Basic Information

Affiliation
Division of Computer Science
Title
Professor
E-Mail
sding@u-aizu.ac.jp
Web site
http://www.u-aizu.ac.jp/~sding/

Education

Courses - Undergraduate
F1 Algorithms and Data StructureA8 Digital Signal ProcessingM10 Introduction to Topology
Courses - Graduate
CSC02 Statistical Signal Processing CSC03 Applied Statistics

Research

Specialization
Educational Background, Biography
Mar. 1996 PhD. in Physics, Tokyo Institute of Technology
Aug. 1996 Department of Yokohama, Fujisoft-ABC, Co., Ltd.
Apr. 1998 Technology Research Laboratory, R&D Division, Clarion Co., Ltd.
May 2005 Associate Professor, University of Aizu
Apr. 2011 ~ present Full Professor, University of Aizu

Sept. 2003-Mar. 2009 Laboratory for Advanced Brian Signal Processing, Brain Science Institute, RIKEN (The Institute of Physical and Chemical Research) (Part-time)
Apr. 2009-Mar. 2011; 2014-present Vice-Chair of Graduate Department of Information Technologies and Project Management, Graduate School, University of Aizu
Current Research Theme
Sparse representation and dictionary learning for signal;Sparse coding and compressive sensing;Independent component analysis;Blind source separation;Optimization via inspirations of quantum tunneling, gravity and artificial bee colony;Applications in signal processing of time reversal acoustics
Key Topic
Sparse representation;Dictionary learning;Sparse coding;Compressive sensing;Independent component analysis (ICA);Blind source separation (BSS);Multi-in and multi-out (MIMO);Swarm intelligence;Time reversal acoustics
Affiliated Academic Society
IEEE, ACM, IEICE

Others

Hobbies
Book reading (Science, History, etc), Igo (Intelligence of Ishi swarm led be a player?)
School days' Dream
I had a dream to become a Physicist.
Current Dream
I have been engaged in independent component analysis, blind source separation, and sparse representation, greatly in recent years. I hope, in the future, these can be used in the realistic world, so that can greatly contribute to the society, and be
Motto
No gain without pain; Any pain has gain.
Favorite Books
[1] Information Theory, Inference, and Learning Algorithm, D. J. MacKay, Cambridge university press, 2005
[2] Independent Component Analysis, Aapo Hyvärinen, Juha Karhunen, Erkki Oja, Wiley-Interscience (2001)
[3] Feynman Lectures on Physics: Mainly Mechanics, Radiation, and Heat v. 1
[4] Feynman Lectures on Physics: Mainly Electromagnetism and Matter v. 2
[5] Feynman Lectures on Physics: Quantum Mechanics v. 3
Messages for Students
Could you please join us, for doing interesting topics, which may influence the world future greatly?
Publications other than one's areas of specialization
2015 Paper of Prof. Ding and his students won the "Best Paper Award" at IEEE DSP 2015

[1] Blind source separation (BSS) and Independent component analysis (ICA)-Prince Shotoku would also be surprised (In Japanese), Japanese Magazine: Fukushima no Shinro, No. 329, pp. 54-57, (Jan. 2010).
[2] Extracting aimed audio source and applications, Nikkei BP Mook, "Reforming University" Series, School of Computer Science and Engineering, The University of Aizu 08-09, pp.62-65 (2008)"

Main research

Intelligent Signal and Information Processing

    |TAB|
  1. Independent component analysis (ICA) and blind source separation (BSS)
    |TAB|In the real world, an observed signal by sensor is usually recorded as a mixture of several sources. How to separate each source signals from only the observation is a very important research topic. This is also called by cocktail party problem. By this we can separate or extract signals by a computer, which is very similar to what is done by a human brain.
  2. |TAB|
  3. Swarm intelligence and Nature-inspired intelligence
    |TAB|In a swarm of, e.g., bird, insects, bacterium and particle, each individual does some simple actions and simply communicates with others. Every individuals are equal. But as a swarm, actions with a great deal of intelligence may be created. By a simulation of such activity, in a computer, we can realize something that usually cannot be done by other methods.
  4. |TAB|
  5. Signal or image reconstruction from incomplete data
    |TAB|Because of restrictions of space or time, or due to noise and interference, the recorded data may by incomplete. How to reconstruct the true signal or image is the purpose of this research. There are many applications such as in communications, medical technology, brain science, etc.

View this research

Dissertation and Published Works

[1] Z. Tang and S. Ding, Dictionary Learning with Incoherence and Sparsity Constraints for Sparse Representation of Nonnegative Signals, IEICE Transactions on Information and Systems, Vol. E96-D, No.5, pp.1192-1203 (May 2013).
[2] Z. Yang, Y. Xiang, S. Xie, and S. Ding, Nonnegative blind source separation by sparse component analysis based on determinant measure, IEEE Transactions on Neural Networks and Learning Systems, Vol. 23, No. 10, pp.1601-1610 (Oct. 2012).
[3] Z. Yang, G. Zhou, S. Xie, S. Ding, J. Yang, and J. Zhang, Blind Spectral Unmixing Based on Sparse Nonnegative Matrix Factorization, IEEE Transactions on Image Processing, Vol. 20, No. 4, pp. 1112-1125 (Apr. 2011).
[4] Z. Yang, S. Ding, S. Xie, Blind Source Separation by Fully Nonnegative Constrained Iterative Volume Maximization, IEEE Signal Processing Letter, Vol. 17, No. 9, pp. 799-802 (Sep. 2010).
[5] W. Liu and S. Ding, An Efficient Method to Determine the Diagonal Loading Factor Using the Constant Modulus Feature, IEEE Transactions on Signal Processing, Vol.56, No.12, pp.6102-6106 (Dec. 2008).
[6] S. Ding, Independent Component Analysis via Learning Updating Using a Form of Orthonormal Transformation Based on the Diagonalization Principle, International Journal of Innovative Computing, Information and Control, Vol. 3, No. 5, pp.1219-1235 (Oct. 2007).
[7] Z. He, S. Xie, S. Ding and A. Cichocki, Convolutive Blind Source Separation in the Frequency Domain Based on Sparse Representation, IEEE Transactions on Audio, Speech and Language Processing, Vol.15, No.5, pp.1551-1563 (Jul. 2007).
[8] S. Ding, J. Huang, D. Wei and A. Cichocki, A Near Real-Time Approach for Convolutive Blind Source Separation, IEEE Transactions on Circuits and Systems, Part I: Regular Papers, Vol.53, No.1, pp.114-128 (Jan. 2006).
[9] H. Zhang, G. Wang, P. Cai, Z. Wu, and S. Ding, A Fast Blind Source Separation Algorithm Based on the Temporal Structure of Signals, Neurocomputing (Elsevier), Vol. 139, No. 9, pp. 261-271 (Sept. 2014).
[10] Y. Sun, Y. Tang, S. Ding, S. Lv, Y. Cui, Diagnose the mild cognitive impairment by constructing Bayesian network with missing data, Expert Systems with Applications (Elsevier), Vol. 38, No. 1, pp. 442?449 (Jan. 2011).