Quality assessment by region in spot images fused by means dual-tree complex wavelet transform

This work is motivated in providing and evaluating a fusion algorithm of remotely sensed images, i.e. the fusion of a high spatial resolution panchromatic image with a multi-spectral image (also known as pansharpening) using the dual-tree complex wavelet transform (DT-CWT), an effective approach for conducting an analytic and oversampled wavelet transform to reduce aliasing, and in turn reduce shift dependence of the wavelet transform. The proposed scheme includes the definition of a model to establish how information will be extracted from the PAN band and how that information will be injected into the MS bands with low spatial resolution. The approach was applied to Spot 5 images where there are bands falling outside PAN’s spectrum. We propose an optional step in the quality evaluation protocol, which is to study the quality of the merger by regions, where each region represents a specific feature of the image. The results show that DT-CWT based approach offers good spatial quality while retaining the spectral information of original images, case SPOT 5. The additional step facilitates the identification of the most affected regions by the fusion process.

Extreme temperature events analyzed with Fast Fourier Transform

Extreme weather and climate events have received increased attention in the last few years, due to the often large loss of agriculture business and exponentially increasing costs associated with them and insurance planning. This increased attention raises the question as to whether extreme weather and climate events are truly increasing, whether this is only a perceived increase exacerbated by enhanced media coverage, or both. There are a number of ways extreme climate events can be defined, such as extreme daily temperatures, extreme daily rainfall amounts, and large areas experiencing unusually warm monthly temperatures, among others. In this study, we will focus our attention in frost and heatstroke events measuring it as the number of days under 0 ºC and number of days with daily maximum over 30ºC monthly respectively. We have studied the trends in these extreme events applying a Fast Fourier Transform to the series to clarify the tendency. Lack of long-term climate data suitable for analysis of extremes is the single biggest obstacle to quantifying whether extreme events have changed over the twentieth century, including high temporal and spatial resolution observations of temperatures. However, several series have been grouped in different ways: chosen the longest series independently, by provinces, by main watersheds and altitude. On the other hand, synthetic series generated by Luna and Balairón (AEMet) were also analyzed. The results obtained by different pooling data are discussed concluding the difficulties to assess the extreme events tendencies and high regional variation in the trends.

Glottal Parameter Estimation by Wavelet Transform for Voice Biometry

Voice biometry is classically based on the parameterization and patterning of speech features mainly. The present approach is based on the characterization of phonation features instead (glottal features). The intention is to reduce intra-speaker variability due to the `text'. Through the study of larynx biomechanics it may be seen that the glottal correlates constitute a family of 2-nd order gaussian wavelets. The methodology relies in the extraction of glottal correlates (the glottal source) which are parameterized using wavelet techniques. Classification and pattern matching was carried out using Gaussian Mixture Models. Data of speakers from a balanced database and NIST SRE HASR2 were used in verification experiments. Preliminary results are given and discussed.

Accelerating FPGA-based evolution of wavelet transform filters by optimized task scheduling

Adaptive embedded systems are required in various applications. This work addresses these needs in the area of adaptive image compression in FPGA devices. A simplified version of an evolution strategy is utilized to optimize wavelet filters of a Discrete Wavelet Transform algorithm. We propose an adaptive image compression system in FPGA where optimized memory architecture, parallel processing and optimized task scheduling allow reducing the time of evolution. The proposed solution has been extensively evaluated in terms of the quality of compression as well as the processing time. The proposed architecture reduces the time of evolution by 44% compared to our previous reports while maintaining the quality of compression unchanged with respect to existing implementations. The system is able to find an optimized set of wavelet filters in less than 2 min whenever the input type of data changes.

Census, reproductive biology, and germination of Astragalus gines-lopezii (Fabaceae), a narrow and endangered endemic species of SW Spain

Astragalus gines-lopezii Talavera, Podlech, Devesa & F.M.Vázquez (Fabaceae) is a threatened endemic species with a distribution restricted to a very small area in Badajoz Province (Extremadura Region, SW Spain) and only 2 populations are known. This species was catalogued in the ?Endangered? category in the 2008 Red List and the 2010 Threatened Spanish Vascular Flora List. Despite its status as an endangered species, at present very little is known about the distribution, census, and reproductive biology of this species. In this study we have carried out an exhaustive census of A. gines-lopezii , and we have evaluated the production of flowers, fruits, and seeds and the existence or not of intra- and interpopulation variability in seed germination. Results have highlighted the high reproductive capacity of this species on the basis of a high production of flowers, fruits, and seeds. Mechanical scarification of seeds was effective for increasing germination. Thus, initial germination (22%?60%) was increased to 97%?99% when seeds were rubbed with sandpapers. A high intra- and interpopulation variability in seed germination was found in this species. A. gines-lopezii produces seeds with different degrees of physical dormancy, varying this grade among different individuals within a population.

Census, reproductive biology, and germination of Astragalus gines-lopezii (Fabaceae),a narrow and endangered endemic species of SW Spain.

Astragalus gines-lopezii Talavera, Podlech, Devesa & F.M.Vazquez (Fabaceae) is a threatened endemic species with a distribution restricted to a very small area in Badajoz Province (Extremadura Region, SW Spain) and only 2 populations are known.This species was catalogued in the "Endangered" category in the 2008 Red List and the 2010 Threatened Spanish Vascular Flora List. Despite its status as an endangered species, at present very little is known about the distribution, census, and reproductive biology of this species. In this study we have carried out anexhaustive census of A. gines-lopezii, and we have evaluated the production of flowers, fruits, and seeds and the existence or not of intra- and interpopulation variability in seed germination. Results have highlighted the high reproductive capacity of this species on the basis of a high production of flowers, fruits, and seeds. Mechanical scarification of seeds was effective for increasing germination. Thus, initial germination (22%-60%) was increased to 97%-99% when seeds were rubbed with sandpapers. A high intra- and interpopulation variability in seed germination was found in this species. A. gines-lopezii produces seeds with different degrees of physical dormancy, varying this grade among different individuals within a population.

Estimation of Symmetric Channels for Discrete Cosine Transform Type-I Multicarrier Systems: A Compressed Sensing Approach

The problem of channel estimation for multicarrier communications is addressed. We focus on systems employing the Discrete Cosine Transform Type-I (DCT1) even at both the transmitter and the receiver, presenting an algorithm which achieves an accurate estimation of symmetric channel filters using only a small number of training symbols. The solution is obtained by using either matrix inversion or compressed sensing algorithms. We provide the theoretical results which guarantee the validity of the proposed technique for the DCT1. Numerical simulations illustrate the good behaviour of the proposed algorithm.

Bridge damage identification from moving load induced deflection based on wavelet transform and Lipschitz exponent

The wavelet transform and Lipschitz exponent perform well in detecting signal singularity.With the bridge crack damage modeled as rotational springs based on fracture mechanics, the deflection time history of the beam under the moving load is determined with a numerical method. The continuous wavelet transformation (CWT) is applied to the deflection of the beam to identify the location of the damage, and the Lipschitz exponent is used to evaluate the damage degree. The influence of different damage degrees,multiple damage, different sensor locations, load velocity and load magnitude are studied.Besides, the feasibility of this method is verified by a model experiment.