Binary metal oxides for composite ultrafiltration membranes

A new ultrafiltration membrane was developed by the incorporation of binary metal oxides inside polyethersulfone. Physico-chemical characterization of the binary metal oxides demonstrated that the presence of Ti in the TiO2?ZrO2 system results in an increase of the size of the oxides, and also their dispersity. The crystalline phases of the synthesized binary metal oxides were identified as srilankite and zirconium titanium oxide. The effect of the addition of ZrO2 can be expressed in terms of the inhibition of crystal growth of anocrystalline TiO2 during the synthesis process. For photocatalytic applications the band gap of the synthesized semiconductors was determined, confirming a gradual increase (blue shift) in the band gap as the amount of Zr loading increases. Distinct distributions of binary metal oxides were found along the permeation axis for the synthesized membranes. Particles with Ti are more uniformly dispersed throughout the membrane cross-section. The physico-chemical characterization of membranes showed a strong correlation between some key membrane properties and the spatial particle distribution in the membrane structure. The proximity of metal oxide fillers to the membrane surface determines the hydrophilicity and porosity of modified membranes. Membranes incorporating binary metal oxides were found to be promising candidates for wastewater treatment by ultrafiltration, considering the observed improvement influx and anti-fouling properties of doped membranes. Multi-run fouling tests of doped membranes confirmed the stability of permeation through membranes embedded with binary TiO2?ZrO2 particles.

Human-computer interaction based on visual hand-gesture recognition using volumetric spatiograms of local binary patterns

A more natural, intuitive, user-friendly, and less intrusive Human–Computer interface for controlling an application by executing hand gestures is presented. For this purpose, a robust vision-based hand-gesture recognition system has been developed, and a new database has been created to test it. The system is divided into three stages: detection, tracking, and recognition. The detection stage searches in every frame of a video sequence potential hand poses using a binary Support Vector Machine classifier and Local Binary Patterns as feature vectors. These detections are employed as input of a tracker to generate a spatio-temporal trajectory of hand poses. Finally, the recognition stage segments a spatio-temporal volume of data using the obtained trajectories, and compute a video descriptor called Volumetric Spatiograms of Local Binary Patterns (VS-LBP), which is delivered to a bank of SVM classifiers to perform the gesture recognition. The VS-LBP is a novel video descriptor that constitutes one of the most important contributions of the paper, which is able to provide much richer spatio-temporal information than other existing approaches in the state of the art with a manageable computational cost. Excellent results have been obtained outperforming other approaches of the state of the art.