21-01-2010, 08:28 PM
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ABSTRACT
While the inclusion of visual, aural, and tactile senses into virtual reality systems is widespread, the sense of smell has been largely ignored .A chemical vapor sensing system is composed of an array of tin-oxide vapor sensors coupled to an artificial neural network. The artificial neural network is used in the recognition of different smells. When a chemical sensor array is combined with an automated pattern identifier, it is often referred to as an electronic or artificial nose. An electronic nose will potentially be a key component in an olfactory input to a telepresent virtual reality system. The identified odor would be electronically transmitted from the electronic nose at one site to an odor generation system at another site. This combination would function as a mechanism for transmitting olfactory information for telepresence olfactory information.
MODELS OF OLFACTORY SYSTEM
The goal of much of the research regarding the olfactory system is to understand how individual odors are identified. Many researchers have produced mathematical models of the olfactory system. These models often include simulations of the neurobiological information processing systems. It is interesting to consider that the mammalian epithelium contains from approximately 1 million sensory neurons in the mouse, to 10 million sensory neurons in the human, to 100 million sensory neurons in the pig.
Electronic noses are much simpler than almost all biological olfactory systems and detect only a small range of odors. However, for many potential tele-smell applications in the near future, a predetermined and limited set of odors is likely. Thus, it is likely an electronic nose will be a key component in an olfactory input to a telepresent virtual reality system.
ELECTRONIC/ARTIFICIAL NOSES
Electronic/artificial noses are being developed as systems for the automated detection and classification of odors, vapors, and gases. The two main components of an electronic nose are the sensing system and the automated pattern recognition system. The sensing system can be an array of several different sensing elements (e.g., chemical sensors), where each element measures a different property of the sensed odor, or it can be a single sensing device (e.g., spectrometer) that produces an array of measurements for each odor, or it can be a combination. Each odor presented to the sensor array produces a signature or pattern characteristic of the odor. By presenting many different odors to the sensor array, a database of signatures is built up. This database of labeled odor signatures is used to train the pattern recognition system. The goal of this training process is to configure the recognition system to produce unique mappings of each odor so that an automated identification can be implemented.
The quantity and complexity of the data collected by sensor arrays can make conventional chemical analysis of data in an automated fashion difficult. [One approach to odor identification is to build an array of sensors, where each sensor in the array is designed to respond to a specific odor. With this approach, the number of unique sensors must be at least as great as the number of odors being monitored. It is both expensive and difficult to build highly selective chemical sensors.
Artificial neural networks (ANNs), which have been used to analyze complex data and for pattern recognition, are showing promising results in chemical vapor recognition. When an ANN is combined with a sensor array, the number of detectable odors is generally greater than the number of sensors [5]. Also, less selective sensors which are generally less expensive can be used with this approach. Once the ANN is trained for odor recognition, operation consists of propagating the sensor data through the network. Since this is simply a series of vector-matrix multiplications, unknown odors can be rapidly identified in the field. Electronic noses that incorporate ANNs have been demonstrated in the following applications: monitoring food and beverage odors, controlling the cooking of food, automated flavor control, analyzing fuel mixtures, discriminating the smoke from different brands of cigarettes, detecting oil leaks, identifying different types of alcohol, identifying odors from household chemicals, and quantifying individual components in gas mixtures. Several ANN configurations have been used in electronic noses including back propagation-trained, feed-forward networks; Kohonen's self-organizing maps (SOMs); Learning Vector Quantizers (LVQs); Hamming networks; Boltzmann machines; and Hopfield networks.
Due to the limitations of current technology, many ANN based electronic noses have less than 20 sensing elements and less than 100 neurons. These systems are designed for specific applications with a limited range of odors. Systems that mimic more of the functionality of the human olfactory system will require a much larger set of sensing elements and a larger ANN.
ARTIFICIAL NEURAL NETWORKS
An Artificial Neural Network (ANN) is an information processing paradigm that was inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurons. This is true of ANNs as well.
Another advantage of the parallel processing nature of the ANN is the speed performance. During development, ANNs are configured in a training mode. This involves a repetitive process of presenting data from known diagnoses to the training algorithm. This training mode often takes many hours. The payback occurs in the field where the actual odor identification is accomplished by propagating the data through the system which takes only a fraction of a second. Since the identification time is similar to the response times of many sensor arrays, this approach permits real-time odor identification.
ELECTRONIC NOSE
The prototype electronic nose, shown in Figure 1, identifies odors from several common household chemicals. It employs an array of nine tin-oxide gas sensors, a humidity sensor, and two temperature sensors to examine the environment. Although each sensor is designed for a specific chemical, each responds to a wide variety of chemical vapors. Collectively, these sensors respond with unique signatures (patterns) to different chemicals. During the training process, various chemicals with known mixtures are presented to the system. In the initial studies, the back propagation algorithm was used to train the ANN to provide the correct analysis of the presented chemicals.
The nine tin-oxide sensors are commercially available Taguchi- type gas sensors obtained from Figaro Co. Ltd. (Sensor 1, TGS 109; Sensors 2 and 3, TGS 822; Sensor 4, TGS 813; Sensor 5, TGS 821; Sensor 6, TGS 824; Sensor 7, TGS 825; Sensor 8, TGS 842; and Sensor 9, TGS 880). Exposure of a tin-oxide sensor to a vapor produces a large change in its electrical resistance [15]. The humidity sensor (Sensor 10: NH- 02) and the temperature sensors (Sensors 11 and 12: 5KD-5) are used to monitor the conditions of the experiment and are also fed into the ANN.
The prototyped ANN was constructed as a multilayer feed forward network and was trained with the back propagation of error algorithm by using a training set from the sensor database [16]. This prototype was initially trained to identify odors from eight household chemicals: acetone, correction fluid, contact cement, glass cleaner, isoproponal alcohol, lighter fluid, rubber cement, and vinegar. Another category, "none," was used denote the absence of all chemicals except those normally found in the air. This resulted in nine output categories from the ANN. Figure 2 illustrates the network layout.
During operation, the sensor array "smells" an odor, the sensor signals are digitized and fed into a computer, and the ANN (implemented in software) then identifies the chemical. This identification time is limited only by the response of the chemical sensors, but the complete process can be completed within seconds. Figure 3 illustrates both the sensor response and the ANN classification of the system for a variety of test chemicals presented to the prototype. The identified odors can then be transmitted to an odor regeneration system.
TELE- SMELL DEMONSTRATION
It is composed of an odor identification system (e.g., electronic nose), transmission channel, and an odor regeneration system. To date, we have only demonstrated the odor identification part. However, we are currently preparing for a demonstration of tele-smell. The goal of this demonstration is to show the concept of tele-smell and olfactory input in a virtual reality environment. In this demonstration, the electronic nose described in the previous section will be placed in a physical mock-up of a house with various household chemicals distributed throughout the house (e.g., vinegar, cleaners, etc.). The odor generation system will be placed in a virtual/artificial reality mock- up of a house. The electronic nose will be moved around the physical house mock-up in a walk-around demonstration. The identified odors will be transmitted to the odor generation system located in the virtual house. This system will then regenerate the detected odor in the virtual environment.
APPLICATIONS
1. Used in tele-medicine
2. Can be used in video games.
3. We will be able to smell and taste products before buying them from a website.
4. Can be used to send a perfumed e-mail.
5. This technique can be used to detect smells in dangerous environments.
CONCLUSION
The electronic nose described in this paper is adequate for demonstrating the concept of tele-smell and olfactory input in a virtual reality environment. This electronic nose has worked well in identifying several common household chemicals. The ultimate goal of our research will be to demonstrate tele- smell with odors of importance in tele-present surgery and tele-present battlefield surgery. This will involve the construction of an automated odor sensing system (electronic nose) that can identify odors generated by the human body (e.g., bile, urine, blood, etc.), integration of the electronic nose with the odor generation system, and demonstration of tele-smell in tele-surgery. This will require a sensing system more sophisticated than the common household chemical odor system described in this paper. Possible sensing technologies for this application include mass spectrometry. However, inclusion of mass spectrometers into portable systems will require a large effort in miniaturizing the mass spectrometer and its associated hardware.
REFERENCES
1. P. Érdi and G. Barna, Neurodynamic Approach to Odor Processing,
In: Proceeding of the 2001 International Joint Conference on Neural
Networks (IJCNN'91). ISBN: 0-7803-0164-1. IEEE Press, Piscataway, NJ,
2001, Vol. 2, pp. 653-656.
2. T. Leen, M. Webb and Steve Rehfuss, Encoding and Classification
in a Model of Olfactory Cortex. In: Proceeding of the 2001
International Joint Conference on Neural Networks (IJCNN'91). ISBN: 0-
7803-0164-1. IEEE Press, Piscataway, NJ, 2001, Vol. 2, pp. 553-559.
3. Z. Li and J.J. Hopfield, Modeling the Olfactory Bulb and its
Neural Oscillatory Processings. Biological Cybernetics 61 (1999) 379-
392.
4. Y. Yao and W.J. Freeman, Pattern Recognition in Olfactory
Systems: Modeling and Simulation. In: Proceeding of the 1999
International Joint Conference on Neural Networks (IJCNN'89). IEEE
Press, Piscataway, NJ, 1999, Vol. 1, pp. 699- 704.
5. B.S. Hoffheins, Using Sensor Arrays and Pattern Recognition to
Identify Organic Compounds. MS-Thesis, The University of Tennessee,
Knoxville, TN, 1999.
CONTENTS
1. MODELS OF OLFACTORY SYSTEM
2. ARTIFICIAL NEURAL NETWORKS
3. ELECTRONIC NOSE
4. TELE-SMELL DEMONSTRATION
5. APPLICATIONS
6. CONCLUSION
7. REFERECES
