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- Stanley C. Ahalt, Ashok K. Krishnamurthy, Prakoon Chen, Douglas E. Melton
- Neural Networks
- 1990

- Ashok K. Krishnamurthy, Stanley C. Ahalt, Douglas E. Melton, Prakoon Chen
- IEEE Journal on Selected Areas in Communications
- 1990

- Aristides S. Galanopoulos, Stanley C. Ahalt
- IEEE Trans. Neural Networks
- 1996

We study the codeword distribution for a conscience-type competitive learning algorithm, frequency sensitive competitive learning (FSCL), using one-dimensional input data. We prove that the asymptotic codeword density in the limit of large number of codewords is given by a power law of the form Q(x)=C.P(x)(alpha), where P(x) is the input data density and… (More)

- James E. Fowler, Matthew R. Carbonara, Stanley C. Ahalt
- IEEE Trans. Circuits Syst. Video Techn.
- 1993

- James E. Fowler, Kenneth C. Adkins, Steven B. Bibyk, Stanley C. Ahalt
- IEEE Trans. Circuits Syst. Video Techn.
- 1995

|This paper describes hardware that has been built to compress video in real time using full-search vector quan-tization (VQ). This architecture implements a diierential-vector-quantization (DVQ) algorithm and features a special-purpose digital associative memory, the VAMPIRE chip, which has been fabricated in 2m CMOS. We describe the DVQ algorithm, its… (More)

- DeLiang Wang, Xiaomei Liu, Stanley C. Ahalt
- Neural Networks
- 1996

We describe hardware that has been built to compress video in real time using full-search vector quantization (VQ). This architecture implements a diierential-vector-quantization (DVQ) algorithm which features entropy-biased codebooks designed using an artiicial neural network (ANN). A special-purpose digital associative memory, the VAMPIRE chip, performs… (More)

- Chao He, Jianyu Dong, Yuan F. Zheng, Stanley C. Ahalt
- ICRA
- 2001

—This paper presents an object tracking method for object-based video processing which uses a two-dimensional (2-D) Gabor wavelet transform (GWT) and a 2-D golden section algorithm. An object in the current frame is modeled by local features from a number of the selected feature points, and the global placement of these feature points. The feature points… (More)

- Aristides S. Galanopoulos, Randolph L. Moses, Stanley C. Ahalt
- IEEE Trans. Neural Networks
- 1997

The focus of this paper is a convergence study of the frequency sensitive competitive learning (FSCL) algorithm. We approximate the final phase of FSCL learning by a diffusion process described by the Fokker-Plank equation. Sufficient and necessary conditions are presented for the convergence of the diffusion process to a local equilibrium. The analysis… (More)

- José-Luis Sancho-Gómez, William E. Pierson, Batu Ulug, Aníbal R. Figueiras-Vidal, Stanley C. Ahalt
- Neurocomputing
- 2000