Multifractal wavelet filter of natural images

Natural images are characterized by the multiscaling properties of their contrast gradient, in addition to their power spectrum. In this work we show that those properties uniquely define an {\em intrinsic wavelet} and present a suitable technique to obtain it from an ensemble of images. Once this wavelet is known, images can be represented as expansions in the associated wavelet basis. The resulting code has the remarkable properties that it separates independent features at different resolution level, reducing the redundancy, and remains essentially unchanged under changes in the power spectrum. The possible generalization of this representation to other systems is discussed.Comment: 4 pages, 4 figures, RevTe

The multi-fractal structure of contrast changes in natural images: from sharp edges to textures

We present a formalism that leads very naturally to a hierarchical description of the different contrast structures in images, providing precise definitions of sharp edges and other texture components. Within this formalism, we achieve a decomposition of pixels of the image in sets, the fractal components of the image, such that each set only contains points characterized by a fixed stregth of the singularity of the contrast gradient in its neighborhood. A crucial role in this description of images is played by the behavior of contrast differences under changes in scale. Contrary to naive scaling ideas where the image is thought to have uniform transformation properties \cite{Fie87}, each of these fractal components has its own transformation law and scaling exponents. A conjecture on their biological relevance is also given.Comment: 41 pages, 8 figures, LaTe

The effect of hydrogen sulphide on ammonium bisulphite when used as an oxygen scavenger in aqueous solutions

Peer reviewedPostprin

Computational Models to Detect Radiation in Urban Environments

Radioactive sources, such as uranium-235, are nuclides that emit ionizing radiation, and which can be used to build nuclear weapons. In public areas, the presence of a radioactive nuclide can present a risk to the population, and therefore, it is imperative that threats are identified by radiological search and response teams in a timely and effective manner. In urban environments, such as densely populated cities, radioactive sources may be more difficult to detect, since background radiation produced by surrounding objects and structures (e.g., buildings, cars) can hinder the effective detection of unnatural radioactive material. This study presents a computational model to detect radioactive sources in urban environments, which uses signal processing techniques to identify radiation signatures. Moreover, the model uses artificial neural networks to identify types of radiation sources, classifying them as innocuous or harmful, and discerning between weapons-grade material and radioactive isotopes used in medical/industrial settings

Developing Computational Models to Detect Radiation in Urban Environments

The main objective of this project is to detect, characterize, and locate radioactive sources in urban environments using computational models based on machine learning and statistical techniques. The project explores multiple approaches such as signal processing methods, and neural networks. Unnatural radiation sources, such as Uranium or Plutonium, can present a risk to the population if they remain undetected by radiological search and response teams. Moreover, the computational model being developed must be capable of identifying the type of radiation source, classifying it as innocuous (i.e., isotopes used in medical and industrial settings) or harmful (nuclear weapons). The project is currently supported by the Pacific Northwest National Laboratory (PNNL), in collaboration with the Department of Mathematics at Embry-Riddle

Numerical simulation of a binary communication channel: Comparison between a replica calculation and an exact solution

The mutual information of a single-layer perceptron with $N$ Gaussian inputs and $P$ deterministic binary outputs is studied by numerical simulations. The relevant parameters of the problem are the ratio between the number of output and input units, $\alpha = P/N$, and those describing the two-point correlations between inputs. The main motivation of this work refers to the comparison between the replica computation of the mutual information and an analytical solution valid up to $\alpha \sim O(1)$. The most relevant results are: (1) the simulation supports the validity of the analytical prediction, and (2) it also verifies a previously proposed conjecture that the replica solution interpolates well between large and small values of $\alpha$.Comment: 6 pages, 8 figures, LaTeX fil

Implementation of Machine Learning Methods for Ionospheric Scintillation Data Analysis

The ionosphere is a region in the Earth’s upper atmosphere, where atoms are ionized due to solar radiation. The behavior of the ionosphere depends on time and location, and it is highly influenced by solar activity. The ionization process creates layers of free electrons at different altitudes, which can cause fluctuations in electromagnetic waves crossing the region. The effect of ionospheric events on radio signals can be measured using Global Navigation Satellite Systems (GNSS) receivers, in terms of ionospheric scintillation and Total Electron Content (TEC). The GNSS team at the Space Physics Research Lab (SPRL) studies ionospheric events using multi-frequency GNSS receivers (NovAtel GPStation-6) capable measuring high and low rate scintillation data as well as TEC values from three different GNSS systems (GPS, GALILEO, and GLONASS). The purpose of this project is to develop a machine learning algorithm, using recurrent neural networks, to detect ionospheric events in low-rate scintillation data. Recurrent neural networks are often used for time-series applications, including forecasting and prediction. The model is being trained using data collected by the GNSS receivers in multiple locations (including Daytona Beach), with a focus on high-latitude data from the Canadian High Artic Ionospheric Network (CHAIN). The machine learning model will be integrated with the Embry-Riddle Ionospheric Scintillation Algorithm (EISA), an existing model capable of processing ionospheric data. EISA was developed by the GNSS team at SPRL. The updated model will allow the team to automate the process of ionospheric event detection, which is currently done manually. Upon this implementation, EISA will become an end-to-end model for ionospheric data collection, processing, and modelling

Auto and crosscorrelograms for the spike response of LIF neurons with slow synapses

An analytical description of the response properties of simple but realistic neuron models in the presence of noise is still lacking. We determine completely up to the second order the firing statistics of a single and a pair of leaky integrate-and-fire neurons (LIFs) receiving some common slowly filtered white noise. In particular, the auto- and cross-correlation functions of the output spike trains of pairs of cells are obtained from an improvement of the adiabatic approximation introduced in \cite{Mor+04}. These two functions define the firing variability and firing synchronization between neurons, and are of much importance for understanding neuron communication.Comment: 5 pages, 3 figure

Periodically rippled graphene: growth and spatially resolved electronic structure

We studied the growth of an epitaxial graphene monolayer on Ru(0001). The graphene monolayer covers uniformly the Ru substrate over lateral distances larger than several microns reproducing the structural defects of the Ru substrate. The graphene is rippled with a periodicity dictated by the difference in lattice parameter between C and Ru. The theoretical model predict inhomogeneities in the electronic structure. This is confirmed by measurements in real space by means of scanning tunnelling spectroscopy. We observe electron pockets at the higher parts of the ripples.Comment: 5 page

Thermodynamics of Extended Bodies in Special Relativity

Relativistic thermodynamics is generalized to accommodate four dimensional rotation in a flat spacetime. An extended body can be in equilibrium when its each element moves along a Killing flow. There are three types of basic Killing flows in a flat spacetime, each of which corresponds to translational motion, spatial rotation, and constant linear acceleration; spatial rotation and constant linear acceleration are regarded as four dimensional rotation. Translational motion has been mainly investigated in the past literature of relativistic thermodynamics. Thermodynamics of the other two is derived in the present paper.Comment: 8 pages, no figur