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A saliency back-electromotive force (EMF)-based wavelet fuzzy neural network (WFNN) torque observer using a new maximum torque per ampere (MTPA) control is proposed in this study to improve the speed estimating performance of a sensorless interior permanent magnet synchronous motor (IPMSM) drive system. First, the characteristics and mathematical model of the saliency back-EMF-based proportional-integral-derivative (PID) torque observer with the mechanical model-based phase-lock-loop (PLL) for the estimation of the rotor flux angle and speed of the IPMSM are discussed. Then, a new saliency back-EMF-based MTPA control suitable for the implementation using digital signal processor (DSP) is introduced. Moreover, the saliency back-EMF-based rotor flux angle and speed estimation method using WFNN torque observer is proposed. Furthermore, detailed network structure and online learning algorithms of WFNN are described. Finally, the feasibility of the proposed control schemes is verified through experimental results.

Whether empirical givenness has the reliability that foundationalists expect is a point about which some philosophers are highly skeptical. Sellars took the doctrine of givenness as a “myth,” denying the existence of immediate perceptual experience. The arguments in contemporary Western epistemology are concentrated on whether sensory experience has conceptual contents, and whether there is any logical relationship between perceptions and beliefs. In fact, once the elements of words and conceptions in empirical perception are affirmed, the logical relationship between perceptual experience and empirical belief is also affirmed. This relationship takes place through perceptual experience acting as evidence for beliefs. The real problem lies in how one should distinguish between the different relationships with perception of singular beliefs and of universal beliefs, and in how singular beliefs can provide justification for universal beliefs.

The coverage probability and range of IEEE 802.16 systems depend on different wireless scenarios. Evaluating the performance of IEEE 802.16 systems over Stanford University Interim (SUI) channels is suggested by IEEE 802.16 specifications. In order to derive an effective method for forecasting the coverage probability and range, this study uses the SUI channel model to analyze the coverage probability with Rayleigh fading for an IEEE 802.16 system. The BER of the IEEE 802.16 system is shown in the simulation results. Then, the maximum allowed path loss can be calculated and substituted into the coverage analysis. Therefore, simulation results show the coverage range with and without Rayleigh fading.

For cognitive radio networks, there is a major spectrum sensing problem, i.e. dynamic spectrum management. It is an important issue to sense and identify the spectrum holes in cognitive radio networks. The first-order derivative scheme is usually used to detect the edge of the spectrum. In this paper, a novel spectrum sensing technique for cognitive radio is presented. The proposed algorithm offers efficient edge detection. Then, simulation results show the performance of the first-order derivative scheme and the proposed scheme and depict that the proposed scheme obtains better performance than does the first-order derivative scheme., {"references":["S. Haykin, \"Cognitive radio: Brain-empowered wireless\ncommunications,\" IEEE J. Sel. Areas Commun., vol. 23, pp. 201-220,\nFeb. 2005.","S. Haykin, \"Fundamental issues in cognitive radio,\" in Cognitive\nWireless Communication Networks, E. Hossain and V. K. Bhargava,\nEds. New York: Springer, 2007, pp. 1-43.","S. Haykin, D. J. Thomsom, and J. H. Reed, \"Spectrum Sensing for\ncognitive radio,\" Proceedings of the IEEE, vol. 97, no.5, pp. 849-877,\nMay, 2009.","I. J. Mitola and G. Q. Maguire, \"Cognitive radio: making software radios\nmore personal,\" IEEE Personal Commun., vol. 6, pp. 13-18, Aug., 1999.","T. Yucek and H. Arslan, \"A Survey of Spectrum Sensing Algorithms for\nCognitive Radio Applications,\" IEEE Commun. Survey & Tutorials, vol.\n11, no. 1, pp. 116-130, 2009.","I. Budiarjo, M, K, Lakshmanan and H. Nikookar, \"Cognitive Radio\nDynamic Access Techniques,\" Wireless Pers. Commun., vol. 45, pp.\n293-324, Feb., 2008.","Zhi Tian, Georgios B Giannakis, \"A Wavelet Approach to Wideband\nSpectrum Sensing for Cognitive Radios\", IEEE 1st Int. Conf. on\nCognitive Radio Oriented Wireless Networks and Communications\n(CROWNCOM), pp. 1-5, 2006.","Y. L. Xu, H. S. Zhang and Z. H. Han, \"The Performance Analysis of\nSpectrum Sensing Algorithms Based on Wavelet Edge Detection,\" IEEE\n1st Int. Conf. on Wireless Commun., Networking and Mobile Computing\n(WiCOM), pp. 1-4, 2009.","S. Mallat, W. Hwang, \"Singularity detection and processing with\nwavelets,\" IEEE Trans. Info. Theory, vol.38, no. 2, pp. 617-643, Mar.,\n1992."]}