PUBLIKATIONSSERVER

Sanaullah Sanaullah

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

29 Publikationen

Alle markieren

[29]
2025 | Artikel | FH-PUB-ID: 5366
Sanaullah S, Schneider A, Waßmuth J, Rückert U, Jungeblut T. RAVSim v2.0: Enhanced visualization and comparative analysis for neural network models. SoftwareX. 2025;29. doi:10.1016/j.softx.2024.102006
HSBI-PUB | DOI
 
[28]
2025 | Konferenzbeitrag | FH-PUB-ID: 5976
Sanaullah S, Honda H, Roy K, Schneider A, Waßmuth J, Jungeblut T. Automating Neural Model Selection in Spiking Neural Networks Using AutoML Techniques*. In: 2025 22nd International Learning and Technology Conference (L&T). IEEE; 2025:274-279. doi:10.1109/LT64002.2025.10941536
HSBI-PUB | DOI
 
[27]
2025 | Artikel | FH-PUB-ID: 6139
Attaullah H, Sanaullah S, Peters A, Ahmed QA, Baudisch J, Jungeblut T. DNA: detecting early signs of neurodegenerative diseases through activity and sleep analysis. Frontiers in Neuroscience. 2025;19. doi:10.3389/fnins.2025.1617758
HSBI-PUB | DOI
 
[26]
2025 | Dissertation | FH-PUB-ID: 6052
Sanaullah S. Design-Space Exploration of Biologically-Inspired SNN Models for Application-Specific Many-Core Systems. Universität Bielefeld; 2025. doi:10.4119/UNIBI/3003814
HSBI-PUB | DOI
 
[25]
2024 | Konferenzbeitrag | FH-PUB-ID: 4782
Sanaullah S, Koravuna S, Rückert U, Jungeblut T. A Spike Vision Approach for Multi-object Detection and Generating Dataset Using Multi-core Architecture on Edge Device. In: Iliadis L, Maglogiannis I, Papaleonidas A, Pimenidis E, Jayne C, eds. Engineering Applications of Neural Networks. 25th International Conference, EANN 2024, Corfu, Greece, June 27–30, 2024, Proceedings. Communications in Computer and Information Science. Cham: Springer Nature Switzerland; 2024:317-328. doi:10.1007/978-3-031-62495-7_24
HSBI-PUB | DOI
 
[24]
2024 | Konferenzbeitrag | FH-PUB-ID: 5367 | OA
Koravuna S, Sanaullah S, Jungeblut T, Rückert U. Spiking Neural Network Models Analysis on Field Programmable Gate Arrays. In: Pudaruth S, Ogudo K, eds. International Conference on Intelligent and Innovative Computing Applications. Vol 2024. Society of Information Technologists and Engineers Ltd; 2024:259-270. doi:10.59200/ICONIC.2024.027
HSBI-PUB | DOI | Download (ext.)
 
[23]
2024 | Konferenzbeitrag | FH-PUB-ID: 5315
Attaullah H, Sanaullah S, Jungeblut T. FL-DL: Fuzzy Logic with Deep Learning, Hybrid Anomaly Detection and Activity Prediction in Smart Homes Data-Sets. In: 2024 IEEE 24th International Symposium on Computational Intelligence and Informatics (CINTI). Budapest, Hungary: IEEE; 2024:43-48. doi:10.1109/CINTI63048.2024.10830825
HSBI-PUB | DOI
 
[22]
2024 | Konferenzbeitrag | FH-PUB-ID: 5098
Sanaullah S, Attaullah H, Jungeblut T. Encryption Techniques for Privacy-Preserving CNN Models: Performance and Practicality in Urban AI Applications. In: Omitaomu OA, Mostafavi A, Randhawa S, Niu H, eds. Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Urban-AI. New York, NY, USA: ACM; 2024:50-53. doi:10.1145/3681780.3697244
HSBI-PUB | DOI
 
[21]
2024 | Konferenzbeitrag | FH-PUB-ID: 5097
Sanaullah S, Koravuna S, Rückert U, Jungeblut T. Advancements in Neural Network Generations. In: Kuhl U, DataNinja.nrw, eds. Proceedings of the SAIOnARA Conference. DataNinja sAIOnARA Conference; 2024. doi:10.11576/DATANINJA-1167
HSBI-PUB | DOI
 
[20]
2024 | Konferenzbeitrag | FH-PUB-ID: 5096
Sanaullah S, Attaullah H, Jungeblut T. Trade-offs Between Privacy and Performance in Encrypted Dataset using Machine Learning Models. In: Kuhl U, DataNinja.nrw, eds. Proceedings of the SAIOnARA Conference. DataNinja sAIOnARA Conference; 2024. doi:10.11576/DATANINJA-1166
HSBI-PUB | DOI
 
[19]
2024 | Artikel | FH-PUB-ID: 4997 | OA
Attaullah H, Sanaullah S, Jungeblut T. Analyzing Machine Learning Models for Activity Recognition Using Homomorphically Encrypted Real-World Smart Home Datasets: A Case Study. Applied Sciences. 2024;14(19). doi:10.3390/app14199047
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.)
 
[18]
2024 | Kurzbeitrag Konferenz | FH-PUB-ID: 4883
Sanaullah S, Roy K, Ruckert U, Jungeblut T. Selection of Optimal Neural Model using Spiking Neural Network for Edge Computing  . In: IEEE, ed. 2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS). IEEE; 2024:1452-1453. doi:10.1109/ICDCS60910.2024.00147
HSBI-PUB | DOI | Inspire
 
[17]
2024 | Konferenzbeitrag | FH-PUB-ID: 4811
Sanaullah S, Attaullah H, Jungeblut T. The Next-Gen Interactive Runtime Simulator for Neural Network Programming. In: Söderberg E, Church L, eds. Companion Proceedings of the 8th International Conference on the Art, Science, and Engineering of Programming. New York, NY, USA: ACM; 2024:8-10. doi:10.1145/3660829.3660833
HSBI-PUB | DOI
 
[16]
2024 | Konferenzbeitrag | FH-PUB-ID: 4783 | OA
Sanaullah S, Roy K, Rückert U, Jungeblut T. A Hybrid Spiking-Convolutional Neural Network Approach for Advancing Machine Learning Models. In: Lutchyn T, Ramírez Rivera A, Ricaud B, eds. Proceedings of the 5th Northern Lights Deep Learning Conference. Vol 233. UiT The Arctic University, Tromsø, Norway: Proceedings of Machine Learning Research (PMLR); 2024. doi:10.48550/arXiv.2407.08861
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.) | WoS | arXiv
 
[15]
2023 | Buchbeitrag | FH-PUB-ID: 3222
Sanaullah S, Koravuna S, Rückert U, Jungeblut T. Streamlined Training of GCN for Node Classification with Automatic Loss Function and Optimizer Selection. In: Iliadis L, Maglogiannis I, Alonso S, Jayne C, Pimenidis E, eds. Engineering Applications of Neural Networks. 24th International Conference, EAAAI/EANN 2023, León, Spain, June 14–17, 2023, Proceedings. Communications in Computer and Information Science. Cham: Springer Nature Switzerland; 2023:191-202. doi:10.1007/978-3-031-34204-2_17
HSBI-PUB | DOI
 
[14]
2023 | Artikel | FH-PUB-ID: 3482 | OA
Sanaullah S, Koravuna S, Rückert U, Jungeblut T. Evaluation of Spiking Neural Nets-Based Image Classification Using the Runtime Simulator RAVSim. International Journal of Neural Systems. 2023. doi:10.1142/S0129065723500442
HSBI-PUB | DOI | Download (ext.)
 
[13]
2023 | Artikel | FH-PUB-ID: 3572 | OA
Sanaullah S, Koravuna S, Rückert U, Jungeblut T. Exploring spiking neural networks: a comprehensive analysis of mathematical models and applications. Frontiers in Computational Neuroscience. 2023;17. doi:10.3389/fncom.2023.1215824
HSBI-PUB | DOI | Download (ext.)
 
[12]
2023 | Artikel | FH-PUB-ID: 3665 | OA
Sanaullah S, Koravuna S, Ruckert U, Jungeblut T. Design-Space Exploration of SNN Models using Application-Specific Multi-Core Architectures. Preprint. 2023. doi:10.13140/RG.2.2.26328.88324
HSBI-PUB | DOI | Download (ext.)
 
[11]
2023 | Konferenzbeitrag | FH-PUB-ID: 3644 | OA
Sanaullah S, Koravuna S, Rückert U, Jungeblut T. Transforming Event-Based into Spike-Rate Datasets for Enhancing Neuronal Behavior Simulation to Bridging the Gap for SNNs. In: Paris France ; 2023. doi:10.13140/RG.2.2.14469.32485
HSBI-PUB | DOI | Download (ext.)
 
[10]
2023 | Konferenzbeitrag | FH-PUB-ID: 4207 | OA
Sanaullah S, Jungeblut T. Analysis of MR Images for Early and Accurate Detection of Brain Tumor using Resource Efficient Simulator Brain Analysis. In: New York USA; 2023. doi:10.5281/zenodo.10457930
HSBI-PUB | DOI | Download (ext.)
 
[9]
2023 | Kurzbeitrag Konferenz | FH-PUB-ID: 4206 | OA
Sanaullah S, Amanullah A, Roy K, Lee J-A, Chul-Jun S, Jungeblut T. A Hybrid Spiking-Convolutional Neural Network Approach for Advancing High-Quality Image Inpainting. In: ; 2023. doi:10.5281/zenodo.10458019
HSBI-PUB | DOI | Download (ext.)
 
[8]
2023 | Konferenzbeitrag | FH-PUB-ID: 4205 | OA
Sanaullah S, Koravuna S, Rückert U, Jungeblut T. A Novel Spike Vision Approach for Robust Multi-Object Detection using SNNs. In: ; 2023. doi:10.5281/zenodo.10262228
HSBI-PUB | DOI | Download (ext.)
 
[7]
2023 | Konferenzbeitrag | FH-PUB-ID: 4208
Koravuna S, Sanaullah S, Jungeblut T, Rückert U. Digit Recognition Using Spiking Neural Networks on FPGA. In: Rojas I, Joya G, Catala A, 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. Lecture Notes in Computer Science. Cham: Springer Nature Switzerland; 2023:406-417. doi:10.1007/978-3-031-43085-5_32
HSBI-PUB | DOI
 
[6]
2023 | Kurzbeitrag Konferenz | FH-PUB-ID: 2919 | OA
Sanaullah S, Koravuna S, Rückert U, Jungeblut T. Evaluating Spiking Neural Network Models: A Comparative Performance Analysis. doi:10.13140/RG.2.2.36122.82886
HSBI-PUB | DOI | Download (ext.)
 
[5]
2022 | Konferenzbeitrag | FH-PUB-ID: 2757
Sanaullah S, Koravuna S, Jungeblut T, Rückert U. NireHApS: Neuro-Inspired and Resource-Efficient Hardware-Architectures for Plastic SNNs. In: ; 2022. doi:10.13140/RG.2.2.16202.85444
HSBI-PUB | DOI
 
[4]
2022 | Kurzbeitrag Konferenz | FH-PUB-ID: 2753
Sanaullah S, Koravuna S, Rückert U, Jungeblut T. Real-Time Resource Efficient Simulator for SNNs-based Model Experimentation. 2022. doi:10.13140/RG.2.2.24761.85607
HSBI-PUB | DOI
 
[3]
2022 | Konferenzbeitrag | FH-PUB-ID: 3480 | OA
Sanaullah S, Koravuna S, Ruckert U, Jungeblut T. Real-Time SNNs Model Analyzing and Visualizing Experimentation. In: Unpublished; 2022. doi:10.13140/RG.2.2.14584.83201/1
HSBI-PUB | DOI | Download (ext.)
 
[2]
2022 | Konferenzbeitrag | FH-PUB-ID: 2276
Sanaullah S, Koravuna S, Rückert U, Jungeblut T. SNNs Model Analyzing and Visualizing Experimentation Using RAVSim. In: Iliadis L, Jayne C, Tefas A, Pimenidis E, eds. Engineering Applications of Neural Networks. 23rd International Conference, EAAAI/EANN 2022, Chersonissos, Crete, Greece, June 17–20, 2022, Proceedings. Communications in Computer and Information Science. Cham: Springer International Publishing; 2022:40-51. doi:10.1007/978-3-031-08223-8_4
HSBI-PUB | DOI
 
[1]
2019 | Konferenzbeitrag | FH-PUB-ID: 2975 | OA
Sanaullah S, Baig H, Lee JA. Accelerating the Threshold and Timing Analysis of Genetic Logic Circuit Models. doi:10.13140/RG.2.2.27989.50402
HSBI-PUB | DOI | Download (ext.)
 
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29 Publikationen

Alle markieren

[29]
2025 | Artikel | FH-PUB-ID: 5366
Sanaullah S, Schneider A, Waßmuth J, Rückert U, Jungeblut T. RAVSim v2.0: Enhanced visualization and comparative analysis for neural network models. SoftwareX. 2025;29. doi:10.1016/j.softx.2024.102006
HSBI-PUB | DOI
 
[28]
2025 | Konferenzbeitrag | FH-PUB-ID: 5976
Sanaullah S, Honda H, Roy K, Schneider A, Waßmuth J, Jungeblut T. Automating Neural Model Selection in Spiking Neural Networks Using AutoML Techniques*. In: 2025 22nd International Learning and Technology Conference (L&T). IEEE; 2025:274-279. doi:10.1109/LT64002.2025.10941536
HSBI-PUB | DOI
 
[27]
2025 | Artikel | FH-PUB-ID: 6139
Attaullah H, Sanaullah S, Peters A, Ahmed QA, Baudisch J, Jungeblut T. DNA: detecting early signs of neurodegenerative diseases through activity and sleep analysis. Frontiers in Neuroscience. 2025;19. doi:10.3389/fnins.2025.1617758
HSBI-PUB | DOI
 
[26]
2025 | Dissertation | FH-PUB-ID: 6052
Sanaullah S. Design-Space Exploration of Biologically-Inspired SNN Models for Application-Specific Many-Core Systems. Universität Bielefeld; 2025. doi:10.4119/UNIBI/3003814
HSBI-PUB | DOI
 
[25]
2024 | Konferenzbeitrag | FH-PUB-ID: 4782
Sanaullah S, Koravuna S, Rückert U, Jungeblut T. A Spike Vision Approach for Multi-object Detection and Generating Dataset Using Multi-core Architecture on Edge Device. In: Iliadis L, Maglogiannis I, Papaleonidas A, Pimenidis E, Jayne C, eds. Engineering Applications of Neural Networks. 25th International Conference, EANN 2024, Corfu, Greece, June 27–30, 2024, Proceedings. Communications in Computer and Information Science. Cham: Springer Nature Switzerland; 2024:317-328. doi:10.1007/978-3-031-62495-7_24
HSBI-PUB | DOI
 
[24]
2024 | Konferenzbeitrag | FH-PUB-ID: 5367 | OA
Koravuna S, Sanaullah S, Jungeblut T, Rückert U. Spiking Neural Network Models Analysis on Field Programmable Gate Arrays. In: Pudaruth S, Ogudo K, eds. International Conference on Intelligent and Innovative Computing Applications. Vol 2024. Society of Information Technologists and Engineers Ltd; 2024:259-270. doi:10.59200/ICONIC.2024.027
HSBI-PUB | DOI | Download (ext.)
 
[23]
2024 | Konferenzbeitrag | FH-PUB-ID: 5315
Attaullah H, Sanaullah S, Jungeblut T. FL-DL: Fuzzy Logic with Deep Learning, Hybrid Anomaly Detection and Activity Prediction in Smart Homes Data-Sets. In: 2024 IEEE 24th International Symposium on Computational Intelligence and Informatics (CINTI). Budapest, Hungary: IEEE; 2024:43-48. doi:10.1109/CINTI63048.2024.10830825
HSBI-PUB | DOI
 
[22]
2024 | Konferenzbeitrag | FH-PUB-ID: 5098
Sanaullah S, Attaullah H, Jungeblut T. Encryption Techniques for Privacy-Preserving CNN Models: Performance and Practicality in Urban AI Applications. In: Omitaomu OA, Mostafavi A, Randhawa S, Niu H, eds. Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Urban-AI. New York, NY, USA: ACM; 2024:50-53. doi:10.1145/3681780.3697244
HSBI-PUB | DOI
 
[21]
2024 | Konferenzbeitrag | FH-PUB-ID: 5097
Sanaullah S, Koravuna S, Rückert U, Jungeblut T. Advancements in Neural Network Generations. In: Kuhl U, DataNinja.nrw, eds. Proceedings of the SAIOnARA Conference. DataNinja sAIOnARA Conference; 2024. doi:10.11576/DATANINJA-1167
HSBI-PUB | DOI
 
[20]
2024 | Konferenzbeitrag | FH-PUB-ID: 5096
Sanaullah S, Attaullah H, Jungeblut T. Trade-offs Between Privacy and Performance in Encrypted Dataset using Machine Learning Models. In: Kuhl U, DataNinja.nrw, eds. Proceedings of the SAIOnARA Conference. DataNinja sAIOnARA Conference; 2024. doi:10.11576/DATANINJA-1166
HSBI-PUB | DOI
 
[19]
2024 | Artikel | FH-PUB-ID: 4997 | OA
Attaullah H, Sanaullah S, Jungeblut T. Analyzing Machine Learning Models for Activity Recognition Using Homomorphically Encrypted Real-World Smart Home Datasets: A Case Study. Applied Sciences. 2024;14(19). doi:10.3390/app14199047
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.)
 
[18]
2024 | Kurzbeitrag Konferenz | FH-PUB-ID: 4883
Sanaullah S, Roy K, Ruckert U, Jungeblut T. Selection of Optimal Neural Model using Spiking Neural Network for Edge Computing  . In: IEEE, ed. 2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS). IEEE; 2024:1452-1453. doi:10.1109/ICDCS60910.2024.00147
HSBI-PUB | DOI | Inspire
 
[17]
2024 | Konferenzbeitrag | FH-PUB-ID: 4811
Sanaullah S, Attaullah H, Jungeblut T. The Next-Gen Interactive Runtime Simulator for Neural Network Programming. In: Söderberg E, Church L, eds. Companion Proceedings of the 8th International Conference on the Art, Science, and Engineering of Programming. New York, NY, USA: ACM; 2024:8-10. doi:10.1145/3660829.3660833
HSBI-PUB | DOI
 
[16]
2024 | Konferenzbeitrag | FH-PUB-ID: 4783 | OA
Sanaullah S, Roy K, Rückert U, Jungeblut T. A Hybrid Spiking-Convolutional Neural Network Approach for Advancing Machine Learning Models. In: Lutchyn T, Ramírez Rivera A, Ricaud B, eds. Proceedings of the 5th Northern Lights Deep Learning Conference. Vol 233. UiT The Arctic University, Tromsø, Norway: Proceedings of Machine Learning Research (PMLR); 2024. doi:10.48550/arXiv.2407.08861
HSBI-PUB | Dateien verfügbar | DOI | Download (ext.) | WoS | arXiv
 
[15]
2023 | Buchbeitrag | FH-PUB-ID: 3222
Sanaullah S, Koravuna S, Rückert U, Jungeblut T. Streamlined Training of GCN for Node Classification with Automatic Loss Function and Optimizer Selection. In: Iliadis L, Maglogiannis I, Alonso S, Jayne C, Pimenidis E, eds. Engineering Applications of Neural Networks. 24th International Conference, EAAAI/EANN 2023, León, Spain, June 14–17, 2023, Proceedings. Communications in Computer and Information Science. Cham: Springer Nature Switzerland; 2023:191-202. doi:10.1007/978-3-031-34204-2_17
HSBI-PUB | DOI
 
[14]
2023 | Artikel | FH-PUB-ID: 3482 | OA
Sanaullah S, Koravuna S, Rückert U, Jungeblut T. Evaluation of Spiking Neural Nets-Based Image Classification Using the Runtime Simulator RAVSim. International Journal of Neural Systems. 2023. doi:10.1142/S0129065723500442
HSBI-PUB | DOI | Download (ext.)
 
[13]
2023 | Artikel | FH-PUB-ID: 3572 | OA
Sanaullah S, Koravuna S, Rückert U, Jungeblut T. Exploring spiking neural networks: a comprehensive analysis of mathematical models and applications. Frontiers in Computational Neuroscience. 2023;17. doi:10.3389/fncom.2023.1215824
HSBI-PUB | DOI | Download (ext.)
 
[12]
2023 | Artikel | FH-PUB-ID: 3665 | OA
Sanaullah S, Koravuna S, Ruckert U, Jungeblut T. Design-Space Exploration of SNN Models using Application-Specific Multi-Core Architectures. Preprint. 2023. doi:10.13140/RG.2.2.26328.88324
HSBI-PUB | DOI | Download (ext.)
 
[11]
2023 | Konferenzbeitrag | FH-PUB-ID: 3644 | OA
Sanaullah S, Koravuna S, Rückert U, Jungeblut T. Transforming Event-Based into Spike-Rate Datasets for Enhancing Neuronal Behavior Simulation to Bridging the Gap for SNNs. In: Paris France ; 2023. doi:10.13140/RG.2.2.14469.32485
HSBI-PUB | DOI | Download (ext.)
 
[10]
2023 | Konferenzbeitrag | FH-PUB-ID: 4207 | OA
Sanaullah S, Jungeblut T. Analysis of MR Images for Early and Accurate Detection of Brain Tumor using Resource Efficient Simulator Brain Analysis. In: New York USA; 2023. doi:10.5281/zenodo.10457930
HSBI-PUB | DOI | Download (ext.)
 
[9]
2023 | Kurzbeitrag Konferenz | FH-PUB-ID: 4206 | OA
Sanaullah S, Amanullah A, Roy K, Lee J-A, Chul-Jun S, Jungeblut T. A Hybrid Spiking-Convolutional Neural Network Approach for Advancing High-Quality Image Inpainting. In: ; 2023. doi:10.5281/zenodo.10458019
HSBI-PUB | DOI | Download (ext.)
 
[8]
2023 | Konferenzbeitrag | FH-PUB-ID: 4205 | OA
Sanaullah S, Koravuna S, Rückert U, Jungeblut T. A Novel Spike Vision Approach for Robust Multi-Object Detection using SNNs. In: ; 2023. doi:10.5281/zenodo.10262228
HSBI-PUB | DOI | Download (ext.)
 
[7]
2023 | Konferenzbeitrag | FH-PUB-ID: 4208
Koravuna S, Sanaullah S, Jungeblut T, Rückert U. Digit Recognition Using Spiking Neural Networks on FPGA. In: Rojas I, Joya G, Catala A, 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. Lecture Notes in Computer Science. Cham: Springer Nature Switzerland; 2023:406-417. doi:10.1007/978-3-031-43085-5_32
HSBI-PUB | DOI
 
[6]
2023 | Kurzbeitrag Konferenz | FH-PUB-ID: 2919 | OA
Sanaullah S, Koravuna S, Rückert U, Jungeblut T. Evaluating Spiking Neural Network Models: A Comparative Performance Analysis. doi:10.13140/RG.2.2.36122.82886
HSBI-PUB | DOI | Download (ext.)
 
[5]
2022 | Konferenzbeitrag | FH-PUB-ID: 2757
Sanaullah S, Koravuna S, Jungeblut T, Rückert U. NireHApS: Neuro-Inspired and Resource-Efficient Hardware-Architectures for Plastic SNNs. In: ; 2022. doi:10.13140/RG.2.2.16202.85444
HSBI-PUB | DOI
 
[4]
2022 | Kurzbeitrag Konferenz | FH-PUB-ID: 2753
Sanaullah S, Koravuna S, Rückert U, Jungeblut T. Real-Time Resource Efficient Simulator for SNNs-based Model Experimentation. 2022. doi:10.13140/RG.2.2.24761.85607
HSBI-PUB | DOI
 
[3]
2022 | Konferenzbeitrag | FH-PUB-ID: 3480 | OA
Sanaullah S, Koravuna S, Ruckert U, Jungeblut T. Real-Time SNNs Model Analyzing and Visualizing Experimentation. In: Unpublished; 2022. doi:10.13140/RG.2.2.14584.83201/1
HSBI-PUB | DOI | Download (ext.)
 
[2]
2022 | Konferenzbeitrag | FH-PUB-ID: 2276
Sanaullah S, Koravuna S, Rückert U, Jungeblut T. SNNs Model Analyzing and Visualizing Experimentation Using RAVSim. In: Iliadis L, Jayne C, Tefas A, Pimenidis E, eds. Engineering Applications of Neural Networks. 23rd International Conference, EAAAI/EANN 2022, Chersonissos, Crete, Greece, June 17–20, 2022, Proceedings. Communications in Computer and Information Science. Cham: Springer International Publishing; 2022:40-51. doi:10.1007/978-3-031-08223-8_4
HSBI-PUB | DOI
 
[1]
2019 | Konferenzbeitrag | FH-PUB-ID: 2975 | OA
Sanaullah S, Baig H, Lee JA. Accelerating the Threshold and Timing Analysis of Genetic Logic Circuit Models. doi:10.13140/RG.2.2.27989.50402
HSBI-PUB | DOI | Download (ext.)
 
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