Imaging and Optics Technology in Modern Business Communication

Imaging and optic applications are truly ancient, and people quickly recognized the innovation these devices represented. "Two thousand years ago," Peter Weiss notes, "Roman Emperor Nero peered through an emerald monocle to better see his gladiators in combat. Twelve hundred or so years later, eyeglasses started to adorn faces" (p. 200). To date, optical lenses have primarily served one purpose: to provide the viewer with a more visible image of the world. Things are changing, however, and they are changing fast. "Now, there are inanimate observers that can also benefit from lenses," Weiss says, and "more and more, computers are being tasked with making sense of the visual world in ways that people can't" (p. 200).

According to Rudolf Kingslake and Brian J. Thompson (2005), "A new era in optics commenced in the early 1950s following the impact of certain branches of electrical engineering—most notably communication and information theory. This impetus was sustained by the development of the laser in the 1960s" (p. 5). The initial association between optics and communication theory emerged based on the wide range of similarities between the two subjects as well as the similar mathematical techniques used to formally describe the behavior of both electrical circuits and optical systems (Kingslake & Thompson, 2005). In the Digital Age, these considerations have assumed new levels of importance.

A topic of considerable concern since the invention of the lens as an optical imaging device has always been the description of the optical system that forms the image; information about the object is relayed and presented as an image. As Kingslake and Thompson explain, "the optical system can be considered a communication channel and can be analyzed as such. There is a linear relationship (i.e., direct proportionality) between the intensity distribution in the image plane and that existing in the object, when the object is illuminated with incoherent light (e.g., sunlight or light from a large thermal source)" (2005, p. 5). Consequently, the linear theory developed to describe new electronic systems can also be applied to optical image-forming systems. For instance, an electronic circuit can be characterized by its impulse response—that is, its output for a brief impulse input of current or voltage. Similarly, an optical system can be characterized by an impulse response that, for an incoherent imaging system, is the intensity distribution in the image of a point source of light. The primary difference is that the optical impulse is spatial rather than temporal; otherwise, the fundamental concept is identical (Kingslake & Thompson, 2005).

These are important considerations for industries seeking better ways to accomplish data processing and process management, because it means that computer-assisted technologies can be applied to once-limited optical devices. As Kingslake and Thompson point out, "Once the appropriate impulse response function is known, the output of that system for any object intensity distribution can be determined by a linear superposition of impulse responses suitably weighted by the value of the intensity at each point in the object. For a continuous object intensity distribution this sum becomes an integral" (2005, p. 6). Although this example concerns the use of an optical imaging system—by far the most common use of optical elements today—the concept can actually be applied independently of whether the receiving plane is an image plane or not. Therefore, an impulse response can be defined for an optical system that is deliberately defocused, or for systems used for the display of Fresnel or Fraunhofer diffraction patterns. The Fraunhofer diffraction takes place when the light source and diffraction patterns are effectively at infinite distances from the diffracting system; by contrast, Fresnel diffraction occurs when one or both of the distances are finite (Kingslake & Thompson, 2005).

In his essay "Pictures Only a Computer Could Love," Peter Weiss of Science News (2003) reports, "With a new generation of optics, engineers are recasting visual scenes for computers' consumption. To the human eye, these pictures are visual gibberish, hardly worth a single word, let alone a thousand. To computers, such data can be worth more words than you'd care to count" (p. 200). Underlying all of these innovations in digitally enhanced processing are improvements in new styles of lenses. Rather than employing the traditional concave and convex disks to develop images, optical engineers today are using oddly shaped, radically different lenses that are customized to take advantage of the strengths of computers. According to Joseph N. Mait of the Army Research Laboratory and the National Defense University in Washington, D.C., "Once you break away from thinking that the optics have to form something [people] recognize as an image, there are many things that you can do." Similarly, Eustace L. Dereniak points out that "There's no reason to go ahead and form an image" (in Weiss, 2003, p. 200).

Even in nature, there are some species of beetles able to navigate by detecting specific colors or by the polarization of light in space without forming a conventional image. To date, there has been a "human-centrist" tendency to avoid such techniques because humans have modeled optical instruments—such as cameras—after our own image-making eyeballs (Weiss, 2003). Innovations on the horizon also transcend optical phenomena altogether; researchers expect to be able to make lenses that process other segments of the electromagnetic spectrum. According to David J. Brady, "It's a general change in the way you think about sensing" (in Weiss, 2003, p. 201). Technologies that stand to benefit from these radical new approaches include radar, computerized axial tomography (CAT) X-ray scanners, and magnetic resonance imaging (MRI) systems. These and other current and emerging technologies that rely on imaging and optical scanning provide improved methods of personal identification, better security, and the elimination of human errors compared with past methods.

More and more industries today are enjoying the benefits of improved imaging and optical scanning techniques. These advantages extend to improved inventory management and warehousing requirements, improved supply chains, better security, and—increasingly—real-time tracking of inventory as it moves through entire production, marketing, and delivery systems. These technologies also allow for automated identification and verification of individuals, a topic that is receiving increasing attention in a world characterized by unexpected acts of terrorism. Furthermore, these technologies are providing companies in almost every industry with an improved ability to manage information, something that has assumed new levels of importance in the Age of Information.

The term "automatic identification" refers to "a means of identifying a product mechanically and entering the data obtained automatically into a computer" (A Dictionary of Business, 1996, p. 40). The most common applications of automatic identification include bar codes, optical character recognition (OCR), magnetic ink character recognition (MICR), magnetic stripes, and voice systems (A Dictionary of Business, 1996). The ability of companies to automatically identify components and finished goods as they move through the supply chain has assumed increasing importance in a globalized marketplace.

Data capture applications have also been extended to global mapping systems that have revolutionized consumer products by adding global positioning systems (GPS) originally developed for military purposes (Bildirici, 2004). According to Bildirici, "Computer technology has been widely used in cartography since the 1970s. New technologies have led to a rapid development in the field of data capture in cartography and related disciplines. In developed countries in particular, the era of data capture is almost complete" (p. 43). Such automated techniques also helped the U.S. Census Bureau perform a wide range of functions for Census 2000, including data collection and data capture activities (Longini, Marshall, Palensky et al., 2002). In terms of navigation, a longer-term technology could involve electronic fixed-response transponders scanned via antennas embedded in the pavement, thereby providing obstruction-free scanning (Bowman, Hakim & Seidenstat, 1996).

By the beginning of the 1980s, various optical character recognition (OCR) technologies became available that made it possible to convert text to electronic form without manual keyboarding (Dry & Lawler, 1998). A scanner's ability to translate typewriter characters from a bit-mapped image into ASCII text depends on a number of factors, including the sensitivity of the device and the legibility and preparation method of the original document. Improvements are being made continually, and even formerly graphic-based scanning systems such as Adobe's PDF applications have incorporated character recognition systems that allow for textual scanning. Some of these systems incorporate features that provide output options to convert text into formats used by common word-processing programs such as RTF and Word.

According to Dry and Lawler, the term "scanning" is frequently used today to describe the process of creating digitized images; in this approach, a graphic picture of the page, rather than an actual transcription of the text itself, is stored in the computer. "While this provides an effective means of delivering text for reading, the text itself in such an image cannot be processed in any way by the computer" (Dry & Lawler, p. 106).

Anyone who has ever written or received a check has been a beneficiary of magnetic ink character recognition (MICR) technology. MICR employs a special type of magnetic ink typically used on checks and other documents to allow them to be automatically sorted, with characters read and fed into a computer. The technology was first introduced in the 1950s, when Bank of America began using MICR techniques to improve check processing capabilities. According to William Serrin, "Machine-readable numbers began appearing on checks—the number of a check and of the bank account as well as the bank's routing number. Reader-sorter machines were developed, and checks could be handled more quickly and by fewer workers. Productivity increased, costs were reduced" (p. 50). The MICR system is comprised of two distinct components: the character set that is used and the type of ink.

In his essay "Check Imaging: Banks Are Getting the Picture," Mark Arend predicted in 1992 that "Banks that believed in imaging's potential all along are being rewarded now, because they are among the first to see significant reductions in check processing costs. Image-based systems, which reduce the physical handling of paper checks, are now becoming commercially available, and in time, they are likely to redefine check processing" (p. 44). This prediction has certainly proved accurate, and today MICR applications abound.

Optical mark recognition (OMR) applications represent one of the earliest efforts at automated data capture and have typically been used in academic and other settings where standard responses are recorded—for example, where students mark their tests "using a number two pencil only" (Tansey, 2002, p. 148).

In the 1980s, many companies began using statistical process control (SPC) techniques as part of the broader Total Quality Management (TQM) practices that were becoming common in most manufacturing industries. According to Cortada (2004), "These kinds of data provided automated systems with early warning of out-of-control situations. By the end of the century, all [manufacturing companies] had engineering databases, bar codes, magnetic cards, and sensors to track inventories. They had very effective software to provide in-line yield analysis by deploying digital image-processing and laser-scanning machines to inspect chips and wafers in various stages of fabrication" (p. 37).

According to "Bar Coding Basics" (2005), "Bar codes are a fast, easy and accurate data entry method used in the process known as automatic data collection. Bar coding enables products to be tracked efficiently and accurately at speeds not possible using manual data entry systems" (p. 1). The key advantages of using bar codes involve the automated collection of data that was previously accomplished manually. "Bar Coding Basics" points out that the "primary benefit of this process is that it is truly automatic, occurring instantaneously as a transaction or process takes place, commonly referred to as real-time data capture and exchange within the industry. Improved accuracy is yet another benefit" (p. 2). Research has shown that entry and read error rates are reduced dramatically when using automated data collection systems—approximately 1 error in 1 million characters, versus 1 error for every 300 characters in manual key entry (Bar Coding Basics, 2005).

Tansey notes that the width of each line on a bar code can be read by a laser reader attached to a computerized cash register as a number that provides a code for each product. "The obvious advantage of the system is quicker work by cash register operators," he says, "reducing labor costs and/or customer waiting times. In addition, more labor is saved by no longer having to use in-store labor to attach price labels to each product" (p. 147). Flexibility in pricing merchandise is also achieved because the system translates a product code into a price rather than printing a price on the good at the factory or packing station. While early users of bar codes were able to achieve competitive advantage through the practice, the technology is so commonplace today that companies must use it just to remain competitive (Tansey, 2002).

Bar coding systems can also be used to analyze and improve sales performance and strategies, as well as providing managers with the ability to organize a daily delivery system. According to Duffner, "Laser bar code scanners not only speed up customers moving through checkout lines, but also allow the store owner to keep accurate track of the inventory to determine which products are selling well. The Marsh Supermarket in Troy, Ohio, was the first to install and use laser bar code scanners in 1974" (1997, p. 318). At the commercial level, small helium-neon lasers have progressed to the point where they accurately read bar codes on grocery items at supermarket checkout counters in a fraction of a second, with the item's price displayed instantly on the cash register (Duffner, 1997).

The enormous popularity of the IBM PC in the early 1980s fueled the rapid growth of bar coding applications for automatic data collection. "Over the past 20 years," note industry observers, "bar coding has become a virtual necessity for the collection and processing of information in a quick and timely manner, enabling companies in every conceivable industry to maximize and dramatically increase their productivity and overall efficiency" (Bar Coding Basics, 2005, p. 3). Bar codes also allow companies to track information and activity in real time, thereby providing managers with the ability to base decisions on timely rather than dated information. Companies such as UPS and FedEx use hand-held bar code scanners in the field to collect real-time delivery information.

Radio frequency identification (RFID) techniques are becoming increasingly commonplace across a wide range of industries. One alternative that uses RFID, now available in almost every major retail store, is electronic article surveillance (EAS). According to Longmore-Etheridge (1998), "The technology, how and when it is applied, and what it can do for retailers continues to evolve. A major issue with regard to EAS tags is when they will be attached to the product and by whom: will it be at the retail store itself, taking the time of store employees, or at the manufacturing stage, referred to as source tagging. Given the benefits of the latter, source tagging has steadily gained momentum in the industry since its tentative beginnings in 1993" (p. 44).

As an example, an electronics store could use an RFID to provide potential customers with video-based information about different available models of a television set or computer; likewise, at a cosmetics counter, an RFID tag could trigger a video showing other colors in a lipstick line or providing information about a cosmetic's hypoallergenic status (Longmore-Etheridge, 1998). "All of these business bonuses to what began as a security device," Longmore-Etheridge says, "should help EAS prove its worth. And that should help loss prevention specialists sell their protection program to retail management" (p. 45). Active tags used in RFID applications are battery-powered; by contrast, passive tags do not contain a radio transmitter and can be either battery- or non-battery-powered.

Mercedes-Benz began offering a service known as TeleAid for all its model-year 2000 S-Class sedans marketed in North America, also using RFID technologies (Barnes, 1999). A Motorola-provided GPS receiver allows drivers to receive road or other types of assistance using an "SOS" button mounted on the rearview mirror. According to Barnes, "For navigation assistance the driver presses the 'i' button and talks with a service representative who provides directions, current traffic conditions, and concierge-type services, such as help with hotel reservations, event ticketing, and restaurant recommendations" (p. 62).

Like a 911 emergency call system, the customer's location is automatically provided to the company's computerized map to allow better assessment of the situation. In emergency cases, the driver can press the "i" button to reach a central dispatch center instantly, also alerting appropriate emergency services and dispatching them automatically based on the car's GPS coordinates. Barnes says, "Many of these emergency responders now navigate based on an in-vehicle moving-map display that takes them directly to those coordinates. For mechanical breakdowns the driver presses a wrench symbol, and a tow truck is dispatched in much the same manner" (1999, p. 62).

EPROM, or "Erasable Programmable Read Only Memory," was developed in response to efforts to build a non-volatile semiconductor storage device in the early 1970s (Moore, 1996, p. 55). According to Dr. Gordon E. Moore (of "Moore's Law" fame), in the first half of 1974, Intel's profit before tax exceeded 40 percent; the company's business at that time was based principally on a family of memory products: the il103, the 12102, a 2048-bit EPROM, and the i1702 (Moore, 1996). The electrically erasable and programmable read-only-memory (EEPROM) market that has emerged since the EPROM's original introduction has experienced exponential growth (Eisenhardt & Schoonhoven, 1990).

Other innovations in the field have included optical, magnetic, and so-called "smart card" applications that are becoming increasingly commonplace around the world today (Divanna, 2002). Early word-processing and toll-booth technologies employed magnetic cards, but these are being increasingly replaced by modern streamlined applications (Bowman & Hakim, 1996). Magnetic card technologies are still commonly used in transportation systems such as the Washington, D.C. Metro (Bowman & Hakim, 1996).

According to Orla O'Sullivan (1997), "These [smart] cards are designed to make the transition from magnetic cards, that offer no security, to smart cards, that offer full security—without forcing a change on the merchant community. Banks are trying to get merchants to bear the cost of moving to smart cards" (p. 68). This transition is worth the cost, O'Sullivan argues, because of the increasing incidence of fraud on the previous striped cards. "Mag-stripe readers (which can read the security coding) are freely available and the cards themselves are being replicated within 20 minutes by sophisticated criminals" (O'Sullivan, 1997, p. 68).

Past attempts to use lenses to go beyond mere imaging were not productive. In the 1960s, for instance, the military tried to develop so-called optical correlators that could detect threats by optically comparing reconnaissance images with patterns of enemy vehicles stored holographically. "When digital processing was in its infancy, the most elegant way to process the information was optically," Weiss reports. Yet the approach failed because "optics doesn't provide the kind of accuracy that is needed for detecting threats in complex and cluttered military scenes" (Weiss, 2003, p. 200). According to Duffner, Dennis Gabor invented holography in 1947 using ordinary light to illuminate photographic plates to produce a hologram. He taught at London's Imperial College, pioneering experiments in the physics of holography for which he won the Nobel Prize in Physics in 1971 (p. 318).

Technology has changed dramatically since that time, and the ability of modern computer processors has contributed to this evolution in substantive ways. There have also been major strides in mathematical analytical tools and advances in optics fabrication, providing researchers with more complex lenses—such as the wavefront coding lenses developed by Dowski, discussed further below. "Now," says Weiss, "all the tools are in place to unlock a world of possibilities that have long been hidden to the human eye" (Weiss, 2003, p. 200). Using computer-assisted technology to manipulate images is nothing new; as Weiss points out, "Anybody with a copy of Photoshop or other image-processing programs can do it routinely on his or her desktop. However, what's new is the strategy of modifying images first to make them better suited for the computer mind" (2003, p. 200).

Research by Dr. Edward R. Dowski Jr. resulted in a new type of lens that would provide significant improvements in autofocusing. According to Weiss, "Conventional cameras, microscopes, and other optical instruments use sets of convex and concave lenses to focus light onto flat pieces of film or electronic detectors. An autofocus camera typically shifts the positions of some of those optical elements forward and backward until a sensor that monitors contrast differences in the field of view detects sufficient detail" (p. 200). The concept behind Dowski's work was to eliminate this requirement for image manipulation by inserting an additional lens between the camera's pre-installed set of lenses and the detector. Weiss explains, "It would generate a computer-readable pattern of light that indicated how far out of focus the camera's subject was. The in-camera computer could then calculate how far to move the motor-driven lens" (p. 200).