1 citations · 2 across the 4 of their papers we have counts for
11 papers
A Fiber Measurement System with Approximate Deconvolution Based on the Analysis of Fault Clusters in Linearized Bregman Iterations
Yuneisy Garcia Guzman, Felipe Calliari, Gustavo C. Amaral +1
Automatic detection of faults in optical fibers is an active area of research that plays a significant role in the design of reliable and stable optical networks. A fiber measureme…
Efficient Majority Voting in Digital Hardware
Stefan Baumgartner, Mario Huemer, Michael Lunglmayr
In recent years, machine learning methods became increasingly important for a manifold number of applications. However, they often suffer from high computational requirements impai…
Fast approximate reciprocal approximations for iterative algorithms
Michael Lunglmayr, Oliver Ploder
The reciprocal function, 1/x, is important for many real-time algorithms. It is used in a large variety of algorithms from areas ranging from iterative estimation to machine learni…
FPGA-Embedded Linearized Bregman Iterations Algorithm for Trend Break Detection
Felipe Calliari, Gustavo C. Amaral, Michael Lunglmayr
Detection of level shifts in a noisy signal, or trend break detection, is a problem that appears in several research fields, from biophysics to optics and economics. Although many…
On Quasi-Isometry of Threshold-Based Sampling
Bernhard Moser
The problem of isometry for threshold-based sampling such as integrate-and-fire (IF) or send-on-delta (SOD) is addressed. While for uniform sampling the Parseval theorem provides i…
A stochastic computing architecture for iterative estimation
Michael Lunglmayr, Daniel Wiesinger, Werner Haselmayr
Stochastic computing (SC) is a promising candidate for fault tolerant computing in digital circuits. We present a novel stochastic computing estimation architecture allowing to sol…