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Sanaullah Sanaullah

https://orcid.org/0000-0003-4112-802X

30 Publikationen

Alle markieren

[30]
2027 | Artikel | FH-PUB-ID: 7251
MindGuard: Embedded agentic VLM for real-time multimodal threat detection in physical AI
Sanaullah, Sanaullah, MindGuard: Embedded agentic VLM for real-time multimodal threat detection in physical AI. Neurocomputing 709 (). , 2027
HSBI-PUB | DOI
 
[29]
2025 | Artikel | FH-PUB-ID: 6139
DNA: detecting early signs of neurodegenerative diseases through activity and sleep analysis
H. Attaullah, S. Sanaullah, A. Peters, Q.A. Ahmed, J. Baudisch, T. Jungeblut, Frontiers in Neuroscience 19 (2025).
HSBI-PUB | DOI
 
[28]
2025 | Dissertation | FH-PUB-ID: 6052
Design-Space Exploration of Biologically-Inspired SNN Models for Application-Specific Many-Core Systems
S. Sanaullah, Design-Space Exploration of Biologically-Inspired SNN Models for Application-Specific Many-Core Systems, Universität Bielefeld, 2025.
HSBI-PUB | DOI
 
[27]
2025 | Konferenzbeitrag | FH-PUB-ID: 5976
Automating Neural Model Selection in Spiking Neural Networks Using AutoML Techniques*
S. Sanaullah, H. Honda, K. Roy, A. Schneider, J. Waßmuth, T. Jungeblut, in: 2025 22nd International Learning and Technology Conference (L&T), IEEE, 2025, pp. 274–279.
HSBI-PUB | DOI
 
[26]
2025 | Artikel | FH-PUB-ID: 5366
RAVSim v2.0: Enhanced visualization and comparative analysis for neural network models
S. Sanaullah, A. Schneider, J. Waßmuth, U. Rückert, T. Jungeblut, SoftwareX 29 (2025).
HSBI-PUB | DOI
 
[25]
2024 | Artikel | FH-PUB-ID: 4997 | OA
Analyzing Machine Learning Models for Activity Recognition Using Homomorphically Encrypted Real-World Smart Home Datasets: A Case Study
H. Attaullah, S. Sanaullah, T. Jungeblut, Applied Sciences 14 (2024).
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.)
 
[24]
2024 | Konferenzbeitrag | FH-PUB-ID: 5096
Trade-offs Between Privacy and Performance in Encrypted Dataset using Machine Learning Models
S. Sanaullah, H. Attaullah, T. Jungeblut, in: U. Kuhl, DataNinja.nrw (Eds.), Proceedings of the SAIOnARA Conference, DataNinja sAIOnARA Conference, 2024.
HSBI-PUB | DOI
 
[23]
2024 | Konferenzbeitrag | FH-PUB-ID: 5097 | OA
Advancements in Neural Network Generations
S. Sanaullah, S. Koravuna, U. Rückert, T. Jungeblut, in: U. Kuhl, DataNinja.nrw (Eds.), Proceedings of the SAIOnARA Conference, DataNinja sAIOnARA Conference, 2024.
HSBI-PUB | DOI | Download (ext.)
 
[22]
2024 | Konferenzbeitrag | FH-PUB-ID: 5098
Encryption Techniques for Privacy-Preserving CNN Models: Performance and Practicality in Urban AI Applications
S. Sanaullah, H. Attaullah, T. Jungeblut, in: O.A. Omitaomu, A. Mostafavi, S. Randhawa, H. Niu (Eds.), Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Urban-AI, ACM, New York, NY, USA, 2024, pp. 50–53.
HSBI-PUB | DOI
 
[21]
2024 | Konferenzbeitrag | FH-PUB-ID: 4811
The Next-Gen Interactive Runtime Simulator for Neural Network Programming
S. Sanaullah, H. Attaullah, T. Jungeblut, in: E. Söderberg, L. Church (Eds.), Companion Proceedings of the 8th International Conference on the Art, Science, and Engineering of Programming, ACM, New York, NY, USA, 2024, pp. 8–10.
HSBI-PUB | DOI
 
[20]
2024 | Konferenzbeitrag | FH-PUB-ID: 5367 | OA
Spiking Neural Network Models Analysis on Field Programmable Gate Arrays
S. Koravuna, S. Sanaullah, T. Jungeblut, U. Rückert, in: S. Pudaruth, K. Ogudo (Eds.), International Conference on Intelligent and Innovative Computing Applications, Society of Information Technologists and Engineers Ltd, 2024, pp. 259–270.
HSBI-PUB | DOI | Download (ext.)
 
[19]
2024 | Konferenzbeitrag | FH-PUB-ID: 5315
FL-DL: Fuzzy Logic with Deep Learning, Hybrid Anomaly Detection and Activity Prediction in Smart Homes Data-Sets
H. Attaullah, S. Sanaullah, T. Jungeblut, in: 2024 IEEE 24th International Symposium on Computational Intelligence and Informatics (CINTI), IEEE, Budapest, Hungary, 2024, pp. 43–48.
HSBI-PUB | DOI
 
[18]
2024 | Kurzbeitrag Konferenz | FH-PUB-ID: 4883
Selection of Optimal Neural Model using Spiking Neural Network for Edge Computing
S. Sanaullah, K. Roy, U. Ruckert, T. Jungeblut, in: IEEE (Ed.), 2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS), IEEE, 2024, pp. 1452–1453.
HSBI-PUB | DOI | Inspire
 
[17]
2024 | Konferenzbeitrag | FH-PUB-ID: 4782
A Spike Vision Approach for Multi-object Detection and Generating Dataset Using Multi-core Architecture on Edge Device
S. Sanaullah, S. Koravuna, U. Rückert, T. Jungeblut, in: L. Iliadis, I. Maglogiannis, A. Papaleonidas, E. Pimenidis, C. Jayne (Eds.), Engineering Applications of Neural Networks. 25th International Conference, EANN 2024, Corfu, Greece, June 27–30, 2024, Proceedings, Springer Nature Switzerland, Cham, 2024, pp. 317–328.
HSBI-PUB | DOI
 
[16]
2024 | Konferenzbeitrag | FH-PUB-ID: 4783 | OA
A Hybrid Spiking-Convolutional Neural Network Approach for Advancing Machine Learning Models
S. Sanaullah, K. Roy, U. Rückert, T. Jungeblut, in: T. Lutchyn, A. Ramírez Rivera, B. Ricaud (Eds.), Proceedings of the 5th Northern Lights Deep Learning Conference, Proceedings of Machine Learning Research (PMLR), UiT The Arctic University, Tromsø, Norway, 2024.
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.) | WoS | arXiv
 
[15]
2023 | Konferenzbeitrag | FH-PUB-ID: 4205 | OA
A Novel Spike Vision Approach for Robust Multi-Object Detection using SNNs
S. Sanaullah, S. Koravuna, U. Rückert, T. Jungeblut, in: 2023.
HSBI-PUB | DOI | Download (ext.)
 
[14]
2023 | Artikel | FH-PUB-ID: 3572 | OA
Exploring spiking neural networks: a comprehensive analysis of mathematical models and applications
S. Sanaullah, S. Koravuna, U. Rückert, T. Jungeblut, Frontiers in Computational Neuroscience 17 (2023).
HSBI-PUB | DOI | Download (ext.)
 
[13]
2023 | Artikel | FH-PUB-ID: 3482 | OA
Evaluation of Spiking Neural Nets-Based Image Classification Using the Runtime Simulator RAVSim
S. Sanaullah, S. Koravuna, U. Rückert, T. Jungeblut, International Journal of Neural Systems (2023).
HSBI-PUB | DOI | Download (ext.)
 
[12]
2023 | Kurzbeitrag Konferenz | FH-PUB-ID: 4206 | OA
A Hybrid Spiking-Convolutional Neural Network Approach for Advancing High-Quality Image Inpainting
S. Sanaullah, A. Amanullah, K. Roy, J.-A. Lee, S. Chul-Jun, T. Jungeblut, in: 2023.
HSBI-PUB | DOI | Download (ext.)
 
[11]
2023 | Artikel | FH-PUB-ID: 3665 | OA
Design-Space Exploration of SNN Models using Application-Specific Multi-Core Architectures
S. Sanaullah, S. Koravuna, U. Ruckert, T. Jungeblut, Preprint (2023).
HSBI-PUB | DOI | Download (ext.)
 
[10]
2023 | Konferenzbeitrag | FH-PUB-ID: 4207 | OA
Analysis of MR Images for Early and Accurate Detection of Brain Tumor using Resource Efficient Simulator Brain Analysis
S. Sanaullah, T. Jungeblut, in: New York USA, 2023.
HSBI-PUB | DOI | Download (ext.)
 
[9]
2023 | Konferenzbeitrag | FH-PUB-ID: 4208
Digit Recognition Using Spiking Neural Networks on FPGA
S. Koravuna, S. Sanaullah, T. Jungeblut, U. Rückert, in: I. Rojas, G. Joya, A. Catala (Eds.), Advances in Computational Intelligence. 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Ponta Delgada, Portugal, June 19–21, 2023, Proceedings, Part I, Springer Nature Switzerland, Cham, 2023, pp. 406–417.
HSBI-PUB | DOI
 
[8]
2023 | Konferenzbeitrag | FH-PUB-ID: 3644 | OA
Transforming Event-Based into Spike-Rate Datasets for Enhancing Neuronal Behavior Simulation to Bridging the Gap for SNNs
S. Sanaullah, S. Koravuna, U. Rückert, T. Jungeblut, in: Paris France , 2023.
HSBI-PUB | DOI | Download (ext.)
 
[7]
2023 | Buchbeitrag | FH-PUB-ID: 3222
Streamlined Training of GCN for Node Classification with Automatic Loss Function and Optimizer Selection
S. Sanaullah, S. Koravuna, U. Rückert, T. Jungeblut, in: L. Iliadis, I. Maglogiannis, S. Alonso, C. Jayne, E. Pimenidis (Eds.), Engineering Applications of Neural Networks. 24th International Conference, EAAAI/EANN 2023, León, Spain, June 14–17, 2023, Proceedings, Springer Nature Switzerland, Cham, 2023, pp. 191–202.
HSBI-PUB | DOI
 
[6]
2023 | Kurzbeitrag Konferenz | FH-PUB-ID: 2919 | OA
Evaluating Spiking Neural Network Models: A Comparative Performance Analysis
S. Sanaullah, S. Koravuna, U. Rückert, T. Jungeblut, in: n.d.
HSBI-PUB | DOI | Download (ext.)
 
[5]
2022 | Kurzbeitrag Konferenz | FH-PUB-ID: 2753
Real-Time Resource Efficient Simulator for SNNs-based Model Experimentation
S. Sanaullah, S. Koravuna, U. Rückert, T. Jungeblut, (2022).
HSBI-PUB | DOI
 
[4]
2022 | Konferenzbeitrag | FH-PUB-ID: 3480 | OA
Real-Time SNNs Model Analyzing and Visualizing Experimentation
S. Sanaullah, S. Koravuna, U. Ruckert, T. Jungeblut, in: Unpublished, 2022.
HSBI-PUB | DOI | Download (ext.)
 
[3]
2022 | Konferenzbeitrag | FH-PUB-ID: 2757
NireHApS: Neuro-Inspired and Resource-Efficient Hardware-Architectures for Plastic SNNs
S. Sanaullah, S. Koravuna, T. Jungeblut, U. Rückert, in: 2022.
HSBI-PUB | DOI
 
[2]
2022 | Konferenzbeitrag | FH-PUB-ID: 2276
SNNs Model Analyzing and Visualizing Experimentation Using RAVSim
S. Sanaullah, S. Koravuna, U. Rückert, T. Jungeblut, in: L. Iliadis, C. Jayne, A. Tefas, E. Pimenidis (Eds.), Engineering Applications of Neural Networks. 23rd International Conference, EAAAI/EANN 2022, Chersonissos, Crete, Greece, June 17–20, 2022, Proceedings, Springer International Publishing, Cham, 2022, pp. 40–51.
HSBI-PUB | DOI
 
[1]
2019 | Konferenzbeitrag | FH-PUB-ID: 2975
Accelerating the Threshold and Timing Analysis of Genetic Logic Circuit Models
S. Sanaullah, H. Baig, J.A. Lee, in: n.d.
HSBI-PUB | DOI | Download (ext.)
 
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30 Publikationen

Alle markieren

[30]
2027 | Artikel | FH-PUB-ID: 7251
MindGuard: Embedded agentic VLM for real-time multimodal threat detection in physical AI
Sanaullah, Sanaullah, MindGuard: Embedded agentic VLM for real-time multimodal threat detection in physical AI. Neurocomputing 709 (). , 2027
HSBI-PUB | DOI
 
[29]
2025 | Artikel | FH-PUB-ID: 6139
DNA: detecting early signs of neurodegenerative diseases through activity and sleep analysis
H. Attaullah, S. Sanaullah, A. Peters, Q.A. Ahmed, J. Baudisch, T. Jungeblut, Frontiers in Neuroscience 19 (2025).
HSBI-PUB | DOI
 
[28]
2025 | Dissertation | FH-PUB-ID: 6052
Design-Space Exploration of Biologically-Inspired SNN Models for Application-Specific Many-Core Systems
S. Sanaullah, Design-Space Exploration of Biologically-Inspired SNN Models for Application-Specific Many-Core Systems, Universität Bielefeld, 2025.
HSBI-PUB | DOI
 
[27]
2025 | Konferenzbeitrag | FH-PUB-ID: 5976
Automating Neural Model Selection in Spiking Neural Networks Using AutoML Techniques*
S. Sanaullah, H. Honda, K. Roy, A. Schneider, J. Waßmuth, T. Jungeblut, in: 2025 22nd International Learning and Technology Conference (L&T), IEEE, 2025, pp. 274–279.
HSBI-PUB | DOI
 
[26]
2025 | Artikel | FH-PUB-ID: 5366
RAVSim v2.0: Enhanced visualization and comparative analysis for neural network models
S. Sanaullah, A. Schneider, J. Waßmuth, U. Rückert, T. Jungeblut, SoftwareX 29 (2025).
HSBI-PUB | DOI
 
[25]
2024 | Artikel | FH-PUB-ID: 4997 | OA
Analyzing Machine Learning Models for Activity Recognition Using Homomorphically Encrypted Real-World Smart Home Datasets: A Case Study
H. Attaullah, S. Sanaullah, T. Jungeblut, Applied Sciences 14 (2024).
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.)
 
[24]
2024 | Konferenzbeitrag | FH-PUB-ID: 5096
Trade-offs Between Privacy and Performance in Encrypted Dataset using Machine Learning Models
S. Sanaullah, H. Attaullah, T. Jungeblut, in: U. Kuhl, DataNinja.nrw (Eds.), Proceedings of the SAIOnARA Conference, DataNinja sAIOnARA Conference, 2024.
HSBI-PUB | DOI
 
[23]
2024 | Konferenzbeitrag | FH-PUB-ID: 5097 | OA
Advancements in Neural Network Generations
S. Sanaullah, S. Koravuna, U. Rückert, T. Jungeblut, in: U. Kuhl, DataNinja.nrw (Eds.), Proceedings of the SAIOnARA Conference, DataNinja sAIOnARA Conference, 2024.
HSBI-PUB | DOI | Download (ext.)
 
[22]
2024 | Konferenzbeitrag | FH-PUB-ID: 5098
Encryption Techniques for Privacy-Preserving CNN Models: Performance and Practicality in Urban AI Applications
S. Sanaullah, H. Attaullah, T. Jungeblut, in: O.A. Omitaomu, A. Mostafavi, S. Randhawa, H. Niu (Eds.), Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Urban-AI, ACM, New York, NY, USA, 2024, pp. 50–53.
HSBI-PUB | DOI
 
[21]
2024 | Konferenzbeitrag | FH-PUB-ID: 4811
The Next-Gen Interactive Runtime Simulator for Neural Network Programming
S. Sanaullah, H. Attaullah, T. Jungeblut, in: E. Söderberg, L. Church (Eds.), Companion Proceedings of the 8th International Conference on the Art, Science, and Engineering of Programming, ACM, New York, NY, USA, 2024, pp. 8–10.
HSBI-PUB | DOI
 
[20]
2024 | Konferenzbeitrag | FH-PUB-ID: 5367 | OA
Spiking Neural Network Models Analysis on Field Programmable Gate Arrays
S. Koravuna, S. Sanaullah, T. Jungeblut, U. Rückert, in: S. Pudaruth, K. Ogudo (Eds.), International Conference on Intelligent and Innovative Computing Applications, Society of Information Technologists and Engineers Ltd, 2024, pp. 259–270.
HSBI-PUB | DOI | Download (ext.)
 
[19]
2024 | Konferenzbeitrag | FH-PUB-ID: 5315
FL-DL: Fuzzy Logic with Deep Learning, Hybrid Anomaly Detection and Activity Prediction in Smart Homes Data-Sets
H. Attaullah, S. Sanaullah, T. Jungeblut, in: 2024 IEEE 24th International Symposium on Computational Intelligence and Informatics (CINTI), IEEE, Budapest, Hungary, 2024, pp. 43–48.
HSBI-PUB | DOI
 
[18]
2024 | Kurzbeitrag Konferenz | FH-PUB-ID: 4883
Selection of Optimal Neural Model using Spiking Neural Network for Edge Computing
S. Sanaullah, K. Roy, U. Ruckert, T. Jungeblut, in: IEEE (Ed.), 2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS), IEEE, 2024, pp. 1452–1453.
HSBI-PUB | DOI | Inspire
 
[17]
2024 | Konferenzbeitrag | FH-PUB-ID: 4782
A Spike Vision Approach for Multi-object Detection and Generating Dataset Using Multi-core Architecture on Edge Device
S. Sanaullah, S. Koravuna, U. Rückert, T. Jungeblut, in: L. Iliadis, I. Maglogiannis, A. Papaleonidas, E. Pimenidis, C. Jayne (Eds.), Engineering Applications of Neural Networks. 25th International Conference, EANN 2024, Corfu, Greece, June 27–30, 2024, Proceedings, Springer Nature Switzerland, Cham, 2024, pp. 317–328.
HSBI-PUB | DOI
 
[16]
2024 | Konferenzbeitrag | FH-PUB-ID: 4783 | OA
A Hybrid Spiking-Convolutional Neural Network Approach for Advancing Machine Learning Models
S. Sanaullah, K. Roy, U. Rückert, T. Jungeblut, in: T. Lutchyn, A. Ramírez Rivera, B. Ricaud (Eds.), Proceedings of the 5th Northern Lights Deep Learning Conference, Proceedings of Machine Learning Research (PMLR), UiT The Arctic University, Tromsø, Norway, 2024.
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.) | WoS | arXiv
 
[15]
2023 | Konferenzbeitrag | FH-PUB-ID: 4205 | OA
A Novel Spike Vision Approach for Robust Multi-Object Detection using SNNs
S. Sanaullah, S. Koravuna, U. Rückert, T. Jungeblut, in: 2023.
HSBI-PUB | DOI | Download (ext.)
 
[14]
2023 | Artikel | FH-PUB-ID: 3572 | OA
Exploring spiking neural networks: a comprehensive analysis of mathematical models and applications
S. Sanaullah, S. Koravuna, U. Rückert, T. Jungeblut, Frontiers in Computational Neuroscience 17 (2023).
HSBI-PUB | DOI | Download (ext.)
 
[13]
2023 | Artikel | FH-PUB-ID: 3482 | OA
Evaluation of Spiking Neural Nets-Based Image Classification Using the Runtime Simulator RAVSim
S. Sanaullah, S. Koravuna, U. Rückert, T. Jungeblut, International Journal of Neural Systems (2023).
HSBI-PUB | DOI | Download (ext.)
 
[12]
2023 | Kurzbeitrag Konferenz | FH-PUB-ID: 4206 | OA
A Hybrid Spiking-Convolutional Neural Network Approach for Advancing High-Quality Image Inpainting
S. Sanaullah, A. Amanullah, K. Roy, J.-A. Lee, S. Chul-Jun, T. Jungeblut, in: 2023.
HSBI-PUB | DOI | Download (ext.)
 
[11]
2023 | Artikel | FH-PUB-ID: 3665 | OA
Design-Space Exploration of SNN Models using Application-Specific Multi-Core Architectures
S. Sanaullah, S. Koravuna, U. Ruckert, T. Jungeblut, Preprint (2023).
HSBI-PUB | DOI | Download (ext.)
 
[10]
2023 | Konferenzbeitrag | FH-PUB-ID: 4207 | OA
Analysis of MR Images for Early and Accurate Detection of Brain Tumor using Resource Efficient Simulator Brain Analysis
S. Sanaullah, T. Jungeblut, in: New York USA, 2023.
HSBI-PUB | DOI | Download (ext.)
 
[9]
2023 | Konferenzbeitrag | FH-PUB-ID: 4208
Digit Recognition Using Spiking Neural Networks on FPGA
S. Koravuna, S. Sanaullah, T. Jungeblut, U. Rückert, in: I. Rojas, G. Joya, A. Catala (Eds.), Advances in Computational Intelligence. 17th International Work-Conference on Artificial Neural Networks, IWANN 2023, Ponta Delgada, Portugal, June 19–21, 2023, Proceedings, Part I, Springer Nature Switzerland, Cham, 2023, pp. 406–417.
HSBI-PUB | DOI
 
[8]
2023 | Konferenzbeitrag | FH-PUB-ID: 3644 | OA
Transforming Event-Based into Spike-Rate Datasets for Enhancing Neuronal Behavior Simulation to Bridging the Gap for SNNs
S. Sanaullah, S. Koravuna, U. Rückert, T. Jungeblut, in: Paris France , 2023.
HSBI-PUB | DOI | Download (ext.)
 
[7]
2023 | Buchbeitrag | FH-PUB-ID: 3222
Streamlined Training of GCN for Node Classification with Automatic Loss Function and Optimizer Selection
S. Sanaullah, S. Koravuna, U. Rückert, T. Jungeblut, in: L. Iliadis, I. Maglogiannis, S. Alonso, C. Jayne, E. Pimenidis (Eds.), Engineering Applications of Neural Networks. 24th International Conference, EAAAI/EANN 2023, León, Spain, June 14–17, 2023, Proceedings, Springer Nature Switzerland, Cham, 2023, pp. 191–202.
HSBI-PUB | DOI
 
[6]
2023 | Kurzbeitrag Konferenz | FH-PUB-ID: 2919 | OA
Evaluating Spiking Neural Network Models: A Comparative Performance Analysis
S. Sanaullah, S. Koravuna, U. Rückert, T. Jungeblut, in: n.d.
HSBI-PUB | DOI | Download (ext.)
 
[5]
2022 | Kurzbeitrag Konferenz | FH-PUB-ID: 2753
Real-Time Resource Efficient Simulator for SNNs-based Model Experimentation
S. Sanaullah, S. Koravuna, U. Rückert, T. Jungeblut, (2022).
HSBI-PUB | DOI
 
[4]
2022 | Konferenzbeitrag | FH-PUB-ID: 3480 | OA
Real-Time SNNs Model Analyzing and Visualizing Experimentation
S. Sanaullah, S. Koravuna, U. Ruckert, T. Jungeblut, in: Unpublished, 2022.
HSBI-PUB | DOI | Download (ext.)
 
[3]
2022 | Konferenzbeitrag | FH-PUB-ID: 2757
NireHApS: Neuro-Inspired and Resource-Efficient Hardware-Architectures for Plastic SNNs
S. Sanaullah, S. Koravuna, T. Jungeblut, U. Rückert, in: 2022.
HSBI-PUB | DOI
 
[2]
2022 | Konferenzbeitrag | FH-PUB-ID: 2276
SNNs Model Analyzing and Visualizing Experimentation Using RAVSim
S. Sanaullah, S. Koravuna, U. Rückert, T. Jungeblut, in: L. Iliadis, C. Jayne, A. Tefas, E. Pimenidis (Eds.), Engineering Applications of Neural Networks. 23rd International Conference, EAAAI/EANN 2022, Chersonissos, Crete, Greece, June 17–20, 2022, Proceedings, Springer International Publishing, Cham, 2022, pp. 40–51.
HSBI-PUB | DOI
 
[1]
2019 | Konferenzbeitrag | FH-PUB-ID: 2975
Accelerating the Threshold and Timing Analysis of Genetic Logic Circuit Models
S. Sanaullah, H. Baig, J.A. Lee, in: n.d.
HSBI-PUB | DOI | Download (ext.)
 
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