The following online publications describe some of the algorithms of the image enhancement and super resolution project, as well as other bat and dolphin sonar inspired technology capable of fusing multiple sonar pings and improving the accuracy and robustness to noise of echo localization.
G. Amit, J. Lessick, N. Gavriely and N. IntratorAcoustic Indices of Cardiac FunctionalityInternational Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS), Vol 2, pp 77-83 (2008).
O. Pasternak, N. Sochen, N. Intrator and Y. Assaf Mapping Neuronal Fibers Through Partial Volume Voxels Proceedings of the 14th meeting of the Organisation for Human Brain Mapping (HBM), Melbourne, Australia (2008).
O. Pasternak, N. Sochen, N. Intrator and Y. Assaf Free water extraction from Diffusion ImagesProceeding of the 16th International Society for Magnetic Resonance in Medicine (ISMRM), Toronto, Canada (2008).
N. Bar-Yaakov, Z. Grossman and N. Intrator.Using Iterative Ridge Regression to Explore Associations Between Conditioned Variables J. of Computational Biology (To appear).
O. Berkman and N. Intrator Robust Inference in Bayesian Networks with Application to Gene Expression Temporal Data International Conference on Multiple Experts, Lecture Notes in Computer Science, (4472) 479-489. Springer-Verlag, (2007).
I. Stainvas and N. Intrator Regularization of Projection Directions via Best Basis Selection Approach Int. J. of Applied Mathematics and Statistics, 2006.
D. Remondini, B. O’Connell, N. Intrator, J. M. Sedivy, N. Neretti, G. C. Castellani and L. N Cooper. Targeting c-Myc activated genes via a correlation method: Detection of global changes in large gene expression network dynamics PNAS 102(19) pp. 6902-6906, May, 2005.
N. Efron and N. Intrator The Effect of Noisy Bootstrapping on the Robustness of Supervised Classification of Gene Expression data. IEEE International Workshop on Machine Learning for Signal Processing pp. 411-420, Brazil, Sep. 2004.