Kernel adaptive filter

About: Kernel adaptive filter is a research topic. Over the lifetime, 8771 publications have been published within this topic receiving 142711 citations.

Evolutionary Computation on Programmable Robust IIR Filter Pole-Placement Design

Abstract: This paper explores the pole-placement design problem of a robust stable infinite-impulse-response (IIR) filter to attenuate or eliminate the undesired measurement noise and proposes a strategy based on an adaptive differential evolution (ADE) algorithm to design a filter. The results are compared to the results of other popular evolutionary algorithms, e.g., particle swarm optimization (PSO), genetic algorithm (GA), and improved genetic algorithm (IGA). The stability robustness for an IIR filter will be achieved by placing all poles inside a disk D(α, r) contained in the unit disk, in which α is the center, and r is the radius of the disk. This investigation first uses a robust stability criterion, called the D(α, r)-stability criterion, to ensure that digital filter poles lie inside a disk D(α, r). The proposed strategy checks the criterion during differential evolution (DE) and adaptively adjusts the DE parameters, depending on the current DE performance. This paper also introduces two design examples of a bandpass IIR filter and a low-pass IIR filter for the measurement of a speech signal. These examples show that the proposed strategy performance based on the proposed ADE is better than designs based on PSO, GA, and IGA. Finally, this paper implements an IIR filter on the field-programmable gate array (FPGA) chip to verify the designed filter performance in practical electronic devices and uses speech signals as an input signal to the FPGA chip to verify that the measurement noise of the speech signal is attenuated by the designed IIR filter.

Spectral Graph Wavelets and Filter Banks With Low Approximation Error

Abstract: We propose filter banks in the graph spectral domain, where each filter is defined by a sum of sinusoidal waves. The main advantages of these filter banks are that (a) they have low approximation errors even if a lower-order shifted Chebyshev polynomial approximation is used, (b) the upper bound of the error after the $p$ th order Chebyshev polynomial approximation can be calculated rigorously without complex calculations, and (c) their parameters can be efficiently obtained from any real-valued linear phase finite impulse response filter banks in regular signal processing. The proposed filter bank has the same filter characteristics as the corresponding classical filter bank in the frequency domain and inherits the original properties, such as tight frame and no DC leakage. Furthermore, their approximation orders can be determined from the desired approximation accuracy. The effectiveness of our approach is evaluated by comparing them with existing spectral graph wavelets and filter banks.

Advanced periodic signal enhancement

Abstract: An enhancement system improves the perceptual quality of a processed speech. The system includes a delay unit that delays a signal received through a discrete input. A spectral modifier linked to the delay unit is programmed to substantially flatten the spectral character of a background noise. An adaptive filter linked to the spectral modifier adapts filter characteristics to match a response of a non-delayed signal. A programmable filter is linked to the delay unit. The programmable filter has a transfer function functionally related to a transfer function of the adaptive filter.

Threshold Calibration in CLARIT Adaptive Filtering.

Abstract: In this paper, we describe the system and methods used for the CLARITECH entries in the TREC–7 Filtering Track. Our main aim was to study algorithms, designs, and parameters for Adaptive Filtering, as this comes closest to actual applications. For efficiency's sake, however, we adapted a system largely geared towards retrieval and introduced a few critical new components. The first of these components, the delivery ratio mechanism, is used to obtain a profile threshold when no feedback has been received. A second method, which we call beta–gamma regulation, is used for threshold updating. It takes into account the number of judged documents processed by the system as well as an expected bias in optimal threshold calculation. Several parameters were determined empirically: apart from the parameters pertaining to the new components, we also experimented with different choices for the reference corpus, and different "chunk" sizes for processing news stories. Gradually increasing chunk sizes over "time" appears to help profile learning. Finally, we examined the effect of terminating underperforming queries over the AP90 corpus and found that the utility metric over AP88–AP89 was a good predictor. All of the above innovations contributed to the success of the CLARITECH system in the adaptive filtering track.