Low level data processing functions, like FIR filtering, pattern recognition or correlation, where the parallel implementation is supported by architecture matched special purpose arithmetic; high throughput FPGA circuits easily outperform even the most advanced DSP processors. In this paper investigates a high-speed nonlinear Adaptive median filter implementation is presented. Then Adaptive Me...

In adaptive filtering, undetected noise bursts often disturb the adaptation and may lead to instabilities and divergence of the adaptive filter. The sensitivity against noise bursts increases with the convergence speed of the adaptive filter and limits the performance of signal processing methods where fast convergence is required. Typical applications which are sensitive against noise bursts a...

The TREC–11 filtering track measures the ability of systems to build persistent user profiles which successfully separate relevant and non-relevant documents in an incoming stream. It consists of three major subtasks: adaptive filtering, batch filtering, and routing. In adaptive filtering, the system begins with only a topic statement and a small number of positive examples, and must learn a be...

A family of adaptive filtering algorithms for processing signals which have energy concentrated in a relatively small number of component subspaces in the spectral domain is introduced. The approach is based on transform domain signal decomposition and linear least squares filtering of the selected subset of transform domain signal components. The derivation is based on the linear least squares...

In this paper, we discuss the performance of Blind Equalization algorithms, which can deal with the cases that the input signals are correlated and decrease the filtering error. We describe the algorithm process and analysis the error performance and computational complexity and an example of adaptive blind equalization is given to verify these algorithms. Results and conclusions are made which...