Under non-homogeneous solar irradiation, photovoltaic (PV) panels receive different solar irradiance, resulting in a decrease in efficiency of the PV generation system. There are a few technical options to fix this issue that goes under the name of mismatch. One of these is the reconfiguration of the PV generation system, namely changing the connections of the PV panels from the initial configuration to the optimal one. Such technique has been widely considered for small systems, due to the excessive number of required switches. In this paper, the authors propose a new method for increasing the efficiency of large PV systems under non-homogeneous solar irradiation using Series-Parallel (SP) topology.
In the first part of the paper, the authors propose a method containing two key points: a switching matrix to change the connection of PV panels based on SP topology and the proof that the SP-based reconfiguration method can increase the efficiency of the photovoltaic system up to 50%.
In the second part, the authors propose the extension of the method proposed in the first part to improve the efficiency of large solar generation systems by means of a two-levels architecture to minimize the cost of fab- rication of the switching matrix.

In existing image retrieval systems using a multipoint query, the neighborhood of the various optimal query points is determined in the same way. The different nature of the various optimal query points was not taken into consideration. This approach confines the performance of the system. To overcome this confinement, in this paper, we propose an image retrieval method through adaptive weights (Aweight) which is possible to compute the various optimal query points, optimal weights and improved distance functions to improve accuracy. In addition, our method constructs clusters without re-clustering the whole feedback image set. The experiments were performed on a set of 10,800 images and the results demonstrate that the proposed method improves performance of system in terms of accuracy.

Over the years, many content-based image retrieval (CBIR) methods, which use SVM-based relevant feedback, are proposed to improve the performance of image retrieval systems. However, the performance of these methods is low due to the following limitations: (1) ignore the unlabeled samples; (2) only exploit the global Euclidean structure and (3) not taking advantage of the various useful aspects of the object. In order to solve the first problem, we propose a graph-based semisupervised learning (GSEL), which can add positive samples and construct balanced sets. With the second problem, we propose a manifold learning for dimensional reduction (MAL), which exploits the geometric properties of the manifold data. With the third problem, we propose a combination of classifiers by aspect (CCA), which exploits the various useful aspects of the object. Experimental results reported in the Corel Photo Gallery (with 31,695 images), which demonstrate the accuracy of our proposed method in improving the performance of the content-based image retrieval system. Keywords: Keyword one, keyword two, keyword three, keyword four, keyword five

Many previous techniques were designed to retrieve semantic images in a particular neighborhood of the image query and thus bypassing the semantically related images in the whole feature space. Several methods were originally designed to retrieve semantically images in the same space feature but with low precision. In this paper, we propose a semantic - Related Image Retrieval method (SRIR), which can retrieve semantic images of the same space in high precision. Our method takes advantage of the user feedback to determine the semantic importance of each query and the importance of each feature. In addition, the retrieval time of our method does not increase with the number of user feedback. We also provide experimental results to demonstrate the effectiveness of our method.

Quality, efficiency and scalability are the key issues in the design of image retrieval systems for large image databases. Although the quality of image retrieval methods still depends strongly on the application domain, color based retrieval techniques have been shown to be competitive and generally applicable. However, the retrieval algorithms based on color histograms largely ignore spatial information in the matching process. In this paper, we propose a technique to improve the retrieval process by image regions matching. We carried out an experiment on an image database containing 8000 images. The experimental results show that our proposed technique is more effective than the other retrieval techniques such as color histogram based and Color Based Clustering based techniques.