5,825 research outputs found
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
A Novel Adaptive Spectrum Noise Cancellation Approach for Enhancing Heartbeat Rate Monitoring in a Wearable Device
This paper presents a novel approach, Adaptive Spectrum Noise Cancellation (ASNC), for motion artifacts removal in Photoplethysmography (PPG) signals measured by an optical biosensor to obtain clean PPG waveforms for heartbeat rate calculation. One challenge faced by this optical sensing method is the inevitable noise induced by movement when the user is in motion, especially when the motion frequency is very close to the target heartbeat rate. The proposed ASNC utilizes the onboard accelerometer and gyroscope sensors to detect and remove the artifacts adaptively, thus obtaining accurate heartbeat rate measurement while in motion. The ASNC algorithm makes use of a commonly accepted spectrum analysis approaches in medical digital signal processing, discrete cosine transform, to carry out frequency domain analysis. Results obtained by the proposed ASNC have been compared to the classic algorithms, the adaptive threshold peak detection and adaptive noise cancellation. The mean (standard deviation) absolute error and mean relative error of heartbeat rate calculated by ASNC is 0.33 (0.57) beats·min-1 and 0.65%, by adaptive threshold peak detection algorithm is 2.29 (2.21) beats·min-1 and 8.38%, by adaptive noise cancellation algorithm is 1.70 (1.50) beats·min-1 and 2.02%. While all algorithms performed well with both simulated PPG data and clean PPG data collected from our Verity device in situations free of motion artifacts, ASNC provided better accuracy when motion artifacts increase, especially when motion frequency is very close to the heartbeat rate
Methods of power line interference elimination in EMG signals
Electromyogram (EMG) recordings are often corrupted by the wide range of artifacts, which one of them is power line interference (PLI). The study focuses on some of the well-known signal processing approaches used to eliminate or attenuate PLI from EMG signal. The results are compared using signal-to-noise ratio (SNR), correlation coefficients and Bland-Altman analysis for each tested method: notch filter, adaptive noise canceller (ANC) and wavelet transform (WT). Thus, the power of the remaining noise and shape of the output signal are analysed. The results show that the ANC method gives the best output SNR and lowest shape distortion compared to the other methods.Web of Science40706
The Application of Blind Source Separation to Feature Decorrelation and Normalizations
We apply a Blind Source Separation BSS algorithm to the decorrelation of Mel-warped cepstra. The observed cepstra are modeled as a convolutive mixture of independent source cepstra. The algorithm aims to minimize a cross-spectral correlation at different lags to reconstruct the source cepstra. Results show that using "independent" cepstra as features leads to a reduction in the WER.Finally, we present three different enhancements to the BSS algorithm. We also present some results of these deviations of the original algorithm
Multi-Level Pre-Correlation RFI Flagging for Real-Time Implementation on UniBoard
Because of the denser active use of the spectrum, and because of radio
telescopes higher sensitivity, radio frequency interference (RFI) mitigation
has become a sensitive topic for current and future radio telescope designs.
Even if quite sophisticated approaches have been proposed in the recent years,
the majority of RFI mitigation operational procedures are based on
post-correlation corrupted data flagging. Moreover, given the huge amount of
data delivered by current and next generation radio telescopes, all these RFI
detection procedures have to be at least automatic and, if possible, real-time.
In this paper, the implementation of a real-time pre-correlation RFI
detection and flagging procedure into generic high-performance computing
platforms based on Field Programmable Gate Arrays (FPGA) is described,
simulated and tested. One of these boards, UniBoard, developed under a Joint
Research Activity in the RadioNet FP7 European programme is based on eight
FPGAs interconnected by a high speed transceiver mesh. It provides up to ~4
TMACs with Altera Stratix IV FPGA and 160 Gbps data rate for the input data
stream.
Considering the high in-out data rate in the pre-correlation stages, only
real-time and go-through detectors (i.e. no iterative processing) can be
implemented. In this paper, a real-time and adaptive detection scheme is
described.
An ongoing case study has been set up with the Electronic Multi-Beam Radio
Astronomy Concept (EMBRACE) radio telescope facility at Nan\c{c}ay Observatory.
The objective is to evaluate the performances of this concept in term of
hardware complexity, detection efficiency and additional RFI metadata rate
cost. The UniBoard implementation scheme is described.Comment: 16 pages, 13 figure
Machine-learning nonstationary noise out of gravitational-wave detectors
Signal extraction out of background noise is a common challenge in high-precision physics experiments, where the measurement output is often a continuous data stream. To improve the signal-to-noise ratio of the detection, witness sensors are often used to independently measure background noises and subtract them from the main signal. If the noise coupling is linear and stationary, optimal techniques already exist and are routinely implemented in many experiments. However, when the noise coupling is nonstationary, linear techniques often fail or are suboptimal. Inspired by the properties of the background noise in gravitational wave detectors, this work develops a novel algorithm to efficiently characterize and remove nonstationary noise couplings, provided there exist witnesses of the noise source and of the modulation. In this work, the algorithm is described in its most general formulation, and its efficiency is demonstrated with examples from the data of the Advanced LIGO gravitational-wave observatory, where we could obtain an improvement of the detector gravitational-wave reach without introducing any bias on the source parameter estimation
Towards Comfortable Cycling: A Practical Approach to Monitor the Conditions in Cycling Paths
This is a no brainer. Using bicycles to commute is the most sustainable form
of transport, is the least expensive to use and are pollution-free. Towns and
cities have to be made bicycle-friendly to encourage their wide usage.
Therefore, cycling paths should be more convenient, comfortable, and safe to
ride. This paper investigates a smartphone application, which passively
monitors the road conditions during cyclists ride. To overcome the problems of
monitoring roads, we present novel algorithms that sense the rough cycling
paths and locate road bumps. Each event is detected in real time to improve the
user friendliness of the application. Cyclists may keep their smartphones at
any random orientation and placement. Moreover, different smartphones sense the
same incident dissimilarly and hence report discrepant sensor values. We
further address the aforementioned difficulties that limit such crowd-sourcing
application. We evaluate our sensing application on cycling paths in Singapore,
and show that it can successfully detect such bad road conditions.Comment: 6 pages, 5 figures, Accepted by IEEE 4th World Forum on Internet of
Things (WF-IoT) 201
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