scholarly journals Quantized Constant-Q Gabor Atoms for Sparse Binary Representations of Cyber-Physical Signatures

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 936
Author(s):  
Milton A. Garcés
Keyword(s):  
Machine Learning ◽  
Wavelet Transforms ◽  
Signal To Noise Ratio ◽  
Continuous Wavelet ◽  
Signal To Noise ◽  
Weighting Functions ◽  
Signal Characteristics ◽  
Multiple Sensor ◽  
New Challenges ◽  
Physical Signal

Increased data acquisition by uncalibrated, heterogeneous digital sensor systems such as smartphones present new challenges. Binary metrics are proposed for the quantification of cyber-physical signal characteristics and features, and a standardized constant-Q variation of the Gabor atom is developed for use with wavelet transforms. Two different continuous wavelet transform (CWT) reconstruction formulas are presented and tested under different signal to noise ratio (SNR) conditions. A sparse superposition of Nth order Gabor atoms worked well against a synthetic blast transient using the wavelet entropy and an entropy-like parametrization of the SNR as the CWT coefficient-weighting functions. The proposed methods should be well suited for sparse feature extraction and dictionary-based machine learning across multiple sensor modalities.

scholarly journals Constant-Q Gabor Atoms for Sparse Binary Representations of Cyber-Physical Signatures

2020 ◽  
Author(s):  
Milton Garces
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Data Acquisition ◽  
Sparse Representation ◽  
Wavelet Transforms ◽  
Sensor Systems ◽  
Weighting Functions ◽  
Wavelet Entropy ◽  
Signal Characteristics ◽  
Physical Signal

Data acquisition by uncalibrated, heterogeneous digital sensor systems such as smartphones present emerging signal processing challenges. Binary metrics are proposed for the quantification of cyber-physical signal characteristics and features, and a highly standardized constant-Q variation of the Gabor atom is developed for use with wavelet transforms. Two different CWT reconstruction schemas are presented and tested under different SNR conditions. A sparse representation of the Nth order Gabor atoms worked well against a test blast synthetic using the wavelet entropy and a comparable entropy-like parametrization of the SNR as the CWT coefficient-weighting functions. The proposed methods should be well suited for dictionary-based machine learning.

scholarly journals Detection of Volcanic Plumes by GPS: the 23 November 2013 Episode on Mt. Etna

2015 ◽  
Vol 57 ◽  
Author(s):  
Massimo Aranzulla ◽  
Flavio Cannavò ◽  
Simona Scollo
Keyword(s):  
Signal To Noise Ratio ◽  
Explosive Eruptions ◽  
Volcanic Plume ◽  
Test Case ◽  
Signal To Noise ◽  
Volcanic Plumes ◽  
Gps Network ◽  
Mt Etna ◽  
New Challenges ◽  
Aviation Operations

<p>The detection of volcanic plumes produced during explosive eruptions is important to improve our understanding on dispersal processes and reduce risks to aviation operations. The ability of Global Position-ing System (GPS) to retrieve volcanic plumes is one of the new challenges of the last years in volcanic plume detection. In this work, we analyze the Signal to Noise Ratio (SNR) data from 21 permanent stations of the GPS network of the Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo, that are located on the Mt. Etna (Italy) flanks. Being one of the most explosive events since 2011, the eruption of November 23, 2013 was chosen as a test-case. Results show some variations in the SNR data that can be correlated with the presence of an ash-laden plume in the atmosphere. Benefits and limitations of the method are highlighted.</p>

scholarly journals GeNets: A unified web platform for network-based analyses of genomic data

2018 ◽  
Author(s):  
Taibo Li ◽  
April Kim ◽  
Joseph Rosenbluh ◽  
Heiko Horn ◽  
Liraz Greenfeld ◽  
...  
Keyword(s):  
Machine Learning ◽  
Functional Genomics ◽  
Rna Sequencing ◽  
Signal To Noise Ratio ◽  
Genomic Data ◽  
Learning Model ◽  
Signal To Noise ◽  
Machine Learning Model ◽  
Optimal Network ◽  
Web Platform

Functional genomics networks are widely used to identify unexpected pathway relationships in large genomic datasets. However, it is challenging to quantitatively compare the signal-to-noise ratio of different networks, the biology they describe, and to identify the optimal network to interpret a particular genetic dataset. Via GeNets users can train a machine-learning model (Quack) to make such comparisons; and they can execute, store, and share analyses of genetic and RNA sequencing datasets.

scholarly journals ODUSSEAS: a machine learning tool to derive effective temperature and metallicity for M dwarf stars

2020 ◽  
Vol 636 ◽  
pp. A9 ◽  
Author(s):  
A. Antoniadis-Karnavas ◽  
S. G. Sousa ◽  
E. Delgado-Mena ◽  
N. C. Santos ◽  
G. D. C. Teixeira ◽  
...  
Keyword(s):  
Machine Learning ◽  
Signal To Noise Ratio ◽  
Computational Tool ◽  
M Dwarfs ◽  
Signal To Noise ◽  
Dwarf Stars ◽  
M Dwarf Stars ◽  
M Dwarf ◽  
Python Package ◽  
Machine Learning Tool

Aims. The derivation of spectroscopic parameters for M dwarf stars is very important in the fields of stellar and exoplanet characterization. The goal of this work is the creation of an automatic computational tool able to quickly and reliably derive the Teff and [Fe/H] of M dwarfs using optical spectra obtained by different spectrographs with different resolutions. Methods. ODUSSEAS (Observing Dwarfs Using Stellar Spectroscopic Energy-Absorption Shapes) is based on the measurement of the pseudo equivalent widths for more than 4000 stellar absorption lines and on the use of the machine learning Python package “scikit-learn” for predicting the stellar parameters. Results. We show that our tool is able to derive parameters accurately and with high precision, having precision errors of ~30 K for Teff and ~0.04 dex for [Fe/H]. The results are consistent for spectra with resolutions of between 48 000 and 115 000 and a signal-to-noise ratio above 20.

Application of machine learning methods to identify englacial seismicity in a Distributed Acoustic Sensing dataset from Store Glacier, West Greenland

2021 ◽  
Author(s):  
Andrew Pretorius ◽  
Emma Smith ◽  
Adam Booth ◽  
Poul Christofferson ◽  
Andy Nowacki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Crystal Orientation ◽  
Time Series Data ◽  
Signal To Noise Ratio ◽  
Series Data ◽  
Seismic Events ◽  
Signal To Noise ◽  
Controlled Source ◽  
Distributed Acoustic Sensing

&lt;p&gt;Seismic surveys are widely used to study the properties of glaciers, basal material and conditions, ice temperature and crystal orientation fabric. The emerging technology of Distributed Acoustic Sensing (DAS) uses fibre optic cables as pseudo-seismic receivers,&lt;br&gt;reconstructing seismic measurements at a higher spatial and temporal resolution than is possible using traditional geophone deployments. DAS generates large volumes of data, especially in passive mode, which can be costly in time and cumbersome to analyse. Machine learning tools provide an effective means of automatically identifying events within these records, avoiding a bottleneck in the data analysis process. Here we present initial trials of machine learning for a borehole-deployed DAS system on Store Glacier, West Greenland. Data were acquired in July 2019, using a Silixa iDAS interrogator and a BRUsens fibre optic cable installed in a 1043 m-deep borehole. The interrogator sampled at 4000 Hz, recording both controlled-source Vertical Seismic Profiles (VSPs), made with hammer-and-plate source, and a 3-day passive record of cryoseismicity.&lt;/p&gt;&lt;p&gt;We used a Convolutional Neural Network (CNN) to identify seismic events within the seismic record. A CNN is a deep learning algorithm that uses a series of convolutional filters to extract features from a 2-dimensional matrix of values. These features are then used to train a model&lt;br&gt;that can recognise objects or patterns within the dataset. CNNs are a powerful classification tool, widely applied to the analysis of both images and time series data. Previous research has demonstrated the ability of CNNs to recognise seismic phases in time series data for long-range&lt;br&gt;earthquake detection, even when the phases are masked by a low signal-to-noise ratio. For the Store Glacier data, initial results were obtained using a CNN trained on hand-labelled, uniformly-sized windows. At present, these windows have been targeted around high signal-to-noise ratio seismic events in the controlled-source VSPs only. Once trained, the CNN achieved accuracy of 90% in recognising whether new windows contained coherent seismic&lt;br&gt;energy.&lt;/p&gt;&lt;p&gt;The next phase of analysis will be to assess the performance of the CNN when trained and tested on large passive DAS datasets. The method will then be used for the identification and flagging of seismic events within the passive record for interpretation and event location. The identified signals will be used to provide information on the glacier&amp;#8217;s seismic velocity structure, ice temperature and ice crystal orientation fabric and anisotropy. Basal reflections were identified and will be used to provide information on subglacial material properties and conditions of Store Glacier. The efficiency of the CNN allows detailed insight to be made into the origins and style of glacier seismicity, facilitating further advantages of passive DAS instrumentation.&lt;/p&gt;

scholarly journals Optimizing High-Efficiency Quantum Memory with Quantum Machine Learning for Near-Term Quantum Devices

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Laszlo Gyongyosi ◽  
Sandor Imre
Keyword(s):  
Machine Learning ◽  
Signal To Noise Ratio ◽  
Quantum Memory ◽  
Quantum Computers ◽  
Signal To Noise ◽  
Quantum Devices ◽  
Retrieval Efficiency ◽  
Quantum Machine Learning ◽  
Near Term ◽  
Quantum Internet

AbstractQuantum memories are a fundamental of any global-scale quantum Internet, high-performance quantum networking and near-term quantum computers. A main problem of quantum memories is the low retrieval efficiency of the quantum systems from the quantum registers of the quantum memory. Here, we define a novel quantum memory called high-retrieval-efficiency (HRE) quantum memory for near-term quantum devices. An HRE quantum memory unit integrates local unitary operations on its hardware level for the optimization of the readout procedure and utilizes the advanced techniques of quantum machine learning. We define the integrated unitary operations of an HRE quantum memory, prove the learning procedure, and evaluate the achievable output signal-to-noise ratio values. We prove that the local unitaries of an HRE quantum memory achieve the optimization of the readout procedure in an unsupervised manner without the use of any labeled data or training sequences. We show that the readout procedure of an HRE quantum memory is realized in a completely blind manner without any information about the input quantum system or about the unknown quantum operation of the quantum register. We evaluate the retrieval efficiency of an HRE quantum memory and the output SNR (signal-to-noise ratio). The results are particularly convenient for gate-model quantum computers and the near-term quantum devices of the quantum Internet.

scholarly journals Agro Image De-Noising (Aid) for Enhanced Agricultural Images

2019 ◽  
Vol 9 (2) ◽  
pp. 2474-2478
Keyword(s):  
Image Processing ◽  
Wavelet Transforms ◽  
Signal To Noise Ratio ◽  
Discrete Wavelet ◽  
Signal To Noise ◽  
Photographic Images ◽  
Spline Wavelets ◽  
Soft Threshold ◽  
Noise Ratio ◽  
Processing Techniques

Several Noises may be present in acquired images. This is an undesired feature for image processing techniques that analyze these images. Image de-noising helps improve efficiency of image processing. Many image de-noising methods have been proposed and exist in literature. Image de-noising methods for agricultural images have been proposed to a lesser extent when compared to the bright medical or photographic images. This paper proposes Agricultural Image De-noising (AID) which uses a discrete wavelet transform (DWT) to eliminate noise in agricultural images. This study uses specific kind of wavelet family spline wavelet transforms with appropriate decomposition level and the wavelet coefficients are analysed with hard and soft threshold methods. The denoised image using various spline wavelets is compared of hard threshold and soft threshold are assessed. The performance of AID is calculated using the peak signal to noise ratio (PSNR) and signal to noise ratio (SNR).

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