VLSI Array processors
- S. Kung
- Computer ScienceIEEE ASSP Magazine
- 1985
A general overview of VLSI array processors is provided and a unified treatment from algorithm, architecture, and application perspectives is provided.
A new identification and model reduction algorithm via singular value decomposition
- S. Kung
- Computer Science
- 1978
Information Exchange in Wireless Networks with Network Coding and Physical-layer Broadcast
It is shown that mutual exchange of independent information between two nodes in a wireless network can be performed by exploiting network coding and the physical-layer broadcast property offered by the wireless medium.
Variable-phase-shift-based RF-baseband codesign for MIMO antenna selection
- Xinying Zhang, A. Molisch, S. Kung
- Computer Science, BusinessIEEE Transactions on Signal Processing
- 1 November 2005
This work introduces a novel soft antenna selection approach for multiple antenna systems through a joint design of both RF (radio frequency) and baseband signal processing that requires only simple, variable phase shifters and combiners to reduce the number of RF chains.
Face recognition/detection by probabilistic decision-based neural network
- Shang-Hung Lin, S. Kung, Long-Ji Lin
- Computer ScienceIEEE Trans. Neural Networks
- 1997
The paper demonstrates a successful application of PDBNN to face recognition applications on two public (FERET and ORL) and one in-house (SCR) databases and experimental results on three different databases such as recognition accuracies as well as false rejection and false acceptance rates are elaborated.
Kernel Methods and Machine Learning
- S. Kung
- Computer Science
- 23 June 2014
This chapter discusses kernel methods for estimation, prediction, and system identification, as well as kNN, PNN, and Bayes classifiers, and their applications in machine learning and cluster discovery.
Principal Component Neural Networks: Theory and Applications
- K. Diamantaras, S. Kung
- Computer Science
- 1996
A review of Linear Algebra, Principal Component Analysis, and VLSI Implementation.
State-space and singular-value decomposition-based approximation methods for the harmonic retrieval problem
New high-resolution methods for the problem of retrieving sinusoidal processes from noisy measurements are presented by use of the so-called principal-components method, which is a singular-value-decomposition-based approximate modeling method.
A neural network learning algorithm for adaptive principal component extraction (APEX)
- S. Kung, K. Diamantaras
- Computer Science, MathematicsIEEE International Conference on Acoustics…
- 3 April 1990
An algorithm called APEX which can recursively compute the principal components of a vector stochastic process using a linear neural network is proposed, and its computational advantages over previously proposed methods are demonstrated.
Adaptive Principal component EXtraction (APEX) and applications
- S. Kung, K. Diamantaras, J. Taur
- Computer ScienceIEEE Transactions on Signal Processing
- 1 May 1994
A neural network model (APEX) for multiple principal component extraction that is applicable to the constrained PCA problem where the signal variance is maximized under external orthogonality constraints and the exponential convergence of the network is formally proved.
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