A Zero-Gravity Instrument to Study Low Velocity Collisions of Fragile Particles at Low Temperatures

We discuss the design, operation, and performance of a vacuum setup constructed for use in zero (or reduced) gravity conditions to initiate collisions of fragile millimeter-sized particles at low velocity and temperature. Such particles are typically found in many astronomical settings and in regions of planet formation. The instrument has participated in four parabolic flight campaigns to date, operating for a total of 2.4 hours in reduced gravity conditions and successfully recording over 300 separate collisions of loosely packed dust aggregates and ice samples. The imparted particle velocities achieved range from 0.03-0.28 m s^-1 and a high-speed, high-resolution camera captures the events at 107 frames per second from two viewing angles separated by either 48.8 or 60.0 degrees. The particles can be stored inside the experiment vacuum chamber at temperatures of 80-300 K for several uninterrupted hours using a built-in thermal accumulation system. The copper structure allows cooling down to cryogenic temperatures before commencement of the experiments. Throughout the parabolic flight campaigns, add-ons and modifications have been made, illustrating the instrument flexibility in the study of small particle collisions.Comment: D. M. Salter, D. Hei{\ss}elmann, G. Chaparro, G. van der Wolk, P. Rei{\ss}aus, A. G. Borst, R. W. Dawson, E. de Kuyper, G. Drinkwater, K. Gebauer, M. Hutcheon, H. Linnartz, F. J. Molster, B. Stoll, P. C. van der Tuijn, H. J. Fraser, and J. Blu

High volume colour image processing with massively parallel embedded processors

Currently Oc´e uses FPGA technology for implementing colour image processing for their high volume colour printers. Although FPGA technology provides enough performance it, however, has a rather tedious development process. This paper describes the research conducted on an alternative implementation technology: software defined massively parallel processing. It is shown that this technology not only leads to a reduction in development time but also adds flexibility to the design

A machine learning-based approach to optimize repair and increase yield of embedded flash memories in automotive systems-on-chip

Nowadays, Embedded Flash Memory cores occupy a significant portion of Automotive Systems-on-Chip area, therefore strongly contributing to the final yield of the devices. Redundancy strategies play a key role in this context; in case of memory failures, a set of spare word- and bit-lines are allocated by a replacement algorithm that complements the memory testing procedure. In this work, we show that replacement algorithms, which are heavily constrained in terms of execution time, may be slightly inaccurate and lead to classify a repairable memory core as unrepairable. We denote this situation as Flash memory false fail. The proposed approach aims at identifying false fails by using a Machine Learning approach that exploits a feature extraction strategy based on shape recognition. Experimental results carried out on the manufacturing data show a high capability of predicting false fails