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Kandel A, ed (1991) Fuzzy Expert Systems CRC Press, Boca Raton, Florida Kasabov NK (1998) Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering MIT Press, Cambridge, Massachusetts Kecman V (2001) Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Systems (Complex Adaptive Systems) MIT Press, Cambridge, Massachusetts Klir GJ, Yuan B (1995) Fuzzy Sets and Fuzzy Logic: Theory and Applications Prentice-Hall PTR, Upper Saddle River, New Jersey Klir GJ, Yuan B, eds (1996) Fuzzy Sets, Fuzzy Logic and Fuzzy Systems: Selected Papers by Lot A Zadeh World Scienti c, Singapore Mamdani EH (1976) Advances in the linguistic synthesis of fuzzy controller International Journal of Man-Machine Studies 8(6): 669 678 McDermott J (1980) R1: an expert in the computing system domain In Proceedings of the National Conference on Arti cial Intelligence, 269 271 Minsky M (1975) A framework for representing knowledge.

In The Psychology of Computer Vision, Winston PH, ed, McGraw-Hill, New York Mitchell TM (1997) Machine Learning McGraw-Hill, New York Newell A, Simon HA (1972) Human Problem Solving Prentice-Hall, Englewood Cliffs, New Jersey Pedrycz W (1995) Fuzzy Sets Engineering CRC Press, Boca Raton, Florida Quillian MR (1968) Semantic Memory In Semantic Information Processing, Minsky M, ed, 227 270 MIT Press, Cambridge, Massachusetts Ruspini EH (1982) Possibility approaches for advanced information systems Computer 15: 83 91 Schank RC, Childers PG (1984) The Cognitive Computer: on Language, Learning and Arti cial Intelligence Pearson Addison-Wesley, New York Scott AC, Clayton JE, Gibson EL (1991) A Practical Guide to Knowledge Acquisition Addison-Wesley, New York Shortliffe EH (1976) Computer-Based Medical Consultations: MYCIN Elsevier, New York Siler W, Tucker D, Buckley J (1987).

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A parallel rule ring production system with resolution of memory con icts by weak fuzzy monotonicity, applied to the classi cation of multiple objects characterized by multiple uncertain features International Journal of Man-Machine Studies 26: 321 332 Thomas SF (1995) Fuzziness and Probability ACG Press, Wichita, Kansas Weinschenk JJ, Marks II RJ, Combs WE (2003) Layered URC fuzzy systems: a novel link between fuzzy systems and neural networks Proceedings of the IEEE International Joint Conference on Neural Networks, Portland, Oreson: 2995 3000 Zadeh LA (1965) Fuzzy sets Information and Control 8: 338 353 Zadeh LA (1972) A fuzzy-set-theoretic interpretation of linguistic hedges Journal of Cybernetics 2: 4 34 Zadeh LA (1974) On the analysis of large-scale systems In Fuzzy Sets and their Application to Cognitive and Decision Processes , Academic Press, New York..

However, these cameras exhibit a lag in response time that results in the appearance of comet tails for rapidly moving objects, a falloff in resolution from the center to the periphery of the image, gain instability, and significant geometric distortion and shading, which make them less desirable for quantitative applications..

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1.9 Summary

Life was easier when your employer bought your health insurance, paid half your social security obligation, and withheld taxes all year so that you stayed in the good graces of the IRS. Once you re in business for yourself you have to pay taxes as you go. The IRS is not about to give you a free loan until next April 15th rolls around. If you expect to owe tax of $1,000 or more when you file your annual return, you likely need to pay estimated taxes four times a year. There is a loophole though see the prior year safe harbor discussion coming up.

For each burst, the end-device also constructs a Burst Control Packet (BCP), which contains information about the burst, such as the burst length and the burst destination address. This control packet is immediately sent along the route of the burst and is electronically processed at each node. The function of the control packet is to inform the nodes of the impending data burst and to set up an end-to-end optical path between the source and the destination. Upon receipt of the control packet, an OBS core node schedules a free wavelength on the desired output port and configures its switching fabric to transparently switch the upcoming burst. After a delay time, known as the offset, the end-device also transmits the burst itself. The burst travels as an optical signal over the end-to-end optical path set up by its control packet. This optical path is torn down after the burst transmission is completed. Figure 12.5 shows a generic model of an edge-OBS node and its functionality. The separation of the control information and the burst data is one of the main advantages of OBS. It facilitates efficient electronic control while it allows for a great flexibility in the format and transmission rate of the user data. This is because the bursts are transmitted entirely as an optical signal, which remains transparent throughout the network. 12.6.1.1 Connection provisioning

and Industry Association of New Jersey), (February 2002), 28 29.

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