Detection of neuronal signatures by means of data-driven tomography

Abstracts from Twenty Second Annual Computational Neuroscience Meeting: CNS*2013. Paris, France. 13-18 July 2013This work was supported by MINECO TIN2012-30883 and TIN-2010-19607

Signal-adapted tomography as a tool for dust devil detection

Dust devils are important phenomena to take into account to understand the global dust circulation of a planet. On Earth, their contribution to the injection of dust into the atmosphere seems to be secondary. Elsewhere, there are many indications that the dust devil’s role on other planets, in particular on Mars, could be fundamental, impacting the global climate. The ability to identify and study these vortices from the acquired meteorological measurements assumes a great importance for planetary science. Here we present a new methodology to identify dust devils from the pressure time series testing the method on the data acquired during a 2013 field campaign performed in the Tafilalt region (Morocco) of the North- Western Sahara Desert. Although the analysis of pressure is usually studied in the time domain, we prefer here to follow a different approach and perform the analysis in a time signal-adapted domain, the relation between the two being a bilinear transformation, i.e. a tomogram. The tomographic technique has already been successfully applied in other research fields like those of plasma reflectometry or the neuronal signatures. Here we show its effectiveness also in the dust devils detection. To test our results, we compare the tomography with a phase picker time domain analysis. We show the level of agreement between the two methodologies and the advantages and disadvantages of the tomographic approach

Signals on graphs : transforms and tomograms

Development of efficient tools for the representation of large datasets is a precondition for the study of dynamics on networks. Generalizations of the Fourier transform on graphs have been constructed through projections on the eigenvectors of graph matrices. By exploring mappings of the spectrum of these matrices we show how to construct more general transforms, in particular wavelet-like transforms on graphs. For time-series, tomograms, a generalization of the Radon transforms to arbitrary pairs of non-commuting operators, are positive bilinear transforms with a rigorous probabilistic interpretation which provide a full characterization of the signals and are robust in the preseninfo:eu-repo/semantics/publishedVersio

Kombinacija vremensko-frekvencijske analize signala i strojnoga učenja uz primjer u detekciji gravitacijskih valova

This paper presents a method for classifying noisy, non-stationary signals in the time-frequency domain using artificial intelligence. The preprocessed time-series signals are transformed into time-frequency representations (TFrs) from Cohen’s class resulting in the TFr images, which are used as input to the machine learning algorithms. We have used three state-of-the-art deep-learning 2d convolutional neural network (Cnn) architectures (ResNet-101, Xception, and EfficientNet). The method was demonstrated on the challenging task of detecting gravitational-wave (gw) signals in intensive real-life, non-stationary, non-gaussian, and non-white noise. The results show excellent classification performance of the proposed approach in terms of classification accuracy, area under the receiver operating characteristic curve (roC auC), recall, precision, F1 score, and area under the precision-recall curve (PR AUC). The novel method outperforms the baseline machine learning model trained on the time-series data in terms of all considered metrics. The study indicates that the proposed technique can also be extended to various other applications dealing with non-stationary data in intensive noise.Ovaj rad predstavlja metodu klasifikacije šumom narušenih nestacionarnih signala u vremensko-frekvencijskoj domeni korištenjem umjetne inteligencije. Naime, signali u obliku vremenskih nizova transformirani su nakon predobrade u vremensko-frekvencijske prikaze (TFR) iz Cohenove klase, rezultirajući TFR slikama korištenim kao ulaz u algoritme strojnoga učenja. Korištene su tri suvremene metode dubokoga učenja u obliku 2D arhitektura konvolucijskih neuronskih mreža (CNN) (ResNet-101, Xception i EfficientNet). Metoda je demonstrirana na zahtjevnom problemu detekcije signala gravitacijskih valova (GW) u intenzivnom stvarnom i nestacionarnom šumu koji nema karakteristike ni Gaussovog ni bijelog šuma. Rezultati pokazuju izvrsne performanse klasifikacije predloženoga pristupa s obzirom na točnost klasifikacije, površinu ispod krivulje značajke djelovanja prijamnika (ROC AUC), odziv, preciznost, F1-mjeru i površinu ispod krivulje preciznost-odziv (PR AUC). Nova metoda nadmašuje osnovni model strojnoga učenja treniran na podatcima u obliku vremenskih nizova s obzirom na razmatrane metrike. Istraživanje pokazuje da se predložena tehnika može proširiti i na različite druge primjene koje uključuju nestacionarne podatke u intenzivnom šumu

Characterization of multiphase flows integrating X-ray imaging and virtual reality

Multiphase flows are used in a wide variety of industries, from energy production to pharmaceutical manufacturing. However, because of the complexity of the flows and difficulty measuring them, it is challenging to characterize the phenomena inside a multiphase flow. To help overcome this challenge, researchers have used numerous types of noninvasive measurement techniques to record the phenomena that occur inside the flow. One technique that has shown much success is X-ray imaging. While capable of high spatial resolutions, X-ray imaging generally has poor temporal resolution. This research improves the characterization of multiphase flows in three ways. First, an X-ray image intensifier is modified to use a high-speed camera to push the temporal limits of what is possible with current tube source X-ray imaging technology. Using this system, sample flows were imaged at 1000 frames per second without a reduction in spatial resolution. Next, the sensitivity of X-ray computed tomography (CT) measurements to changes in acquisition parameters is analyzed. While in theory CT measurements should be stable over a range of acquisition parameters, previous research has indicated otherwise. The analysis of this sensitivity shows that, while raw CT values are strongly affected by changes to acquisition parameters, if proper calibration techniques are used, acquisition parameters do not significantly influence the results for multiphase flow imaging. Finally, two algorithms are analyzed for their suitability to reconstruct an approximate tomographic slice from only two X-ray projections. These algorithms increase the spatial error in the measurement, as compared to traditional CT; however, they allow for very high temporal resolutions for 3D imaging. The only limit on the speed of this measurement technique is the image intensifier-camera setup, which was shown to be capable of imaging at a rate of at least 1000 FPS. While advances in measurement techniques for multiphase flows are one part of improving multiphase flow characterization, the challenge extends beyond measurement techniques. For improved measurement techniques to be useful, the data must be accessible to scientists in a way that maximizes the comprehension of the phenomena. To this end, this work also presents a system for using the Microsoft Kinect sensor to provide natural, non-contact interaction with multiphase flow data. Furthermore, this system is constructed so that it is trivial to add natural, non-contact interaction to immersive visualization applications. Therefore, multiple visualization applications can be built that are optimized to specific types of data, but all leverage the same natural interaction. Finally, the research is concluded by proposing a system that integrates the improved X-ray measurements, with the Kinect interaction system, and a CAVE automatic virtual environment (CAVE) to present scientists with the multiphase flow measurements in an intuitive and inherently three-dimensional manner