The combination of shot blasting (SB) and micro-arc oxidation (or anodic oxidation--AO) in titanium surfaces was shown to provide enhanced conditions for cell differentiation and osseointegration than those provided by SB or AO alone. This study associated both methods aiming to attain titania layers on Ti with adequate tribo-mechanical features for bone implants. SB was performed using alumina particles, and titania layers were grown by AO using a CaP-based electrolyte. Mechanical properties and scratch resistance were characterized at nanoscale by instrumented indentation and nanoscratch, and correlated with morphological and microstructural changes (XRD, SEM, EDS, AFM, and profilometry). Analytical methods were employed to correct roughness and substrate effects on the indentation results. CaP-containing TiO2 layers were produced on AO and SB + AO. The latter presented small pore size and inhomogeneous layer thickness and Ca/P ratios, caused by the non-uniform surface straining by SB that affects the oxide growth kinetics in the electrochemical process. Elastic modulus of SB + AO layer (37 GPa) were lower than the AO one (45 GPa); both of them were smaller than bulk Ti (130 GPa) and close to bone values. The hardness profiles of AO and SB + AO were similar to the substrate ones. Because of the improved load bearing capacity and unique layer features, the critical load to remove the SB + AO titania coating in scratch tests was three times as much or higher than in AO. Results indicate improved mechanical biocompatibility and tribological strength of anodic titania layers grown on sand blasted Ti surfaces.
Real-time image processing and computer vision systems are now in the mainstream of technologies enabling applications for cyber-physical systems, Internet of Things, augmented reality, and Industry 4.0. These applications bring the need for Smart Cameras for local real-time processing of images and videos. However, the massive amount of data to be processed within short deadlines cannot be handled by most commercial cameras. In this work, we show the design and implementation of a manycore vision processor architecture to be used in Smart Cameras. With massive parallelism exploration and application-specific characteristics, our architecture is composed of distributed processing elements and memories connected through a Network-on-Chip. The architecture was implemented as an FPGA overlay, focusing on optimized hardware utilization. The parameterized architecture was characterized by its hardware occupation, maximum operating frequency, and processing frame rate. Different configurations ranging from one to eighty-one processing elements were implemented and compared to several works from the literature. Using a System-on-Chip composed of an FPGA integrated into a general-purpose processor, we showcase the flexibility and efficiency of the hardware/software architecture. The results show that the proposed architecture successfully allies programmability and performance, being a suitable alternative for future Smart Cameras.
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