). This server creates a virtual computer (See "Virtualization flies under the mass-media radar") with enough resources to run an application program (which it preloads onto the virtual computer) to do whatever it is you want to do with the file (which is stored somewhere in the computing cloud). When you're done doing your thing, the server updates the file and dissolves the virtual computer into - nothing.

Thin clients have been around for a long time. The old time-shared computer terminals we used in the 1970s to access minicomputers were very much like today's thin clients, which you know as netbooks.

The term was coined in the early 1990s by Tim Negris, VP of Server Marketing at Oracle Corp. The technology has been growing in popularity and usefulness ever since. Expect in the future (probably less than 5 years) that this style of computing will be almost universal, with everything from mobile devices to home entertainment centers architected as thin clients allowing users to interface over the Internet with service providers, such as banks, online stores, news providers, and entertainment content providers. I'm already writing this blog entry using exactly this technology!

Don't invest in companies that make personal computers.

So, how does the Google vs. Microsoft struggle fit into this landscape? They both see it coming and want to provide you with the means to partake of its bounties. The problem is that they have competition. All the makers of mobile devices, household appliances, TV set-top-boxes, telecommunications suppliers, and virtually anyone who makes anything with even the potential for Internet connectivity sees it coming, too. Especially, all the Internet service providers building all the computer clouds see it coming. Google and Microsoft are really just struggling to avoid being left behind!

Google does have one advantage, at least relative to Microsoft. Google is wisely basing its Chrome OS on Linux, which is the Open Source leader. To develop application software in a Linux-based thin-client environment, a company can hire a few pimply-faced ex-hackers who learned to roll their own Linux distribution before they reached puberty. Software engineers with expertise in the latest of the never-ending stream of Windows versions are harder to come by.

Basically, the days when anybody cares what operating system or browser your Internet-connected device uses are gone. In the thin-client/cloud-computing world of the future, like in the post-Civil-War land of Gone With the Wind, frankly, my dear, nobody is going to give a damn.

And, you thought the infrastructure-spending portion of the Obama Administration's Stimulus Plan was a waste of time and money. CG/LA Infrastructure LLC means to dispel that idea. One way to do so is to identify the top 100 infrastructure projects now in the works, and publish the list, which they've now done.

The Top 100 US Strategic Infrastructure Projects is available as a free download. Readers interested in this topic might want to register for the the North America Strategic Infrastructure Leadership Forum to be held September 22 - 24, 2009 at the Omni Shoreham Hotel in Washington, DC.

"These projects will form the backbone for a new, competitive US economy and breathe life into the Obama government's vision going forward," according to Norman F. Anderson, President and CEO of CG/LA. Led by both the Obama Administration's commitment to improving the nation's faltering infrastructure stock and by a regional drive towards carbon-neutral energy and productive infrastructure, the North American Leadership Forum will host not only the top projects in the US, but also the leading projects in Canada and Mexico. "The US economy is in trouble, and these projects define a powerfully competitive, critical path forward," says Anderson.

The Top 100 projects were identified as possessing three specific criteria: (1) strong probability of going forward in the next 12 months; (2) critical as building blocks for US competitiveness; and (3) strong relevance to the Obama government's 'connect the dots' infrastructure priorities.

The organization separated projects into three classes:

Smart Grid projects will provide an upgraded power distribution system. It is best understood as the operating system for the new economy, and is what Warren Buffet calls "the single most important investment in the US economy." Fourteen of the 100 projects are tied to the Smart Grid, either directly or through the projects that the Grid enables, including: 6 transmission projects ($25.1 billion), lead by the Midwest's Green Power Express project; and 8 renewable energy projects ($15.3 billion), including wind, solar and energy efficiency, the largest of which is T. Boone Picken's Pampa project.

New Infrastructure will provide "building blocks" for finance and physical capacity creation to drive a globally competitive US economy. These projects are largely "carbon-neutral" and will include 6 high-speed passenger rail projects ($109.4 billion, the largest spend by far on our list), lead by the San Francisco/Los Angeles and Midwest Rail Initiative; and 18 urban mass transit projects ($44.4 billion) including Michigan's Regional Rail Link and Northern Virginia's Dulles Access Corridor project. The visionary $10 billion electric freight rail initiative would also fall into this category.

Traditional Infrastructure projects will rebuild what we normally think of as infrastructure; the physical structures created 50 years ago that have allowed our economy to be competitive and have created opportunities for Americans over the last half-century. These projects were selected based on their ability to renew that competitiveness, including: 17 project in surface transportation ($58.3 billion); 7 projects in ports & logistics ($5 billion); 4 projects in traditional electricity generation ($21.4 billion); 9 projects in natural gas, including pipelines, LNG terminals and exploration ($55.1 billion); and 14 projects in the 'forgotten' infrastructure of water/wastewater ($19 billion).

It is said that roughly 2 million new jobs would be created each year from 2010 through 2014, directly and indirectly, through the development of these 100 strategic infrastructure projects.

Technical market analysis applies mathematical analysis of patterns in 2-D data sets to stock market (actually, any financial market) buy, sell, and hold decisions. The two dimensions are, of course, market price and time. This entry looks at one reason such data sets should be viewed as chaotic systems.

You've all heard that the so-called Butterfly Effect is a characteristic of chaotic systems. It is the fact that small, seeming unrelated happenings can have major effects on results in a chaotic system. The classic version says that a butterfly flapping its wings in China (or some other remote location) can cause a hurricane to hit South Florida.

Since few of us have a real feel for how global weather patterns develop, we all tend to say: "Yeah, yeah. Blah, blah, blah. I've heard it before, and sure it might be real to weather patterns, but it's not real to me."

This story, however, illustrates in a way we all can relate to how the Butterfly Effect works in another chaotic system -- financial markets of all kinds. I offer it up to help unbelievers understand that these markets are truly chaotic. How chaos rules stock market price movements, and how to understand its effects, is mathematically complex, but the starting point is to accept that accurate financial market analysis is impossible without using chaos theory.

That's not to say technical market analysis based on chaos theory tells the whole story. The full development shows that fundamental stock analysis is necessary to understand the forces driving stock-price movements, which technical analysis only describes.

This story depicts characters and events that are fictitious. Any similarities.... You know the rest. That the story depicts how real people and events interrelate you can judge for yourself.

In 1992, Xin Hua was a wildlife photographer in China on assignment as part of a Peoples Republic grant to Professor Yau Khan of Beijing University to study insect flight dynamics for the purpose of improving the performance of high-speed aircraft. Prof. Yau wanted to obtain high-speed video of butterflies in free flight, rather than tethered in a wind tunnel.

Such free-flight photography is difficult because, unlike the situation in a wind tunnel where the insect is constrained to be stationary in the vision system's field of view, the insect is free to move in three dimensions, while the camera must follow it. For this reason, Prof. Yao hired Xin, reputed to be the best photographer in China for this type of assignment, rather than try to do it himself or assign it to a graduate student.

Xin spent weeks trying to obtain a few minutes of film that would meet Prof. Yau's specifications. The weeks turned into months as Xin filled dozens of CDs with hours of clips showing hundreds of different butterflies flitting across fields in rural China. Finally, he had an hour or so of video that could be used in Prof. Yau's study, along with days worth of video that was beautiful, but not quite what Yau needed.

Being one of China's hungry young entrepreneurs, Xin negotiated a contract with Beijing University and the People's Republic Central Committee that would allow him to market his excess butterfly video worldwide through stock photography service Corbis. Xin shared the royalties with Beijing University.  The Central Committee was pleased to have economic stimulus.

Akira Matsumori was an advertising executive at a firm in Tokyo, Japan. One of his firm's client's was the Japanese Tourism Bureau, and, in early 1994, he was assigned to create a wonderful new ad campaign to promote the Cherry Blossom festival to potential tourists throughout the world. Matsumori developed a campaign composed of a number of ads that all climaxed with a butterfly landing on a cherry blossom.

There was no way he was ever going to obtain actual footage of a butterfly flying in from the left to land on a cherry blossom located on the right side of the screen, with all of the action limited to the bottom third of the frame, so he hired the American film graphics company Industrial Light and Magic to create a lifelike sequence using CGI technology.

To ensure that the butterfly flight was realistic, ILM artists purchased stock video from Corbis showing slow-motion butterfly free flight. The video they studied was, of course, Xin Hua's, because it was practically the only slow-motion close-up video of butterflies in free flight existent.

From Xin's video, the ILM artists developed models of how a butterfly's wings would move if it were flying horizontally from the left and landing on a cherry blossom on the right. They then used thier models to render a butterfly animation to match Matsumori's art director's specifications. They gave the butterfly a dramatic wing coloration pattern based on stock photos of North American Monarch butterflies, with black borders and an arresting gold-color fill.

By the Fall of 1994, the Matsumori's ad campaign was ready to roll out worldwide to generate interest for the 1995 Cherry Blossom Festival in late Spring. By January of 1995, the ads were blanketing television channels all over North America, from Mexico to Canada.

Maria Delgado was a travel agent in Tijuana, Mexico. Her husband, Manuel, had a highly successful leather-goods store catering to tourists. Part of Manuel's sales strategy was to mark relatively high prices on his goods, so that he could give deep "discounts" during protracted negotiations with customers. This was lots of fun for the tourists, and gave them a sense of accomplishment by negotiating the deep discounts. Manuel's shop did very well.

Manuel brought in a large stock of handbags, boots, leather jackets, and pants to sell during the annual motorcycle run starting from downtown San Diego, California and ending in a big parade along Tijuana's main thoroughfare. It was to be a huge tourist marketing event, and Manuel's shop was busting at the seams!

The week before the motorcycle run, Maria saw Matsumori's Cherry Blossom Festival advertisement. Being in the travel business, she had arranged vacations for hundreds of tourists, but, being an aggressive Mexican entrepreneur like her husband, she had never taken a vacation herself. Matsumori's ad made the travel bug bite her hard!

She loved the exotic scenes of Japanese landscapes. She wanted to go shopping in the Ginza. She wanted to visit Buddhist temples. Especially, she absolutely fell head over heels in love with ILM's CGI animated butterfly. It was so lifelike and moved so beautifully.

Making an instant decision, she threw caution to the winds and negotiated a practically free flight for her, Manuel, and the children to go for a month-long tour of Japan during Cherry Blossom time. With her travel-business connections, she obtained cut-rate accommodations near all the great Japanese tourist attractions. It was going to be a huge surprise for Manuel when she told him at dinner that night.

Oh, it was a surprise alright. In fact, it was more of a shock. "We can't do that! My shop is full to bursting with stuff for the motorcycle run as well as the stuff for the rest of the Summer. I can't just walk away and leave it for a month."

"We can afford it," countered Maria, "We've been doing very well. We can afford to take a month off for the children to see Japan before they grow up. Besides, I can't cancel all these reservations. I had to put down deposits and everything."

"But, all our cash is tied up with this stock."

"You'll just have to sell it during the motorcycle run. Clear the store out , and we'll have cash to enjoy our trip."

During the mid-1990s, my wife, Bonnie, and I were living in western Arizona, and we attended the Tijuana Run every year with friends from the local motorcycle repair shop across the border in Needles, California. In 1995, I'd just sold a struggling magazine I'd started and been trying to keep afloat. For the first time in a couple of years, we had a little money in our pockets.

After the motorcycle run and the parade, we settled down for some serious shopping. Bonnie was still carrying around the beige handbag I'd bought her several years before. It certainly didn't go with her black leather motorcycling outfit, and was well past its prime, anyway. So, she was in the market for a replacement that would be more appropriate. She found it in Manuel's shop.

Now, I never really enjoyed the kind of aggressive negotiations Manuel specialized in. I don't much like confrontation, and don't much care whether a pocketbook costs $30 or $50 as long as I have the cash and think it's not a ripoff. Bonnie, however, likes negotiating a bargain. So, I always let her do the deals.

"You pick out anything you want, and get the price you want. I'll pay for it," is what I told her.

Because Manuel's travel-agent wife had fallen in love with an animated butterfly based on a real butterfly photographed by a Chinese wildlife photographer, the market price for that handbag in that shop at that time on that morning was $15 -- well below the $45 I'd have expected to pay for it in the States.

Notice that the handbag did have an intrinsic value based on the cost of the materials, labor, energy to make it and transport it to Manuel's shop, along with carrying costs Manuel incurred by keeping it in the store, and having a store to keep it in. That intrinsic value is the value a financial accountant would arrive at by fundamental analysis.

It is not, however, the market value. The market value is simply set by two parties negotiating one sale. It's based on a host of factors including, but not limited to, the two parties' ability to negotiate, their desire to make the sale, and the amount of coffee they'd drunk within one hour of starting the negotiations.The intrinsic value of the the item is only one factor among many.

In this case, the flapping of the Chinese butterfly's wings had a profound affect on the two parties relative willingness to make a deal, which had a major affect on the price arrived at.

The take home lesson is that hundreds of such seemingly unrelated events affect each and every sale of a financial instrument, whether the deal is struck between amateurs, professionals or 10 year olds swapping baseball cards. That fact makes stock markets -- indeed all markets -- chaotic systems.