Hardware Architecture for the Implementation of the Discrete Wavelet Transform in two Dimensions

Resumen El artículo presenta una arquitectura hardware que desarrolla la transformada Wavelet en dos dimensiones sobre una FPGA, en el diseño se buscó un balance entre número de celdas lógicas requeridas y la velocidad de procesamiento. El artículo inicia con una revisión de trabajos previos, después se presentan los fundamentos teóricos de la transformación, posteriormente se presenta la arquitectura propuesta seguida por un análisis comparativo. El sistema se implementó en la FPGA Ciclone II EP2C35F672C6 de Altera utilizando un diseño soportado en el sistema Nios II. Abstract This paper presents a hardware architecture developed by the two-dimensional wavelet transform on an FPGA, in the design it was searched a balance between the number of required logic cells and the processing speed. The design is based on a methodology to reuse the input data with a parallel-pipelined structure and a calculation of the coefficients is performed using a method of odd and even numbers, which is achieved by calculating a cycle ratio after 2 cycles latency, to store the data processing result of the SDRAM memory is used IS42S16400, the control unit uses a design architecture supported by Nios II processor. The system was implemented in the FPGA Altera Cyclone II EP2C35F672C6 using a design that combines descriptions in VHDL, schematics and control connection via a general purpose processor

On the relationship between spectroscopic constants of diatomic molecules: a machine learning approach

Through a machine learning approach, we show that the equilibrium distance, harmonic vibrational frequency and binding energy of diatomic molecules are related, independently of the nature of the bond of a molecule; they depend solely on the group and period of the constituent atoms. As a result, we show that by employing the group and period of the atoms that form a molecule, the spectroscopic constants are predicted with an accuracy of <5%, whereas for the A-excited electronic state it is needed to include other atomic properties leading to an accuracy of <11%

Complex Reaction Network Thermodynamic and Kinetic Autoconstruction Based on Ab Initio Statistical Mechanics: A Case Study of O₂ Activation on Ag₄ Clusters

An approach based on ab initio statistical mechanics is demonstrated for autoconstructing complex reaction networks. Ab initio molecular dynamics combined with Markov state models are employed to study relevant transitions and corresponding thermodynamic and kinetic properties of a reaction. To explore the capability and flexibility of this approach, we present a study of oxygen activation on Ag4 as a model reaction. Specifically, with the same sampled trajectories, it is possible to study the structural effects and the reaction rate of the cited reaction. The results show that this approach is suitable for automatized construction of reaction networks, especially for non-well-studied reactions, which can benefit from this ab initio molecular dynamics based approach to construct comprehensive reaction networks with Markov state models without prior knowledge about the potential energy landscape

Tracking the variable North Atlantic sink for atmospheric CO2

3 páginas, 1 tabla, 3 figuras.-- Watson, Andrew J. ... et al.The oceans are a major sink for atmospheric carbon dioxide (CO2). Historically, observations have been too sparse to allow accurate tracking of changes in rates of CO2 uptake over ocean basins, so little is known about how these vary. Here, we show observations indicating substantial variability in the CO2 uptake by the North Atlantic on time scales of a few years. Further, we use measurements from a coordinated network of instrumented commercial ships to define the annual flux into the North Atlantic, for the year 2005, to a precision of about 10%. This approach offers the prospect of accurately monitoring the changing ocean CO2 sink for those ocean basins that are well covered by shipping routes.Peer reviewe

Spectroscopic constants from atomic properties: a machine learning approach

We present a machine-learning approach toward predicting spectroscopic constants based on atomic properties. After collecting spectroscopic information on diatomics and generating an extensive database, we employ Gaussian process regression to identify the most efficient characterization of molecules to predict the equilibrium distance, vibrational harmonic frequency, and dissociation energy. As a result, we show that it is possible to predict the equilibrium distance with an absolute error of 0.04 {\AA} and vibrational harmonic frequency with an absolute error of 36 $\text{cm}^{-1}$, including only atomic properties. These results can be improved by including prior information on molecular properties leading to an absolute error of 0.02 {\AA} and 28 $\text{cm}^{-1}$ for the equilibrium distance and vibrational harmonic frequency, respectively. In contrast, the dissociation energy is predicted with an absolute error $\lesssim 0.4$ eV. Alongside these results, we prove that it is possible to predict spectroscopic constants of homonuclear molecules from the atomic and molecular properties of heteronuclear. Finally, based on our results, we present a new way to classify diatomic molecules beyond chemical bond properties

Fast and reliable storage using a 5 bit, nonvolatile photonic memory cell

This is the final version. Available from Optical Society of America via the DOI in this record.Optically storing and addressing data on photonic chips is of particular interest as such capability would eliminate optoelectronic conversion losses in data centers. It would also enable on-chip non-von Neumann photonic computing by allowing multinary data storage with high fidelity. Here, we demonstrate such an optically addressed, multilevel memory capable of storing up to 34 nonvolatile reliable and repeatable levels (over 5 bits) using the phase change material Ge2Sb2Te5 integrated on a photonic waveguide. Crucially, we demonstrate for the first time, to the best of our knowledge, a technique that allows us to program the device with a single pulse regardless of the previous state of the material, providing an order of magnitude improvement over previous demonstrations in terms of both time and energy consumption. We also investigate the influence of write-and-erase pulse parameters on the single-pulse recrystallization, amorphization, and readout error in our multilevel memory, thus tailoring pulse properties for optimum performance. Our work represents a significant step in the development of photonic memories and their potential for novel integrated photonic applications.Engineering and Physical Sciences Research Council (EPSRC)European CommissionDeutsche Forschungsgemeinschaft (DFG)Horizon 2020 Framework Programme (H2020

The chemistry of AlF and CaF production in buffer gas sources

In this work, we explore the role of chemical reactions on the properties of buffer gas cooled molecular beams. In particular, we focus on scenarios relevant to the formation of AlF and CaF via chemical reactions between the Ca and Al atoms ablated from a solid target in an atmosphere of a fluorine-containing gas, in this case, SF6 and NF3. Reactions are studied following an ab initio molecular dynamics approach, and the results are rationalized following a tree-shaped reaction model based on Bayesian inference. We find that NF3 reacts more efficiently with hot metal atoms to form monofluoride molecules than SF6. In addition, when using NF3, the reaction products have lower kinetic energy, requiring fewer collisions to thermalize with the cryogenic helium. Furthermore, we find that the reaction probability for AlF formation is much higher than for CaF across a broad range of kinetic temperatures

Development of a dairy fouling model to assess the efficacy of cleaning procedures using alkaline and enzymatic products

Dairy fouling is defined as the accumulation of thermally insulating materials or deposits from process fluids which are especially formed on heat transfer surfaces. The selection of suitable cleaning strategies to remove dairy fouling requires the understanding of its composition and the relationships with the surfaces where it is formed. For the industry, the development of novel strategies to test cleaning products, as well reducing water and energy consumption during the dairy processing operations is of enormous interest. The results showed the development of a laboratory-milk fouling model (MFM) with an average content of 52.8 mg/cm2 of fouling in the test coupons. Seven different cleaners were tested with a fouling removal effectiveness of between 55% and 97%. Additionally, for evaluating the cleaning process of the model, the turbidity of the cleaning solutions was assessed. We presented an enzymatic alternative to the use of traditional cleaning products, with a similar efficacy against the dairy fouling. 78% of fouling removal after the use of enzymatic solution, in comparison to the 72% of fouling removal after the use of alkaline cleaning products. A reduction in water (−33.3%) and temperature (−28.5%), as well as shorter cleaning times (−33%) than its chemical alternative, was observed.info:eu-repo/semantics/acceptedVersio

Net sea-air CO2 flux uncertainties in the Bay of Biscay based on the choice of wind speed products and gas transfer parameterizations

25 páginas, 4 figuras, 1 tablaThe estimation of sea-air CO2 fluxes are largely dependent on wind speed through the gas transfer velocity parameterization. In this paper, we quantify uncertainties in the estimation of the CO2 uptake in the Bay of Biscay resulting from using different sources of wind speed such as three different global reanalysis meteorological models (NCEP/NCAR 1, NCEP/DOE 2 and ERA-Interim), one regional high-resolution forecast model (HIRLAM-AEMet) and QuikSCAT winds, in combination with some of the most widely used gas transfer velocity parameterizations. Results show that net CO2 flux estimations during an entire seasonal cycle may differ up to 240% depending on the wind speed product and the gas exchange parameterization. The comparison of satellite and model derived winds with observations at buoys advises against the systematic overestimation of NCEP-2 and the underestimation of NCEP-1. In this region, QuikSCAT has the best performing, although ERA-Interim becomes the best choice in areas near the coastline or when the time resolution is the constraint.This work was developed and funded by the ECO project (MCyT REN2002-00503/MAR) and EU FP7 project CARBOCHANGE “Changes 5 in carbon uptake and emissions by oceans in a changing climate” under agreement no. 264879Peer reviewe