Search results for "EXTRACTION"
showing 10 items of 2072 documents
Analysis of data fusion techniques for multi-microphone audio event detection in adverse environments
2017
Acoustic event detection (AED) is currently a very active research area with multiple applications in the development of smart acoustic spaces. In this context, the advances brought by Internet of Things (IoT) platforms where multiple distributed microphones are available have also contributed to this interest. In such scenarios, the use of data fusion techniques merging information from several sensors becomes an important aspect in the design of multi-microphone AED systems. In this paper, we present a preliminary analysis of several data-fusion techniques aimed at improving the recognition accuracy of an AED system by taking advantage of the diversity provided by multiple microphones in …
Approximate 3D Partial Symmetry Detection Using Co-occurrence Analysis
2015
This paper addresses approximate partial symmetry detection in 3D point clouds, a classical and foundational tool for analyzing geometry. We present a novel, fully unsupervised method that detects partial symmetry under significant geometric variability, and without constraints on the number and arrangement of instances. The core idea is a matching scheme that finds consistent co-occurrence patterns in a frame-invariant way. We obtain a canonical partition of the input shape into building blocks and can handle ambiguous data by aggregating co-occurrence information across both all building block instances and the area they cover. We evaluate our method on several benchmark data sets and dem…
Signal-to-noise ratio in reproducing kernel Hilbert spaces
2018
This paper introduces the kernel signal-to-noise ratio (kSNR) for different machine learning and signal processing applications}. The kSNR seeks to maximize the signal variance while minimizing the estimated noise variance explicitly in a reproducing kernel Hilbert space (rkHs). The kSNR gives rise to considering complex signal-to-noise relations beyond additive noise models, and can be seen as a useful signal-to-noise regularizer for feature extraction and dimensionality reduction. We show that the kSNR generalizes kernel PCA (and other spectral dimensionality reduction methods), least squares SVM, and kernel ridge regression to deal with cases where signal and noise cannot be assumed inde…
Footprint of carbonyl compounds in hand scent by in-tube solid-phase microextraction coupled to nano-liquid chromatography/diode array detection
2019
Abstract In the present work, the footprint of carbonyl compounds in hand scent was achieved by a miniaturized method consisting of sampling with cotton gauze, extraction and derivatization using 2,4-dinitrophenylhydrazine (DNPH) and preconcentration, separation and detection by in-tube solid-phase microextraction (IT-SPME) coupled to nano-liquid chromatography/Uv–vis diode array detection. The coupling IT-SPME-nanoLC-DAD was solved by using a two-valve system: the first valve for loading the sample and the second one to perform IT-SPME. To this aim, a nanoparticle-based capillary column was employed. Firstly, the transfer time from the load loop to the NP-based capillary column in the IT-S…
A Comparative Analysis of Residual Block Alternatives for End-to-End Audio Classification
2020
Residual learning is known for being a learning framework that facilitates the training of very deep neural networks. Residual blocks or units are made up of a set of stacked layers, where the inputs are added back to their outputs with the aim of creating identity mappings. In practice, such identity mappings are accomplished by means of the so-called skip or shortcut connections. However, multiple implementation alternatives arise with respect to where such skip connections are applied within the set of stacked layers making up a residual block. While residual networks for image classification using convolutional neural networks (CNNs) have been widely discussed in the literature, their a…
Rapid extraction of short-lived isotopes from a buffer gas cell for use in gas-phase chemistry experiments. Part I: Off-line studies with 219Rn and …
2021
Abstract To study the chemical properties of the heaviest elements, a fast and efficient stopping and extraction of the highly energetic residues from heavy ion fusion reactions into the chemistry setup is essential. Currently used techniques like Recoil Transfer Chambers (RTC) relying on gas flow extraction provide high efficiencies for chemically non-reactive volatile species, but operate at extraction times t extr of about 0.5 s or more. Buffer Gas Cells (BGC) with electric and Radio-Frequency (RF) fields offer much faster extraction times. Here, we demonstrate the successful coupling of a BGC to a gas chromatography setup as is used for studies of chemical properties of superheavy eleme…
Rate capability of a cryogenic stopping cell for uranium projectile fragments produced at 1000 MeV/u
2016
At the Low-Energy Branch (LEB) of the Super-FRS at FAIR, projectile and fission fragments will be produced at relativistic energies, separated in-flight, energy-bunched, slowed down and thermalized in a cryogenic stopping cell (CSC) filled with ultra-pure He gas. The fragments are extracted from the stopping cell using a combination of DC and RF electric fields and gas flow. A prototype CSC for the LEB has been developed and successfully commissioned at the FRS Ion Catcher at GSI. Ionization of He buffer gas atoms during the stopping of energetic ions creates a region of high space charge in the stopping cell. The space charge decreases the extraction efficiency of stopping cells since the …
Cryogenic helium as stopping medium for high-energy ions
2008
We have investigated the survival and transport efficiency of Ra-219 ions emitted by a Ra-223 source in high-density cryogenic helium gas, with ionisation of the gas induced by a proton beam. The combined efficiency of ion survival and transport by an applied electric field was measured as a function of ionisation rate density for electric fields up to 160 V/cm and for three temperature and density combinations: 77 K, 0.18 mg/cm(3), 10 K, 0.18 mg/cm(3) and 10 K, 0.54 mg/cm(3). At low beam intensity or high electric field, an efficiency of 30%, is obtained, confirming earlier results. A sharp drop in efficiency is observed at a "threshold" ionisation rate density which increases with the squ…
Auto-Adaptive Trigger and Pulse Extraction for Digital Processing in Nuclear Instrumentation
2015
International audience; This paper presents a novel auto-adaptive method for pulse triggering and extraction. Pulse triggering uses a threshold that must be placed as close as possible to the noise level. We do this by means of an adaptive threshold level based on real-time noise level estimation. A dynamic estimation of the pulse length is also used for pulse selection. The proposed approach is largely insensitive to noise and enables autonomous extraction of pulses regardless of their shape, height or length. The proposed approach can be used with numerous types of detectors from an analog-to-digital converter, and can be used in conjunction with various pulse processing techniques such a…
Generalization of the model-independent Laurent–Pietarinen single-channel pole-extraction formalism to multiple channels
2016
A method to extract resonance pole information from single-channel partial-wave amplitudes based on a Laurent (Mittag-Leffler) expansion and conformal mapping techniques has recently been developed. This method has been applied to a number of reactions and provides a model-independent extraction procedure which is particularly useful in cases where a set of amplitudes is available only at discrete energies. This method has been generalized and applied to the case of a multi-channel fit, where several sets of amplitudes are analyzed simultaneously. The importance of unitarity constraints is discussed. The final result provides a powerful, model-independent tool for analyzing partial-wave amp…