Propagação in vitro de barueiro (Dipteryx alata Vog.)

Savannah is the second biome in biodiversity in Brazil, presenting great vegetation endemism. Dipteryx alata Vog. (Fabaceae), native from this biome, is an economically important species, with an incipient market due to the lack of commercial plantations. This highlights the need to develop and provide the basis for the domestication of this species. Thus, this study determined the best conditions for in vitro establishment, multiplication, elongation and rooting of stem tips of D. alata plantlets grown vitro. Two culture media (MS and WPM) were evaluated in different salt concentrations (25, 50, 75 and 100%) for plantlet establishment. Four concentrations of 6– Benzylaminopurine (BAP) (0, 1, 2, 3 and 4 mg L-1) amended with 0.25 mg L-1 naphthalene-acetic acid (NAA) were studied for multiplication. Simultaneous elongation and rooting were studied with four concentrations of NAA (0, 1, 2, 3 and 4 mg L-1) together with 0.5 mg L-1 IBA. The variables analyzed were: shoot length (CPA), root length (CP), fresh matter (MF), dry matter (MSC), stem diameter (DC) and number of leaves (NF), 120 days after inoculation, with the exception of number of shoots, which was evaluated in the multiplication stage only. The medium MS at the original salt concentration (100%) was effective for the in vitro establishment of E. alata, resulting in greater root length (27.65 cm) and number of leaves per plantlet (26.0). The concentration of 4 mg L-1 BAP was the best one for multiplication; however, greater concentrations can boost multiplication. The effect of NAA and IBA were noticeable on in vitro elongation and rooting, with best CPA (3.14 cm) and CR (15.84 cm). Therefore, it is possible to state that the medium MS increases the success probability of in vitro establishment of stem tips of Dipteryx alata. NAA concentrations below 3 mg L-1 were favorable for in vitro development of the species, with essential characteristics for acclimatization success|.

Algorithms for super-resolution of images based on Sparse Representation and Manifolds

lmage super-resolution is defined as a class of techniques that enhance the spatial resolution of images. Super-resolution methods can be subdivided in single and multi image methods. This thesis focuses on developing algorithms based on mathematical theories for single image super­ resolution problems. lndeed, in arder to estimate an output image, we adopta mixed approach: i.e., we use both a dictionary of patches with sparsity constraints (typical of learning-based methods) and regularization terms (typical of reconstruction-based methods). Although the existing methods already per- form well, they do not take into account the geometry of the data to: regularize the solution, cluster data samples (samples are often clustered using algorithms with the Euclidean distance as a dissimilarity metric), learn dictionaries (they are often learned using PCA or K-SVD). Thus, state-of-the-art methods still suffer from shortcomings. In this work, we proposed three new methods to overcome these deficiencies. First, we developed SE-ASDS (a structure tensor based regularization term) in arder to improve the sharpness of edges. SE-ASDS achieves much better results than many state-of-the- art algorithms. Then, we proposed AGNN and GOC algorithms for determining a local subset of training samples from which a good local model can be computed for recon- structing a given input test sample, where we take into account the underlying geometry of the data. AGNN and GOC methods outperform spectral clustering, soft clustering, and geodesic distance based subset selection in most settings. Next, we proposed aSOB strategy which takes into account the geometry of the data and the dictionary size. The aSOB strategy outperforms both PCA and PGA methods. Finally, we combine all our methods in a unique algorithm, named G2SR. Our proposed G2SR algorithm shows better visual and quantitative results when compared to the results of state-of-the-art methods.