Magnetic susceptibility dataset from the early Givetian to early Frasnian in the La Thure section

Recent advances in radiometric dating result in significant improvements in the geological timescale and provide better insight into the timing of various processes and evolutions within the Earth's system. However, no radiometric ages are contained within the Givetian. Consequently, the absolute ages of the Givetian Stage boundaries, as well as the stage's duration, remain poorly constrained. As an alternative, the analysis of sedimentary cycles allows for the estimation of the duration of this stage. We examined the high-resolution magnetic susceptibility signals of four Givetian outcrops in the Givet area for a possible astronomical imprint, to fully understand the rates of evolutionary and environmental change. All four sections are firmly correlated and wavelet analyses of the magnetic susceptibility signals reveal the imprint of astronomical eccentricity forcing. The highly stable 405 kyr cycles constrain the duration of the Givetian Stage at 4.35±0.45 Myr, which is in good agreement with the International Chronostratigraphic Chart (5.0 Myr). The studied sections also exhibit an imprint of obliquity, suggesting a climatic teleconnection between low and high latitudes. The corresponding microfacies curves demonstrate similar astronomical imprint, and thereby indicate that the observed 10**5 year-scale cyclicity is the result of climatic and environmental change.

Radiocarbon ages, carbon and oxygen isotopes and Mg/Ca and Sr/Ca ratios of Globigerinoides in sediment core MD99-2203, Cape Hatteras

This study presents high-resolution foraminiferal-based sea surface temperature, sea surface salinity and upper water column stratification reconstructions off Cape Hatteras, a region sensitive to atmospheric and thermohaline circulation changes associated with the Gulf Stream. We focus on the last 10,000 years (10 ka) to study the surface hydrology changes under our current climate conditions and discuss the centennial to millennial time scale variability. We observed opposite evolutions between the conditions off Cape Hatteras and those south of Iceland, known today for the North Atlantic Oscillation pattern. We interpret the temperature and salinity changes in both regions as co-variation of activities of the subtropical and subpolar gyres. Around 8.3 ka and 5.2-3.5 ka, positive salinity anomalies are reconstructed off Cape Hatteras. We demonstrate, for the 5.2-3.5 ka period, that the salinity increase was caused by the cessation of the low salinity surface flow coming from the north. A northward displacement of the Gulf Stream, blocking the southbound low-salinity flow, concomitant to a reduced Meridional Overturning Circulation is the most likely scenario. Finally, wavelet transform analysis revealed a 1000-year period pacing the d18O signal over the early Holocene. This 1000-year frequency band is significantly coherent with the 1000-year frequency band of Total Solar Irradiance (TSI) between 9.5 ka and 7 ka and both signals are in phase over the rest of the studied period.

Potassium measurements and ages of sediment cores GeoB5546-2 and GeoB6007-2

The northward extent of the influence of the W African monsoon during humid periods of interglacials is subject to investigations highlighting feedback mechanisms, such as vegetation. To detect this regional variation and the climate system acting farther to the north will be the aim of this paper focussing on the Holocene. We present two very high-resolution Holocene sediment records off NW Africa located at 31°N and 27°N. The well-known mid-Holocene climate change from the "African Humid Period" to present arid conditions is well reflected by the terrigenous input in the southern core. In contrast, in the northern core spectral and wavelet analyses indicate a periodic oscillation of about 900 years of the terrigenous input throughout the last 9000 years B.P. We conclude that the W African monsoonal influence characterized by the abrupt climatic change at 5000 years B.P. can be separated from the influence of the N Atlantic climate system reflected by a periodic oscillation throughout the Holocene.

Paleomagnetic and rock magnetic data from IODP Site U1308

Integrated Ocean Drilling Program (IODP) Site U1308 (central North Atlantic) records paleomagnetic directional and relative paleointensity (RPI) variations for the last 1.5 Myr, in 110 m of the sediment sequence at a mean sedimentation rate of 7.3 cm/kyr. A detailed benthic oxygen isotope record was combined with RPI to produce an integrated, high-resolution magneto-isotopic stratigraphy for Site U1308. Apart from the well-known polarity reversals in this interval, the Punaruu excursion is recorded at 1092 ka and the Cobb Mountain Subchron in the 1182-1208 ka interval. The paleointensity proxies are determined as slopes of NRM versus ARM and NRM versus ARMAQ (ARM acquisition) with linear correlation coefficients to monitor the quality of the linear fit. The RPI record for Site U1308 is compared with the three other paleointensity records (one from the Western Equatorial Pacific and two from the North Atlantic) that cover the same time interval and have accompanying oxygen isotope records. The Match protocol of Lisiecki and Lisiecki (2002) is used to optimize the correlation of paleointensity records. Beginning with the original (published) age models for each record, the Match routine is used to optimize the RPI correlations to Site U1308, with checks to ensure compatibility with oxygen isotope records. Squared wavelet coherence (WTC) indicates significant improvement in RPI (and oxygen isotope) correlations after matching each RPI record to Site U1308, particularly for periods > 10 kyr. The level of coherence for the Atlantic RPI records and the lower resolution Pacific record implies synchronous global variability (at scales > 10 kyr) that can be attributed to the axial dipole geomagnetic field.

Bedform characterization (Knudedyb, Danish Wadden Sea)

Continuous Wavelet Transform was applied to bed elevation profiles (BEP) and used in the study in order to recognise the spatial distribution of bedforms and discriminate between their hierarchical scales. In particular, the spatial distribution of the hierarchical scales is highlighted by averaging wavelet power spectra over different bands, and displayed as the wavelet variance of the BEP (see map). Four dune classes were defined, following Ashley (1990): small dunes (1-5 m), medium dunes (5-10 m), large dunes (10-100 m), and very large dunes (>100 m).

Nuevas Técnicas de Transmisión de Muy Alta Velocidad para Redes de Computadores: Una aproximación mediante ondículas.

Indoor multpropagation channel is modeled by the Kaiser electromagnetic wavelet. A method for channel characterization is proposed by modeling all the reflections of indoor propagation in a kernel function instead of its impulse response. This led us to consider a fractal modulation scheme in which Kaiser wavelets substitute the traditional sinusoidal carrier.

Toward reduction of artifacts in fused images

Most fusion satellite image methodologies at pixel-level introduce false spatial details, i.e.artifacts, in the resulting fusedimages. In many cases, these artifacts appears because image fusion methods do not consider the differences in roughness or textural characteristics between different land covers. They only consider the digital values associated with single pixels. This effect increases as the spatial resolution image increases. To minimize this problem, we propose a new paradigm based on local measurements of the fractal dimension (FD). Fractal dimension maps (FDMs) are generated for each of the source images (panchromatic and each band of the multi-spectral images) with the box-counting algorithm and by applying a windowing process. The average of source image FDMs, previously indexed between 0 and 1, has been used for discrimination of different land covers present in satellite images. This paradigm has been applied through the fusion methodology based on the discrete wavelet transform (DWT), using the à trous algorithm (WAT). Two different scenes registered by optical sensors on board FORMOSAT-2 and IKONOS satellites were used to study the behaviour of the proposed methodology. The implementation of this approach, using the WAT method, allows adapting the fusion process to the roughness and shape of the regions present in the image to be fused. This improves the quality of the fusedimages and their classification results when compared with the original WAT method

Two Different Approaches of Feature Extraction for Classifying the EEG Signals

The electroencephalograph (EEG) signal is one of the most widely used signals in the biomedicine field due to its rich information about human tasks. This research study describes a new approach based on i) build reference models from a set of time series, based on the analysis of the events that they contain, is suitable for domains where the relevant information is concentrated in specific regions of the time series, known as events. In order to deal with events, each event is characterized by a set of attributes. ii) Discrete wavelet transform to the EEG data in order to extract temporal information in the form of changes in the frequency domain over time- that is they are able to extract non-stationary signals embedded in the noisy background of the human brain. The performance of the model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed scheme has potential in classifying the EEG signals.

The DLMT. An alternative to the DCT

In the last recent years, with the popularity of image compression techniques, many architectures have been proposed. Those have been generally based on the Forward and Inverse Discrete Cosine Transform (FDCT, IDCT). Alternatively, compression schemes based on discrete “wavelets” transform (DWT), used, both, in JPEG2000 coding standard and in the next H264-SVC (Scalable Video Coding), do not need to divide the image into non-overlapping blocks or macroblocks. This paper discusses the DLMT (Discrete Lopez-Moreno Transform). It proposes a new scheme intermediate between the DCT and the DWT (Discrete Wavelet Transform). The DLMT is computationally very similar to the DCT and uses quasi-sinusoidal functions, so the emergence of artifact blocks and their effects have a relative low importance. The use of quasi-sinusoidal functions has allowed achieving a multiresolution control quite close to that obtained by a DWT, but without increasing the computational complexity of the transformation. The DLMT can also be applied over a whole image, but this does not involve increasing computational complexity. Simulation results in MATLAB show that the proposed DLMT has significant performance benefits and improvements comparing with the DCT

Thresholding Soil Surface Images

Soil erosion is a complex phenomenon involving the detachment and transport of soil particles, storage and runoff of rainwater, and infiltration. The relative magnitude and importance of these processes depends on several factors being one of them surface micro-topography, usually quanti[U+FB01]ed trough soil surface roughness (SSR). SSR greatly affects surface sealing and runoff generation, yet little information is available about the effect of roughness on the spatial distribution of runoff and on flow concentration. The methods commonly used to measure SSR involve measuring point elevation using a pin roughness meter or laser, both of which are labor intensive and expensive. Lately a simple and inexpensive technique based on percentage of shadow in soil surface image has been developed to determine SSR in the field in order to obtain measurement for wide spread application. One of the first steps in this technique is image de-noising and thresholding to estimate the percentage of black pixels in the studied area. In this work, a series of soil surface images have been analyzed applying several de-noising wavelet analysis and thresholding algorithms to study the variation in percentage of shadows and the shadows size distribution

Signal analysis and feature generation for pattern identification of partial discharges in high-voltage equipment

This paper proposes a method for the identification of different partial discharges (PDs) sources through the analysis of a collection of PD signals acquired with a PD measurement system. This method, robust and sensitive enough to cope with noisy data and external interferences, combines the characterization of each signal from the collection, with a clustering procedure, the CLARA algorithm. Several features are proposed for the characterization of the signals, being the wavelet variances, the frequency estimated with the Prony method, and the energy, the most relevant for the performance of the clustering procedure. The result of the unsupervised classification is a set of clusters each containing those signals which are more similar to each other than to those in other clusters. The analysis of the classification results permits both the identification of different PD sources and the discrimination between original PD signals, reflections, noise and external interferences. The methods and graphical tools detailed in this paper have been coded and published as a contributed package of the R environment under a GNU/GPL license.

Dysfunctional and compensatory duality in mild cognitive impairment during a continuous recognition memory task

One of the current issues of debate in the study of mild cognitive impairment (MCI) is deviations of oscillatory brain responses from normal brain states and its dynamics. This work aims to characterize the differences of power in brain oscillations during the execution of a recognition memory task in MCI subjects in comparison with elderly controls. Magnetoencephalographic (MEG) signals were recorded during a continuous recognition memory task performance. Oscillatory brain activity during the recognition phase of the task was analyzed by wavelet transform in the source space by means of minimum norm algorithm. Both groups obtained a 77% hit ratio. In comparison with healthy controls, MCI subjects showed increased theta (p < 0.001), lower beta reduction (p < 0.001) and decreased alpha and gamma power (p < 0.002 and p < 0.001 respectively) in frontal, temporal and parietal areas during early and late latencies. Our results point towards a dual pattern of activity (increase and decrease) which is indicative of MCI and specific to certain time windows, frequency bands and brain regions. These results could represent two neurophysiological sides of MCI. Characterizing these opposing processes may contribute to the understanding of the disorder.

Caracterización multiescala de objetos como herramienta para la clasificación de imágenes de alta resolución espacial

This paper presents a new methodology, simple and affordable, for the definition and characterization of objects at different scales in high spatial resolution images. The objects have been generated by integrating texturally and spectrally homogeneous segments. The former have been obtained from the segmentation of Wavelet coefficients of the panchromatic image. The multi-scale character of this transform has yielded texturally homogeneous segments of different sizes for each of the scales. The spectrally homogeneous segments have been obtained by segmenting the classified corresponding multispectral image. In this way, it has been defined a set of objects characterized by different attributes, which give to the objects a semantic meaning, allowing to determine the similarities and differences between them. To demonstrate the capabilities of the methodology proposed, different experiments of unsupervised classification of a Quickbird image have been carried out, using different subsets of attributes and 1-D ascendant hierarchical classifier. Obtained results have shown the capability of the proposed methodology for separating semantic objects at different scales, as well as, its advantages against pixel-based image interpretation.

Electrical Power Fluctuations in a Network of DC/AC inverters in a Large PV Plant: relationship between correlation, distance and time scale

This paper analyzes the correlation between the fluctuations of the electrical power generated by the ensemble of 70 DC/AC inverters from a 45.6 MW PV plant. The use of real electrical power time series from a large collection of photovoltaic inverters of a same plant is an impor- tant contribution in the context of models built upon simplified assumptions to overcome the absence of such data. This data set is divided into three different fluctuation categories with a clustering proce- dure which performs correctly with the clearness index and the wavelet variances. Afterwards, the time dependent correlation between the electrical power time series of the inverters is esti- mated with the wavelet transform. The wavelet correlation depends on the distance between the inverters, the wavelet time scales and the daily fluctuation level. Correlation values for time scales below one minute are low without dependence on the daily fluctuation level. For time scales above 20 minutes, positive high correlation values are obtained, and the decay rate with the distance depends on the daily fluctuation level. At intermediate time scales the correlation depends strongly on the daily fluctuation level. The proposed methods have been implemented using free software. Source code is available as supplementary material.

Statistical models for short-term wind power ramp forecasting

La predicción de energía eólica ha desempeñado en la última década un papel fundamental en el aprovechamiento de este recurso renovable, ya que permite reducir el impacto que tiene la naturaleza fluctuante del viento en la actividad de diversos agentes implicados en su integración, tales como el operador del sistema o los agentes del mercado eléctrico. Los altos niveles de penetración eólica alcanzados recientemente por algunos países han puesto de manifiesto la necesidad de mejorar las predicciones durante eventos en los que se experimenta una variación importante de la potencia generada por un parque o un conjunto de ellos en un tiempo relativamente corto (del orden de unas pocas horas). Estos eventos, conocidos como rampas, no tienen una única causa, ya que pueden estar motivados por procesos meteorológicos que se dan en muy diferentes escalas espacio-temporales, desde el paso de grandes frentes en la macroescala a procesos convectivos locales como tormentas. Además, el propio proceso de conversión del viento en energía eléctrica juega un papel relevante en la ocurrencia de rampas debido, entre otros factores, a la relación no lineal que impone la curva de potencia del aerogenerador, la desalineación de la máquina con respecto al viento y la interacción aerodinámica entre aerogeneradores. En este trabajo se aborda la aplicación de modelos estadísticos a la predicción de rampas a muy corto plazo. Además, se investiga la relación de este tipo de eventos con procesos atmosféricos en la macroescala. Los modelos se emplean para generar predicciones de punto a partir del modelado estocástico de una serie temporal de potencia generada por un parque eólico. Los horizontes de predicción considerados van de una a seis horas. Como primer paso, se ha elaborado una metodología para caracterizar rampas en series temporales. La denominada función-rampa está basada en la transformada wavelet y proporciona un índice en cada paso temporal. Este índice caracteriza la intensidad de rampa en base a los gradientes de potencia experimentados en un rango determinado de escalas temporales. Se han implementado tres tipos de modelos predictivos de cara a evaluar el papel que juega la complejidad de un modelo en su desempeño: modelos lineales autorregresivos (AR), modelos de coeficientes variables (VCMs) y modelos basado en redes neuronales (ANNs). Los modelos se han entrenado en base a la minimización del error cuadrático medio y la configuración de cada uno de ellos se ha determinado mediante validación cruzada. De cara a analizar la contribución del estado macroescalar de la atmósfera en la predicción de rampas, se ha propuesto una metodología que permite extraer, a partir de las salidas de modelos meteorológicos, información relevante para explicar la ocurrencia de estos eventos. La metodología se basa en el análisis de componentes principales (PCA) para la síntesis de la datos de la atmósfera y en el uso de la información mutua (MI) para estimar la dependencia no lineal entre dos señales. Esta metodología se ha aplicado a datos de reanálisis generados con un modelo de circulación general (GCM) de cara a generar variables exógenas que posteriormente se han introducido en los modelos predictivos. Los casos de estudio considerados corresponden a dos parques eólicos ubicados en España. Los resultados muestran que el modelado de la serie de potencias permitió una mejora notable con respecto al modelo predictivo de referencia (la persistencia) y que al añadir información de la macroescala se obtuvieron mejoras adicionales del mismo orden. Estas mejoras resultaron mayores para el caso de rampas de bajada. Los resultados también indican distintos grados de conexión entre la macroescala y la ocurrencia de rampas en los dos parques considerados. Abstract One of the main drawbacks of wind energy is that it exhibits intermittent generation greatly depending on environmental conditions. Wind power forecasting has proven to be an effective tool for facilitating wind power integration from both the technical and the economical perspective. Indeed, system operators and energy traders benefit from the use of forecasting techniques, because the reduction of the inherent uncertainty of wind power allows them the adoption of optimal decisions. Wind power integration imposes new challenges as higher wind penetration levels are attained. Wind power ramp forecasting is an example of such a recent topic of interest. The term ramp makes reference to a large and rapid variation (1-4 hours) observed in the wind power output of a wind farm or portfolio. Ramp events can be motivated by a broad number of meteorological processes that occur at different time/spatial scales, from the passage of large-scale frontal systems to local processes such as thunderstorms and thermally-driven flows. Ramp events may also be conditioned by features related to the wind-to-power conversion process, such as yaw misalignment, the wind turbine shut-down and the aerodynamic interaction between wind turbines of a wind farm (wake effect). This work is devoted to wind power ramp forecasting, with special focus on the connection between the global scale and ramp events observed at the wind farm level. The framework of this study is the point-forecasting approach. Time series based models were implemented for very short-term prediction, this being characterised by prediction horizons up to six hours ahead. As a first step, a methodology to characterise ramps within a wind power time series was proposed. The so-called ramp function is based on the wavelet transform and it provides a continuous index related to the ramp intensity at each time step. The underlying idea is that ramps are characterised by high power output gradients evaluated under different time scales. A number of state-of-the-art time series based models were considered, namely linear autoregressive (AR) models, varying-coefficient models (VCMs) and artificial neural networks (ANNs). This allowed us to gain insights into how the complexity of the model contributes to the accuracy of the wind power time series modelling. The models were trained in base of a mean squared error criterion and the final set-up of each model was determined through cross-validation techniques. In order to investigate the contribution of the global scale into wind power ramp forecasting, a methodological proposal to identify features in atmospheric raw data that are relevant for explaining wind power ramp events was presented. The proposed methodology is based on two techniques: principal component analysis (PCA) for atmospheric data compression and mutual information (MI) for assessing non-linear dependence between variables. The methodology was applied to reanalysis data generated with a general circulation model (GCM). This allowed for the elaboration of explanatory variables meaningful for ramp forecasting that were utilized as exogenous variables by the forecasting models. The study covered two wind farms located in Spain. All the models outperformed the reference model (the persistence) during both ramp and non-ramp situations. Adding atmospheric information had a noticeable impact on the forecasting performance, specially during ramp-down events. Results also suggested different levels of connection between the ramp occurrence at the wind farm level and the global scale.