In a study conducted by social psychologists Tversky and Kahneman, people were asked to guess the percentage of African nations that were members of the United Nations. Two groups were first asked to estimate whether this number was lower or higher than a threshold: the first group was asked whether it is more or less than 45 percent and the second group was asked whether it is more or less than 65 percent. Then both groups were asked to give their estimates of the actual percentage. The researchers demonstrated that the group that was given the lower threshold estimated a lower value for the actual percentage of African nations that are members of the United Nations. The group that was given the higher threshold estimated a higher percentage. The pattern has held in other experiments for a wide variety of different subjects of estimation. A bias in the estimation of African members of the United Nations may not sound like a big deal, but how about decisions that affect people’s lives seriously?

Consider sentencing in court trials. Social psychologist Mussweiler and colleagues asked trial judges with more than 15 years of experience to consider sentencing demands made by non-experts in a legal crime case before issuing a final sentence. The two sentencing demands were 34 months and 12 months. The judges, despite their experience and despite the fact that the crime was the same, were influenced by the demands. Judges who considered the high demand of 34 months prior to their decision gave final sentences that were almost 8 months longer than judges who considered a low demand of 12 months initially. If prior exposure to a piece of information can make a difference as much as 8 more months in prison, then we ought to know what is going on. The examples above illustrate a psychological heuristic known as “anchoring and adjustment.” The heuristic suggests that when faced with a decision-making or estimation situation, people start with an anchor, or a reference point, and make adjustments to it to reach their final estimate. The anchor serves as a first approximation and then the person makes adjustments to it to reach a final estimate or decision. Why does the human mind utilize this heuristic? The answer is simple—we do not always have a whole lot of information to reach an accurate estimate or best decision.

Therefore, the mind sometimes needs shortcuts, especially under pressing circumstances. To understand this process, let’s have a look at the limitations of human cognition. Models of human cognition Cognitive psychologists and sociologists have worked to develop models of human social cognition. These models emphasize four aspects of our social cognition, which is the way we perceive others. The first is the role of prior knowledge versus information immediately available. For example, when we see a policeman directing the traffic, we use our prior knowledge in our perception. We may assume that he is carrying a gun and he has communications equipment to talk with his station. We deduce these features from our prior knowledge about the traffic police, even if we may not be in a position to observe that particular policeman’s gun or communications device. Relying on prior information in our judgments is called “top-down” processing as opposed to “bottom-up” or data-driven processing. Typically, relying on top-down processing, such as relying on stereotypes, requires fewer processing resources.

The second aspect is the limitation of our cognitive processing capacity. The human cognitive system is modeled to consist of our sensory organs, a sensory register (memory) that temporarily stores our perceptions of external stimuli, a short-term memory, a long-term memory, attention resources and executive control processes. When we receive information in the form of audio-visual or other sensory stimuli, they are processed by our cognitive system and transferred to our short-term memory. Through a process of encoding and categorization, the information is organized and stored in the long-term memory. A part of the long-term memory is “active” or readily accessible. Our further use of information in our long-term memory activates the information, and the lack of use deactivates it, making it less readily accessible. Our behavioral response results from our processing of information. According to this information-processing model of human cognition, the amount of information that can be processed by our cognitive system is restricted in terms of storage, flow and inference (Huitt, 2003).

The third aspect is the amount of cognitive processing that is determined by capacity (amount of free resources) and motivation. Factors such as interest, importance, and relevance determine the motivation to allocate more cognitive resources. We are more likely to devote more cognitive processing resources to subjects that are more interesting, important or relevant in our judgment. The fourth aspect is the interplay between automatic and controlled processing. Automatic processes require fewer resources. Given our limited processing capacity, time and other types of constraints have consequences for our cognitive processing. Under constraints, most individuals tend to simplify their processing by relying on less information, relying on automatic cognitive processes as opposed to conscious ones, or relying on prior information as opposed to information available in the circumstances. Anchoring is one such a simplification. The nature of the situation we are facing will determine which of these mechanisms will be selected. They will be reused or abandoned depending on whether they provide a sound basis for our responses to the social environment. If the simplifications lead to interpretations that harm us, we are likely to abandon them. If, on the other hand, there is no harm or there is a benefit, then we are likely to reuse those simplifications.

Anchoring effect Anchoring is defined as the effect of a prior judgment of an object, the anchor, on our future judgments regarding another object. These judgments may be about a numerical value, a probability, or even a moral or legal judgment. As an example, consider the situation where people are asked whether the population of a city is greater than or smaller than a value. Let’s say two groups of people are asked the same question with two anchors: Group A is asked whether the population of Houston is more than or less than 500,000. Group B is asked whether the population of Houston more than or less than 2,000,000. In this example, the values of 500,000 and 2,000,000 serve as anchors. Both groups are then asked: What is your estimate of the population of Houston? Experiments indicate that the people who were given the lower anchor on average give a lower estimate for the population and vice versa. How does anchoring happen? Cognitive psychologists tell us that human judgment is essentially relative or comparison based, even if we are not asked to make a comparison explicitly. So, in evaluating the present object or person, our minds search for an anchor. A particular anchor may be selected because it is readily accessible, because it is suggested to us, or “self-generated via an insufficient adjustment process” (Mussweiler et al). Our prior cognitive processing of the anchor increases the accessibility of anchor-consistent knowledge, which influences our subsequent judgments. For example, when we meet a person from a country, the first person we met from that country may become our anchor. If we had a positive experience with the first person, we are likely to interpret the actions of this new person with a positive light. While cognitive heuristics such as anchoring help us make quick decisions under constraints, they may also lead to errors. The price we pay for the economy provided by the heuristics is “systematically biased judgments under certain conditions.” (Bless et al., p. 24). For instance, a car dealer may offer you a very high price as an anchor and ask you to make a counter offer. Experiments have demonstrated that under such circumstances, the value of the initial offer has a significant effect on what people will be willing to pay for the final price of the car. If the initial offer is very high, the customer is likely to accept a higher negotiated price and vice versa. The anchoring effect can also be observed when we take a few characteristics of a person and consciously or subconsciously fit them to a stereotype.

In such cases, we may misperceive their motivations or misunderstand their circumstances. Sometimes the anchor can be manipulated by some person other than ourselves, as in the case of the car dealer. Sometimes, we may pick the anchor unintentionally based on information obtained from the mass media. As the mass media tend to focus on rare events that tend to be negative, the stereotypes formed based on information solely derived from the mass media may be misleading (Said, 1997). What are some of the lessons we can derive from our discussion of the anchoring effect? For one, we need to point the mirror at ourselves and ask: Are we forming stereotypes of others that may be inaccurate? Are we influenced by the anchoring effect in our judgment of other people? To see whether we may have anchors for evaluating people of different backgrounds consider your initial thoughts about Christians, Jews, Muslims, Hindus, Buddhists, Americans, Russians, Asians, Africans, Mexicans, etc. Are these anchors based on scientific data or news media coverage of events or personal encounters with one or more individuals? It may be infeasible to try to collect encyclopedic information about every nationality, religion, or culture that we encounter. But in matters that impact our society we ought to do a better job of researching a diversity of resources. But perhaps a more important lesson is this—the anchoring effect is here to stay as part of the reality of human cognition. If we would like to provide accurate, reliable information to people about ourselves, our culture and values, we should reach them before they form a negative anchor or stereotype. If we would like our cultural background, religion, or values to be understood without distortion, we need to reach out to as many people as possible around us and interact with them. We need to hold conversations, and share meaningful experiences with them to anchor their future judgments in an accurate reference point. The anchoring effect is demonstrated to be pervasive and robust in psychology. It is not likely to disappear in the foreseeable future. However, we do have the opportunity to reach out and help form positive anchors for better human relationships.

Further reading

• Bless, H., Fiedler, K., Strack, F. 2004. Social Cognition, New York: Taylor and Francis.
• Huitt, W. 2003. The Information Processing Approach to Cognition. Educational Psychology Interactive. Valdosta, GA: Valdosta State University.
• Mussweiler, T., & Strack, F. 2001. The Semantics of Anchoring. Organizational Behaviour and Human Decision Processes, 86, 234–255.
• Said, E. 1997. Covering Islam: How the Media and the Experts Determine How We See the Rest of the World, New York: Random House.

Human beings are visually-oriented in their daily life; they use the sense of sight more than any of the other senses with which they have been endowed. The modern understanding of human vision and the underlining principles were only discovered in the past couple centuries. The nineteenth and twentieth centuries witnessed the development of photographic and digital imaging camera systems, which partially mimic human visual systems. We will open a small window on the history of human vision and camera systems, and try to compare today’s state-of-the-art cameras with the human visual system, focusing mainly on solid-state image sensors, or camera chips, and the image-sensing element of the human visual system, the eye.

History of human vision

Human vision has been the subject of conflicting interpretations since ancient times. Many ancient physicians and philosophers believed in the theory of extramission, or the active eye. According to this theory, the eye perceives objects by emanating light and seizing objects with its rays. It was in medieval Islamic culture that research on human vision and optics developed into a system similar to the modern theory of vision. Among others, Ibn Al-Haytham (Alhazen) (965-1040 A.D.), a Muslim physicist, astronomer, and mathematician in the tenth century, played a great part in this field by promoting the intromission theory which states that vision only occurs because of light rays entering the eye. Ibn Al-Haytham founded physiological optics, which distinguished the functioning of the eye from the behavior of light. On the other hand, ten centuries after Ibn Al-Haytham, Winer et al. (2002) have found recent evidence that as many as 50% of American college students believe in the extramission theory.1

Although the fundamental features, anatomy, and physiology of the eye were documented by Galen (129–200 A.D.), an ancient Greek physician, in the second century A.D., it was Kepler, a close reader of Ibn Al-Haytham, who offered the first theory of the retinal image and the correct operation of the eye in 1604. He proclaimed, “Therefore vision occurs through a picture of the visible things on the white, concave surface of the retina.” Progress came slowly after Kepler, because little was known about the nervous system until the nineteenth century, and only recently have scientists acquired a more knowledge about how the brain apprehends the retinal image. But many questions still elude us.

History of the camera

In parallel with curiosity about human vision, human beings have also tried to mimic human vision by capturing images of objects with instruments. Around 1000 A.D., Ibn Al-Haytham, also known as the father of modern optics, invented the pinhole camera,2 and explained why the image was upside down. It was Johannes Kepler who further suggested the use of a lens to improve the pinhole camera in the 1600s. Capturing an image on a photographic plate was first achieved in the early 1800s. Consequently, photographic cameras began to be mass-marketed in the twentieth century. The photographic equipment with which we are all familiar today, such as the 35mm camera, flash bulb, Polaroid camera, and the point-and-shoot auto focus camera, all were developed in the twentieth century. The invention of the camera as we know it today paved the way for other technologies, including the moving image capture, and, later, the digital camera, in which electronic image-capture devices were used. In 1972, chemically processing an image onto photographic paper no longer became the sole destination of an image, because the first filmless electronic camera was patented by Texas Instruments Corporation. Filmless electronic cameras were made possible with the invention of solid-state image-capture devices called charge coupled devices (CCD) and metal-oxide-semiconductor (MOS) image sensors in the late 1960s. Since the invention of solid-state imagers, people have become more visually stimulated and oriented than ever before in history.

A comparison of camera chips and the human eye

The technological advancements of solid-state image-capture camera chip design and manufacturing during the past twenty-five years has made digital imaging more affordable and accessible to the general public. These advancements have become more visible to consumers in mobile products, particularly in cellular phones, in which there are still and video-camera functions. Although digital cameras are easily available today, the state-of-the-art image sensor chips used in these cameras exhibit a performance gap when compared with the capabilities of the human eye. How good these image sensor chips are today when compared to our eyes is a question that will be elaborated on.

It is possible to compare the capabilities of the human eye and state-of-the-art image sensor chips used in cellular phones or in mainstream PC and digital still cameras. It is also possible to compare the capabilities of the human visual system, including the eyes, the optic nerve, the visual cortex, etc. with a digital camera system which includes optics, image-capture and signal-processing chips and other camera apparatuses. The capabilities include ability to see different colors (spectral response), photo-element (pixel) characteristics (size, density, distribution), light sensitivity, light-intensity response range, functionality and operation modes, and signal processing capabilities.

Spectral response

A single light-sensing element in a solid-state image sensor is called a pixel. In the human eye it is called the photoreceptor. Both elements convert impinging light or photons into electrical signals. The human eye sees in the so-called visible spectrum, between 380nm (blue) and 750 nm (red), and utilizes two kinds of photoreceptors on the retina; rods and cones. The cones are used for color and daylight vision. Rods are responsible for night vision. There are three types of cone photoreceptors on the retina that contain different types of photosensitive pigments. The three types of cones are L, M, and S, and they have pigments that respond best to wavelengths of light that are long or red (peak at 564 nm), medium or green (peak at 534 nm), and short or blue (peak at 420 nm), respectively. The rods (R) are most sensitive at a wavelength of approximately 498 nm (green), as seen in Figure 1.3 Image sensor pixels in digital cameras mimic the photoreceptors in the human eye for color vision. They utilize three kinds of color filters (red, green, blue) on top of each pixel to convert light rays into electrical signals in different visible spectrums. Unlike the cones in the human eye, camera pixels and color filters can be designed to cover wide spectrums that are not visible to the human eye, for instance, the x-ray, ultraviolet, and infrared spectrums. In the category of spectral response range, camera pixels exhibit greater flexibility than those of the photoreceptors of the human eye. On the other hand, interestingly enough, the eyesight that humans possess has similar spectral characteristics as the sun. The solar light emission peaks in the visible spectrum as seen in Figure 2.4

Figure 1. Spectral absorption curves of the short (S), medium (M), and long (L) wavelength pigments in human cone and rod cells.3

Figure 2. The daylight solar spectral power distribution on earth.4

Pixel and array size

The size of pixels in today’s modern digital cameras is getting closer to the size of the photoreceptors in human eye. The typical human eye contains an average of 130 million photoreceptors. The diameter of the rods and cones varies between 1.0m and 8.0m, depending on their location on the retina.5 Today’s state-of-the-art image sensor chips contain 10 to 30 million pixels. Each pixel can be as small as 1.4m in diameter. To date there has been no image sensor that is 1.4m pixel in size or more than 8 million pixels. However, the human being has been equipped with photoreceptors that are as small as 1.0m and has more than 100 million photoreceptors; and this is since the beginning of existence. It is also estimated that the resolution of the human eye is equivalent to an imager sensor chip of 576 million pixels with a 120 degree field of view.6 Thus we still have a long way to go in improving the image-sensor pixel and array sizes used in cameras if we are to match the human eye.

Pixel distribution and formation

In the human eye the photoreceptor size and densities change, depending on their location on the retina. For example, no rods exist on the focus center of the eye, which is called the fovea. Color vision photoreceptors, which total only 10% of the eye’s photoreceptors, are located mostly on the fovea. There is an irregular distribution of photoreceptors which is unique for every human being, like a fingerprint. Yet, we all see things the same, such as colors (with the exception of people who are colorblind). In camera chips, however, pixels are arrayed regularly, in two-dimensions. As the image-processing techniques and algorithms used in camera systems are linear and do not closely mimic the signal processing that exists in the human visual system, regularly arrayed pixels are required.

Light sensitivity and response range

Although the pixel sizes in image-sensor chips are approaching the size of the photoreceptors in the human eye, camera systems are not yet close to being able to match performance in terms of light sensitivity and response range. The human visual system and photoreceptors can easily adapt to very dim and bright light, with a light-intensity response range of ten billion to one (1010:1).7 This response range goes from light conditions on a bright sunny day to dim night vision. Typically, a conventional consumer camera pixel has a light intensity response range of one thousand to one (103:1).8 In a camera system, details of a captured scene are either concealed in the dark regions or washed out by the bright light, depending on the exposure settings of the system. Thus, one could say that the human visual system works ten million times (107) more efficiently than that of consumer cameras in terms of transferring scenes into images.

Operation principle

In terms of operation principles, the photoreceptors in the human eye convert light rays into electrical signals with extremely rapid electro-chemical reactions which can detect a single photon. Typically, in the image sensor pixel of a digital camera the photoelectric effect is used to convert impinging photons into electrical charges. Electrical charges are collected and stored in each pixel during the exposure period. Collected electric charges in each pixel are amplified and converted into digital ones (logic-1) and zeros (logic-0) during image readout before the image is sent to higher processing elements, such as a personal computer, digital-still or video camera. It is possible for a single photon-counting camera to be developed. However, very special and larger pixel sizes and extra apparatuses are required to build such a camera system. Thus, we could say that it is almost impossible to build imaging pixels that have the capability and dimensions of the photoreceptors of the human eye with today’s state-of-the-art technology.

Signal processing capabilities

The captured image in the human eye is preprocessed before it is sent to the visual cortex of the brain. This preprocessing consists of a data reduction operation in which nothing is lost, with a compression ratio of 130 to 1, as only 1 million optic nerves leave each eye carrying the information from 130 million photoreceptors. This compression allows the brain to process information at a rate of 25 to 150 scenes or frames per second. Typically, every pixel in an image sensor chip is first transferred to higher processing units. A data compression method is either carried out with some loss of details in the image or the compression is never used. The transfer of frames in camera chips typically takes place sequentially, reducing the speed of the image-capture operation or frame rate. Different techniques are used to maintain a capture rate of, at most, 25 frames-per-second in camera chips. With today’s technology, image sensors that have a capture rate of one million frames per second have been proposed and can be manufactured for scientific applications.

The inherent inefficiencies of image-capture in today’s image-sensor chips are hidden by employing the limitations of the human eye. For example, solid-state image sensors have always been produced with row or column-vice uncanny stripes which are easily picked up by the human eye. However, psycho-visual experiments have shown that the human eye can only detect contrasts between two adjacent gray lines when the difference is greater than 0.5%. Thus, if a camera chip is designed to have a column to column or row to row contrast of less than 0.5%, these odd stripes would not be visible.

Conclusion

Humans are visually oriented and without a doubt, our eyes are considered to be our primary source of information. It is obvious that the human visual system is extremely complex and this complexity has fascinated human beings throughout history. Yet, the underlining principles and basic functions of human vision and the eye have only been discovered during the last two centuries. These discoveries have led research in how to mimic these functions, which has resulted in moving and still-photographic and camera equipment, and the image sensors chips used in digital cameras today. Even though human beings are only taking baby steps in fully mimicking the human eye, curiosity and scientific inquiry allows us to discover functions and features of the eye and the visual pathways that will increase our knowledge and help us to build better pixels and image sensor chips.

References

1. Winer, G. A., Cottrell, J. E., Gregg, V., Fournier, J. S., & Bica, L. A., “Fundamentally misunderstanding visual perception: Adults’ beliefs in visual emissions.” American Psychologist, 57, 417-424, 2002.

2. Ertan Salik, “Pinhole Cameras, Imaging, and The Eye” The Fountain Magazine, Issue 54, pp. 30-33, April – June 2006.

3. URL: http://en.wikipedia.org/wiki/Image:Cone-response.png

4. URL: http://www.handprint.com/HP/WCL/color3.html

5. Stefan Winkler, Digital Video Quality – Vision Models and Metrics, John-Wiley & Sons, Ltd., 2005.

6. URL: http://www.clarkvision.com/imagedetail/eye-resolution.html

7. R.C. Gonzalez and R.E. Woods, Digital Image Processing, Addison-Wesley, 1993.

8. M. Schanz, et al., “A high-dynamic-range CMOS image sensor for automotive applications”, IEEE Journal of Solid-State Circuits, vol. 35, no. 7, pp.932-938, July 2000.

Increasing brainpower

We can improve our brain’s memory, creativity, and intelligence by our own conscious effort and free will. Even though our brain is made of nerve tissues, it can grow if it is used, just like a muscle. Scientists are constantly amazed at its plasticity “ the ability to grow. Even for people over 80 years old, significant life-quality improvements can be achieved through intellectual activity.

Researchers at the University of California-Los Angeles studied the brains of 20 dead people. After examining the dendrites (tree-like communicating arms between muscles), they discovered that their length increased proportionally with a person’s education and lifestyle. Those with a college education and a mentally active lifestyle had longer dendrites than those with less education and an intellectually inactive lifestyle.

Animal studies seem to confirm the same result. For example, rats exposed to maze learning show an increase in dendrite growth and enhanced problem-solving ability. They form new synapses between neurons, which facilitates further learning. When they are moved to dull, non-challenging environments, dendritic material decreases and synapses regress. Neurons can grow and change throughout one’s life.

French philosopher and mathematician Rene Descartes (1596-1650) once said: It is not enough to have a good mind. The main thing is to use it well.(1) In this ever-changing information society, successful adaptation depends on expanding our minds through learning and creativity. Knowledge helps us advance to happiness, because happiness usually is tied to less stress (in our career or daily life), gives us greater travel and leisure opportunities, more autonomy and even more money. Love grows in a relaxed and happy atmosphere, so even emotional well-being depends on improving our brainpower.

By stimulating our brain more intensively, a curious thing happens: The inter-connections between neurons increase by developing new dendrites. These surplus connections make our brain work better, improve our memory, and protect us against diseases like Alzheimer’s by providing alternative connections.

Learning and brainpower

An increased sense of self-confidence and awareness originates from a large fund of knowledge. Even the number and variety of friends we have is directly proportional to the number of topics of interest and discussion we acquire. Knowledge also improves our ability to foresee future political, economic, and historical trends. Moreover, an improved understanding of history and cultures help us avoid the hazard of prejudice.

Understanding the world is like a jigsaw puzzle. The more pieces that we can fit in, the clearer the image and the greater the urge to learn and fill in more pieces. The more we learn, the more we wish to continue learning. Increasing knowledge is like an avalanche; for it gains a momentum of its own once it starts. Perseverance and some initial prompting are required, but the rewards soon pay off. Minds are kept young by continual use, and mentally active people tend to live longer. Learning is as important to our brain as exercise is to our body. Hence the process of learning should not cease right after graduating from high school or college.

Students are presented with an enormous amount of information to learn and memorize during the academic year. During vacation time, however, they shy away from reading or learning even about non-curricular topics because of the mistaken notion that the brain has a limited capacity and that new information will overwrite previous information. The brain has a virtually unlimited capacity to absorb, sort, and retain information. But stimulation is required. An athlete improves by exercise and training; the brain gets into shape the more it is used.

According to Life magazine’s July 1994 feature article on Brain Calisthenics, Golden claims that exercising your brain may do as much for your health as exercising your body. Research on an elderly group of nuns in Minnesota revealed that a daily diet of brain games kept them healthy, youthful, and relatively free from Alzheimer’s and other degenerative brain disorders. And, they were happy, active, and mentally sharp well into their nineties. Researchers attribute this to the brain’s capacity to grow new connections after receiving the proper kind of stimulation. Although the connection between continued brain activity and health remains unclear, we can say that neural stimulation seems to have a direct role in keeping the brain and body balanced, energetic, and healthy. And, it is surmised that increased brain stimulation provides more pronounced and long-lasting benefits.

We can increase our dendrites by engaging in newer activities. When we learn something, we use our whole brain and build new brain circuits. But once something becomes a routine, we only use a small portion of our brain, leaving the rest to atrophy. To prevent the loss or actually to increase the number of dendrites, they must be stimulated. In a Life magazine interview published in July 1994, Arnold Scheible, head of UCLA’s Brain Research Institute, suggested: The important thing is to be actively involved in areas that are unfamiliar to you. Anything that’s intellectually challenging can probably serve as a kind of stimulus for dendritic growth, which means it adds to the computational reserves in your brain.

However, the article pointed out a caveat for the occasional brain developer. It seems that although learning a new skill creates more neural dendritic connections, the growth stops once the skill is learned and the new connections may actually atrophy. Thus, the brain needs random and interactive exercise so that it cannot learn the exercise so well that it becomes a routine and so that it can better learn new things continually.

Learning is an art that requires practical tools to gather a broad knowledge base. For example, Dr. S. Ray has an interesting suggestion: Look up unfamiliar words in the dictionary and then write a reminder by the word indicating where you encountered it, whet-her in a book, a newspaper, or a conversation. If you saw it in Newsweek, write NW beside the word. If your professor mentioned it, write his name next to the word. If you come across a word in this article, put a smiling face near it. Next time you encounter this word, your previous annotation will help you form a better association and you will remember the word better.

Once you start learning new words, it is a pleasant surprise to encounter them again. Each new word becomes a personal friend that reinforces a memory. Consider the dictionary one of your most interesting friends. The more words we learn, the more we become aware of our surroundings. Words enrich our memory. Words form the thread on which we string our experiences, said the British philosopher Aldous Huxley (1894-1963).

Physical activity and brainpower

Physical activity increases mental function. Exercise induces the growth of capillaries (tiny blood vessels) in the brain. Aging can lead to the brain receiving less blood. Exercise throughout life works against the decreased mental functioning associated with old age. Yet, one must not overemphasize physical exercise.

In their book Healthy Pleasures, Ornstein (a psychologist) and Sobel (a physician) argue that our current health practices should be shifted toward a more intellectual approach. They claim that diets and physical exercises can punish and even harm the body, and that they might have side-effects and only a limited effectiveness. They opine that some of the rigorous body controls we tend to practice are more linked to the Protestant work ethic than real health benefits. Hence going to the gym is a work-out.

Stress and brainpower

One side-effect of stress is dullness in the brain. Therefore, whatever removes stress increases brainpower. There are several relatively new methods to improve brainpower (intelligence, creativity, learning ability). For example, listening to a precise combination of audio signals embedded on a cassette or CD beneath soothing music and environmental sounds can give the brain a very specific audio stimulus that gently creates deep meditation, removes stress, and creates emotional healing at a deep level. This causes new connections to be created in the brain.

This is accompanied by a deep, trance-like meditative state in which the brain produces a whole host of pleasurable neurochemicals, such as endorphins. Such a trance or meditation can be attained easily during deep and concentrated prayer. A soothing and relaxing atmosphere can be achieved during chanting, remembrance, or reciting holy texts and hymns.

The new pathways caused by these meditative states connect and synchronize the brain’s hemispheres, thereby causing whole brain functioning. The improvements in brainpower include learning, intuition, mental clarity, creativity, focus and concentration, and intelligence. This research is backed by Centerpointe Institute, which has a commercial product (Holosync) based on the above principle.

Stress also is tied to the burdens we are obliged to carry. Usually we think that we control our lives entirely and thus can control everything around us. As this is not even near the truth, the resulting condition is stress accompanied by depression. Again, a fine balance between faith in destiny and free will help us remove this stress and live an alleviated and spiritually relieved life. Said Nursi analyzes this concept in his Twenty-sixth Word.(2)

In addition to removing stress, the nervous system’s reorganization of itself to a new and higher level increases the stress threshold. At that point, many uncomfortable, dysfunctional feelings and behaviors go away, even if they might have been persistent until then. This is a deep and dramatic change in mental health: the release of anger, fear, and sadness, as well as the release of self-defeating behaviors, childhood traumas, and limiting decisions. Once these are gone, we can expect better relationships to emerge.

Thus it is like a virtuous cycle, because people who reach higher levels want to move to deeper meditation levels, just as athletes increase their mileage after mastering a certain distance. In these in-creased levels, the mind expands, grows, and becomes self-aware to a greater extent. Perhaps it also experiences a deeper meditative experience.

Health and brainpower

Adding years to your life and life to your years have always been challenging. The first one has been achieved for some, as life expectancy in developed countries increased by 30 years during the twentieth century due to improved food supply, housing, medicine, and many other factors. However, research in the last few decades proves that one can become even smarter by dendrite generation. At first, researchers told us that life-long physical training was one way to keep in good health. Now, since scientists know that the brain can be developed and enlarged, brain calisthenics have become even more important. The age-old dilemma of mind over matter has been resolved in favor of the mind.

A group of researchers has demonstrated that pleasure and positive states of mind are better for our health. This new intellectual approach to health is not only more powerful, but also has no side effects. Central to this claim are recent findings that even getting an education may add as much as 10 years to your health. That is why National Geographic featured John de Rosen in its 1986 book The Incredible Machine, which discussed old age. De Rosen, an artist, continued to paint until the week he died at age 91. The book notes: Some scientists believe that retirement to a sedentary lifestyle initiates or aggravates medical problems, thus shortening life. According to a study of retired people, adults over 65 can learn a creative skill, like oil painting, as readily as younger students. So retiring from a job in a sense means retiring from life unless supplemented by some other (preferably new) activity.

Investigating mental processes and brain efficiency

With their new imaging machines, scientists literally look into the brain and photograph the paths of mental processing to learn how the brain handles information. Each of the 100 billion neurons has dendrites (receptors), a central processor, and a cable to send the messages to the next neuron. Some years ago, scientists thought that these physical aspects were fixed at birth. Then Marion Diamond, a pioneer brain researcher who dissected Einstein’s brain, published Enriching Heredity. In it, she claimed to have found that the key areas of Einstein’s brain were very rich in dendrites due to the increased usage, and thus established that the brain is not fixed by heredity.

Another aspect of brainpower is efficiency. Dr. Richard Haier, a professor of psychology at the University of California, Irvine School of Medicine, used PET (positron emission tomography) scan images of brain metabolism to indicate mental efficiency. Newsweek (29 February 1988) reported his finding that smarter brains use less energy. Wired (May 1994) introduced to the general public his first works using PET to analyze the brain changes of Tetris players. In this article, he revealed the importance of neural efficiency. In other words, give a smart brain a hard cognitive task, like Tetris, and it will quickly learn to solve it using less of the brain and less rigorously. Less efficient brains seem to have difficulty localizing the task to the most appropriate processing centers. Therefore, there is wasted brain energy and needless redundancy (noise) in neural-net processing activity.

Newsweek (27 March 1988) featured Haier’s PET scans of SAT (Scholastic Aptitude Test) takers. The outcome seemed to differentiate be-tween sexes. Smart men who scored above 700 on the SAT math section worked their brains harder (less efficiently) than smart women. The PET images reveal how efficiently the brain works when processing a cognitive task. Therefore, before and after PET images may show how smart the brain, or neural-cognitive system, is as a function of how fast it learned how to minimize extraneous brain processing areas and focus energy on smaller, more productive areas.

We have been led to believe that we use very little of our brain. Ironically, the smarter the person is, the less of his or her brain seems to be involved in any particular task. It seems that dendrites use the shortest or most feasible path possible, and that the more numerous and longer the dendrites are, the more likely they are to have shorter paths between the nodes in the brain’s neural network. When the mind starts to process a task, it apparently engages a greater area of the brain to feel it out. However, the smarter brain-mind system will narrow in on the brain’s most appropriate processing area(s). Then, the rest of the brain is released to do something else, such as noisy chatter or just rest (which is rare). A less intelligent system might use more of the brain than is necessary. This may create a source of noise on the neural-net that distracts, disrupts, or derails the fast and efficient (consistent and accurate) data processing by the appropriate brain center(s).

Brain efficiency seems to be highly correlated with intelligence (or brainpower). Apparently, the less moving parts the less friction and noise! This would agree with certain research findings of meditators whose quieter and more alpha- and theta-dominant brains appear to be better at various cognitive and mental reflex tasks.

Conclusion

In conclusion, brainpower depends on many things: daily diet, physical and mental exercise, emotional state, stress, heredity and so on. It is not fixed or static, but has the ability to change and progress, so one can improve it by a combination of techniques that suit the individual. The discovery of new or improving existing techniques is open for further research in cognitive science.

Footnotes

1. http://serendip.brynmawr.edu/Mind/Descartes.html#Descartes.
2. The Words is available online at:www.sozler.com.tr/risnur/warning_word.htm.

References

• Centerpointe Institute, Holosync, http://www.trans4mind.com/holosync/.
• Diamond, Marion. Enriching Heredity: The Impact of the Environment on the Anatomy of the Brain. Free Press: 1988.
• Ornstein, R. and D. Sobel. Healthy Pleasures. Addison-Wesley, 1989.
• Poole, R. M. (ed.) The Incredible Machine. National Geographic Society: 1992.
• Report from the Biomedical Newsletter. Lippincott-Raven Publishers, n.d.
• Sahelian, Ray. Be Happier Starting Now: A Medical Doctor Explores the Fascinating Field of Happiness. Longevity Research Center: 1995. See Chapter 9.
• www.brain.com.

Radar, a contraction of the words radio detection and ranging, is an electronic device for detecting and locating objects. It operates by transmitting a particular waveform pattern and detects the nature of the echo (return) signal.1 Radar is used to extend the capability of the man’s senses, especially that of vision. We can think of radar as being a substitute for the eye, although it can do so much more: it can see objects through such impervious conditions as darkness, haze, fog, rain, and snow, for its wavelengths are much longer than those of visible or infrared light. The human eye works as a passive device, since the object is illuminated by sunlight or other light sources. However, radar produces its own illumination via electromagnetic waves, which means that it is an active device. 

APPLICATIONS OF RADAR AND RADAR IMAGING

Radar is used in civilian applications as air-traffic-control radar to guide aircraft to a safe landing, and in commercial aircraft as radar altimeters to determine height and weather avoidance, as well as wind-shear radars to navigate in severe weather conditions.

The military uses radar for surveillance and weapons control. Examples of such radars are DEW (Distant Early Warning) and AEW (Airborne Early Warning), which detect aircraft, long-range search radars, and guided missile radars.2

Research scientists use radar as a measurement tool. Radars have been placed on satellites, space modules, and shuttles to explore meteors, planets, and other objects in the solar system.

In the case of an imaging radar, the radar travels along an airplane’s or a space shuttle’s flight path. The area underneath is illuminated by the radar, and the radar architecture builds the image as it moves on the top of its footprint (Fig.1). The radar image’s finer resolution is achieved by using a very long antenna array to focus transmitted and received energy into a sharp beam.2 The beam’s sharpness defines the resolution. Similarly, such optical systems as telescopes require large apertures (mirrors or lenses that are analogous to the radar antenna) to obtain fine imaging resolution. Synthetic Aperture Radar (SAR) is a common and very popular technique in radar imaging that achieves a very fine resolution.3 In the following sections, we introduce and explain different types of SAR imaging techniques.

SYNTHETIC APERTURE RADAR (SAR)

SAR refers to a technique that synthesizes a very long antenna by combining echoes received by the radar when it travels.4.5 Typically, SAR is used to produce a two-dimensional (2-D) image. One dimension in the image is called range (or along track), and is a measure of the “line-of-sight” distance from the radar to the target (Fig.l). Range is determined by precisely measuring the time from a pulse’s transmission to receiving the echo from target. The range resolution is determined by the transmitted pulse’s width (i.e., narrow pulses yield fine range resolution).

The other dimension is called azimuth (or cross track), and is perpendicular to range. Usually, the length of the radar antenna determines azimuth resolution. However, a good azimuth resolution requires a radar antenna that is not practically carried by an airborne platform, for imaging radars are much lower in frequency (1 to 10 GHz) than optical systems (4,000 to 8,000 GHz). The length of the required antenna could be around several hundred meters, which obviously cannot be carried by an air vehicle.

However, SAR differs from other radars in that it collects data along the flight path when it travels, instead of using a large antenna. Therefore, a very small antenna is adequate for the job. After collecting the data, it processes this aperture data as if it came from a physically long antenna. The distance the aircraft flies in synthesizing the antenna is known as the synthetic aperture. A narrow synthetic beamwidth results from the relatively long synthetic aperture, which yields finer resolution than what is possible from a smaller physical antenna.

SARs are not as simple as described above. Transmitting short pulses to provide range resolution is generally not practical. Typically, longer pulses with wide-bandwidth modulation are transmitted, which complicates range processing but decreases peak power requirements on the transmitter. For even moderate azimuth resolutions, a target’s range to each location on the synthetic aperture changes along the synthetic aperture. The energy reflected from the target must be “mathematically focused” to compensate for the range dependence across the aperture prior to image formation. Additionally, for fine-resolution systems, range and azimuth processing is coupled (dependent on each other), which greatly increases computational processing. The trick in SAR processing is to correctly match the variation in frequency due to motion (moving target or moving radar) for each point in the image.

An example of SAR imaging is shown in Fig. 2. The colors in the image reflect the received signal intensity. The strongest signal level is red, whereas the weakest is black. The figure is a SAR image of San Francisco, California, obtained by the Spaceborne Imaging Radar-C/X-band Synthetic Aperture (SIR-C/X-SAR) imaging radar when it flew aboard the space shuttle Endeavour on October 3, 1994. The size of the image is about 26 miles by 36 miles. The center of the area is 37.83 degrees north latitude, 122.38 degrees east longitude.

This particular SAR image is a good illustration of how SAR distinguishes urban areas from nearby relatively less populated areas. Such densely populated regions as downtown San Francisco (center) and the city of Oakland (at the right across the San Francisco Bay) show up as red images due to the alignment of streets and buildings vis A vis the incoming radar beam. The bridges in the area are easily detected by the imaging radar, including the Golden Gate Bridge (left center) at the opening of San Francisco Bay, the Bay Bridge (right center), and the San Mateo Bridge (bottom center). All dark regions on the image represent smooth water. Radar also easily detects the major faults in the area: those bounding the San Francisco-Oakland urban areas and the San Andreas Fault (at the lower left), As seen from the image, faults are shown as dark straight lines in the SAR image.

INCERSE SAR (ISAR)

While SAR images a region of the Earth from an airplane or an air shuttle, Inverse SAR (ISAR) images a flying object, such as airplane or an asteroid, from land-based radar. ISAR is very popular, and also very critical in military applications.6 It is commonly used for identification purposes. In a possible war scenario where there are too many aircraft in the sky, it is almost impossible to guess which one is friendly or hostile. In that case, ISAR imaging technique is used to identify the approaching aircraft and classify it from a collection of possible targets.

In theory, ISAR is an imaging technique that maps the locations of dominant scattering points of a target based on the multi-frequency, multi-aspect, backscattered data.7 In this data, the signal’s amplitude reflects the magnitude information of the scattering points on the target, while the backscattered signal’s phase is related to the location information of the scattering point off the target. After collecting this 2-D raw data, several signal-processing tools extract from this data the amplitude and location information of the scattering centers. Then, a 2-D image of the target is constructed by using a convenient image processing technique.

An example of ISAR imagery is shown in Fig. 3. The model of the test airplane (C-29 model) is shown at the lower portion, while a 2-D ISAR image of the airplane is constructed at the upper portion of Fig.3. The measurement is taken at the center frequency of 10 GHz, where the frequency bandwidth is 16 GHz. The data is collected from 0.10 steps to cover the entire 3600 azimuth. At the end, a 2048 by 2048 2-D grid is constructed by using the ISAR algorithm. By comparing both, it is seen that ISAR imaging provides accurate target information. By looking at this image, it is very easy to identify and classify the aircraft.

ISAR is an active operation of the radar at the target’s far field. Both receiving and transmitting antennas must be far away from the target. Recently, new ISAR imaging techniques that allow passive radar operation have been discovered. Antenna SAR (ASAR) and Antenna Coupling (ACSAR) imaging techniques use direct radiation from an antenna mounted on the near field of an airplane or a ship to image the dominant radiation points off these platforms. In these cases, the radar functions only as a receiver, for the target’s own antenna provides illumination to the target. These techniques are mainly used to determine the dominant radiation points off the target to explore ways to cancel or mitigate undesired extra radiation from the target’s platform.

The development of fast computers during the 1980s allowed researchers to apply intensive computational electromagnetic (CEM) tools that ultimately led them to develop new SAR/ISAR algorithms. One of the most appreciated and widely used tool is Interferometric Synthetic Aperture Radar (INSAR) imaging, which allows the extraction of height information that can be used to render 3-D topographic views of a SAR scene.

INTERFEROMETRIC SAR (INSAR)

Radar interferometry involves coherently combining radar measurements made by two or more radar antennas displaced by a relatively small distance.8 Depending on the relative geometry of the two antennas, the combined measurements can be turned into measurements of surface topography, topographic change, or displacement over time. Mapping precision of around 2m in three dimensions over a wide area is now possible from airborne interferometric radars.

Here is how an INSAR works: A radar system launches electromagnetic energy to scan the ground terrain to be imaged. Two radar antennas collect the backscattered wave to obtain two different snapshots of SAR image. To avoid phase ambiguity, these antennas must be close enough to each other. Since the waves travel different distances from a particular scatterer to each antenna, the resultant phases of each SAR image is different. In the next step, an image called interferogram is formed by multiplying one SAR image by the complex conjugate of the other SAR image. The phase of the interferogram represents the differences in range to the scattering centers of each pixel in the image. These differences are caused by the terrain’s topography. Then, a signal-processing algorithm converts this phase information to extract the terrain’s topographic features. Finally, a 3-D INSAR image of the region is formed by combining the SAR images with the height information.

An example of INSAR imaging is illustrated in Fig.4, which depicts the Long Valley of east central California. The images were taken by the Spaceborne Imaging Radar-C/X-band Synthetic Aperture Radar (SIR-C/X-SAR) aboard the space shuttle Endeavour during its two flights in April and October 1994. The four images show the steps necessary to produce 3-D data from radar interferometry. The image covers an area of 21 by 37 miles. The radar illumination is from the top of the image. The bright areas are hilly regions of big rocks and pine forest; the darker areas are the relatively smooth, sparsely vegetated valley floors. The curving ridge running across the image’s center from top to bottom is the northeast rim of the Long Valley caldera, a remnant crater from a massive volcanic eruption roughly 750,000 years ago.

The image in the upper right is an interferogram of the same region, constructed by combining data from the April and October flights. The different phases are shown as different color levels. These variations are caused by elevation differences in the area. The same color levels indicate that those regions have same altitudes. The image in the lower left shows a topographic map derived from the interferometric data. The black bold contour lines represent levels of elevation. In this particular image, elevation levels are spaced at 250-meter intervals. The last image is a 3-D view of the northeast rim of the caldera, looking toward the northwest. As can be seen from the image, it is possible to extract such geologic structural and landform features as elevation, vegetation, and soil type with the help of INSAR processing.

Another example of INSAR imaging is shown in Fig. 5, which depicts the Washington, DC, Mall area. A similar approach is used to form this 3-D image. The region starts from the Capitol building (top) to the Lincoln Memorial and the Arlington Memorial Bridge (toward the right bottom). The Washington Monument is very easy to observe at the center of the image. The bright areas (from white to yellow) represent higher elevation places; darker colors (from green to dark blue) represent the areas of lower elevation. The Potomac river (right bottom of the image) and the reflecting pool (from the Lincoln Memorial toward the Washington Monument) are all in dark blue because of the water and the lowest elevations. We can also clearly distinguish Constitution Avenue running from bottom to top. The green regions are intermediate elevation consisting mostly of vegetation. As seen from the image, the highest elevation is the top of the Washington Monument, the Library of Congress building, and the Capitol building.

CONCLUSION

In this paper, we presented a survey study of radar basics and radar imagery. It is obvious that radar has been a very important and useful tool throughout the 20th century, both in the military and industry. With developments in the computer era and new imaging algorithms, it looks like it will be a very critical tool in the 21st century as well. It is now possible to simulate very complex models and targets in a reasonable computation time in radar frequencies thanks to new developments in computational electromagnetics methods (CEM). Examples of those are Xpatch9 (a high frequency code that can predict the scattering from large, complex bodies) and FISC10 (a fast simulator of electromagnetic bodies at high frequencies). While computers continue to grow faster and faster, new electromagnetic simulators are also getting faster and more efficient. As a result, more compact, fancier, faster, and more accurate radar-imaging techniques are being developed.

REFERENCES

1. Morris, G. V. and Harkness, L. (1996) ‘Airborne Pulsed Doppler Radar’, Artech House.
2. Mensa, D. L. (1981) ‘High Resolution Radar Imaging’, pp. 185-189, Artech House.
3. Wehner, D. R. (1994) ‘High-Resolution Radar’. Artech House.
4. Carrara, W. C., Goodman, R. S. and Majewski, R. M. (1995) ‘Spotlight Synthetic Aperture Radar: Signal Processing Algorithms’, Artech House.
5. Franceschetti, G. and Lanari,. (1999) ‘Synthetic Aperture Radar Processing’, C. R. C. Press LLC.
6. Baltes, H. P. (1980) ‘Inverse Scatteiing Problems in Optics’, Springer-Verlag.
7. Chu, T. H. and Lin, D. B. (1991) ‘Microwave diversity imaging of perfectly conducting objects in the near-field region’, IEEE Trans. Antennas Propagat., vol. 39, pp. 480-487.
8. Askne, J., et al. (1997) ‘C-band repeat-pass inter ferometric SAR observations of forest, IEEE Trans. on Geoscience and Remote Sensing, vol.35, pp. 25-35.
9. Lee, S. W. (1992) ‘Test cases for XPATCH’, Electromagn. Lab. Tech. Rept., ARTI-92-4, Univ. of Illinois.
10. Ctr. Computat. Electromagn. (1997) ‘User’s Manual for FISC (Fast Illinois Solver Code)’, Univ. Illinois, Urbana-Champaign, and DEMACO. Inc.

Another feature of the human visual system is the ease with which interpretation is carried out. We see a scene as it is – trees in a landscape, books on a desk, products in a factory. No obvious deductions are needed and no overt effort is required to interpret each scene. In addition, answers are immediate and available normally within a tenth of a second. The important point is that we are for the most part unaware of the complexities of vision. Seeing is not a simple process.

We are still largely ignorant of the process of human vision. However, man is inventive and he is now trying to get machines to do much of his work for him. For the simplest tasks there should be no particular difficulty in mechanization but for more complex tasks the machine must be given man’s prime sense, i.e. that of vision. Efforts have been made to achieve this, sometimes in modest ways, for well over 30 years. At first, such tasks seemed trivial and schemes were devised for reading, for interpreting chromosome images and so on. But when such schemes were confronted with rigorous practical tests, the problems often turned out to be more difficult.

Computer vision blends optical processing and sensing, computer architecture, mechanics and a deep knowledge of process control. Despite some success, in many fields of application it really is still in its infancy.

With the current state of computing technology, only digital images can be processed by our machines. Because our computers currently work with numerical rather than pictorial data, an image must be converted into numerical form before processing.

The field of machine or computer vision may be sub-divided into six principal areas (1) sensing, (2) pre-processing, (3) segmentation, (4) description (5) recognition and (6) interpretation. Sensing is the process that yields a visual image. The sensor, most commonly a TV camera, acquires an image of the object that is to be recognised or inspected. The digitizer converts this image into an array of numbers, representing the brightness values of the image at a grid of points; the numbers in the array are called pixels. Pre-processing deals with techniques such as noise reduction and the enhancement of details. The pixel array is fed into the processor, a general-purpose or custom-built computer that analyses the data and makes the necessary decisions. Segmentation is the process that partitions an image into objects of interest. The segmentation of images should result in regions which correspond to objects, parts of objects or groups of objects which appear in the image. These features of these entities, along with their positions relative to the entire image, help us to make a meaningful interpretation. Description deals with the computation of features such as size, shape, texture, etc. suitably for differentiating one type of object from another. Recognition is the process that identifies these objects. Finally, interpretation assigns meaning to an ensemble of recognised objects.

Any description of the human visual system only serves to illustrate how far computer vision has to go before it approaches human ability.

In terms of image acquisition, the eye is totally superior to any camera system yet developed. The retina, on which the upside-down image is projected, contains two classes of discrete light receptors – cones and rods. There are between 6 and 7 million cones in the eye, most of them located in the central part of the retina called the fovea. These cones are highly sensitive to colour and the eye muscles rotate the eye so that the image is focused primarily on the fovea. The cones are also sensitive to bright light and do not operate in dim light. Each cone is connected by its own nerve to the brain.

There are at least 75 million rods in the eye distributed across the surface of the retina. They are sensitive to light intensity but not to colour.

The range of intensities to which the eye can adapt is of the order of 1010, from the lowest visible light to the highest bearable glare. In practice the eye per forms this amazing task by altering its own sensitivity depending on the ambient level of brightness.

Of course, one of the ways in which the human visual system gains over the machine is that the brain possesses some 1010 cells (or neurons), some of which have well over 10,000 contacts (or synapses) with other neurons. If each neuron acts as a type of microprocessor, then we have an immense computer in which all the processing elements can operate concurrently. Probably, the largest manmade computer still contains less than a million processing elements, so the majority of the visual and mental processing tasks that the eye-brain system can perform in a flash have no chance of being performed by present-day man-made systems.

Added to these problems of scale is the problem of how to organize such a large processing system, and also how to program it. Clearly, the eye- rain system is partly hard-wired but there is also an interesting capability to program it dynamically by training during active use. This need for a large parallel processing system with the attendant complex control problems illustrates clearly that machine vision must indeed be one of the most difficult intellectual problems to tackle.

Part of the problem lies in the fact that the sophistication of the human visual system makes robot vision systems pale by comparison.

Developing general-purpose computer vision systems has been proved surprisingly difficult and complex. This has been particularly frustrating for vision researchers, who experience daily the apparent ease and spontaneity of human perception.

As can be seen from the information given above, developing a computer-vision system requires knowledge. Hence the eye performs better and better than even the best computer-vision system, the very complex eye-brain system also requires more comprehensive knowledge to build. When man understands and discovers the functioning of the eye-brain system, better computer-vision systems will be developed. That means the eye-brain system (like other systems in the body of human being) is a very deep and rich knowledge source. As this system has links with other systems in the body, it obviously shows that the maker of these systems is One who knows everything about every single part of the whole body, for not only the eye-brain system but the entire body develops accordingly.

So, who can be the maker of this fantastic eye-brain system? If it is said that it is self-creating, this has no meaning because everybody knows that such an important system cannot create itself, just as a computer-vision system cannot give itself existence. As for chance, is it possible for such a system, which is full of knowledge for human being to imitate in order to develop computer- vision systems, to be made by chance at all? Of course not. So who is the maker of the eye-brain system?

The maker or the creator of this system can only be One who has supernatural power. He says in His Holy Book:

‘Have we not made for him (human being) a pair of eyes?’ (The Holy Qur’an 90.8).

Yes, indeed, He made them just as He made the whole of the rest of the cosmos with His unlimited knowledge.

REFERENCES

• DAVIES, B. R. (1990) Machine vision: theory, algorithms, practicalities, Academic Press, New York.
• GOWRISBANKAR, T. R, & BOURBAKIS, N. G. (June 1990) Specifications for the development of a knowledge-based image-interpretation systems, Engineering Applications of Al, Vol. 3, pp.79-9O.
• MAJUMDER, D. D. (1988) Computer vision and knowledge based computer systems, Institution of Electronics and Telecom. Engineers, Vol. 34, No 3, pp. 230-245.
• MULLER. B. & REINHARDT. J. (1990) Neural networks, an introduction. Springer- Verlag Publications, New York.
• RODD, M. G. (1990) Knowledge based vision systems, Knowledge Engineering, Vol. 2. ed, Adeli, II., McGraw [Jill, pp.245-276.
• ROSENFELD, A. (August 1985) Machine vision for industry: tasks, tools and techniques, Image and Vision Computing, Vol. 3, No 3. pp.122-135.
• SANDERSON, 1

WHY OPTICAL?

Despite the many benefits that classical computers (‘classical’ here means computers in which the signals are carried electrically) have brought to our lives, they have some limitations which prevent any improvement in the speed or volume of signals carried. These limitations are inherent to the way these computers work.

For example, classic electric circuits carry information units serially, one by one, and there are some lower limits beyond which such circuits cannot be built-below that limit they simply cannot process the information reliably. Another handicap is that electrons floating in circuits can interfere with each other-and this interference, incidentally, is one reason why engineers cannot produce smaller circuits. By contrast, photons, light particles, which are the main signal or information carrying agent simply do not interact with each other because they do not carry a charge.

An optical computer could be run faster than one running electrons, theoretically at the speed of light, along optical fibres which are specifically designed guide-wires to transfer light-photons in and out between chips in an optical computer without distortion.

One of the main advantages of optical computers is their capability of processing more than one piece of information at the same moment. That means multi-beams can be processed in one chip. This would allow engineers to use parallel processing which greatly enhances the speed of the computer.

THE DIFFICULTIES

Lasers would, naturally, be the source of light in this new generation of computers. Scientists and engineers all over the world are trying to build appropriately tiny lasers emitting precise frequencies of infrared light. But they face a number of practical hurdles. One has to do with making lasers of appropriate size and efficiency. Current technology does not have the means to build optical chips comparable in size to ‘classical’ ones. The efficiency of the lasers is not high enough for the specifications required. Most of the energy to run these lasers escapes as heat and is not used. Since one or at most two percent of this energy can be transformed into the useful form of light, the rest can generate a lot of heat which is dangerous to the condition of the chips.

Making the right lasers is not the only problem on the way to fully optical computers. Switches are at the heart of optical computers, but as photons do not interact with each other, there are substaintial difficulties in building switches.

SOME PROPOSED SOLUTIONS

One solution to this problem is to build computers which are part electrical, part optical. Many scientists now believe that the most viable use for optical technology is in this type of hybrid system combining optics and electronics. Researchers are now focusing their work on optical interconnections between chips, which could be a reality in as little as one or two years. This type of connection can vastly increase the amount of data moving in and out of chips.

Such a machine would have to contain prisms, mirrors, and lasers to channel the light, as well as gallium arsenide chips that convert pulses of laser light into electrons so as to function as switches. If all this does happen, there will be a need for new computer architectures, that is, new computer structures.

However, there are some scientists following a different route. They are trying to find ways to use current transistor technology so as to detect laser beams in the information processing. NPN type transistors without a metal cover would be appropriate because they are faster. This approach also allows for adaptation of existing designs, with all the advantages in time and savings that brings.

FUTURE

The first optical processor developed at AT&T Bell Labs measured about two feet by two feet. Scientists hope some day to fit it all into three square inches. A fully optical computer is more than five years away.

Scientists have set themselves a target for the year 2000: 1,000 I/O (input and output) channels running at 1 giga-bit/sec. That is a thousand times faster than current modern computers.

It is a pity that we must wait for a decade, while scientists and engineers try to accomplish this difficult task. But what an exciting wait!

•  FURTHER READING
• ‘Bright future’, Scientific American, May 1990.
• ‘Now easier optical’, Electronics, May 1990.
• ‘Slacken lights up’, Scientific American, July 1991.
• ‘Optical computer no longer lighters away’, Byte, April 1992.
• ‘Optical computing sheds ‘blue sky’ image’ Electronics, April 1990.