Towards a real-time 3D object recognition pipeline on mobile GPGPU computing platforms using low-cost RGB-D sensors

In this project, we propose the implementation of a 3D object recognition system which will be optimized to operate under demanding time constraints. The system must be robust so that objects can be recognized properly in poor light conditions and cluttered scenes with significant levels of occlusion. An important requirement must be met: the system must exhibit a reasonable performance running on a low power consumption mobile GPU computing platform (NVIDIA Jetson TK1) so that it can be integrated in mobile robotics systems, ambient intelligence or ambient assisted living applications. The acquisition system is based on the use of color and depth (RGB-D) data streams provided by low-cost 3D sensors like Microsoft Kinect or PrimeSense Carmine. The range of algorithms and applications to be implemented and integrated will be quite broad, ranging from the acquisition, outlier removal or filtering of the input data and the segmentation or characterization of regions of interest in the scene to the very object recognition and pose estimation. Furthermore, in order to validate the proposed system, we will create a 3D object dataset. It will be composed by a set of 3D models, reconstructed from common household objects, as well as a handful of test scenes in which those objects appear. The scenes will be characterized by different levels of occlusion, diverse distances from the elements to the sensor and variations on the pose of the target objects. The creation of this dataset implies the additional development of 3D data acquisition and 3D object reconstruction applications. The resulting system has many possible applications, ranging from mobile robot navigation and semantic scene labeling to human-computer interaction (HCI) systems based on visual information.

Real-time monitoring of electrochemically active biofilm developing behavior on bioanode by using EQCM and ATR/FTIR

In the current study, the relationship between current and biomass and bio-adhesion mechanism of electrogenic biofilm on electrode were investigated using EQCM and ATR-SEIRAS linking electrochemistry. The results indicated that cellular biomass of biofilm on QCM-crystal surface showed maximum value of 6.0 μg/cm2 in initial batch and 11.5 μg/cm2 in the second batch on mature biofilm, producing a similar maximum current density of 110 μA/μg. Especially, the optimum cell biomass linking high electricity production ratio (110 μA/μg) occurred before maximum biomass coming, implying that over-growth mature biofilm is not an optimum state for enhancing power output of MFCs. On the other hand, the spectra using ATR-SEIRAS technique linking electrochemistry obviously exhibited water structure adsorption change at early biofilm formation and meanwhile the water adsorption accompanied the adsorbed bacteria and the bound cells population on the electrode increased with time. Meanwhile, the direct contact of bacteria and electrode via outer-membrane protein can be confirmed via a series spectra shift at amide I and amide II modes and water movement from negative bands displacing by adsorbed bacteria. Our study provided supplementary information about the interaction between the microbes and electrode beyond traditional electrochemistry.