Hybrid ArtificialNN and SpikingNN for closed-loop motor control

Using the LuI silicon neurons in a spiking neural network converting the sound processing output of an ANN into spike trains to move a servo motor. Another ANN classifies the spike trains to determine the motor direction.

Team:

• Selin Schmitt - schmittse79503@th-nuernberg.de
• Timon Löwl - loewlti80780@th-nuernberg.de
• Sven Remy - remysv80967@th-nuernberg.de

Spiking Silicon Neurons for Text2Morse Conversion

Neuromorphic data encoding and decoding using LuI silicon neurons for implementing an efficient Text2Morse converter based on spike trains.

Team:

• Tan Phat, Nguyen - phatnguyen@gmx.de
• Paul Schmachtl - paul.schmachtl@outlook.com

Event-based camera spatial and temporal clustering for predictive maintenance

Using a neuromorphic event-based camera to design and develop a spatial and temporal clustering algorithm for frequency detection used in machine state estimation and predictive maintenance.

Team:

• Benedikt Fischer - fischerbe98484@th-nuernberg.de
• Felix Sixdorf - sixdorffe80095@th-nuernberg.de
• David Stiegler - stieglerda78912@th-nuernberg.de

Spiking PID Controller for Mobile Robot Trajectory Tracking

Neuromorphic PID implemented using Nengo to control a differential mobile robot trajectory tracking performance under uncertainty.

Team:

• Adrian Stangl - stanglad98626@th-nuernberg.de
• Bastian Wunderlich - wunderlichba98628@th-nuernberg.de

Follow the leader robot swarm

Neuromorphic vision based frequency tracking algorithm and closed-loop control. Embedded processing for collaborative mini-robots.

Scene understanding for robot motion

Neuromorphic visual sensing fusion with LiDAR for scene understanding and planning for mobile robot motion control.

Camera-based instruments for karaoke

Users can accompany their favourite songs instrumentally by imitating selected instruments with gestures sensed using a neuromorphic vision sensor.

Team:

• Selin Schmitt - schmittse79503@th-nuernberg.de
• Kay Hartmann - hartmannka80488@th-nuernberg.de

Crowd-sourced visuals

Privacy-preserving club visuals generation based on synchronized neuromorphic video sensing and sound generation.

Team:

• Selin Schmitt - schmittse79503@th-nuernberg.de
• Kay Hartmann - hartmannka80488@th-nuernberg.de

Event-based camera-LiDAR fusion for clustering-based depth estimation

This project addresses the challenge of estimating depth using a single DVS (Dynamic Vision Sensor) camera and a 2D LiDAR. Built using ROS2 for communication and data processing, the system can run on various platforms, including wheeled robots with Jetson Nano or similar hardware. A custom clustering algorithm was developed to estimate the depth of objects outside the LiDAR's measurement plane, enhancing depth perception while maintaining affordability.

Team:

• Annika Igl - aiglgg@web.de
• Timo Kapellner - timo.kapellner@gmail.com