Books

Tulay Adali and Simon Haykin, Adaptive Signal Processing: Next Generation Solutions, 424 pages, Wiley-IEEE Press, Hoboken, NJ, 2010.

Eric Moreau and Tulay Adali, Blind Identification and Separation of Complex Signals, 112 pages, ISTE and Wiley, London, UK and Hoboken, NJ, 2013.

Recent Overviews

On ICA & IVA

T. Adali, M. Anderson, and G.-S. Fu, "Diversity in independent component and vector analyses: Identifiability, algorithms, and applications in medical imaging," IEEE Signal Processing Magazine, vol. 31, no. 3, pp. 18-33, May 2014.
This overview article provides a general view of independent component analysis (ICA) using mutual information rate such that multiple types of statistical properties—higher-order-statistics (non-Gaussianity), sample-to-sample dependence (nonwhiteness), nonstationarity, and when samples are complex-valued, noncircularity—can be jointly taken into account. The overview article also introduces independent vector analysis (IVA) that generalizes ICA to multiple datasets and adds the use of statistical dependence across multiple datasets to the overall general framework. Such a general view of ICA & IVA significantly extends their power, enabling identification of multiple Gaussians (multiple correlated in the case of ICA and multiple iid Gaussians in the case of IVA) and provides guidance for the design of efficient ICA & IVA algorithms. It is shown how various ICA (including Infomax, FastICA, EFICA, SOBI, WASOBI and many others) and IVA methods (including CCA and multiset CCA) fall under this umbrella.

On Data Fusion

T. Adali, Y. Levin-Schwartz, and V. D. Calhoun, "Multimodal data fusion using source separation: Two effective models based on ICA and IVA and their properties," Proc. IEEE, vol. 103, no. 9, pp. 1478-1493, Sep. 2015.
This paper introduces two powerful data-driven models and provides guidance on the selection of a given model and its implementation while emphasizing the general applicability of the two models.

T. Adali Y. Levin-Schwartz, and V. D. Calhoun, "Multimodal data fusion using source separation: Application to medical imaging," Proc. IEEE, vol. 103, no. 9, pp. 1494-1506, Sep. 2015.
This paper demonstrates the application of the two models introduced in the previous paper to fusion of medical imaging data from three modalities: functional magnetic resonance imaging (MRI), structural MRI, and electroencephalography data
and discusses the tradeoffs in various modeling and parameter choices.

D. Lahat, T. Adali and C. Jutten, "Multimodal data fusion: An overview of methods, challenges, and prospects," Proc. IEEE, vol. 103, no. 9, pp. 1449-1477, Sep. 2015.
This paper defines data fusion as the process that lets data from multiple modalities to fully interact and influence each other, and reviews the main data-driven models that have been proposed to achieve this challenging task. Then, the challenges across various disciplines are reviewed with emphasis on two key issues: "why we need data fusion?" and "how we perform it."

On Complex-Valued Signal Processing

T. Adali and P. J. Schreier, “Optimization and estimation of complex-valued signals: Theory and applications in filtering and blind source separation,” IEEE Signal Processing Magazine, vol. 31, no. 5, pp. 112–128, Sep. 2014.
Complex-valued signals occur in many areas of science and engineering and are thus of fundamental interest. When developing signal processing methods in the complex domain, there are two key issues: making use of the full statistical information and optimization. In this article, we review the necessary tools to address these two key issues and provide examples in filtering and blind source separation that utilize these tools.

T. Adali, P. J. Schreier, and L. L. Scharf, "Complex-valued signal processing: The proper way to deal with impropriety," IEEE Trans. Signal Processing, vol. 59, no. 11, pp. 5101-5123, Nov. 2011.
In this more detailed overview article, we review the necessary tools for detection and estimation of complex-valued signals, among which are widely linear transformations, augmented statistical descriptions, and Wirtinger calculus. We also present some selected recent developments in the field of complex-valued signal processing, addressing the topics of model selection, filtering, and source separation.

Complete list of publications at http://mlsp.umbc.edu/publications.html