scholarly journals Wind Turbine Noise Reduction from Seismological Data

2021 ◽  
Author(s):  
Janis Heuel ◽  
Wolfgang Friederich
Keyword(s):  
Signal To Noise Ratio ◽  
Low Cost ◽  
Computational Cost ◽  
Noise Signal ◽  
Noise Model ◽  
Threshold Function ◽  
High Signal ◽  
Time Data ◽  
Negative Effects ◽  
Before And After

<p>Over the last years, installations of wind turbines (WTs) increased worldwide. Owing to<br>negative effects on humans, WTs are often installed in areas with low population density.<br>Because of low anthropogenic noise, these areas are also well suited for sites of<br>seismological stations. As a consequence, WTs are often installed in the same areas as<br>seismological stations. By comparing the noise in recorded data before and after<br>installation of WTs, seismologists noticed a substantial worsening of station quality leading<br>to conflicts between the operators of WTs and earthquake services.</p><p>In this study, we compare different techniques to reduce or eliminate the disturbing signal<br>from WTs at seismological stations. For this purpose, we selected a seismological station<br>that shows a significant correlation between the power spectral density and the hourly<br>windspeed measurements. Usually, spectral filtering is used to suppress noise in seismic<br>data processing. However, this approach is not effective when noise and signal have<br>overlapping frequency bands which is the case for WT noise. As a first method, we applied<br>the continuous wavelet transform (CWT) on our data to obtain a time-scale representation.<br>From this representation, we estimated a noise threshold function (Langston & Mousavi,<br>2019) either from noise before the theoretical P-arrival (pre-noise) or using a noise signal<br>from the past with similar ground velocity conditions at the surrounding WTs. Therefore, we<br>installed low cost seismometers at the surrounding WTs to find similar signals at each WT.<br>From these similar signals, we obtain a noise model at the seismological station, which is<br>used to estimate the threshold function. As a second method, we used a denoising<br>autoencoder (DAE) that learns mapping functions to distinguish between noise and signal<br>(Zhu et al., 2019).</p><p>In our tests, the threshold function performs well when the event is visible in the raw or<br>spectral filtered data, but it fails when WT noise dominates and the event is hidden. In<br>these cases, the DAE removes the WT noise from the data. However, the DAE must be<br>trained with typical noise samples and high signal-to-noise ratio events to distinguish<br>between signal and interfering noise. Using the threshold function and pre-noise can be<br>applied immediately on real-time data and has a low computational cost. Using a noise<br>model from our prerecorded database at the seismological station does not improve the<br>result and it is more time consuming to find similar ground velocity conditions at the<br>surrounding WTs.</p>

scholarly journals Fourier Rotman Lens Enabled Directional Modulation Transmitter

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Yuan Ding ◽  
Yunhua Zhang ◽  
Vincent Fusco
Keyword(s):  
Bit Error Rate ◽  
Data Streams ◽  
Data Transmission ◽  
Signal To Noise Ratio ◽  
High Signal ◽  
Independent Data ◽  
Time Data ◽  
Dual Beam ◽  
Directional Modulation ◽  
Ber Performance

A 10 GHz Fourier Rotman lens enabled dynamic directional modulation (DM) transmitter is experimentally evaluated. Bit error rate (BER) performance is obtained via real-time data transmission. It is shown that Fourier Rotman DM functionality enhances system security performance in terms of narrower decodable low BER region and higher BER values associated with BER sidelobes especially under high signal to noise ratio (SNR) scenarios. This enhancement is achieved by controlled corruption of constellation diagrams in IQ space by orthogonal injection of interference. Furthermore, the paper gives the first report of a functional dual-beam DM transmitter, which has the capability of simultaneously projecting two independent data streams into two different spatial directions while simultaneously scrambling the information signals along all other directions.

Internally Modulated Chirped Pulse Based Direct Detection Type φ-OTDR System with High Signal-to-Noise Ratio and Low Cost

2019 ◽  
Vol 46 (8) ◽  
pp. 0806003
Author(s):  
李鲁川 Luchuan Li ◽  
卢斌 Bin Lu ◽  
王校 Xiao Wang ◽  
梁嘉靖 Jiajing Liang ◽  
郑汉荣 Hanrong Zheng ◽  
...  
Keyword(s):  
Direct Detection ◽  
Signal To Noise Ratio ◽  
Low Cost ◽  
High Signal ◽  
Signal To Noise ◽  
Chirped Pulse ◽  
Noise Ratio

Speech in noise testing before and after grommet insertion

2012 ◽  
Vol 126 (10) ◽  
pp. 1010-1015 ◽  
Author(s):  
V Possamai ◽  
G Kirk ◽  
A Scott ◽  
D Skinner
Keyword(s):  
Small Group ◽  
Background Noise ◽  
Signal To Noise Ratio ◽  
Sound Field ◽  
Noise Signal ◽  
Signal To Noise ◽  
Speech In Noise ◽  
Before And After ◽  
Noise Test ◽  
Noise Ratio

AbstractObjectives:To assess the feasibility of designing and implementing a speech in noise test in children before and after grommet insertion, and to analyse the results of such a test in a small group of children.Methods:Twelve children aged six to nine years who were scheduled to undergo grommet insertion were identified. They underwent speech in noise testing before and after grommet insertion. This testing used Arthur Boothroyd word lists read at 60 dB in four listening conditions presented in a sound field: firstly in quiet conditions, then in signal to noise ratios of +10 (50 dB background noise), 0 (60 dB) and −10 (70 dB).Results:Mean phoneme scores were: in quiet conditions, 28.1 pre- and 30 post-operatively (p = 0.04); in 50 dB background noise (signal to noise ratio +10), 24.2 pre- and 29 post-operatively (p < 0.01); in 60 dB background noise (signal to noise ratio 0), 22.6 pre- and 27.5 post-operatively (p = 0.06); and in 70 dB background noise (signal to noise ratio −10), 13.9 pre- and 21 post-operatively (p = 0.05).Conclusion:This small study suggests that speech in noise testing is feasible in this scenario. Our small group of children demonstrated a significant improvement in speech in noise scores following grommet insertion. This is likely to translate into a significant advantage in the educational environment.

scholarly journals Implementation of vehicle ventilation system using NodeMCU ESP8266 for remote monitoring

2021 ◽  
Vol 10 (1) ◽  
pp. 327-336
Author(s):  
Amirun Murtaza Abd Jalil ◽  
Roslina Mohamad ◽  
Nuzli Mohamad Anas ◽  
Murizah Kassim ◽  
Saiful Izwan Suliman
Keyword(s):  
Mobile Applications ◽  
Mobile Application ◽  
Transmission Control Protocol ◽  
Low Cost ◽  
Ventilation System ◽  
Time Data ◽  
Transmission Control ◽  
Iot Platform ◽  
Before And After ◽  
And Control

In this paper, an implementation of vehicle ventilation system using microcontroller NodeMCU is described, as an internet of things (IoT) platform. A low-cost wireless fidelity (Wi-Fi) microchip ESP8266 integrated with NodeMCU provides full-stack transmission control protocol/internet protocol (TCP/IP) to communicate between mobile applications. This chip is capable to monitor and control sensor devices connected to the IoT platform. In this reserach, data was collected from a temperature sensor integrated to the platform, which then monitored using Blynk application. The vehicle ventilation system was activated/deactivated through mobile application and controlled using ON/OFF commands sent to the connected devices. From the results, the vehicle ventilation system built using NodeMCU microcontroller is capable to provide near real-time data monitoring for temperature in the car before and after the ventilation system was applied.

scholarly journals Double-Layer Flexible Neural Probe With Closely Spaced Electrodes for High-Density in vivo Brain Recordings

2021 ◽  
Vol 15 ◽  
Author(s):  
Sara Pimenta ◽  
José A. Rodrigues ◽  
Francisca Machado ◽  
João F. Ribeiro ◽  
Marino J. Maciel ◽  
...  
Keyword(s):  
Double Layer ◽  
Signal To Noise Ratio ◽  
Low Cost ◽  
High Signal ◽  
Composition Analysis ◽  
Assessment Tests ◽  
Flexible Polymer ◽  
Mouse Cortex ◽  
Neural Probe

Flexible polymer neural probes are an attractive emerging approach for invasive brain recordings, given that they can minimize the risks of brain damage or glial scaring. However, densely packed electrode sites, which can facilitate neuronal data analysis, are not widely available in flexible probes. Here, we present a new flexible polyimide neural probe, based on standard and low-cost lithography processes, which has 32 closely spaced 10 μm diameter gold electrode sites at two different depths from the probe surface arranged in a matrix, with inter-site distances of only 5 μm. The double-layer design and fabrication approach implemented also provides additional stiffening just sufficient to prevent probe buckling during brain insertion. This approach avoids typical laborious augmentation strategies used to increase flexible probes’ mechanical rigidity while allowing a small brain insertion footprint. Chemical composition analysis and metrology of structural, mechanical, and electrical properties demonstrated the viability of this fabrication approach. Finally, in vivo functional assessment tests in the mouse cortex were performed as well as histological assessment of the insertion footprint, validating the biological applicability of this flexible neural probe for acquiring high quality neuronal recordings with high signal to noise ratio (SNR) and reduced acute trauma.

scholarly journals Deep Learning Based Inversion of Locally Anisotropic Weld Properties from Ultrasonic Array Data

2022 ◽  
Vol 12 (2) ◽  
pp. 532
Author(s):  
Jonathan Singh ◽  
Katherine Tant ◽  
Anthony Mulholland ◽  
Charles MacLeod
Keyword(s):  
Real Time ◽  
Signal To Noise Ratio ◽  
Computational Cost ◽  
Scattered Wave ◽  
Training Data ◽  
Grain Structure ◽  
Time Data ◽  
Grain Orientations ◽  
Weld Properties ◽  
Crystallographic Orientations

The ability to reliably detect and characterise defects embedded in austenitic steel welds depends on prior knowledge of microstructural descriptors, such as the orientations of the weld’s locally anisotropic grain structure. These orientations are usually unknown but it has been shown recently that they can be estimated from ultrasonic scattered wave data. However, conventional algorithms used for solving this inverse problem incur a significant computational cost. In this paper, we propose a framework which uses deep neural networks (DNNs) to reconstruct crystallographic orientations in a welded material from ultrasonic travel time data, in real-time. Acquiring the large amount of training data required for DNNs experimentally is practically infeasible for this problem, therefore a model based training approach is investigated instead, where a simple and efficient analytical method for modelling ultrasonic wave travel times through given weld geometries is implemented. The proposed method is validated by testing the trained networks on data arising from sophisticated finite element simulations of wave propagation through weld microstructures. The trained deep neural network predicts grain orientations to within 3° and in near real-time (0.04 s), presenting a significant step towards realising real-time, accurate characterisation of weld microstructures from ultrasonic non-destructive measurements. The subsequent improvement in defect imaging is then demonstrated via use of the DNN predicted crystallographic orientations to correct the delay laws on which the total focusing method imaging algorithm is based. An improvement of up to 5.3 dB in the signal-to-noise ratio is achieved.

scholarly journals Particle Imaging Velocimetry Gyroscope

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4734
Author(s):  
Ahmed A. Youssef ◽  
Naser El-Sheimy
Keyword(s):  
Signal To Noise Ratio ◽  
Low Cost ◽  
Particle Imaging Velocimetry ◽  
High Signal ◽  
Inertial Measurement Units ◽  
Signal To Noise ◽  
Inertial Measurement ◽  
Measurement Units ◽  
Particle Imaging ◽  
Bias Instability

Inertial measurement units (IMUs) are typically classified as per the performance of the gyroscopes within each system. Consequently, it is critical for a system to have a low bias instability to have better performance. Nonetheless, there is no IMU available commercially that does not actually suffer from bias-instability, even for the navigation grade IMUs. This paper introduces the proposition of a novel fluid-based gyroscope, which is referred to hereafter as a particle imaging velocimetry gyroscope (PIVG). The main advantages of the PIVG include being nearly drift-free, a high signal-to-noise ratio (SNR) in comparison to commercially available high-end gyroscopes, and its low cost.

IMPLEMENTATION OF AN INEXPENSIVE AND IMPROVED PURPOSE-BUILT HIGH SNR BIOMEDICAL SIGNAL CAPTURE, RECORD, AND ANALYSIS SYSTEM

2020 ◽  
pp. 2150004
Author(s):  
Tung-Tai Kuo ◽  
Rong-Chin Lo ◽  
Yuan-Hao Chen ◽  
Chung-Ling Tseng
Keyword(s):  
Signal To Noise Ratio ◽  
Low Cost ◽  
High Signal ◽  
Record System ◽  
Brain Wave ◽  
Biomedical Signal ◽  
Accuracy And Precision ◽  
Analysis System ◽  
The Brain ◽  
Brain Study

It is very important for the brain study to design a multi-channel and high signal-to-noise ratio (SNR) bio-medical signal capture, record, and analysis system, which can effectively enhance accuracy and precision of the signal capture under dozens to hundreds of microvolts. Unfortunately, the system for data acquisition is very easily interfered by the environment, the power, and the bio-amplifier, so that the results will lead to a failed promotion of capturing high SNR signals, especially in the tiny brain wave signal. In this study, it has been designed an inexpensive, purpose-built, high SNR brainwave signals measurement system. The system is composed of an improved capture system, record system, and analysis system. To better consider the strength and characteristics of the tiny brainwave signals, the system was designed to include a suitable bio-amplifier to make each channel of the invasive microelectrode able to collect the brainwave signals correctly and it provides recording and analysis software, which can not only extract the characteristics of brainwave signals, but also quickly classify signals. The system can collect biological signals from 10[Formula: see text][Formula: see text]V to 420[Formula: see text][Formula: see text]V and has a high SNR[Formula: see text]30. The proposed system is easy to make and can be fabricated for the relatively low cost of only US$203. The brain wave signals from the three actions can also be easily classified, with a correct rate up to 46.70%. The system has six improvements: good SNR, the ability to capture small signals, modularity, a low price, easy fabrication, and simple operation.

scholarly journals A Solution to Ambiguities in Position Estimation for Solenoid Actuators by Exploiting Eddy Current Variations

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3441 ◽  
Author(s):  
Niklas König ◽  
Matthias Nienhaus
Keyword(s):  
Eddy Current ◽  
Signal To Noise Ratio ◽  
Low Cost ◽  
High Sensitivity ◽  
Position Estimation ◽  
High Signal ◽  
Wide Field ◽  
Fusion Algorithm ◽  
Case Scenario ◽  
Position Detection

Position estimation techniques for solenoid actuators are successfully used in a wide field of applications requiring monitoring functionality without the need for additional sensors. Most techniques, which also include standstill condition, are based on the identification of the differential inductance, a parameter that exhibits high sensitivity towards position variations. The differential inductance of some actuators shows a non-monotonic dependency over the position. This leads to ambiguities in position estimation. Nevertheless, a unique position estimation in standstill condition without prior knowledge of the actuator state is highly desired. In this work, the eddy current losses inside the actuator are identified in terms of a parallel resistor and are exploited in order to solve the ambiguities in position estimation. Compared to other state-of-the-art techniques, the differential inductance and the parallel resistance are estimated online by approaches requiring low implementation and computation effort. Furthermore, a data fusion algorithm for position estimation based on a neural network is proposed. Experimental results involving a use case scenario of an end-position detection for a switching solenoid actuator prove the uniqueness, the precision and the high signal-to-noise ratio of the obtained position estimate. The proposed approach therefore allows the unique estimation of the actuator position including standstill condition suitable for low-cost applications demanding low implementation effort.

scholarly journals Simple and low-cost amplifier system for the study of a ventral nerve action potential in earthworm

2019 ◽  
Author(s):  
Orlando Jorquera
Keyword(s):  
Action Potential ◽  
Signal To Noise Ratio ◽  
Low Cost ◽  
Experimental System ◽  
Lumbricus Terrestris ◽  
Simulation Software ◽  
High Signal ◽  
Giant Fiber ◽  
Basic Principles ◽  
Met Expectations

AbstractBasic Biophysics or Neurobiology courses encounter problems when starting their practices due to the complexity and high cost of the equipment. As a consequence, these experiences are replaced by simulation software, leading students to boredom and disinterest, and finally to the lack of understanding of the basic principles of the phenomenon observed. Classical nerve conduction studies for the visualization of action potentials are an example of this, being able to be developed with simple and low-cost amplification circuits, as described in this article. The system showed stability, expected amplification and high signal-to-noise ratio. The action potential of the medial giant fiber (MGF) as lateral giant fiber (LGF) in the ventral nerve cord of the earthworms Lumbricus terrestris was recorded. The experimental system has met expectations and regains practical, effective and stimulating learning for students.

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