a computational proof of complexity of some restricted counting problems

South Central University

Evaluation of a combined wavelet and a combined principal component analysis classification system for BCG diagnostic problem

Heart disease is one of the main factor causing death in the developed countries. Over several decades, variety of electronic and computer technology have been developed to assist clinical practices for cardiac performance monitoring and heart disease diagnosis. Among these methods, Ballistocardiography (BCG) has an interesting feature that no electrodes are needed to be attached to the body during the measurement. Thus, it is provides a potential application to asses the patients heart condition in the home. In this paper, a comparison is made for two neural networks based BCG signal classification models. One system uses a principal component analysis (PCA) method, and the other a discrete wavelet transform, to reduce the input dimensionality. It is indicated that the combined wavelet transform and neural network has a more reliable performance than the combined PCA and neural network system. Moreover, the wavelet transform requires no prior knowledge of the statistical distribution of data samples and the computation complexity and training time are reduced.

Intelligence Without Reason

Computers and Thought are the two categories that together define Artificial Intelligence as a discipline. It is generally accepted that work in Artificial Intelligence over the last thirty years has had a strong influence on aspects of computer architectures. In this paper we also make the converse claim; that the state of computer architecture has been a strong influence on our models of thought. The Von Neumann model of computation has lead Artificial Intelligence in particular directions. Intelligence in biological systems is completely different. Recent work in behavior-based Artificial Intelligenge has produced new models of intelligence that are much closer in spirit to biological systems. The non-Von Neumann computational models they use share many characteristics with biological computation.

Coherent Behavior from Incoherent Knowledge Sources in the Automatic Synthesis of Numerical Computer Programs

A fundamental problem in artificial intelligence is obtaining coherent behavior in rule-based problem solving systems. A good quantitative measure of coherence is time behavior; a system that never, in retrospect, applied a rule needlessly is certainly coherent; a system suffering from combinatorial blowup is certainly behaving incoherently. This report describes a rule-based problem solving system for automatically writing and improving numerical computer programs from specifications. The specifications are in terms of "constraints" among inputs and outputs. The system has solved program synthesis problems involving systems of equations, determining that methods of successive approximation converge, transforming recursion to iteration, and manipulating power series (using differing organizations, control structures, and argument-passing techniques).

An artificial immune system for continuous analysis of time-varying data

M. Neal, An Artificial Immune System for Continuous Analysis of Time-Varying Data, in Proceedings of the 1st International Conference on Artificial Immune Systems (ICARIS), 2002, eds J Timmis and P J Bentley, volume 1, pages 76-85,

Meta-stable memory in an artificial immune network

Neal, M., Meta-stable memory in an artificial immune network, Proceedings of the 2nd International Conference on Artificial Immune Systems {ICARIS}, Springer, 168-180, 2003,LNCS 2787/2003

Knowledge based error recovery in industrial robots

M. H. Lee, D. P. Barnes, and N. W. Hardy. Knowledge based error recovery in industrial robots. In Proc. 8th. Int. Joint Conf. Artificial Intelligence, pages 824-826, Karlsruhe, FDR., 1983.

Augmenting an artificial immune network using ordering, self-recognition and histo-compatibility operators.

Timmis J Neal M J and Hunt J. Augmenting an artificial immune network using ordering, self-recognition and histo-compatibility operators. In Proceedings of IEEE international conference of systems, man and cybernetics, pages 3821-3826, San Diego, 1998. IEEE.

Data analysis with artificial immune systems, cluster analysis and kohonen networks: some comparisons.

Neal M J Timmis J and Hunt J. Data analysis with artificial immune systems, cluster analysis and kohonen networks: some comparisons. In Proceedings of IEEE international conference of systems, man and cybernetics, pages 922-927, Tokyo, 1999. IEEE.

An artificial immune system for data analysis

Timmis J and Neal M J. An artificial immune system for data analysis. In Proceedings of 3rd international workshop on information processing in cells and tissues (IPCAT), Indianapolis, U.S.A., 1999.

A resource limited artificial immune system for data analysis.

Timmis J and Neal M J. A resource limited artificial immune system for data analysis. In Proceedings of ES2000 - Research and Development of Intelligent Systems, pages 19-32, Cambrige, U.K., 2000. Springer.

Investigating the evolution and stability of a resource limited artificial immune system.

Timmis J and Neal M J. Investigating the evolution and stability of a resource limited artificial immune system. In Proceedings of GECCO - special workshop on artificial immune systems, pages 40-41. AAAI press, 2000.

Qualitative Modelling of Linear Networks in Engineering Applications

Lee M.H., Qualitative Modelling of Linear Networks in Engineering Applications, in Proc. ECAI?2000, 14th European Conf. on Artificial Intelligence, Berlin, August 19th - 25th 2000, pp161-5.

Growth of Motor Coordination in Early Robot Learning

M. H. Lee and Q. Meng, Growth of Motor Coordination in Early Robot Learning, IJCAI-05, 2005.

Qualitative Circuit Models in Failure Analysis Reasoning

Lee M.H., Qualitative Circuit Models in Failure Analysis Reasoning, AI Journal. vol 111, pp239-276.1999.