Search results for "adaptive filter"
showing 10 items of 30 documents
Predicting the Short-Term Exchange Rate Between United State Dollar and Czech Koruna Using Hilbert-Huang Transform and Fuzzy Logic
2017
In this paper, the combination of the Hilbert-Huang Transform, fuzzy logic and an embedding theorem is described to predict the short-term exchange rate from United States dollar to Czech Koruna. By Using the Hilbert-Huang Transform as an adaptive filter, the proposed method decreases the embedding dimension space from five (original samples) to four (de-noising samples). This dimension space provides the number of inputs to the fuzzy rule base system, which causes the number of rules, the time for training and the inference process to decrease. Experimental results indicated that this method achieves higher accuracy prediction than the direct use of original data.
Experimental study on the effects of physical training on the defibrillation threshold
2011
Background: Chest compression artifacts during cardiopulmonary resuscitation (CPR) deteriorate the rhythm diagnosis of automated external defibrillators (AED). Cardiopulmonary resuscitation must therefore be interrupted for a reliable shock/no-shock decision. However, these hands-off intervals adversely affect the defibrillation success, and, in addition, pauses in chest compressions compromise circulation. An accurate diagnosis of the rhythmwhile performing CPR is therefore needed to minimize these hands-off intervals. Methods: The characteristics of the CPR artifact are very variable, and the artifact presents an important spectral overlap with human cardiac arrest rhythms. Consequently, …
FPGA Implementation of an Adaptive Filter Robust to Impulsive Noise: Two Approaches
2011
Adaptive filters are used in a wide range of applications such as echo cancellation, noise cancellation, system identification, and prediction. Its hardware implementation becomes essential in many cases where real-time execution is needed. However, impulsive noise affects the proper operation of the filter and the adaptation process. This noise is one of the most damaging types of signal distortion, not always considered when implementing algorithms, particularly in specific hardware platforms. Field-programmable gate arrays (FPGAs) are used widely for real-time applications where timing requirements are strict. Nowadays, two main design processes can be followed for embedded system design…
Descriptor-type Robust Kalman Filter and Neural Adaptive Speed Estimation Scheme for Sensorless Control of Induction Motor Drive Systems
2012
Abstract This paper deals with robust estimation of speed and rotor flux for sensorless control of motion control systems which use induction motors as actuators. Due to the observability lack of five and six order Extended Kalman Filters, speed is here estimated by means of a Total Least Square algorithm with Neural Adaptive mechanism. This allows the use of a fourth-order Kalman Filter for estimating rotor flux and to filter stator currents. To cope with motor-load parameter variations, a descriptor-type robust Kalman Filter is designed taking explicitly into account these variations. The descriptor-type structure allows direct translation of parameter variations into variations of the co…
Hardware implementation of a robust adaptive filter: Two approaches based in High-Level Synthesis design tools
2009
Abstract Adaptive filters are used in a wide range of applications. Impulsive noise affects the proper operation of the filter and the adaptation process. This noise is one of the most damaging types of signal distortion, not always considered when implementing algorithms. Field Programmable Gate Array (FPGA) are widely used for applications where timing requirements are strict. Nowadays, two main design processes can be followed, namely, Hardware Description Language (HDL) and a High Level Synthesis (HLS) design tool for embedded system design. This paper describes the FPGA implementation of an adaptive filter robust to impulsive noise using two approaches based in HLS and the implementati…
Adaptive Kernel Learning for Signal Processing
2018
Adaptive filtering is a central topic in digital signal processing (DSP). By applying linear adaptive filtering principles in the kernel feature space, powerful nonlinear adaptive filtering algorithms can be obtained. This chapter introduces the wide topic of adaptive signal processing, and explores the emerging field of kernel adaptive filtering (KAF). In many signal processing applications, the problem of signal estimation is addressed. Probabilistic models have proven to be very useful in this context. The chapter discusses two families of kernel adaptive filters, namely kernel least mean squares (KLMS) and kernel recursive least‐squares (KRLS) algorithms. In order to design a practical …
Sensor Fusion Combining 3-D and 2-D for Depth Data Enhancement
2012
Time-of-Flight (ToF) cameras are known to be cost-efficient 3-D sensing systems capable of providing full scene depth information at a high frame rate. Among many other advantages, ToF cameras are able to provide distance information regardless of the illumination conditions and with no texture dependency, which makes them very suitable for computer vision and robotic applications where reliable distance measurements are required. However, the resolution of the given depth maps is far below the resolution given by standard 2-D video cameras which, indeed, restricts the use of ToF cameras in real applications such as those for safety and surveillance. In this thesis, we therefore investigate…
Implementation of a new adaptive algorithm using fuzzy cost function and robust to impulsive noise
2012
Adaptive filters are used in a wide range of applications such as noise cancellation, system identification, and prediction. One of the main problems for theses filters is the impulsive noise as it generates algorithm unstability. This work shows the development, simulation and hardware implementation of a new algorithm robust to impulsive noise. Hardware implementation becomes essential in many cases where a real time execution, reduced size, or low power system is needed. An efficient hardware architecture is proposed and different optimizations for size and speed are developed: no need for control state machine, reduced computation requirements due to simplifications, etc. Furthermore, t…
Flat-top ultra-wideband photonic filters based on mutual coherence function synthesis
2008
A novel all-incoherent optical circuit that allows for band-pass microwave-photonic filter design is presented and verified through numerical simulation. In contrast to conventional spectrum-sliced optical architectures that operate on the basis of a finite number of discrete taps, our proposal is based on arbitrary shaping of the spectrum of the broadband optical source in a conventional frequency encoder. This fact dramatically increases the free spectral range of the filter with respect to the conventional discrete-time optical processing. The filter transfer function is given by the mutual coherence function of the filtered source which allows, through an inverse problem, sculpting the …
Adaptive Metropolis algorithm using variational Bayesian adaptive Kalman filter
2013
Markov chain Monte Carlo (MCMC) methods are powerful computational tools for analysis of complex statistical problems. However, their computational efficiency is highly dependent on the chosen proposal distribution, which is generally difficult to find. One way to solve this problem is to use adaptive MCMC algorithms which automatically tune the statistics of a proposal distribution during the MCMC run. A new adaptive MCMC algorithm, called the variational Bayesian adaptive Metropolis (VBAM) algorithm, is developed. The VBAM algorithm updates the proposal covariance matrix using the variational Bayesian adaptive Kalman filter (VB-AKF). A strong law of large numbers for the VBAM algorithm is…