RBIR Based on Signature Graph

This paper approaches the image retrieval system on the base of visual features local region RBIR (region-based image retrieval). First of all, the paper presents a method for extracting the interest points based on Harris-Laplace to create the feature region of the image. Next, in order to reduce the storage space and speed up query image, the paper builds the binary signature structure to describe the visual content of image. Based on the image's binary signature, the paper builds the SG (signature graph) to classify and store image's binary signatures. Since then, the paper builds the image retrieval algorithm on SG through the similar measure EMD (earth mover's distance) between the image's binary signatures. Last but not least, the paper gives an image retrieval model RBIR, experiments and assesses the image retrieval method on Corel image database over 10,000 images.Comment: 4 pages, 4 figure

Biocompatible chitosan-functionalized upconverting nanocomposites

Simultaneous integration of photon emission and biocompatibility into nanoparticles is an interesting strategy to develop applications of advanced optical materials. In this work, we present the synthesis of biocompatible optical nanocomposites from the combination of near-infrared luminescent lanthanide nanoparticles and water-soluble chitosan. NaYF4:Yb,Er upconverting nanocrystal guests and water-soluble chitosan hosts are prepared and integrated together into biofunctional optical composites. The control of aqueous dissolution, gelation, assembly, and drying of NaYF4:Yb,Er nanocolloids and chitosan liquids allowed us to design novel optical structures of spongelike aerogels and beadlike microspheres. Well-defined shape and near-infrared response lead upconverting nanocrystals to serve as photon converters to couple with plasmonic gold (Au) nanoparticles. Biocompatible chitosan-stabilized Au/NaYF4:Yb,Er nanocomposites are prepared to show their potential use in biomedicine as we find them exhibiting a half-maximal effective concentration (EC50) of 0.58 mg mL–1 for chitosan-stabilized Au/NaYF4:Yb,Er nanorods versus 0.24 mg mL–1 for chitosan-stabilized NaYF4:Yb,Er after 24 h. As a result of their low cytotoxicity and upconverting response, these novel materials hold promise to be interesting for biomedicine, analytical sensing, and other applications

Architecture Parallel for the Renewable Energy System

This chapter present one possible evolution is the parallel topology on the high-voltage bus for the renewable energy system. The system is not connected to a chain of photovoltaic (PV) modules and the different sources renewable. This evolution retains all the advantages of this system, while increasing the level of discretization of the Maximum Power Point Tracker (MPPT). So it is no longer a chain of PV modules that works at its MPPT but each PV module. In addition, this greater discretization allows a finer control and monitoring of operation and a faster detection of defects. The main interest of parallel step-up voltage systems, in this case, lies in the fact that the use of relatively high DC voltages is possible in these architectures distributed

CAMELLIA HOABINHENSIS (THEACEAE: SECT. CHRYSANTHA), A NEW YELLOW-FLOWERED SPECIES FROM NORTHERN VIETNAM

A new species, Camellia hoabinhensis (section Chrysantha, Theaceae), is described and illustrated based on specimens collected from a lowland forest on limestone hills in Hoa Binh Province, northern Vietnam. The species is characterized by its small habit to 4.5 m tall, large flowers 9.0–9.5 cm in diameter with 18–19 light yellow petals, and hairy filaments, ovaries, and styles. A comparison of the new species with similar species, C. euphlebia, C. impressinervis, C. phanii, and C. velutina, is provided. The IUCN conservation status of the proposed species is Data Deficient (DD)

Attention correlated appearance and motion feature followed temporal learning for activity recognition

Recent advances in deep neural networks have been successfully demonstrated with fairly good accuracy for multi-class activity identification. However, existing methods have limitations in achieving complex spatial-temporal dependencies. In this work, we design two stream fusion attention (2SFA) connected to a temporal bidirectional gated recurrent unit (GRU) one-layer model and classified by prediction voting classifier (PVC) to recognize the action in a video. Particularly in the proposed deep neural network (DNN), we present 2SFA for capturing appearance information from red green blue (RGB) and motion from optical flow, where both streams are correlated by proposed fusion attention (FA) as the input of a temporal network. On the other hand, the temporal network with a bi-directional temporal layer using a GRU single layer is preferred for temporal understanding because it yields practical merits against six topologies of temporal networks in the UCF101 dataset. Meanwhile, the new proposed classifier scheme called PVC employs multiple nearest class mean (NCM) and the SoftMax function to yield multiple features outputted from temporal networks, and then votes their properties for high-performance classifications. The experiments achieve the best average accuracy of 70.8% in HMDB51 and 91.9%, the second best in UCF101 in terms of 2DConvNet for action recognition