References
- Gezici S, Tian Z, Giannakis G B, Kobayashi H, Molisch A F, Poor H V and Sahinoqlu Z, “Localization via ultra-wideband radios: a look at positioning aspects for future sensor networks”, IEEE Signal Processing, vol. 22, no. 4, pp. 70-84, 2005.
- Patwari N, Ash J N, Kyperountas S, Hero A O, Moses R L and Correal N S, “Locating the nodes: cooperative localization in wireless sensor networks”, IEEE Signal Processing, vol. 22, no. 4, pp. 54-69, 2005.
- Biswas P, Lian T C and Wang T C, “Semi-definite programming based algorithms for sensor localization”, ACM Trans Sensor Networks, vol. 2, no. 2, pp. 188-200, 2006.
- Kushki A, Plataniotis K and Venetsanopoulos A, “Intelligent dynamic radio tracking in indoor wireless local area networks”, IEEE Transactions on Mobile Computing, vol. 9, no. 3, pp. 405419, 2010.
- Ahn H S and Yu W, “Environmental adaptive RSSI based indoor localization”, Automation Science and Engineering, vol. 6, no. 10, pp. 626-633, 2009.
- Gurrieri L E, Willink T J, Petosa A and Noghanian S, “Characterization of the angle, delay and polarization of multipath signals for indoor environments”, Antennas and Propagation, vol. 56, no. 8, pp. 2710-2719, 2008.
- M. Roseline Juliana and S.Srinivasan, “Seladg: secure energy efficient location aware data gathering approach for wireless sensor networks”, International Journal On Smart Sensing and Intelligent Systems(S2IS), vol. 8, no. 3, pp. 1748-1767, 2015.
- Ahadul Imam, Justin Chi and Mohammad Mozumdar, “Data compression and visualization for wireless sensor networks”, International Journal On Smart Sensing and Intelligent Systems(S2IS), vol. 8, no. 4, pp. 2083-2115, 2015.
- Yi Zhao, Valentin Gies and Jean-Marc Ginoux, “WSN based thermal modeling: a new indoor energy efficient solution”, International Journal On Smart Sensing and Intelligent Systems(S2IS), vol. 8, no. 2, pp. 869-895, 2015.
- Sayed A H, Tarighat A and Khajehnuri N, “Networked based wireless location”. IEEE Signal Process, vol. 22, no. 4, pp. 24-40, 2005.
- Li Xinrong. “RSS-based location estimation with unknowm pathloss model”. IEEE Transactions on Wireless Communications, vol. 5, no. 12, pp. 3626-3633, 2006.
- Shang Y, Ruml W, Zhang Y and Fromherz M, “Localization from connectivity in sensor networks”, IEEE Transactions on Parallel and Distributed Systems, vol. 15, no. 11, pp. 961-974, 2004.
- Jian Y, Frangi A F, Jing-Yu Y,Zhang D and Zhong J, “KPCA plus LDA: a complete kernel fisher disctiminant framework for feature extraction and recognition”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 2, pp. 230-244, 2005.
- Natarajan U, Periasamy V M and Aravanan R, “Application of particle swarm optimisation in artificial neural network for the prediction of tool life”, The International Journal of Advanced Manufacturing Technology, vol. 31, no. 9, pp. 871-876, 2007.
- Lahiri S K and Ghanta K C, “Development of an artificial neural network correlation for prediction of hold-up of slurry transport in pipelines”, Chemical Engineering Science, vol. 63, no. 1, pp. 1497-1509, 2007.
- D. Riordan, P. Doody and J. Walsh, “The use of artificial neural networks in the estimation of the perception of sound by the hunman auditory system”, vol. 8, no. 3, pp. 1806-1836, 2015.
- Alcala C F and Qin S J, “Reconstruction-based contribution for process monitoring with kernel principal component analysis”, Industrial and Engineering Chemistry Research, vol. 49, pp. 7849-7857, 2010.
- Wei Jing Wonga, Andrew B.J. Teohb, Yau Hee Khoc and M.L. Dennis Wonga, “Kernel PCA enabled bit-string representation for minutiae-based cancellable fingerprint template”, Pattern Recognition, vol. 51, pp. 197-208, 2016.
- Jian Huang and Xuefeng Yan, “Related and independent variable fault detection based on KPCA and SVDD”, Journal of Process Control, vol. 39, pp. 88-99, 2016.
- Majdi Mansouri, Mohamed Nounou, Hazem Nounou and Nazmul Karim, “Kernel PCA-based GLRT for nonlinear fault detection of chemical processes”, Journal of Loss Prevention in the Process Industries, vol. 40, pp. 334-347, 2016.
- Sakuntala Mahapatra, Raju Daniel, Deep Narayan Dey and Santanu Kumar Nayak, “Induction Motor Control Using PSO-ANFIS”, Procedia Computer Science, vol. 48, pp. 753768, 2015.
- Haidar Samet, Farid Hashemi and Teymoor Ghanbari, “Minimum non detection zone for islanding detection using an optimal Artificial Neural Network algorithm based on PSO”, Renewable and Sustainable Energy Reviews, vol. 52, pp. 1-18, 2015.
- Abhijit Suresh, K.V. Harish and N. Radhika, “Particle swarm optimization over back propagation neural network for length of stay prediction”, vol. 46, pp. 268-275, 2015.
- Li Deng, Gen Lu, Yuying Shao, Minrui Fei, Huosheng Hu, “A novel camera calibration technique based on differential evolution particle swarm optimization algorithm”, vol. 174, no. 22, pp. 456-465, 2016.