Skip to main content Accessibility help
×
  • Cited by 225
Publisher:
Cambridge University Press
Online publication date:
June 2012
Print publication year:
2012
Online ISBN:
9780511818363

Book description

The principles of cognition are becoming increasingly important in the areas of signal processing, communications and control. In this groundbreaking book, Simon Haykin, a pioneer in the field and an award-winning researcher, educator and author, sets out the fundamental ideas of cognitive dynamic systems. Weaving together the various branches of study involved, he demonstrates the power of cognitive information processing and highlights a range of future research directions. The book begins with a discussion of core topics such as cognition and sensing, dealing, in particular, with the perception-action cycle. Bayesian filtering, machine learning and dynamic programming are then addressed. Building on these foundations, there is detailed coverage of two important practical applications, cognitive radar and cognitive radio. Blending theory and practice, this insightful book is aimed at all graduate students and researchers looking for a thorough grounding in this fascinating field.

Reviews

"The author has led the Cognitive System Laboratory at McMaster University for many years, contributing fundamental papers to cognitive dynamic systems theory. It is therefore fortunate that this book has been written, the first one on this integrative new field. This very clear and highly pedagogical book will be greatly appreciated by many applied researchers."
-M. Iosifescu, Mathematical Reviews

Refine List

Actions for selected content:

Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Save to Kindle
  • Save to Dropbox
  • Save to Google Drive

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
×

Contents

References
References
Aghassi, M. and Bertsimas, D. (2006). Robust game theory. Mathematical Programming, Series B, 107, 231–73.
Aleksander, I. and Morton, H. (1990). An Introduction to Neural Computing. London: Chapman and Hall.
Anderson, J. (1995). An Introduction to Neural Networks. Cambridge, MA: MIT Press.
Anderson, B. D. O. and Moore, J. B. (1979). Linear Optimal Control. Englewood Cliffs, NJ: Prentice-Hall.
Annastasio, T. J. (2003). Vestibulo-occular reflex. In M. A. Arbib, ed., The Handbook of Brain Theory and Neural Networks, second edition. Cambridge, MA: MIT Press, pp. 1192–96.
Apt, K. R. and Witzel, A. (2006). A generic approach to coalition formation. International Game Theory Review, 11, 347–67.
Arasaratnam, I. and Haykin, S. (2009). Cubature Kalman filters. IEEE Transactions on Automatic Control, 54, 1254–69.
Arasaratnam, I., Haykin, S., and Hurd, T. R. (2010). Cubature Kalman filtering for continuousdiscrete systems: theory and simulations. IEEE Transactions on Signal Processing, 58, 4977–4993.
Athans, M., Wishner, R. P., and Bertolini, A. (1968). Suboptimal state estimation for continuoustime nonlinear systems from discrete noise measurements. IEEE Transactions on Automatic Control, AC13, 504–14.
Autmann, R. J. and Peleg, B. (1960). Von Neumann–Morgenstern solutions to cooperative games without side payments. Bulletin of the American Mathematical Society, 6, 173–9.
Baddeley, A. (2003). Working memory: looking back and looking forward. Nature Reviews Neuroscience, 4, 829–39.
Baird, L. C. (1999). Reinforcement learning through gradient descent. Ph.D. thesis, Carnegie- Mellon University, May.
Barlow, H. (1961). The coding of sensory images. In W. H., Thorpe and O. L., Zangwill, eds, Current Problems in Animal Behaviour. Cambridge: Cambridge University Press, pp. 331–60.
Barlow, H. (2001). Redundancy reduction revisited. Network: Computational Neural Systems, 12, 241–53.
Bar-Shalom, Y., Li, X., and Kirburajan, T. (2001). Estimation with Applications to Tracking and Control. Wiley.
Barto, A. G., Sutton, R. S., and Anderson, C. W. (1983). Neuronlike elements that can solve difficult learning control problems. IEEE Transactions on Systems, Man, and Cybernetics, 13, 835–46.
Basar, T. and Bernhard, P. (eds) (1995). H∞-Optimal Control and Related Minimax Design Problems: A Dynamic Game Approach, second edition, Boston. MA: Birkhäuser.
Basar, T. and Olsder, G. J. (1999). Dynamic Noncooperative Game Theory. SIAM.
Bellman, R. E. (1957). Dynamic Programming. Princeton, NJ: Princeton University Press.
Bellman, R. E. (1961). Adaptive Control Processes: A Guided Tour. Princeton, NJ: Princeton University Press.
Bellman, R. E. and Dreyfus, S. E. (1962). Applied Dynamic Programming. Princeton, NJ: Princeton University Press.
Bengio, Y. and LeCun, Y. (2007). Scaling learning algorithms toward AI. In L., Bottou, O., Chapelle, D., DeCoste, and J., Weston, eds, Large Scale Kernel Machines. Cambridge, MA: MIT Press. pp. 321–59.
Bennett, B.Hoffman, D.Nichola, J., and Prokash, C. (1989). Structure from two orthographic views of rigid motion. journal of the Optical Society of America, A.6, 1052–69.
Bernardo, J. M. and Smith, A. F. M. (1998). Bayesian Theory. Wiley.
Bertsekas, D. P. (2005). Dynamic Programming and Optimal Control, vol. 1, third edition. Athena Scientific.
Bertsekas, D. P. (2007). Dynamic Programming and Optimal Control, vol. 2, third edition. Athena Scientific.
Bertsekas, D. P. and Tsitsiklis, J. N. (1996). Neuro-Dynamic Programming. Belmont, MA: Athena Scientific.
Bertsekas, D. P. and Tsitsiklis, J. N. (2008). Introduction to Probability, second edition. Belmont, MA: Athena Scientific.
Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
Boyd, S. and Vandenberghe, L. (2004). Convex Optimization. Cambridge University Press.
Bradtke, S. J. and Barto, A. G. (1996). Linear least-squares algorithms for temporal difference learning. Machine Learning, 22, 33–57.
Brady, M. H. and Cioffi, J. M. (2006). The worst-case interference in DSL systems employing dynamic spectrum management. EURASIP Journal on Advanced Signal Processing, 1–11.
Bronez, T. P. (1992). On the performance advantage of multitaper spectral analysis. IEEE Transactions on Signal Processing, 40, 2941–46.
Buddhiko, M. M. (2007). Understanding dynamic spectrum access: models taxonomy, and challenges. In Proceedings of IEEE DYSPAN, April.
Cappé, O., Moulines, E., and Ryden, T. (2005). Inference in Hidden Markov Models. Springer.
Churchland, P. S. and Sejnowski, T. J. (1992). The Computational Brain. Cambridge, MA: MIT Press.
Clancy, T. C. and Goergen, N. (2008). Security in cognitive radio networks: threats and mitigation. In International Conference on Cognitive Radio Oriented Wireless Networks and Communucations (Crowncom), May, pp. 1–8.
Cohen, L. (1995). Time–Frequency Analysis. Englewood Cliffs, NJ: Prentice-Hall.
Cools, R. (1997). Constructing cubature formulae: the science behind the art. Acta Numerica, 6, 1–54.
Cover, T. M. and Thomas, J. A. (2006). Elements of Information Theory, second edition. New York: Wiley.
Danskin, J. (1967). The Theory of Max–Min. Springer-Verlag.
Dayan, P. and Abott, L. (2001). Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. Cambridge, MA: MIT Press.
Donoho, D. L. and Elad, M. (2003). Optimally sparse representation in general (nonorthogonal) dictionaries via l1 minimization. Proceedings of the National Academy of Sciences of the United States of America, 100(5), 2197–202.
Doya, K., Ishi, S., Pouget, A., and Rao, R. (2007). Bayesian Brain: Probabilistic Approaches to Neural Coding. Cambridge, MA: MIT Press.
Drosopoulos, A. and Haykin, S. (1992). Adaptive radar parameter estimation with Thomson's multiple-window method. In S., Haykin and A., Steinhardt, eds, Radar Detection and Estimation. New York: Wiley.
Dupuy, J.-P. (2009). On the Origins of Cognitive Science: The Mechanization of the Mind. Cambridge, MA: MIT Press.
Elliott, R. J. and Haykin, S. (2010). A Zakai equation derivation of the extended Kalman filter, Automatica, 46, 620–4.
FCC. (2002). Spectrum Policy Task Force, Report ET Docket No. 02-135, Federal Communications Commission, November.
FCC. (2008). Second Report and Order and Memorandum Opinion and Order In the Matter of Unlicensed Operation in the TV Broadcast Bands, Additional Spectrum for Unlicensed Devices Below 900 MHz and in the 3 GHz Band. Docket number 08-260, Federal Communication Commission, November.
FCC. (2010). Second Memorandum Opinion and Order In the Matter of Unlicensed Operation in the TV Broadcast Bands, Additional Spectrum for Unlicensed Devices Below 900 MHz and in the 3 GHz Band. Docket number 10-174, Federal Communication Commission, September.
Fisher, R. A. (1912). On an absolute criteria for fitting frequency curves. Messenger of Mathematics, 41, 155–60.
Fisher, R. A. (1922). On the mathematical foundation of theoretical statistics. Philosophical Transactions of the Royal Society of London, Series A, 222, 309–68.
Frigo, M. and Johnson, S. G. (2005). The design and implementation of FFTW3. Proceedings of the IEEE, 93, 216–31.
Fukushima, M. (2007). Stochastic and robust approaches to optimization problems under uncertainty. Proceedings of International Conference on Informatics Research for Development of Knowledge Society Infrastructure (ICKS), pp. 87–94.
Fundenberg, D. and Levine, D. K. (1999). The Theory of Learning in Games. Cambridge, MA: MIT Press.
Fuster, J. M. (2003). Cortex and Mind: Unifying Cognition. Oxford, UK: Oxford University Press.
Gardner, W. A. (1988). Signal interception: a unifying theoretical framework for feature detection. IEEE Transactions on Communications, 36, 897–906.
Gardner, W. A. ed. (1994). Cyclostationarity in Communications and Signal Processing. New York: IEEE Press.
Geman, S. and Geman, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-6, 721–41.
Giannakis, G. B. and Serpedin, E. (1998). Blind identification of ARMA channels with periodically modulated inputs. IEEE Transactions on Signal Processing, 46, 3099–104.
Gjessing, D. T. (1986). Target Adaptive Matched Illumination Radar: Principles and Applications. Peter Peregrinus Ltd on Behalf of the Institution of Electrical Engineers, London, UK.
Glimcher, P. W. (2003). Decisions, Uncertainty, and the Brain: The Science of Neuroeconomics. Cambridge, MA: MIT Press.
Golub, G. H. and Van Loan, C. F. (1996). Matrix Computations, third edition. Johns Hopkins University Press.
Grenander, U. (1976–1981). Lectures in Pattern Theory I, II and III: Pattern Analysis, Pattern Synthesis and Regular Structures. Springer-Verlag.
Gross, B. (1964). The Managing of Organizations: The Administrative Struggle. New York: Free Press of Glencoe.
Grossberg, S. (1988). Neural Networks and Natural Intelligence. Cambridge, MA: MIT Press.
Guerci, J. R. (2010). Cognitive Radar: The Knowledge-Aided Fully Adaptive Approach. Artech House.
Habibi, S. R. and Burton, R. (2003). The variable structure filter. Journal of Dynamic Systems Measurement and Control, 125, 287–93.
Hanzo, L., Akhtman, Y., Wang, L., and Jiang, M. (2010). MIMO-OFDM for LTE, WiFi and WiMAX: Coherent versus Non-Coherent and Cooperative Turbo-Transceivers. Wiley–IEEE.
Harker, P. T. and Pang, J.-S. (1990). Finite-dimensional variational inequality and nonlinear complementarity problems: a survey of theory, algorithms and applications. Mathematical Programming, 48, 161–220.
Hastie, T., Tishbirani, R., and Friedman, J. (2001). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Canada: Springer.
Haykin, S. (2000). Communication Systems, third edition. New York: Wiley.
Haykin, S. (2002). Adaptive Filter Theory, fourth edition. Prentice-Hall.
Haykin, S. (2005a). Cognitive radio: brain-empowered wireless communications. IEEE Journal on Selected Areas in Communications, 23, 201–20.
Haykin, S. (2005b). Cognitive radar networks. In 1st IEEE Workshop on Computational Advances in Multi-sensor Adaptive Processing, Jalisco State, Mexico.
Haykin, S. (2006a). Cognitive dynamic systems, point-of-view article. Proceedings of the IEEE, 94, 1910–11.
Haykin, S. (2006b). Cognitive radar: a way of the future. IEEE Signal Processing Magazine, 23, 30–41.
Haykin, S. ed. (2007). Adaptive Radar Signal Processing. Wiley.
Haykin, S. (2009). Neural Networks and Learning Machines. Upper Saddle River, NJ: Prentice-Hall.
Haykin, S. and Thomson, D. J. (1998). Signal detection in a nonstationary environment reformulated as an adaptive pattern classification problem. Proceedings of the IEEE, 86, 2325–44.
Haykin, S., Bakker, R., and Currie, B. (2002). Uncovering nonlinear dynamics: the case study of sea clutter. Proceedings of the IEEE, 90, 860–81.
Haykin, S., Thomson, D. J., and Reed, J. H. (2009). Spectrum sensing for cognitive radio. Proceedings of the IEEE, 97, 849–77.
Haykin, S., Zia, A., Xue, Y., and Arasaratnam, I. (2011). Control-theoretic approach to tracking radar: first step towards cognition. Digital Signal Processing 21, 576–85.
Haykin, S. and Xue, Y. (2012). Cognitive Radar. To be published.
Hebb, D. O. (1949). The Organization of Behavior: A Neurosychological Theory. New York: Wiley.
Hecht-Nielsen, R. (1995). Replicator neural networks for universal optimal source and coding. Science, 269, 1860–63.
Ho, Y. C. and Lee, R. C. K. (1964). A Bayesian approach to problems in stochastic estimation and control. IEEE Transactions on Automatic Control, AC-9, 333–9.
Hurd, H. L. and Miamee, A. (2007). Periodically Correlated Random Sequences. New York: Wiley.
Julier, S. J. and Uhlmann, J. K. (2004). Unscented filtering and nonlinear estimation. Proceedings of the IEEE, 92, 401–422.
Julier, S. J., Uhlmann, J. K., and Durrant-Whyte, H. F. (2000). A new method for nonlinear transformation of means and covariances in filters and estimators. IEEE Transactions on Automatic Control, 45, 472–82.
Kalman, R. (1960). A new approach to linear filtering and prediction problems. Transactions of the ASME, Journal of Basic Engineering, Series D, 82, 35–45.
Kavukcuoglu, K., Ranzato, M., and LeCun, Y. (2008). Fast inference in sparse coding algorithms with applications to object recognition. Technical Report, CBLL, Courant Institute, NYU, CBLL-TR-2008-12-01.
Kershaw, D. J. and Evans, R. J. (1994). Optimal waveform selection for tracking systems. IEEE Transactions on Information Theory, 40, 1536–50.
Kersten, D. (1990). Statistical limits to image understanding. In C., Blakemore, ed., Vision: Coding and Efficiency. Cambridge, UK: Cambridge University Press.
Khalil, H. K. (2002). Nonlinear Systems, third edition. Upper Saddle River, NJ: Prentice-Hall.
Khozeimeh, F. and Haykin, S. (2009). Dynamic spectrum management for cognitive radio: an overview. Wireless Communications and Mobile Computing, 9, 1447–59.
Khozeimeh, F. and Haykin, S. (2010). Self-organizing dynamic spectrum management for cognitive radio networks. In The 8th Conference on Communication Networks and Services Research (CNSR-2010), pp. 1–8.
Knill, D. C. and Pouget, A. (2004). The Bayesian brain: the role of uncertainty in neural coding and computation for action. Trends in Neuroscience, 12, 712–19.
Knill, D. C. and Richards, W. eds. (1996). Perception as Bayesian Inference. Cambridge, UK: Cambridge University Press.
Krishnamurthy, V. and Djonin, D. V. (2009). Optimal threshold policies for POMDPs in radar resource management. IEEE Transactions on Signal Processing, 57, 3954–69.
Kumarasan, R. and Tufts, D. W. (1983). Estimating the angles of arrival of multiple plane waves. IEEE Transactions on Aerospace and Electronic Systems, AES-19, 134–9.
Li, Y. and Stüber, G. (2006). Orthogonal Frequency Division Multiplexing for Wireless Communications. Springer.
Lo, J. T. and Bassu, D. (2001). Adaptive vs. accommodative neural networks for adaptive system identification. In Proceedings of the International Joint Conference on Neural Networks, Washington, DC, July, pp.1279–1284.
Lo, J. T. and Yu, L. (1995). Adaptive neural filtering by using the innovations approach. In Proceedings of the 1995 World Congress on Neural Networks, vol. 2, July, pp. 29–35.
Loève, M. (1946). Fonctions aléatoires de second ordrer. Revue Scientifique Paris, 84, 195–206.
Loève, M. (1963). Probability Theory. Van Nostrand.
Luo, Z. and Pang, J. (2006). Analysis of iterative waterfilling algorithm for multiuser power control in digital subscriber lines. EURASIP Journal of Applied Signal Processing, 2006, ID 24012, 1–10.
Maei, H. R. and Sutton, R. S. (2010). GQ(λ): a general gradient algorithm for temporal difference prediction learning with eligibility traces. In E., Baum, M., Hutter, and E., Kitzelmann, eds, AGI 2010. Atlantis Press, pp. 91–6.
Maei, H. R., Szepesva′ri, Cs., Bhatnagar, S., and Sutton, R. S. (2010). Toward off-policy learning control with function approximation. In Proceedings of the 27th International Conference on Machine Learning, Haifa, Israel.
Marple, L. S. L. (1987). Digital Spectral Analysis with Applications. Englewood Cliffs, NJ: Prentice-Hall.
Maybeck, P. S. (1982). Stochastic Models, Estimation, and Control, vol. 2. New York: Academic Press.
Maynard Smith, J. (1974). The theory of games and the evolution of animal conflicts. Journal of Theoretical Biology, 47, 209–21.
Maynard Smith, J. (1982). Evolution and the Theory of Games. Cambridge, UK: Cambridge University Press.
McLaughlin, D., Payne, D., Chandrasekar, V., Philips, B., Kurose, J., Zink, M., et al. (2009). Short-wavelength technology and the potential for distributed networks of small radar systems. Bulletin of the American Meteorological Society, 90, 1797–1817.
Mitola, J. (2000). Cognitive radio: an integrated agent architecture for software defined radio. Doctor of Technology Dissertation, Royal Institute of Technology, Sweden.
Mitola, J. and McGuire, G. Q. (1999). Cognitive radio: making software radios more personal. IEEE Personal Communications, 6(4), 13–18.
Molisch, A. F. (2005). Wireless Communications. Chichester, UK: IEEE Press/Wiley.
Molisch, A. F., Greenstein, L. J., and Shafi, M. (2009). Propogations issues for cognitive radio, Proceeding of the IEEE, 97, 787–804.
Mooers, C. N. K. (1973). A technique for the cross-spectrum analysis of pairs of complex-valued time series, with emphasis on properties of polarized components and rotational invariants. Deep-Sea Research, 20, 1129–41.
Morrone, C. and Burr, D. (2009). Visual stability during saccadic eye movements. In M. S., Gazzaniga, editor-in-chief, The Cognitive Neurosciences, fourth edition. MIT Press, pp. 511–24.
Morse, P. M. and Feshbach, H. (1953). Methods of Theoretical Physics, Part I. New York: McGraw-Hill.
Nagurney, A. and Zhang, D. (1996). Projected Dynamical Systems and Variational Inequalities with Applications. Springer.
Narendra, K. S. and Parthasarathy, K. (1990). Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks, 1, 4–27.
Nash, J. F. (1950). Equilibrium points in n-person games. Proceedings of the National Academy of Sciences of the United States of America, 86, 48–9.
Nash, J. F. (1951). Non-cooperative games. Annals of Mathematics, 54, 286–95.
Nicolis, G. and Prigogine, I. (1989). Exploring Complexity: An Introduction. W. H. Freeman.
Olshausen, B. A. (2011). Personal communication.
Olshausen, B. A. and Field, D. J. (1996). Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 381, 607–9.
Ortiz, S. (2008). The wireless industry begins to embrace femtocells. Computer, 41, 14–17.
Parsons, J. (2000). The Mobile Radio Propagation Channel. New York: Wiley.
Percival, D. B. and Walden, A. T. (1993). Spectral Analysis for Physical Applications. Cambridge, UK: Cambridge University Press.
Picinbone, B. (1996). Second-order complex random vectors and normal distributions. IEEE Transactions on Signal Processing, 44, 2637–40.
Posner, M., ed. (1989). Foundations of Cognitive Science. Cambridge, MA: MIT Press.
Powell, W. B. (2007). Approximate Dynamic Programming: Solving the Curses of Dimensionality. Hoboken, NJ: Wiley.
Press, W. and Teukolsky, S. (1990). Orthogonal polynomials and Gaussian quadrature with nonclassical weighting functions. Computers in Physics, 4, 423–26.
Puskorius, G. V. and Feldkamp, L. A. (2001). Parameter-based Kalman filter training: theory and implementation. In S., Haykin, ed., Kalman Filtering and Neural Networks. Wiley.
Pylyshyn, Z. (1984). Computation and Cognition: Toward a Foundation for Cognitive Science. Cambridge, MA: MIT Press.
Ranzato, M., Boureau, Y., and LeCun, Y. (2007). Sparse feature learning for deep belief networks. In Neural Information Processing Systems (NIPS), Vancouver, B.C., Canada.
Reber, A. (1995). Dictionary of Psychology. London: Penguin Books.
Ristic, B., Arulampalam, S., and Gordon, N. (2004). Beyond the Kalman Filter: Particle Filters for Tracking Applications. Boston, MA: Artech House.
Robbins, H. and Monro, S. (1951). A stochastic approximation method. Annals of Mathematical Statistics, 22, 400–7.
Robert, C. P. (2007). The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementations, second edition. Springer.
Robert, C. P. and Casella, G. (2004). Monte Carlo Statistical Methods, second edition. Springer.
Rogers, T. and McLelland, J. L. (2004). Semantic Cognition: A Parallel Distributed Processing Approach. Cambridge, MA: MIT Press.
Ross, S. M. (1983). Introduction to Stochastic Dynamic Programming. New York: Academic Press.
Rumelhart, D. E. and McLelland, J. L. eds. (1986). Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1. Cambridge, MA: MIT Press.
Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986). Learning internal representations by error propagation. In D. E., Rumelhart and J. L., McLelland, eds, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1. Cambridge, MA: MIT Press, pp. 318–62.
Saad, W., Han, Z., Debbah, M., Horungnes, A., and Basar, T. (2009). Coalitional game theory for communication networks. IEEE Signal Processing Magazine, 26, 77–97.
Sanger, T. D. (1989). Optimal unsupervised learning in a single-layer linear feedforward neural network. Neural Networks, 12, 459–73.
Sarkka, S. (2007). On unscented Kalman filtering for state estimation of continuous-time nonlinear systems. IEEE Transactions on Automatic Control, 52, 1631–41.
Schmidt, R. (1981). A signal subspace approach to multiple emitter of location and spectral estimation. Ph.D. dissertation, Stanford University, Stanford, CA.
Schultz, W. (1998). Predictive reward signal of dopamine neurons. Journal of Neurophysiology, 80, 1–27.
Schuster, H. G. (2001). Complex Adaptive Systems: An Introduction. Springer-Verlag.
Sejnowski, T. J. (2010). Personal communication.
Selfridge, O. G. (1958). Pandamonium: a paradigm for learning. In Proceedings of a Symposium held at the National Physical Laboratory, November. London: HMSO.
Serpedin, E., Panduru, F., Sari, I., and Giannakis, G. B. (2005). Bibliography on cyclostationarity. Signal Processing, 85, 2233–303.
Setoodeh, P. (2010). Dynamic models of cognitive radio networks Ph.D. thesis, McMaster University, Ontario.
Setoodeh, P. and Haykin, S. (2009). Robust transmit power control for cognitive radio. Proceedings of the IEEE, 97, 915–39.
Seung, S. (2000). Half a century of Hebb. Nature Neuroscience, 3, 1166–1167.
Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27, 379–423, 623–56.
Shellhammer, S. J. (2008). Spectrum sensing in IEEE 802.22. In Cognitive Information Processing Workshop, Greece, June 2008.
Shellhammer, S. J. (2010). Personal communication.
Simmons, J. A., Saillant, P. A., and Dear, S. P. (1992). Through a bat's ear. IEEE Spectrum, 29(3), 46–8.
Skolnik, M. I. (2008). Radar Handbook. McGraw-Hill.
Slepian, D. (1965). Some asymptotic expansions for prolate spheroidal wave functions. Journal of Mathematics and Physics, 44, 99–140.
Slepian, D. (1978). Prolate spheroidal wave functions, Fourier analysis and uncertainty. Bell System Technical Journal, 57, 1371–1430.
Stein, D. L., ed. (1989). Lectures in the Sciences of Complexity. Addison-Wesley.
Stevenson, C. R., Chouinand, G., Lei, Z., Hu, W., Shellhammer, S. J., and Caldwell, W. (2009). IEEE 802.22: the first cognitive radio wireless regional area network standard. IEEE Communications Magazine, 47(1), 130–38.
Stroud, A. H. (1966). Gaussian Quadrature Formulas. Englewood Cliffs, NJ: Prentice-Hall.
Stroud, A. H. (1971). Approximate Calculation of Multiple Integrals. Englewood Cliffs, NJ: Prentice-Hall.
Subramanian, A. P. and Gupta, S. H. (2007). Fast spectrum allocation in coordinated spectrum access based cellular networks. In 2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, DySPAN 2007, Dubin, 17–20 April, pp. 320–330.
Suga, N. (1990). Cortical computational maps for auditory imaging. Neural Networks, 3, 3–21.
Sutton, R. S. (1984). Temporal credit assignment in reinforcement learning. Ph.D. dissertation, University of Massachusetts, Amherst, MA.
Sutton, R. S. and Barto, A. G. (1998). Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press.
Sutton, R. S. (1988). Learning to predict by the methods of temporal differences. Machine Learning, 3, 9–44.
Teo, K. (2007). Nonconvex robust optimization. Ph.D. thesis, Massachusetts Institute of Technology, Cambridge, MA.
Thomson, D. J. (1982). Spectrum estimation and harmonic analysis. Proceedings of the IEEE, 70, 1055–96.
Thomson, D. J., (2000). Multitaper analysis of nonstationary and nonlinear time series data. In Fitzgerald, W., Smith, R., Walden, A., and Young, P., eds, Nonlinear and Nonstationary Signal Processing, Cambridge, UK: Cambridge University Press.
Trappenberg, T. P. (2010). Fundamentals of Computational Neuroscience. Oxford University Press.
Utkin, V. I. (1992). Sliding Modes in Control and Optimization. Springer-Verlag.
Utkin, V. I., Guldner, J., and Shi, J. (2009). Sliding Mode Control in Electro-Mechanical Systems, second edition. CRC Press.
Van Trees, H. L. (1968). Detection, Estimation, and Modulation Theory, Part 1. Wiley, New York.
Van Trees, H. L. (1971). Detection, Estimation, and Modulation Theory, Part III. New York: Wiley.
Von Neumann, J. and Morgenstern, O. (1944). Theory of Games and Economic Behavior. Princeton, NJ: Princeton University Press.
Watkins, C. I. C. H. (1989). Learning from delayed rewards. Ph.D. thesis, Cambridge University, Cambridge, UK.
Watkins, C. I. C. H. and Dayan, P. (1992). Q-learning. Machine Learning, 8, 279–92.
Weisbunch, G. (1991). Complex System Dynamics. Addison-Wesley.
Welch, P. D. (1967). The use of fast Fourier transform for the estimation of power spectra: a method based on time-averaging over short modified periodograms. IEEE Transactions on Audio and Electroacoustics, AU-15, 70–3.
Werbos, P. (2004). ADP: goals, opportunities and principles. In J., SiA. G., BartoW. B., Powell, and D., Wusch II, eds Handbook of Learning and Approximate Dynamic Programming. Wiley.
Wicks, M. C. (2010). Waveform diversity: the way forward. Keynote Lecture. In 5th International Waveform Diversity and Design Conference, Niagara Falls, Ontario, Canada, August.
Wicks, M. C., Mokole, E., Blunt, S., Schneible, R., and Amuso, V. (2010). Principles of Waveform Diversity and Design. SciTech.
Widrow, B. and Stearns, S. D. (1985). Adaptive Signal Processing. Prentice-Hall.
Wiener, N. (1948). Cybernetics: Or Control and Communication in the Animal and the Machine. Cambridge: MIT Press.
Williams, J. L., Fisher, J. W. III, and Wilsky, A. S. (2007). Approximate dynamic programming for communication-constrained sensor network management. IEEE Transactions on Signal Processing, 55, 4300–11.
Wolfowitz, J. (1952). On the stochastic approximation method of Robbins and Monro. Annals of Mathematical Statistics, 22, 457–61.
Woodward, P. (1953). Probability and Information Theory, with Applications to Radar, London: Pergamon Press.
Xue, Y. (2010). Cognitive radar: theory and simulations, Ph.D. thesis, McMaster University, Ontario.
Younger, S., Hockreiter, S., and Conwall, P. (2001). Meta-learning with backpropagation. In Proceedings of the Joint International Conference on Neural Networks, Washington, DC, pp. 2001–6.
Yu, W. (2002). Competition and cooperation in multi-user communication environments. Doctoral dissertation, Stanford University, Stanford, CA.
Yuille, A. L. and Clark, J. (1993). Bayesian models, deformable templates and competitive priors. In L., Harris and M., Jenkins, eds, Spatial Vision in Humans and Robots. Cambridge, UK: Cambridge University Press.
Zeng, Y., Liang, Y.-C., Hoang, A. T., and Zhang, R. (2010). A review on spectrum sensing for cognitive radio: challenges and solutions. EURASIP Journal on Advances in Signal Processing, 2010, Article ID 381465.
Zhang, Q. T. (2011). Theoretical performance and thresholds of the multitaper method for spectrum sensing, IEEE transaction on Vehicular Technology, 60, 2128–38.
Further reading
Bellman, R. E. (1971). Introduction to the Mathematical Theory of Control Processes, vol. II. New York: Academic Press.
Debreu, G. (1952). A social equilibrium existence theorem. Proceedings of National Academy of Sciences of the United States of America, 38, 886–93.
Dreyfus, S. E. and Law, A. (1977). The Art and Theory of Dynamic Programming. New York: Academic Press.
Facchinei, F. and Pang, J. S. (2003). Finite-Dimensional Variational Inequalities and Complementarity Problems. Springer.
Jazwinski, H. (2007). Stochastic Processes and Filtering Theory. New York: Dover Publications.
Krazios., R. S. and. Stone, L. S (2003). Pursuit eye movements. In M. A., Arbib, ed., The Handbook of Brain Theory and Neural Networks, second edition, MIT Press, pp. 929–34.
Langoudakis, M. and Parr, R. (2003). Least-squares policy iteration. Journal of Machine Learning Research, 4, 1107–49.
Laplace, P. (1812). Théorie Analytique de Probabilités, Paris: Courcier.
Mumford, D. (1996). Pattern theory: a unifying perspective. In D. C., Knill and W., Richards, eds, Perception as Bayesian Inference. Cambridge, UK: Cambridge University Press.
Olshausen, B. A. and Field, D. J. (1997). Sparse coding with an overcomplete basis set: a strategy employed by VI?Vision Research, 37, 3311–25.
Osborne, M. and Rubenstein, A.T. (1994). A Course in Game Theory. Cambridge, MA: MIT Press.
Pang, J. S. and Facchinei, F. (2003). Finite-Dimensional Variational Inequalities and Complementarity Problems. Springer-Verlag.
Putterman, M. L. (1994). Markov Decision Processes: Discrete Stochastic Dynamic Programming. New York: Wiley.

Metrics

Altmetric attention score

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Book summary page views

Total views: 0 *
Loading metrics...

* Views captured on Cambridge Core between #date#. This data will be updated every 24 hours.

Usage data cannot currently be displayed.