This book originated from a training course for engineers at the research and development center of TELEBRAS, the former Brazilian telecommunications holding. That course was taught by the first author back in 1987, and its main goal was to present efficient digital filter design methods suitable for solving some of their engineering problems. Later on, this original text was used by the first author as the basic reference for the digital filters and digital signal processing courses of the Electrical Engineering Program at COPPE/Federal University of Rio de Janeiro.

For many years, former students asked why the original text was not transformed into a book, as it presented a very distinct view that they considered worth publishing. Among the numerous reasons not to attempt such task, we could mention that there were already a good number of well-written texts on the subject; also, after many years of teaching and researching on this topic, it seemed more interesting to follow other paths than the painful one of writing a book; finally, the original text was written in Portuguese and a mere translation of it into English would be a very tedious task.

In later years, the second and third authors, who had attended the signal processing courses using the original material, were continuously giving new ideas on how to proceed. That was when we decided to go through the task of completing and updating the original text, turning it into a modern textbook.

Combine the name of any technology company with “platform” or “ecosystem” in the search engine of your choice and you will get a sense of the maturity and scope of that company's product, platform, and ecosystem strategies. Even early-stage technology companies that don't have much of a strategy beyond issuing press releases are eager to announce an ecosystem partnership with a market leader in their industry. An August 2009 search for “Microsoft ecosystem” on the Bing search engine retrieved 6.4 million results. Most high-tech companies fall somewhere in-between Microsoft and new entrants in the scope of their ecosystem strategies. Like the concept of shaping a corporate value chain to create competitive advantage, an enterprise ecosystem has become a fundamental element in technology product development and distribution.

Applying product platform strategies to computer component design and software development gained favor in the 1990s as a way to keep up with rapid-fire technological advances and accelerating software-update cycles. Platform strategies allowed technology companies to attract more developers and component partners, to create more coherent product roadmaps, and to assure their technical and channel partners that the next generation of products would complement current development and sales plans and be compatible with their growing base of customer installations (McGrath, 1995; Salonen, 2004).

There are many e-commerce success stories in the United States, but consumer privacy protection on the Internet is not one of them. Every click on a website triggers a continuous flow of information reporting, from the navigational path that visitors follow, to the topics that they search and the time that they spend viewing various content. All online actions can be logged and then aggregated with data regarding an individual's actions on previous visits. Aggregated data may also include details about registration on one or more websites, subscriptions to online services, purchases, preferences, and other information that has been collected by marketing affiliates and partners. The popularity of online advertising is driving the mining of ever more detailed consumer information in the quest to monetize websites and deliver highly targeted advertisements and offers. In the past decade this quest has created an internet interactive advertising ecosystem that a 2009 study by the Interactive Advertising Bureau (IAB) estimated was responsible for $300 billion in total economic activity in the USA alone (IAB, 2009).

In an environment where tracking and collecting behavioral data is so closely associated with revenue generation, attempts to protect privacy on the Internet through industry self-regulation and voluntary adherence to privacy best practices have been largely unsuccessful. In 1994 the Federal Trade Commission (FTC) filed its first privacy violation case against an Internet company (Swindle, 1999).

Business plan competitions are popular again in the USA as are incubator programs for early stage start-up companies. To help participants prepare for the rigors of real-world meetings with venture capital investors, incubators often feature a session where fledgling companies can practice and perfect their fund-raising pitch. The theory is that start-up teams will benefit from the candid assessment and advice of the seasoned industry veterans who typically serve as mentors and judges at these events.

In the spring of 2009 a start-up management team took the floor at one such incubator to present its plan for launching a home networking company that would integrate various smart entertainment devices, appliances, energy monitors, and PCs. The team presented ample evidence of unmet market demand and unsolved technical challenges in current home networking platforms. Instead of friendly advice, however, this team received a chorus of negative feedback about its plans. According to the panel of mentors the home networking space was a guaranteed death trap for any small technology company. The skepticism was so pervasive that management eventually shifted the company's focus to a product with a different value proposition. In aspiring to become the lynchpin of home networking, however, the start-up team was undeniably following in some well-trodden and notable technical footsteps.

Samsung trademarked the phrase “Home Wide Web” (HWW) in 1998 for its widely publicized technology platform for connecting PCs, TV sets, remote controls, modems, VCRs, and cable boxes using a standard called IP (Internet Protocol) over IEEE 1394 (Samsung, 1998).

The machines of the world may not be smarter than human beings, at least not yet, but they certainly have us outnumbered. An estimated fifty billion machines are already at work around the globe, compared to the world population of 6.7 billion people in 2009 (M2M Magazine, 2008). Population growth in most developed countries is slowing, but billions of new microcontrollers and machines go into operation every year. Connecting those machines to each other and to a network of wireless modules and sensors is the business of M2M (machine to machine), a well-established industry with enormous global growth potential.

Until quite recently the M2M sector has focused on solutions for the government and for enterprise customers in security-conscious and infrastructure-dependent industries such as energy resource management, manufacturing, transportation, and logistics. With the proliferation of smart products and connected devices in homes, automobiles, and the pockets of more than four billion cellphone subscribers, some M2M vendors and wireless carriers are now eyeing the consumer market as part of a strategy to rebrand M2M as smart services. This chapter analyzes the evolution of enterprise smart services from a foundation of M2M systems and the challenge of transplanting a technology and service culture designed for industrial customers and machines into the consumer environment. It describes the benefits that enterprise managers expect from M2M implementations and contrasts these benefits with the requirements for creating a consumer-oriented value proposition for connected smart services.

Smart products are here to stay. Some will enable ground-breaking business innovations, catapulting the companies that launch them into leadership in enormously profitable new market sectors. Others will disrupt traditional market leaders and open industry sectors to a rush of new entrants. A significant number will fail to attract any consumer following and fade from view. Whether or not a company aspires to create a transformational and market-leading new consumer solution, wants to avoid being blindsided by unexpected competition, or needs to understand the impact of embedded product intelligence for its industry and technology roadmap, smart products will play a prominent role in their future strategic options.

There is no guaranteed formula for smart product success, but there are a lot of predictable pitfalls that managers can avoid through a better understanding of the dynamics of smart product ecosystems and embedded control. This chapter distills the lessons from smart product initiatives across industries to offer recommendations for analyzing the impact of smart products on industry sectors, for selecting a smart ecosystem model, and for developing corporate strategies that deliver visible customer value through smart product platforms and services. It concludes that smart products will succeed to the extent that they make the value of smart services enabled by a smart product platform highly visible and accessible to the consumer.

The intelligent automobile has been promised and prototyped for several decades. Like many smart product concepts, mass-market adoption of smarter automobiles often seems to be stalled just down the road. Take a closer look at the vehicles coming off the production line, however, and it becomes clear that automobiles already demonstrate many smart product characteristics. Embedded systems monitor engine performance, and manage braking and vehicle stability. Features such as cruise control, automatic variable transmission, and power steering are designed to operate cars more safely and efficiently without requiring specific driver instructions. Even tire pressure sensors rely on microcontrollers that communicate with the on-board vehicle network to warn drivers about tire problems. Advanced vehicle safety systems respond to complex inputs in real time and control critical driving maneuvers. This track record of incremental increases in embedded vehicle intelligence provides a solid foundation for the current worldwide push to develop and produce even smarter automobiles.

A review of automobile accident statistics around the world provides a sobering argument that human drivers need more help from their cars in avoiding accidents and keeping the roads safe. According to the US Department of Transportation (DOT), about six million vehicle crashes take place on American roadways annually. Drivers and passengers pay a steep price: approximately 42,000 fatalities and over three million serious injuries from vehicle accidents every year in the USA.

We are surrounded by products that have minds of their own. Computing power, in the form of microcontrollers, microprocessors, sensors, and data storage chips, has become so cheap that manufacturers are building microcomputers and embedded software programs into all types of consumer goods. According to market research firm Databeans, microcontroller shipments worldwide will reach fourteen billion units by the end of 2010 (Databeans, 2009). Along with these chips have come a host of advanced product features and the penetration of embedded product intelligence into daily life.

Everyday appliances can now keep track of how often we use them and remind us when it is time to order new batteries or replacement parts. Alarm clocks get louder and louder, or flash a light if we ignore their morning summons. Coffee pots turn themselves on, grind the beans, and brew our first cup at just the strength we prefer. Mobile phones can download our email, display digital photos, remind us of today's appointments, and let us scan the Internet for breaking news over breakfast. Or we can start the day by listening to music on our iPod, watching the morning news reports on our high-definition television (TV), and setting up the recording of a new television series on the digital video recorder (DVR). If we forget about them in our rush to get to work, our steam irons, coffee pots, and toaster ovens will sound an alert, or simply switch themselves off to save power and avoid overheating.

Tom finally has the family car keys in his pocket. He has waited a long time for this moment, having suffered through driver education and envied his older friends who already have their own cars. Tom's first solo drive won't be quite as exciting as he would like, however. His new set of keys are more than a ticket to ride; they are microcontroller-enabled smart keys linked to Ford Motors' MyKey driver control system, with safety features designed to keep Tom within limits.

The MyKey system allows Tom's parents to set the maximum speed that he can drive their Ford Focus: when he puts the key in the ignition the car reads his personalized driving profile and sets its controls accordingly. To help ensure that Tom keeps his eye on the speedometer and his foot off the gas pedal, a reminder chime will sound when the car reaches preset speed levels of 45, 55, and 65 miles per hour (MPH). The system takes control even before Tom leaves the driveway, enforcing his use of seat belts by sounding a recurring reminder chime and keeping the audio system muted until the driver's belt is buckled.

Tom isn't exactly happy with all these limits on his independent driving. But the alternative would be much worse – waiting until after high school graduation before getting access to the family car.

As the largest wireless carrier in Japan when the original mobile app store, i-mode, was introduced in 1999, NTT DoCoMo's description of itself as just another player in i-mode's ecosystem is a bit disingenuous. DoCoMo was undeniably the dominant force behind i-mode. It is true, however, that in creating a new mobile content and service platform, DoCoMo deliberately resisted devouring all the revenue in sight. The DoCoMo business model allowed i-mode content and application providers to keep an unprecedented 91 percent share of the application revenue generated by i-mode subscribers. In addition, DoCoMo provided direct billing to its subscribers for all i-mode content, relieving content partners of the need to handle financial transactions. These business decisions made the ecosystem attractive to very small participants as well as to partners with well-established brands. Compared to the terms offered to application developers and content partners by wireless carriers in the USA and Europe at that time, DoCoMo's wireless data strategy was structured to encourage and reward widespread partner participation and innovation.

Attached to almost every house, apartment building, and office complex in the United States is a familiar and rather homely device. Inside its glass cover, a counter ticks off the kilowatts as the meter measures and records the amount of energy consumed by kitchen appliances, air conditioners, computers, light bulbs, and other electrically-powered devices inside the premises. For more than a hundred years the basic electric meter design focused on this mission of accurate measurement, providing utility companies with a reliable record of electricity usage that could be turned into a monthly bill for each customer.

When meters finally entered the digital age during the 1990s, the priority for utilities was automating the time-consuming and costly process of meter reading rather than giving their customers more information about their power usage. The possibility that the humble electric meter could cooperate with the average residential consumer in managing peak energy demand cycles and avoiding brown-outs was, at best, a far-fetched vision for the distant future. To the extent that records of monthly electric power consumption at the residential level were used to forecast future demand and power-generation needs, the utility's strategy was straightforward. The electric utilities simply planned for continued year-over-year increases in energy consumption. The historical data for electric power demand in the United States certainly supported the assumption that consumption and demand would continue to increase; according to the Department of Energy (DoE), the demand for power rose almost 30 percent between 1988 and 1998 (US Department of Energy, 2008a).