
Davis Rakhshan
Davis Rakhshan
Wissenschaftlicher Mitarbeiter
Mail: davis.rakhshan[at]tuhh.deTelefon: +49 40 30601 3430
Adresse: Am Schwarzenberg 3 (E), 3.027
Personensuche: Zum Eintrag
Awards und Grants
- 2026: Gustav Polensky Foundation - PhD Poster Competition Award, TUHH UNU Hub Cluster Meeting
Projects
- BioDivKI2: BioDivKI2
- Biodiversity is a key indicator of ecosystem resilience, but current monitoring methods for insect populations are costly, time-consuming, and require expert knowledge. BioDivKI2 develops an AI-supported solution based on acoustic signatures to enable efficient insect monitoring, integrating sensor technology, practical studies, and citizen science to enhance biodiversity assessment and public awareness.
Publications
2026
-
DCOSS
Conference
At the Edge of the Heart: ULP FPGA-Based CNN for on-Device Cardiac Feature Extraction in Smart Health Sensors for Astronauts -
2026 22nd International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT)2026.
0.1109/DCOSS-IoT69657.2026.00019 [BibTex]
-
DCOSS
Demo Abstract
DEMO: Real-Time Bird Detection with a Modular Acoustic Sensing Platform -
2026 22nd International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT)2026.
10.1109/DCOSS-IoT69657.2026.00065 [BibTex]
-
HIEMI
Workshop
From Sound to Species: A Modular Acoustic Sensor for Biodiversity Monitoring -
2026 2nd International Workshop on Hybrid Intelligence for Internet of Everything Models and Implementations (HIEMI)2026.
10.1109/DCOSS-IoT69657.2026.00151 [BibTex]
2024
-
FGSN
Workshop
Potentials and Challenges of Ecoacoustics for Environmental Monitoring: A Small Field Study -
Proceedings of the 21st GI/ITG KuVS Fachgespräch Drahtlose Sensornetze2024Accepted for publication.
[BibTex]
Student Theses
Open Theses Topics
ADC Evaluation for Energy-Efficient Sensor Data Acquisition
Typ: Forschungsprojekt
Status: offen
Supervisors: Davis Rakhshan
Evaluate a new ADC as a potential replacement for the ADC currently used in an embedded sensor system. Compare both solutions in terms of signal quality, sampling performance, energy consumption, and the quality of acquired sensor data.
Status: offen
Supervisors: Davis Rakhshan
Evaluate a new ADC as a potential replacement for the ADC currently used in an embedded sensor system. Compare both solutions in terms of signal quality, sampling performance, energy consumption, and the quality of acquired sensor data.
AI Accelerator for Energy-Efficient Edge AI
Typ: Forschungsprojekt
Status: offen
Supervisors: Davis Rakhshan
Design and evaluate a custom AI accelerator board based on the STM32N6 for integration into an existing sensor system, enabling efficient neural-network inference directly at the edge.
Status: offen
Supervisors: Davis Rakhshan
Design and evaluate a custom AI accelerator board based on the STM32N6 for integration into an existing sensor system, enabling efficient neural-network inference directly at the edge.