99,889 research outputs found
Non-invasive fetal monitoring: a maternal surface ECG electrode placement-based novel approach for optimization of adaptive filter control parameters using the LMS and RLS algorithms
This paper is focused on the design, implementation and verification of a novel method for the optimization of the control parameters (such as step size mu and filter order N) of LMS and RLS adaptive filters used for noninvasive fetal monitoring. The optimization algorithm is driven by considering the ECG electrode positions on the maternal body surface in improving the performance of these adaptive filters. The main criterion for optimal parameter selection was the Signal-to-Noise Ratio (SNR). We conducted experiments using signals supplied by the latest version of our LabVIEW-Based Multi-Channel Non-Invasive Abdominal Maternal-Fetal Electrocardiogram Signal Generator, which provides the flexibility and capability of modeling the principal distribution of maternal/fetal ECGs in the human body. Our novel algorithm enabled us to find the optimal settings of the adaptive filters based on maternal surface ECG electrode placements. The experimental results further confirmed the theoretical assumption that the optimal settings of these adaptive filters are dependent on the ECG electrode positions on the maternal body, and therefore, we were able to achieve far better results than without the use of optimization. These improvements in turn could lead to a more accurate detection of fetal hypoxia. Consequently, our approach could offer the potential to be used in clinical practice to establish recommendations for standard electrode placement and find the optimal adaptive filter settings for extracting high quality fetal ECG signals for further processing. Ultimately, diagnostic-grade fetal ECG signals would ensure the reliable detection of fetal hypoxia.Web of Science175art. no. 115
Detection and Characterization of Actuator Attacks Using Kalman Filter Estimation
In this thesis, two discrete-time control systems subject to noise, are modeled, analyzed and estimated. These systems are then subjected to attack by false signals such as constant and ramp signals. In order to find out how and when the control systems are being attacked by the false signals, several detection algorithms are applied to the systems. This work focuses on actuator attack detection. To detect the presence of false actuator signals, a bank of Kalman filters is set up which uses adaptive estimation and conditional probability density functions for detecting the false signals. The individual Kalman filters are each tuned to satisfy a control system: one of which is the original system and the other of which is the system with a false signal. The use of the bank of Kalman filters to detect actuator attacks is tested in 4 cases; first-order system attacked by a constant or ramp signal and then a second-order system subject to the same types of attack signals. This work shows the bank of Kalman filters can successfully detect the intrusion of false signals for actuator attack by using several different detection algorithms. Simulations show that the false signal is found and detected in all cases
Detection and Characterization of Actuator Attacks Using Kalman Filter Estimation
In this thesis, two discrete-time control systems subject to noise, are modeled, analyzed and estimated. These systems are then subjected to attack by false signals such as constant and ramp signals. In order to find out how and when the control systems are being attacked by the false signals, several detection algorithms are applied to the systems. This work focuses on actuator attack detection. To detect the presence of false actuator signals, a bank of Kalman filters is set up which uses adaptive estimation and conditional probability density functions for detecting the false signals. The individual Kalman filters are each tuned to satisfy a control system: one of which is the original system and the other of which is the system with a false signal. The use of the bank of Kalman filters to detect actuator attacks is tested in 4 cases; first-order system attacked by a constant or ramp signal and then a second-order system subject to the same types of attack signals. This work shows the bank of Kalman filters can successfully detect the intrusion of false signals for actuator attack by using several different detection algorithms. Simulations show that the false signal is found and detected in all cases
Detection of point sources on two-dimensional images based on peaks
This article considers the detection of point sources in two dimensional
astronomical images. The detection scheme we propose is based on peak
statistics. We discuss the example of the detection of far galaxies in Cosmic
Microwave Background experiments throughout the paper, although the method we
present is totally general and can be used in many other fields of data
analysis. We assume sources with a Gaussian profile --that is a fair
approximation of the profile of a point source convolved with the detector beam
in microwave experiments-- on a background modeled by a homogeneous and
isotropic Gaussian random field characterized by a scale-free power spectrum.
Point sources are enhanced with respect to the background by means of linear
filters. After filtering, we identify local maxima and apply our detection
scheme, a Neyman-Pearson detector that defines our region of acceptance based
on the a priori pdf of the sources and the ratio of number densities. We study
the different performances of some linear filters that have been used in this
context in the literature: the Mexican Hat wavelet, the matched filter and the
scale-adaptive filter. We consider as well an extension to two dimensions of
the biparametric scale adaptive filter (BSAF). The BSAF depends on two
parameters which are determined by maximizing the number density of real
detections while fixing the number density of spurious detections. For our
detection criterion the BSAF outperforms the other filters in the interesting
case of white noise.Comment: 21 pages, 3 figures, version accepted for publication on EURASIP
Journal on Applied Signal Processing: Applications of Signal Processing in
Astrophysics and Cosmolog
A Modified Cross Correlation Double Talk Detector using Variable Threshold for Acoustic Echo Cancellation
The presence of echo in the communication systems reduces the speech quality and can be overcome by using Acoustic Echo Cancellers (AEC). Acoustic Echo Canceller is a special device which estimates the echo and subtracts it from the microphone signal. Adaptive filters are used for this purpose. Adaptive filters diverge when in addition to far end signal, near end signal is also present. This situation is known as double talk which is handled by double talk detectors (DTD). Cross correlation based DTD is an attractive approach in which cross correlation between two signals give decision parameter. This parameter is compared to constant threshold to give decision. The constant threshold based double talk detector increases the probability of missed detection and increases the residual error of echo canceller. Thus degrades the performance of Acoustic echo canceller. In this paper, a new variable threshold based double talk detector is proposed in which a threshold value is evaluated with respect to the power of near end signal and far end signal. The proposed method shows the reduced probability of missed detections and mean square error, hence improving AEC performance. For updating of the filter coefficients, Variable Step Size Least Mean Square algorithm is used
IIR Adaptive Filters for Detection of Gravitational Waves from Coalescing Binaries
In this paper we propose a new strategy for gravitational waves detection
from coalescing binaries, using IIR Adaptive Line Enhancer (ALE) filters. This
strategy is a classical hierarchical strategy in which the ALE filters have the
role of triggers, used to select data chunks which may contain gravitational
events, to be further analyzed with more refined optimal techniques, like the
the classical Matched Filter Technique. After a direct comparison of the
performances of ALE filters with the Wiener-Komolgoroff optimum filters
(matched filters), necessary to discuss their performance and to evaluate the
statistical limitation in their use as triggers, we performed a series of
tests, demonstrating that these filters are quite promising both for the
relatively small computational power needed and for the robustness of the
algorithms used. The performed tests have shown a weak point of ALE filters,
that we fixed by introducing a further strategy, based on a dynamic bank of ALE
filters, running simultaneously, but started after fixed delay times. The
results of this global trigger strategy seems to be very promising, and can be
already used in the present interferometers, since it has the great advantage
of requiring a quite small computational power and can easily run in real-time,
in parallel with other data analysis algorithms.Comment: Accepted at SPIE: "Astronomical Telescopes and Instrumentation". 9
pages, 3 figure
- …