Robots need sensors to operate properly. Using a single image sensor, various aspects of a robot operating in its environment can be measured or monitored. Over the past few years, image sensors have improved a lot: frame rate and resolution have increased, while prices have fallen. As a result, data output has increased and in a number of applications data transfer to a processing unit has become the limiting factor for performance. Local processing in the sensor is one way of reducing data transfer. A report on the Vision in Robotics and Mechatronics project
Neighborhood image processing operations on Field Programmable Gate Array (FPGA) are considered as memory intensive operations. A large memory bandwidth is required to transfer the required pixel data from external memory to the processing unit. On-chip image buffers are employed to reduce this data transfer rate. Conventional image buffers, implemented either by using FPGA logic resources or embedded memories are resource inefficient. They exhaust the limited FPGA resources quickly. Consequently, hardware implementation of neighborhood operations becomes expensive, and integrating them in resource constrained devices becomes unfeasible. This paper presents a resource efficient FPGA based on-chip buffer architecture. The proposed architecture utilizes full capacity of a single Xilinx BlockRAM (BRAM36 primitive) for storing multiple rows of input image. To get multiple pixels/clock in a user defined scan order, an efficient duty-cycle based memory accessing technique is coupled with a customized addressing circuitry. This accessing technique exploits switching capabilities of BRAM to read 4 pixels in a single clock cycle without degrading system frequency. The addressing circuitry provides multiple pixels/clock in any user defined scan order to implement a wide range of neighborhood operations. With the saving of 83% BRAM resources, the buffer architecture operates at 278 MHz on Xilinx Artix-7 FPGA with an efficiency of 1.3 clock/pixel. It is thus capable to fulfill real time image processing requirements for HD image resolution (1080 × 1920) @103 fcps.
Article contributers: Annika Reinke, Minu D. Tizabi, Carole H. Sudre, Matthias Eisenmann, Tim Rädsch, Michael Baumgartner, Laura Acion, Michela Antonelli, Tal Arbel, Spyridon Bakas, Peter Bankhead, Arriel Benis, Matthew Blaschko, Florian Buettner, M. Jorge Cardoso, Jianxu Chen, Veronika Cheplygina, Evangelia Christodoulou, Beth Cimini, Gary S. Collins, Sandy Engelhardt, Keyvan Farahani, Luciana Ferrer, Adrian Galdran, Bram van Ginneken, Ben Glocker, Patrick Godau, Robert Haase, Fred Hamprecht, Daniel A. Hashimoto, Doreen Heckmann-Nötzel, Peter Hirsch, Michael M. Hoffman, Merel Huisman, Fabian Isensee, Pierre Jannin, Charles E. Kahn, Dagmar Kainmueller, Bernhard Kainz, Alexandros Karargyris, Alan Karthikesalingam, A. Emre Kavur, Hannes Kenngott, Jens Kleesiek, Andreas Kleppe, Sven Kohler, Florian Kofler, Annette Kopp-Schneider, Thijs Kooi, Michal Kozubek, Anna Kreshuk, Tahsin Kurc, Bennett A. Landman, Geert Litjens, Amin Madani, Klaus Maier-Hein, Anne L. Martel, Peter Mattson, Erik Meijering, Bjoern Menze, David Moher, Karel G.M. Moons, Henning Müller, Brennan Nichyporuk, Felix Nickel, M. Alican Noyan, Jens Petersen, Gorkem Polat, Susanne M. Rafelski, Nasir Rajpoot, Mauricio Reyes, Nicola Rieke, Michael Riegler, Hassan Rivaz, Julio Saez-Rodriguez, Clara I. Sánchez, Julien Schroeter, Anindo Saha, M. Alper Selver, Lalith Sharan, Shravya Shetty, Maarten van Smeden, Bram Stieltjes, Ronald M. Summers, Abdel A. Taha, Aleksei Tiulpin, Sotirios A. Tsaftaris, Ben Van Calster, Gaël Varoquaux, Manuel Wiesenfarth, Ziv R. Yaniv, Paul Jäger, Lena Maier-Hein.
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Structural Biology plays a crucial role in understanding the Chemistry of Life by providing detailed information about the three-dimensional structures of biological macromolecules such as proteins, DNA, RNA and complexes thereof. This knowledge allows researchers to understand how these molecules function and interact with each other, which forms the basis for a molecular understanding of disease and the development of targeted therapies. For decades, X-ray crystallography has been the dominant technique to determine these 3D structures. Only a decade ago, advances in technology and data processing resulted in a dramatic improvement of the resolution at which structures of biomolecular assemblies can be determined using another technique: cryo-electron microscopy (cryo-EM). This has been referred to as “the resolution revolution”. Since then, an ever increasing group of structural biologists are using cryo-EM. They employ a technique named Single Particle Analysis (SPA), in which thousands of individual macromolecules are imaged. These images are then computationally iteratively aligned and averaged to generate a three-dimensional reconstruction of the macromolecule. SPA works best if a very pure and concentrated macromolecule of interest can be captured in random orientations within a thin layer (10-50nm) of vitreous ice. Maastricht University has been the inventor of the machine that is found in most labs worldwide used for this: the VitroBot. We have been the inventor of succeeding technologies that allow for much better control of this process: the VitroJet. In here, we will develop a novel chemical way to expand our arsenal for preparing SPA samples of defined thickness. We will design, produce and test chemical spacers to allow for a controlled sample thickness. If successful, this will provide an easy, affordable solution for the ~1000 laboratories worldwide using SPA, and help them with their in vitro studies necessary for an improved molecular understanding of the Chemistry of Life.
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