1,721,025 research outputs found
WiN-GUI Version 2: A graphical tool for neuron-based encoding
No full textThe WiN-GUI enables real-time exploration of neuron model behaviors by adjusting internal parameters and accounting for input properties such as scale and temporal resolution. The spiking responses are however often difficult to interpret and categorize. The update to version 2 introduces a systematic labeling of spike- patterns to facilitate clearer communication across researchers and disciplines, enabling a common framework to describe neuron responses without sharing data. Labels also allow researchers to intentionally target specific neuronal behaviors, fostering biologically plausible simulations or specific tuning goals. To this end, our WiN-GUI incorporates a spike-pattern classifier for automated identification and analysis of neuron activity, streamlining research and collaboration
EDOPT: Event-camera 6-DoF Dynamic Object Pose Tracking
No full text<p>The dataset can be used to test your event-based 6-DoF pose tracking algorithm.</p>
<p>If you use any of this data, please cite the following publication:</p>
<p>@inproceedings{glover2024,<br> title={EDOPT: Event-camera 6-DoF Dynamic Object Pose Tracking },<br> author={Glover, Arren and Gava, Luna and Li, Zhichao and Bartolozzi, Chiara},<br> booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)},<br> year={2024}<br>}</p>
<p>The dataset includes event-driven data and ground truth of 5 objects: dragon, jell-o, mustard, soup can, and spam. For each object, six different motions on independent axes are recorded. </p>
<p>To import .log files containing events, we suggest <a href="https://github.com/event-driven-robotics/bimvee">bimvee</a> Python library.</p>
<p>Specifically, use the functions to import .log files:</p>
<p>data = importIitYarp(filePathOrName=input_path)</p>
<p>Ground-truth .csv files have 8 columns, each one corresponding to a different measure: </p>
<p>timestamp | x | y | z | qx | qy | qz | qw</p>
<p>x, y, z refer to the object position in the camera reference frame, while qx, qy, qz and qw refer to the object orientation expressed in quaternions. </p>
An event-driven POSFET taxel for sustained and transient sensing
No full textWe present an event-driven tactile sensing element that encodes both the absolute value of the input force and its variation over time. It is based on the POSFET device and Leaky-Integrate and Fire neurons, connected by a transconductance amplifier; the proposed circuit exploits the advantages of the POSFET device, such as high integration scale, fast response, wide bandwidth and force sensitivity, as well as the advantages of event-driven encoding, such as low latency, low power dissipation, and high temporal resolution, coupled with redundancy reduction
luvHarris: a practical corner detector for event-cameras
No full text<p>There have been a number of corner detection methods proposed for event cameras in the last years, since event-driven computer vision has become more accessible. Current state-of-the-art have either unsatisfactory accuracy or real-time performance when considered for practical use; random motion using a live camera in an unconstrained environment. In this paper, we present yet another method to perform corner detection, dubbed look-up event-Harris (luvHarris), that employs the Harris algorithm for high accuracy but manages an improved event throughput. Our method has two major contributions, 1. a novel "threshold ordinal event-surface" that removes certain tuning parameters and is well suited for Harris operations, and 2. an implementation of the Harris algorithm such that the computational load <em>per event</em> is minimised and computational heavy convolutions are performed only 'as-fast-as-possible', i.e. only as computational resources are available. The result is a practical, real-time, and robust corner detector that runs more than the speed of current state-of-the-art; a necessity when using high-resolution event-camera in real-time. We explain the considerations taken for the approach, compare the algorithm to current state-of-the-art in terms of computational performance and detection accuracy, and discuss the validity of the proposed approach for event cameras.</p>
<p> </p>
<pre><code>@article{glover2021luvharris,
title={luvharris: A practical corner detector for event-cameras},
author={Glover, Arren and Dinale, Aiko and Rosa, Leandro De Souza and Bamford, Simeon and Bartolozzi, Chiara},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume={44},
number={12},
pages={10087--10098},
year={2021},
publisher={IEEE}
}</code></pre>
Asynchronous DC-free serial protocol for event-based AER systems
No full textWe developed an Asynchronous DC-free Serial Address-Event Representation (AS-AER) protocol. It allows for full-duplex communication and explicit flow control, does not require any clock data recovery or accurate clock relationship between the transmitter and receiver, and is based on AC-coupling that galvanically isolates communicating devices. The proposed reference implementation does not require any specific hardware and can be implemented on low-cost FPGAs and eventually ASICs. Preliminary tests performed at raw bit transfer rate of 100 Mbps confirm a 32 bit maximum event rate of 2.9Meps. Further tests with 16 bit events show a good tolerance to the clock difference between transmitter and the receiver, with no errors for a frequency difference up to ±2
Neuromorphic decoding of spinal motor neuron behaviour during natural hand movements for a new generation of wearable neural interfaces
Full text linkWe propose a neuromorphic framework to process the activity of human spinal motor neurons for movement intention recognition. This framework is integrated into a non-invasive interface that decodes the activity of motor neurons innervating intrinsic and extrinsic hand muscles. One of the main limitations of current neural interfaces is that machine learning models cannot exploit the efficiency of the spike encoding operated by the nervous system. Spiking-based pattern recognition would detect the spatio-temporal sparse activity of a neuronal pool and lead to adaptive and compact implementations, eventually running locally in embedded systems. Emergent Spiking Neural Networks (SNN) have not yet been used for processing the activity of in-vivo human neurons. Here we developed a convolutional SNN to process a total of 467 spinal motor neurons whose activity was identified in 5 participants while executing 10 hand movements. The classification accuracy approached 0.95 ±0.14 for both isometric and non-isometric contractions. These results show for the first time the potential of highly accurate motion intent detection by combining non-invasive neural interfaces and SNN
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Audio-visual target speaker enhancement on multi-talker environment using event-driven cameras
No full text- …
