Additive manufacturing techniques have been the focus of studies and technological advances in recent years, obtaining the capability to fabricate pieces with complex geometries easily, rapid and with high precision, allowing the use of different materials, the appearance of new techniques, and a range of applications beyond prototyping. However, Additive Manufacturing techniques are still affected by some deficiencies and challenges such as the absence of sensing and control during the fabrication process that would result in a more reliable process and printed part. This paper shows the development of an inference process using probabilistic graphical models, in order to track the motion of the extrusion nozzle during the printing process using linear encoders
Automated Driving requires fusing information from multitude of sensors such as cameras, radars, lidars mounted around car to handle various driving scenarios e.g. highway, parking, urban driving and traffic jam. Fusion also enables better functional safety by handling challenging scenarios such as weather conditions, time of day, occlusion etc. The paper gives an overview of the popular fusion techniques namely Kalman filters and its variation e.g. Extended Kalman filters and Unscented Kalman filters. The paper proposes choice of fusing techniques for given sensor configuration and its model parameters. The second part of paper focuses on efficient solution for series production using embedded platform using Texas Instrument's TDAx Automotive SoC. The performance is benchmarked separately for "predict" and "update" phases on for different sensor modalities. For typical L3/L4 automated driving consisting of multiple cameras, radars and lidars, fusion can supported in real time by single DSP using proposed techniques enabling cost optimized solution.