There are already 1.5 billion smartphone users and a total of 5 billion mobile phones in the world. Smartphone companies are announcing new phone models with improved capabilities every year. These are smarter and better phones with new sensors, powerful chips, and new capabilities. Newspapers and technology blogs often start discussing rumors about an upcoming smartphone several months before its launch.

Over the past few years, we have seen many futuristic prototypes and designs. Consumers request better and smarter phones that can handle more and more tasks with every new model. Although the phone companies are surprising us with new smartphones every year, we are far from seeing some of the technologies presented in prototypes. What do you expect to see in smartphones in the next 5 to 10 years? Let’s talk about some of the technologies that will end up in a smartphone in the near future.

One of the critical components in a smartphone is the battery. While most smartphones only support being charged from a wall plug, some of them are supporting inductive wireless charging out of the box. Although it is not a standard feature for most smartphones, wireless charging can be enabled using third-party cases and charging pads. Current wireless charging systems still require the smartphone to be placed on a pad for charging. A bigger step in powering smartphones will be touchless wireless charging. A new startup company, Ossia [1], recently unveiled the world’s first commercially viable touchless remote wireless power system. Imagine you are walking into your house or office, and the smartphone in your pocket is starting to charge via receiving electric waves through the air.

With wired or wireless charging, the battery technology of smartphones remained largely unchanged for years. A large percentage of smartphone bodies are occupied by lithium-ion batteries. With the developments of new power efficient displays and computing chips, smartphones are now using less and less power. This allows manufacturers to use smaller batteries with the same usage time. Battery technologies are not efficient enough to power smartphones more than a couple of days with average daily usage. People often carry extra batteries and chargers when travelling or spending time outside. Solar charging can help with some of these challenges by enabling smartphones to recharge under sunlight. Researchers are also exploring other materials to replace lithium-ion (e.g. graphene) in batteries to provide extra power for the ever-growing size of smartphone displays.

Smartphone displays are getting bigger and better with ultra-high screen resolutions that are sometimes even stronger than the human eye. Some manufacturers are already working on 4K ultra HD displays (3840 pixels wide by 2160 pixels tall). With the screen sizes getting larger, the distinction between a smartphone and tablet is becoming less clear. Some smartphones are even called phablet, a new category between smartphones and tablets, by newspapers and tech blogs.

A bigger jump in display technology will be through the use of flexible screens. There are already prototypes of 5-inch flexible displays or 50 inch curved televisions from several companies. One benefit of flexible displays would be fitting a tablet size screen on a small smartphone body. Users will be able to slide or unfold the display of a typical size (e.g. 4-inch) smartphone, and convert it to an 8-inch tablet. Companies are still looking for a good use case for flexible displays in daily life. Some applications of the smartphones with flexible displays make them closer to wearable devices. Some of them have appeared as watches or bracelets.

Another development in display technology is transparency. There are already prototypes of transparent displays in various sizes and forms. Transparency is important for heads-up displays and some models where seeing the background is necessary. This technology will allow displays on glasses, contact lenses, windows, and windshields. An obvious application of the transparent displays in cars is a GPS navigation system on the windshield [2]. This will open a full immersive experience by showing directions directly positioned on the road, virtual signs on the sidewalks, and even augmented information about companies and addresses directly on the walls of the buildings. Imagine seeing a large virtual menu or information panel with available seats, business hours, and prices on the wall of a restaurant. While the transparent displays are already available in various prototypes, the main challenge is achieving transparency in electronics and other components in a smartphone.

Even with flexible displays, you may not be able to carry and fold a 50-inch display into typical smartphone size. A major shift from increasing the display size will come via the use of pico-projectors. A smartphone, with a decent display size (e.g. 5 inch) for everyday use, can be turned into a full size (e.g. 50-inch) media consumption system with an integrated pico-projector. Although there are prototypes of pico-projectors embedded into smartphones, there are still many technical limitations in projection size, brightness, battery life, and cost of components.

While larger displays provide a better experience for users, the consumable content is mostly 2-dimensional. Even though there are games and movies designed and recorded in 3-dimensional environments, they still cannot provide a true 3D experience. All of this 3D content is projected onto a 2D screen in smartphones. There are prototypes giving the 3D effect by providing 2 separate images to both eyes by using a stereoscopy or parallax barrier. These technologies have limitations in field of view, viewing angle, interactivity, and distance of viewer. Researchers can create a pseudo 3D experience via head tracking, using camera or sensors to provide a glass-free experience. This allows users to see a different perspective of the scene on the phone by looking from different angles. The technology is still in its early steps with limited applications and hardware support for the full immersive experience and interaction.

The keyboard is main point of interaction in smartphones. One of the major changes in smartphones was eliminating the physical keyboard on the phone. Now most of the front face of the phone is covered with the display. Multi touch displays provide on-screen keyboard for text input. While they are similar in size, on-screen keyboards allow customization for different languages, use cases, and applications. On-screen keyboards try to imitate the haptic feedback through vibrations, but still cover almost half of the screen during typing. There are prototypes with air-inflatable buttons in real 3D forms using a transparent layer on the screen to provide realistic haptic feedback. The size of the keyboard is the main limitation for smartphones. A new prototype eliminates even the on-screen keyboard using laser projection. A small device projects a full size virtual keyboard on a flat surface like table using lasers, and tracks finger movements for recognizing key inputs. This allows both customization and experience of a full size keyboard, while making the screen fully available for other functions on the phone.

While the main input point for the phone is through virtual or on screen keyboards, a smartphone actually gets many inputs from outside through sensors. The full potential of these phones can only be achieved through new technological sensors in the phone. GPS and compass sensors allow navigation and direction capabilities, and replace the need for a GPS device for most users. Many smartphones are now listed as the most used camera devices on photography websites, and are getting closer to the quality of a DSLR camera with new image sensors. Proximity sensors understand the distance between device and user, and allow the device to turn off the display during phone calls to reduce battery usage. Accelerometer and gyro sensors provide new inputs for gaming and user interaction. Barometer, temperature, humidity, gesture, fingerprint, and heart rate sensors are becoming part of many smartphones. Researchers are already experimenting with ultrasound [3] and x-ray scanners [4] for smartphones, and we are getting one step closer to the Star Trek tricorder, a general-purpose science fiction health diagnosis device.

The future of smartphones is almost unlimited. We have already seen novel technologies introduced for smartphones as prototypes and concept designs. We can easily extend the list with intelligent assistants, nano-coating for waterproofing and self-cleaning, seamless integration of voice controls and augmented reality, environmental and medical sensors, and 3D and holographic displays. I can’t wait to see some of these technologies integrated into smartphones in the near future.

While smartphones revolutionized the way we communicate and how we carry out many of our daily tasks, at the same time they are challenging our privacy, safety, behavioral codes, social life, and the use of public space. The problems and discussions will not end here, and they are growing with the introduction of new sensors and capabilities. Increasing awareness through education about the security and privacy risks that smartphones present is the first step for protecting users from future problems. Parents and educators have a critical role for raising responsible generations, and they can start with becoming a good role model [5] on how they use their smartphones…

Acknowledgment: This article is produced at Mergeous [6], an online article and project development service for authors and publishers dedicated to the advancement of technologies in the merging realms of science and spiritual thought.

References

1. Darrell Etherington. 2013. “Cota By Ossia Aims To Drive A Wireless Power Revolution And Change How We Think About Charging,” September.
2. Jared Newman. 2013. “GPS on Your Windshield: Garmin Brings Navigation HUDs to Regular Old Cars,” Time Magazine, July.
3. “Ultrasound scan and health check using your smartphone,” BBC News, December 2013.
4. James Plafke. 2013. “Tiny terahertz chips can give smartphones X-ray vision, tricorder-like functionality,” ExtremeTech, July.
5. Larry Magid. 2012. “Smartphone Guide for Parents of Tweens and Teens,” SafeKids.com, August 2012.
6. Mergeous, Online article and project development service, mergeous.com.

Programs and documents on the computer are held in two areas: software and hardware. For scientists, one of many goals is to make the processors or chips, which are like the human brain that consists of nerves, much smaller, but still powerful enough to process many more calculations. There are many differences between the current chips and earlier ones. Chips which will be produced in the future will be smaller and probably process more calculations more quickly.

Programs, which can be seen as being the mechanical counterpart of the human mind, bring the above-mentioned improvements into daily life. New programs boost the capability of the computer in parallel with the capability of their chips. Without these programs, computers would be no more than ordinary electronic machines.

Developed technologies in fields like industry, communication, or the military bring us face to face with new developments and have made the computer an undeniable part of our lives. Mobile phones equipped with new features, medical machines which can easily make a large number of analyses and provide ease in diagnosis and treatment, robots that can operate with minimum error and have a low cost when used in production, and weapons that automatically focus on the target are all part of this progress.

With time, software and hi-tech sensors have enabled computers to communicate with people; there are now systems that are controlled by the voice, which are able to recognize a person from their iris or fingerprints, control systems which are carried out by touching a screen, etc. Such systems are operated with the help of special sensors or by some signals that carry messages from the person or the environment to the computer. The most important feature of these sensors is that the signals produced at the output are very weak and there are few differences between them. An ATM can recognize a particular person’s iris, thanks to the ability of its computer to compare the signals from the ATM’s iris scanner with previously recorded data. In this process, the computer uses the small differences that one person’s iris has to another’s. In this or similar systems, complicated programs are used, called “expert systems” or “artificial intelligence”. These programs imitate human senses, but they aim to operate with an even keener sensitivity and clearer criteria.

A question that is a subject of fiction comes to mind; “Will computers vie with or even fight with human beings?” In the mid-term, the rapid development of technology will create computers which can communicate with humans, which can understand them, and put forward ideas. A negative outcome of such a situation depends, once again, on man. Such a horrific situation could be the result of technology that can cause environmental disasters; this technology is almost identical to the one that we have described above. If we are able to establish an understanding of “civilization” which does not ignore human values for the sake of technological development, then such fears will be groundless.

Artificial Nervous Systems

As we all know, people have imitated nature in many of their inventions. In a way, artificial nervous systems imitate how a nerve cell learns and how it works. Fuzzy systems however imitate how the judgment of a human being works, rather than the nerve cells of the brain. In these systems people try to form a decision making criterion by assuming that there are endless grey tones between white and black or by assuming that there are infinite values between zero and one.

The purpose of the research on artificial nervous systems is to understand how the brain operates, then to make a system that imitates it and carries out the same operations. Artificial nervous systems are made of simple nerve cells which are bound in parallel, called process elements; these allow for real objects to be seen as if they were biological systems.

Here, the program that resembles the nerve cell operates in the same way as a nerve cell. The main part of a nerve cell is formed from the body, called a “soma”, an “axon” that is bound to the body and many “dendrites”. There are many “roots”, or synapses, on the dendrite of a cell which make contact with the dendrites of other cells. A nerve cell either transmits the electrical stimulus that comes through the axon to the other nerve cells through the synapse, or it does not transmit it, depending on whether or not the signal is over or below the threshold value. So a nerve works by itself, but its activity becomes meaningful when working as a part of a nervous system. It would be useful if we consider how the learning process occurs here. It is thought that the required data are stored in the memory center and this fact is taken as a model for some artificial nervous system software that has been successfully developed to date.

A nerve cell and the process of transporting signals from one cell to another can be written as software. It is clear that a natural nerve cell is more complex and that it is bound to more cells than an artificial one can be. The number of communication ports (synapses) of a natural nerve can vary from between 1,000 to 10,000.

An artificial cell produces output if the input value is over the cell’s threshold value; if this is not the case then there is no production. If there is output – as in natural cells – then this output is transported to the next cell group. Each cell produces its output as an input for the next cell.

A cell is separated into three groups: input, the hidden layer and output. Each group is considered to be made up of one layer, while the hidden layer can consist of more than one, according to the complexity of the job. As can be seen, the placement of the layers is similar in the process of the human body. We can compare the cells on the input layer with human senses. In this way we can teach a robot to avoid heat and cold, we can make them see and act according to this information. (Do not forget that a robot is in fact a computer.) It is natural that some sensors must be bound to the cells on the input layer. For instance, a sensor which is sensitive to heat can make the robot react to heat when the temperature is over the limit value or when the temperature is dramatically low it can move closer to a heat source. Or if pictures received from a video-camera are similar to an object that has been fed into the robot such data input can cause the robot to move to that object.

Artificial nervous systems are not only used in robot applications. They are commonly used in making clinical diagnoses, determining market-customer profiles, recognizing voices or pictures, classifications such as determining micro-structures, like germs and cell materials, economic profiles, energy sources, the futures of market shares, some predictive sciences, such as weather forecast, zipping data for computers, process control in industry, checking resources and some other matters in technological areas. As can be seen, there are many application areas for artificial nervous systems, all of which differ from one another.

The Basic Features of Artificial Nervous Systems

The features of artificial nervous systems can be simplified as follows: firstly, they can learn how to solve problems. In order to do this they use sample data and learning styles and while doing this they do not require any special help. Secondly, they can recognize important features and relations to help them distinguish different data forms.

When an artificial nervous system is operated, the first thing to be carried out is the training process. In order to do this, the program needs to have two alternating operations. It may obtain information concerning some results to be achieved, using results that come from the user, or the program is itself asked to produce some results. These two types of learning are not very different from how a human learns. One shows a young child an animal, and repeats the name. Now the child has learned the name of the animal and correlates it with the picture of the same. If no one teaches a child what a bird is, the child will all the same classify all animals that have wings and beaks and that have a certain physical shape, maybe even creating a name for the animal by him/herself. The difference between the computer and the human in this process is that a human has the ability to judge, while computers classify the animals according to their shapes and groups them thus. Naming and giving a naming feature to the computer is again a decision that a human will make. When the training process is finished, the data can be entered into the computer and the desired results can be attained.

Artificial nervous systems are changing and developing day by day. With each new development they become closer to the human nervous system; they are able to recognize different characteristics of different people and they are learning to make sorting decisions, even limited judgments. Whether or not these machines may one day enact a nightmare scenario, taking over from us is not a great threat, as whatever they are capable of doing is up to us to decide, as their “masters”. We should not fear these systems, but try to develop more of them; such systems help us in every day tasks, from drawing money out of the bank to our annual check-up at the doctor’s.

New Findings in Biology

Olfaction was long considered a uniquely mammalian trait. Scientists have disproven this by showing that many intertebrates can smell. For example, birds were thought to be unable to smell, although they have nostrils in their bills. John Audubon, a famous nineteenth-century bird artist, reached this mistaken conclusion by observing vultures confronted with a covered and an uncovered animal corpse, he concluded that they could not smell. The minute weight of the birds’ olfactory bulb consolidated this widespread misconception. Recent research shows that birds use smell when finding and distinguishing food, choosing proper nesting sites and mates, and following avian navigation routes. Ken Stager, an orinthogist at Los Angeles County Natural History Museum, used turkey vultures to disprove Audubon’s vulture experiment. Marine biologist Betsy Bang, who measured the olfactory bulbs and tissues in the brains of 151 bird species, calculated the olfactory bulb’s mass as being between 3% to 37% of the brain’s entire mass. This shows that the ratio, and not the weight, determines a bird’s ability to smell.

Other examples are as follows:

•Pigeons perceive small amounts of odorants. If their olfactory bulbs are blocked, they become lost.

•Certain seabirds (e.g., white chinned petrels) are sensitive to the chemical emitted by their main food (plankton), and so follow an olfactory path over the sea.

•European starlings smell the best region for their nesting site.

•Chickens detect inedible bugs (e.g., bright-colored bad-tasting caterpillars) through smell and sight.

•Salmon return to their hatching sites years later by using the unique olfactory memory of these sites left in their brains.

Smelling is far more developed in mammals, especially dogs and cats, which can sense parts per billion or trillion and can identify millions of different odorants. Science would benefit greatly if such abilities could be reproduced in sensors. But first, how does the human nose smell?

Perceiving Odors

Scientists divide human olfaction into steps. First, a potential odorant emits an odor’s basic elements: volatile organic compounds (VOCs). We perceive an odor when molecules are transformed into an odor by binding the receptor proteins.(1) After binding with certain types of VOCs, these receptor proteins cause depolarization. The electrical charges produce unique signals, which the epithelium’s sensory cells transmit to our neural network (axons).

These signals then are carried to a cluster of neural networks in the brain (glomeruli).(2) Ultimately, the impulse reaches the hypothalamus and describes the scent through a process of classification and identification.

Quantifying Scents

Human odor panels or gas chromatography and mass spectroscopy (GC/MS) are used to identify odors. Such quantification is problematic, however, because it is hard to quantify the VOC’s perception in the nose as a unit of odor. Quantifying mass, volume, temperature, light intensity, and the molecular concentration of a soluble substance in a solution are reasonably objective and can be measured as a multiple of a standard unit.

But a standard olfactory measure does not exist, for it varies according to time and environment. Odor concentration is expressed as a multiple of a threshold: 50% of human “sniffers” must detect-not necessarily identify-it. This threshold is defined by the American Society for Testing and Materials (ASTM), and is accepted as the absolute threshold of odor perception. It takes 5 or 10 odor units for the human panel to identify the odor. GC/MS also can identify the odor’s chemical composition.

Electronic Nose

After developments in electronic sight and hearing, scientists sought similar progress in odor perception. Research began at the University of Warwick (Coventry, England) in the 1980s. Its participants coined the term “electronic nose,” now commonly known as “e-nose.”(3) Their progress made it a commercial commodity with many applications.

E-noses have moved from being metal oxide devices, to conducting polymers, and now to laptop-size or pocket-size odor sensors. The Swiss Federal Institute of Technology (Zurich) has made one the size of a wrist-watch. However, current e-nose use is largely restricted to labs and military applications. Scientists are trying to match or surpass the human sense of smell’s accuracy and sensitivity, after which they will work on surpassing that of the canine species.

Uses and Advantages

E-noses have a wide application in agriculture. Since they can detect minute differences, an e-nose using polymer materials can determine whether a tomato is sun-ripened, picked green, or internally damaged, and whether apple juice comes from a concentrate or is authentic but pasteurized.

Volunteers often test such products. But who wants to determine if corn oil is rancid or canola oil is oxidized? E-noses, having no such “qualms,” detect changed odors in oil samples and provide far more accurate reports.

In animal science and poultry, e-noses provide detailed reports about spoiled food. Judy Arnold, a microbiologist in Athens, GA, researches food quality for the Agricultural Research Service (ARS).

In 1998, researchers discovered that e-noses can detect gases produced by spoiled poultry products. They claim that an e-nose can determine freshness, period of time in a refrigerator, and the amount of fat in white meat. Such an objective evaluation benefits poultry farmers and producers by eliminating returns of “funny-smelling” poultry. E-noses also can detect meat’s decay rate and bacteria, overall quality and freshness, the composition of mixed meat-part products (e.g., processed meat), and how long a ham has been dry-cured. Given this, the e-nose’s ability to examine a bundle of scents makes it very useful. It can perform hundreds of preliminary assessments that would occupy a chemist for months.

The military uses e-noses to detect land mines and traces of chemical-biological weapons. This is important, for over 100 million land mines litter 62 war-torn countries. Although dog-sniffers are useful, this practice is inhumane (dogs are often injured) and impractical (they need lots of training).

E-noses also are better than metal detectors and ground-penetrating radar and infrared imaging-the former detects even tiny pieces of metal, whereas the latter often images pebbles. As e-noses can identify traces of TNT or similar explosives to the 100 parts per quadrillion level, their detection rate is far more accurate and efficient. Nomadics, a Still-water, OK-based company, produces a cigar-box-sized e-nose for this purpose. Tufts University produces an optical e-nose that is designed and functions much like a mammalian nose.

Environmentalists use e-noses to analyze air. For instance, e-noses can report the chemical makeup of odors emitted by a farm’s store of manure, detect the compounds causing that odor, help minimize leaks, and determine a new diet that will decrease such odors. With their ability to detect toxic VOCs and compounds leaking from a factory’s or waste site’s storage areas, e-noses will help environmentalists force industry to change its practices. The major difficulty here remains sampling, as concentrations vary with time and place.

Caltech has used Department of Defense funding to develop a device that identifies odors in seconds. Its 32 components swell like sponges when exposed to a particular vapor, and its resistance (hence conductivity) changes accordingly. As it can detect any type of odor, doctors at the Children’s Hospital in Los Angeles are studying medical applications. Currently, it is applied to patients’ breath to help diagnose upper respiratory infections.

The major advantages of e-noses over human noses in these areas are objectivity; ability to measure odors over long real-time periods; and immunity to fatigue, infection, mental state, hazardous material, and adaptation (gradual loss of sensitivity).

How E-noses Work

E-noses have three functional components: a sample handler, a gas sensor array, and a signal processing system. Its output identifies the odorant, estimates its concentration, and relates its characteristic properties. A sensor recognizes different types and concentrations of odors through its arrays, each of which has a different sensitivity. The resulting combination provides the response pattern that enables the e-nose to identify odorants.

In a typical e-nose, a vacuum pump pulls the first air sample into the tube housing the electronic sensor arrays. The air sampling unit exposes the odorant to the sensor, after which VOCs interact with the surface and the sensor’s active material until reaching a steady state. The sensor’s response is recorded and transmitted to the signal-processing unit. When completed, a washing gas cleanses the sensor. After the reference gas is applied to the unit, the sensor is ready to measure again.

E-nose Technologies

The sensor is the e-nose’s key element, and the sensor type is its defining characteristic. There are 5 types of e-nose sensors, as follows:

Optical sensors: Optical fiber sensors work through fluorescence and chemoluminescence. The tube’s glass fibers contain a thin encoated active material in their sides and at both ends. As VOCs interact with the organic matrix’s chemical dyes, the dye’s fluorescent emission changes the spectrum. These changes then are measured and recorded for different odorous particles.

Fiber arrays with different dye mixtures can be used as sensors. These are fabricated by dipcoating (binding a plastic solution to a substrate), micro electromechanical system (MEMS), and precision machining. The main advantage is that this adjustable tool can filter out noise. Also, since many dye forms are available in biological research, sensors are cheap and easy to fabricate. But the instrumentation control systems are complex, which adds to the cost, and have a limited lifetime due to photo bleaching (the sensing process slowly consumes the fluorescent dyes).

Optical sensors are sensitive and can measure low ppb (parts per billion); however, they are still in the researach stage of development.

Spectrometry-based Sensors: This group consists of a molecular spectrum-based gas chromatography (GC), an atomic mass spectrum-based mass spectrometry (MS), and a transmitted light spectrum-based light spectrum (LS). The first two can analyze the odor’s components accurately, which is a plus. However, their use of a vapor trap to increase concentration can alter the odor’s characteristics. LS devices do not consume the sample, but do require tunable quantum-well devices. GC and MS devices are commercially available, while LS devices are only at the research stage. All spectrometry-based sensors are fabricated by MEMS and precision machining, and can measure odors to a low ppb level.

The GC tube decomposes the odorant into its molecular constituents, and MS forms a mass spectrum for each peak. The spectra then is compared to a large precompiled database of spectral peaks to classify and identify odorants.

MOSFET (Metal-oxide-silicon field-effect-transistor): The basic principle here is capacitive charge coupling. In other words, VOCs react with the catalytic metal and thereby alter the device’s electrical properties. The device’s selectivity and sensitivity can be fine-tuned by varying the metal catalyst’s thickness and composition. MOSFETs are micro-fabricated and commercially available, but can measure only parts per million. They can be manufactured by electronic interface circuits, which minimizes batch-to-batch variation. However, the gas produced by the VOC-metal reaction must penetrate the MOSFET’s gate.

Conductivity Sensors: The sensor types used here are metal oxide or conducting polymer. Both operate on the principle of conductivity, for their resistance changes as they interact with VOCs. Metal oxide sensors are common, commercially available, inexpensive, and easy to produce (they are micro-fabricated). Their sensitivity ranges from 5-500 ppm. However, they only operate at high temperatures (200°C to 400°C).

In conducting polymer sensors, VOCs bond with the polymer backbone and change the polymer’s conductivity (resistance). They are micro-fabricated together with electroplating and screen printing, are commercially available, and can measure from .1 to 100 ppm. They operate at room temperature, yet are very sensitive to humidity. Moreover, it is hard to electropolymerize the active material, which makes batch-to-batch variation inevitable. Sometimes VOCs penetrate the polymer chain, which means that the sensor must be returned to its neutral and reference state-a very time-consuming process.

Piezoelectric Sensors: These devices, which measure any change in mass, come in two varieties: quartz crystal microbalance (QCM) and surface acoustic wave (SAW) devices.

QCM sensors have a resonating disk and metal electrodes on each side. While applying the gas sample to the resonator’s surface, the polymer surface absorbs VOCs from the environment. Thus its mass increases, which increases resonance frequency. As the U.S. Navy has long used QCMs, this technology is familiar, developed, and commercially available. A QCM sensor is fabricated by screen-printing, wire bonding, and MEMS. Althoug it can measure a 1.0 Ng mass change, its MEMS fabrication and interface electronics is a major disadvantages. QCM sensors are quite linear in mass changes, their sensitivity to temperature can be adjusted, and their response to water can vary for the material used.

MEMS techniques should be handled carefully, for the surface-to-volume ratio increases drastically as dimensions approach the micrometer levels. Measurement accuracy is lost when the increasing surface-to-volume ratio begins to degrade the signal-to-noise ratio. This problem occurs in most micro-fabricated devices. SAW devices have much higher frequencies. Since 3-D MEMS processing is unnecessary, SAW devices are cheaper. As with QCM devices, many polymer coatings are available. The differential devices can be quite sensitive. However, interface electronics require more complex electronics than those of conductivity sensors for both QCM and SAW sensors. Also, as the active membrane ages, resonance frequencies can drift and so must be detected for frequency by time. SAW devices are commercially available and sensitive to mass changes at the 1.0 pg level.

Pattern Recognition

Any e-nose’s primary task is to identify an odorant and perhaps measure its concentration. After the signal processing step comes the crucial step of pattern recognition: preprocessing, feature extraction, classification, and decision-making. A database of odors must be formed for comparison purposes.

Preprocessing accounts for sensor drifts and reduces sample-to-sample variation. This can be done by normalizing sensor response ranges, manipulating sensor baselines, and compressing sensor transients.

Feature extraction involves dimensionality reduction, a crucial step for statistical data analysis, since the database’s examples usually are subject to financial constraints. The higher dimensionality caused by sensor arrays is reduced to relevant pattern-recognition information and thus extracts only significant data. As most dimensions are correlated and dependent, it is better to reduce dimensionality to a few informative axes.

Feature extraction usually is accomplished by classical principal component analysis (PGA) or linear discriminant analysis (LDA). PCA is a linear transformation that finds the maximum variance projections and the most widely used technique for feature extraction. But as PCA ignores class labels, it is not an optimal technique for odor recognition.

LDA seeks to maximize the distance between class label examples and minimize the within distance, and thus is a more appropriate approach. LDA is also a linear transformation. For instance, LDA might better discriminate subtle but crucial odor projections, whereas PCA can remove the high variance random noise in a projection.(4)

The classification stage identifies odors. Classical classification techniques are KNN (k nearest neighbors), Bayesian classifiers, and ANN (artificial neural networks]. KNN with, say, 5 nearest points will find the 5 closest matches from the precompiled database. The closest match will be assigned as the tested material’s odorant class.

Bayesian classifiers first assign a posterior probability to the classes in the lower dimension and then pick the class that maximizes the predetermined probability distribution. ANN is closer to biological odor recognition. After being trained by the odor database, it is exposed to the unknown odorant in order to recognize the largest applicable response odorant class. The classifier estimates the class and places a confidence level on it.

In decision-making, risks and application-specific knowledge are considered in order to modify the classification. All decisions are reported-even a nonmatch.

Conclusion

As this article indicates, we can expect great progress in this area. And with each step forward, science and technology will continue to point toward the Greatest Artist’s most subtle designs and allow us to appreciate them better.

Footnotes

1. There are over 100 million receptor proteins of about 1,000 different types.
2. A human olfactory bulb contains approximately 2,000 glomeruli.
3. The terms “electronic nose” and “e-nose” are incorrect, for these devices cannot be considered “real” noses. The correct terminology should be “electronic arrays for chemical sensory and identification.” However, “e-nose” has gained wide acceptance in the literature since it first appeared during a 1991 NATO workshop in Reykjavik, Iceland.
4. Such nonlinear transformations as Sammon nonlinear maps and Kohonen self organizing maps also are used in feature extraction. These preserve the distance between pairs of examples when reducing dimensionality to 2 or 3.

References

• Baltes, Henry, Dirk Lange, and Andreas Koll. “The Electronic Nose in Lilliput.” IEEE Spectrum (Sept. 1998): 35-38.
• Barinaga, Marcia. “Salmon Follow Watery Odor Home.” Science 286 (22 Oct. 1999): 705-6.
• http://csmt.jpl.nasa.gov/enose.html.
• http://faculty.washington.edu/chudler/nosek.html.
• Malakoff, David. “Following the Scent of Avian Olfaction.” Science 286 (22 Oct. 1999): 704-5.
• Mamberts, Peter. “Seven-Transmembrane Proteins as Odorant and Chemosensory Receptors.” Science 286 (22 Oct. 1999): 707-10.
• Perkins, Sid. “Eau, Brother! Electronic Noses Provide a New Sense of the Future.” Science News 157 (19 Feb. 2000): 125-27.
• Schiffmann, Susan, and H. Troy Nagle. “The How and Why of Electronic Noses.” IEEE Spectrum (Sept. 1998): 22-32.
• Stern, Peter and Jean Marx. “Making Sense of Scents.” Science 286 (22 Oct. 1999): 703.
• Wolfgang, Gopel, and Tilo Weiss. “Design for Smelling.” IEEE Spectrum (Sept. 1998): 32-34.
• www.planetee.com/planetee/servlet/DisplayDocument?ArticleID=6899.
• www.sfn.org/briefings/smell.html.

Smart structures can sense changes in their environment and respond accordingly. These adaptive structures can autonomously modify their shapes to perform the desired task regardless of the particular environmental disturbance.

“It’s difficult to bet on technology-you cannot always pick the winners, but it looks like smart materials would be the next step in engineering design,” says Craig A. Rogers, director of Virginia Tech’s Center for Intelligent Systems and Structures.

By using smart materials instead of adding mass, engineers can endow structures with built-in responses to a myriad of contingencies. In their various forms, these materials can perform as actuators, which can adapt to their environments by changing such characteristics as shape and stiffness, or as sensors, which provide actuators with information about structural and environmental changes.

Smart structures have numerous applications, among them the following:

SPACE STRUCTURES

Large space structures are subject to a variety of dynamic perturbations produced by the crew, the docking of other spacecraft, transient thermal states during the orbit, micrometeorities, and so on. The vibration amplitude of the perturbations has to be dampened in time to avoid further nonstability in the space structure.

In addition to that, the dampening of the flexible models is a necessary ingredient in achieving robust attitude control of the spacecraft.

AIRPLANES

Smart wings. Airplanes that have smart wings will control surfaces that can reshape themselves on the fly. Airplane wings will flex themselves like fish tails. With the help of smart structures, airfoil will be shaped and the aircraft’s lift will be improved. This improved lift will help to get a single-engine fighter off the deck of an aircraft carrier without a catapult.

Replacing current (and heavy) hydraulic control systems with light-weight, high-performance smart materials could increase aircraft payloads by as much as 30 percent and flight range by 50 percent.

Adaptive surfaces would replace stiff structures designed as a compromise among ideal wing shapes for various maneuvers. Eventually vertical tails, ailerons, and stiff structures could be eliminated.

HELICOPTERS

Active helicopter blades that adjust shape continuously to respond to vibration-engendering pressure changes in the air. These fluctuations knock the machinery out of alignment and cause a lot of down time. A helicopter’s maintenance schedule is approximately 15 percent of its time. In the helicopter project, piezoelectric patches on blade surfaces function both as sensors and as actuators, or as generators of counter-force.

Flutter suppression is a particularly important problem. Recent experiments in NASA wing tunnels with unoptimized smart structure designs have shown a 70 percent decrease in displacement and a 20 percent increase in blade speed by utilizing active vibration control concepts.

Active noise suspension for helicopter cabins promise greatly decreased acoustic noise/vibration intensities. This reduces stress upon crew members involved in increasingly longer duration missions.

SUBMARINES

Stealth submarines using smart skins. Smart materials technology may result in stealth submarines. Their acoustically hypersensitive smart skins would detect the pressure of an incoming sonar wave, and then automatically generate an equal but opposite counter-pressure to cancel out the ping. With nothing reflected back to enemy boat, the submarine would be invisible.

CARS

The automotive industry also is eager to incorporate intelligent materials technology. Some of the areas where smart material will be used are:

Smart car seats. Researchers are working on an industry-sponsored project to develop smart car seats that can identify primary occupants and adapt to their preferences for height, leg room, back support, and so forth.

Maintenance information. The technology exists to enable cars to tell owners how much air pressure tires have, when oil changes are needed, and other maintenance information.

Suspension and transmission. Smart materials that can change their viscosity (inherent thickness or resistance to flow) when exposed to electric or magnetic fields. This kind of smart material will lead to new kinds of auto suspensions and transmissions.

SKIS

A revolutionary piezo control module, developed by Active Control eXperts, Inc. (ACX), serves as “the brain inside the ski.”

The ACX “brain” is a small, thin, rectangular card containing piezoelectric smart materials and control circuits, which are embedded while the skis are being made. These materials detect unwanted vibrations in the skis and convert them into useful electrical energy. The control circuitry then uses the energy to smooth out the vibrations, putting the skis back on the snow. The result is a smoother ride, more responsive turning, and “solid stability.”

SOUND

Ultra-high-fidelity stereo speakers. Using piezoelectric actuators, such speakers can expand and contract in thousandths of a second in response to applied voltage. Speaker speakers in their homes and cars to achieve maximum musical effects. Their cars and houses will offer built-in surround-sound.

BUILDINGS

New bridge systems using fiber-optic lines and other sensors as strain indicators. Embedded in building materials, these devices would generate telltale optical or electrical signals when the system is stressed. Eventually, earthquake-resistant structures could be made using materials that would alter their stiffness in response to the ground’s motion, much as horseback riders flex their legs while riding.

Earthquake resistant structures. Smart structures will shake the building to cancel the effect of the earthquake.

Early warning. Smart structures will help determine possible structural damages due to the onset of structural degradation.

To chemically identify substances, scientists around the world are trying to develop artificial taste sensors that mimic the human tongue. Recently researchers at the University of Texas in Austin have developed an electronic sensor that has the potential to detect taste as well as to identify the chemicals of any substances.1 It has uses for food and beverage development as well as medical applications.

Besides the tongue, an electronic nose has also recently been developed to mimic the sense of smell, but it can only detect volatile molecules in the air. Since many chemicals of interest, such as those in food and beverages, are not easily transported into vapor phase, there needs to be a way of detecting a combination of them in solution, such as the electronic tongue.

A team of engineers and chemists in Austin has come up with a prototype of the artificial tongue which resembles the mammalian tongue in some ways. The surface of the human tongue contains cavities which hold chemical receptors known as taste buds. The artificial tongue consists of an array of tiny chemical sensors on a square-centimeter chip. The sensors are actually polymer microbeads placed inside micromachined wells on a silicon wafer which mimics real taste buds on a human tongue. Each bead responds to specific conditions (for example, high acidity or charged ions). A special camera records those colors, which the researchers can then monitor on a computer.

One obvious application of the electronic tongue is in the rapid testing of new foods and beverages; the results could be quickly compared with databases of known popular consumer tastes. When developed further, the electronic tongue should be able to analyze chemical processing streams, biological fluids and other complex mixtures without exposing human beings to possibly harmful substances such as antigens, toxins, and bacteria. For the tasting of ice cream, though, we will likely continue to use our own tongues.

FOOTNOTES

• J.J. Lavigne, S. Savoy, M.B. Clevenger, J.E. Ritchic, B. McDoniel, S.J. Yoo, E.V.
• Ansyln, J. T. McDevitt, J. B. Shear and D. Neikirk, J. Am. Chem. Soc., 1998, 120, 6429-6430.
• See also R. Dagani, Chem. & Eng. News, June 29, 1998, 12.