activity
20172025
most citedIntegrate-and-Fire from a Mathematical and Signal Processing Perspective

2 citations · 5 across the 9 of their papers we have counts for

collaborators
Showing eess.SPShow all

13 papers · 1 filter

eess.SP20252 cited

Integrate-and-Fire from a Mathematical and Signal Processing Perspective

Bernhard A. Moser, Anna Werzi, Michael Lunglmayr

Integrate-and-Fire (IF) is an idealized model of the spike-triggering mechanism of a biological neuron. It is used to realize the bio-inspired event-based principle of information…

eess.SP2024

On the Sampling Sparsity of Neuromorphic Analog-to-Spike Conversion based on Leaky Integrate-and-Fire

Bernhard A. Moser, Michael Lunglmayr

In contrast to the traditional principle of periodic sensing neuromorphic engineering pursues a paradigm shift towards bio-inspired event-based sensing, where events are primarily…

eess.SP2023

SNN Architecture for Differential Time Encoding Using Decoupled Processing Time

Daniel Windhager, Bernhard A. Moser, Michael Lunglmayr

Spiking neural networks (SNNs) have gained attention in recent years due to their ability to handle sparse and event-based data better than regular artificial neural networks (ANNs…

eess.SP2021

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…

eess.SP2021

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…

eess.SP20201 cited

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…