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Network management applications require large numbers of counters in order to collect traffic characteristics for each network flow. However, these counters often barely fit into on-chip SRAM memories. Past papers have proposed using counter estimators instead, thus trading off counter precision for a lower number of bits. But these estimators do not achieve optimal estimation error, and cannot always scale to arbitrary counter values.In this paper, we introduce the CEDAR algorithm for decoupling the counter estimators from their estimation values, which are quantized into estimation levels and shared among many estimators. These decoupled and shared estimation values enable us to easily adjust them without needing to go through all the counters. We demonstrate how our CEDAR scheme achieves the min-max relative error, i.e., can guarantee the best possible relative error over the entire counter scale. We also explain how to use dynamic adaptive estimation values in order to support counter up-scaling and adjust the estimation error depending on the current maximal counter.Finally we implement CEDAR on FPGA and explain how it can run at line rate. We further analyze its performance and size requirements.

We present an algorithm for generating the Voronoi cells for a set of rational C 1 -continuous planar closed curves, which is precise up to machine precision. Initially, bisectors for pairs of curves, (C(t),C i (r)), are generated symbolically and represented as implicit forms in the tr-parameter space. Then, the bisectors are properly trimmed after being split into monotone pieces. The trimming procedure uses the orientation of the original curves as well as their curvature fields, resulting in a set of trimmed-bisector segments represented as implicit curves in a parameter space. A lower-envelope algorithm is then used in the parameter space of the curve whose Voronoi cell is sought. The lower envelope represents the exact boundary of the Voronoi cell.

Self-assembly provides an information-economical route to the fabrication of objects at virtually all scales. However, there is no known algorithm to program self-assembly in macro-scale, solid, complex 3D objects. Here such an algorithm is described, which is inspired by the molecular assembly of DNA, and based on bricks designed by tetrahedral meshing of arbitrary objects. Assembly rules are encoded by topographic cues imprinted on brick faces while attraction between bricks is provided by embedded magnets. The bricks can then be mixed in a container and agitated, leading to properly assembled objects at high yields and zero errors. The system and its assembly dynamics were characterized by video and audio analysis, enabling the precise time- and space-resolved characterization of its performance and accuracy. Improved designs inspired by our system could lead to successful implementation of self-assembly at the macro-scale, allowing rapid, on-demand fabrication of objects without the need for assembly lines.