- H.A. Gonzalez, et al. “A Low-footprint FFT Accelerator for a RISC-V-based Multi-core DSP in FMCW Radars”. IEEE International Symposium on Circuits and Systems (ISCAS) 2024.
- Wang, L. Riemenschneider, C. Wang, L. Panes-Ruiz, M. Hantusch, Y. Chen, J. Zhang, S. Singh, Y. Vaynzof, M. Löffler, S. Huang, and G. Cuniberti. “Zn-Nx Coordination Bonds Modulation on N-doped Graphitic Carbon Derived from Metal-Organic Framework Towards Gas Sensing.” Angewandte Chemie, 2024.
- 3. W. Wanga, M. Al Aitia, B. Ibarluceaa , C. Huang, R. Dong, S. Huang, G. Cuniberti. “Multi-metallic MOFs based Composites for Environmental Applications: Merging Metals Centers, Mixing Metals Interaction.” Nanoscale Horizon, 2024.
- 4. S. Huang, G. Cuniberti. “Low-dimensional Nanomaterials-based Smart Gas Sensors for Odor Identification.” In 2024 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN). IEEE, 2024, May, Dallas, US.
- 5. S. Huang, B. Ibarlucea, G. Cuniberti. “Discrimination of Methanol from Ethanol Using Graphene-based Smart Gas Sensors.” In 2024 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN). IEEE, 2024, May, Dallas, US.
- H. A. Gonzalez, et al. “SpiNNaker2: A large-scale Neuromorphic System for Event-based and Asynchronous Machine Learning.” Workshop on Machine Learning with New Compute Paradigms at NeurIPS 2023.
- S. Huang, B. Ibarlucea, L. Antonio Panes-Ruiz, and G. Cuniberti. “Rapid Detection of SARS-CoV-2 Antigen Utilizing Machine Learning-Enabled Graphene-Based Smart Gas Sensors.” In 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE).
- S. Huang, L. Riemenschneider, L. Antonio Panes-Ruiz, B. Ibarlucea, and G. Cuniberti. “Discrimination of Complex Mixtures Using Carbon Nanotubes-based Multichannel Electronic Nose: Coffee Aromas.” In 2023 IEEE Nanotechnology Materials and Devices Conference (NMDC).
- F. M. Schüffny S.M.A. Zeinolabedin, R. George, L. Guo, A. Weiße, J. Uhlig, J. Meyer, A. Dixius, S. Hänzsche, M. Berthel, S. Scholze, S. Höppner, C. Mayr, “A 64-channel back-gate adapted ultra-low-voltage spike-aware neural recording front-end with on-chip lossless/near-lossless compression engine and 3.3V stimulator in 22nm FDSOI,” IEEE A-SSCC 2022.
- H. A. Gonzalez, B. Vogginger, C. Liu, M. Stolba, F. Kelber, H. Bauer, S. Hänzsche, S. Scholze, M. Berthel, T. Rosmeisl, L. Guo, D. Walter, P. Das, K. K. Nazeer, T. Schubert, S. Höppner, C. Mayr , “A 12-ADC 25-Core Smart MPSoC Using ABB in 22FDX for 77GHz MIMO Radars at 52.6mW Average Power,” IEEE CICC 2023, San Antonio, Texas.
- H. A. Gonzalez, C. Liu, B. Vogginger, S. Höppner, C. Mayr, “Cognitive FMCW radar to enhance velocity disambiguation in MIMO systems,” RADAR 2022.
- B. Vogginger, F. kreutz, J. Lopez-Randulfe, C. Liu, R. Dietrich, H. A. Gonzalez, D. Scholz, N. Reeb, D. Auge, J. Hille, M. Arsalan, F. Mirus, C. Grassmann, A. Knoll, C. Mayr, “Automotive radar processing with spiking neural networks: Concepts and challenges,” Frontiers in Neuroscience 2022.
- F. M. Schüffny, S. Höppner, S. Hänzsche, R. M. George, S. M. Ali Zeinolabedin, C. Mayr, “Power Minimisation in Neural Recording Σ Modulators by Adaptive Back-Gate Voltage Tuning,” 2023 IEEE SSC-L.
- F. M. Schüffny, S. Hänzsche, S. Henker, S. M. Ali Zeinolabedin, S. Scholze, S. Höppner, R. M. George, C. Mayr, “A 3.3V Saturation-Aware Neurostimulator with Reset Functionality in 22nm FDSOI,” 2023 IEEE Interregional NEWCAS Conference.
- L. Guo, S. M. Ali Zeinolabedin, F. M. Schüffny, A. Weiße, S. Scholze, R. George, J. Partzsch, C. Mayr, “A 16-channel Real-time Adaptive Neural Signal Compression Engine in 22nm FDSOI,” 2023 IEEE Interregional NEWCAS Conference.
- Hector A. Gonzalez and Jiaxin Huang and Florian Kelber and Khaleelulla Khan Nazeer and Tim Langer and Chen Liu and Matthias Lohrmann and Amirhossein Rostami and Mark Schöne and Bernhard Vogginger and Timo C. Wunderlich and Yexin Yan and Mahmoud Akl and Christian Mayr, “SpiNNaker2: A Large-Scale Neuromorphic System for Event-Based and Asynchronous Machine Learning.”
- Guo, Liyuan and Jobst, Matthias and Partzsch, Johannes and Scholze, Stefan and Dixius, Andreas and Lohrmann, Matthias and Zeinolabedin, Seyed Mohammad Ali and Mayr, Christian, “A Low-Power Hardware Accelerator of MFCC Extraction for Keyword Spotting in 22nm FDSOI,” 2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS), Hangzhou, China, 2023.
- Yildirim, Ertürk Enver, Luis Antonio Panes-Ruiz, Pratyaksh Yemulwar, Ebru Cihan, Bergoi Ibarlucea, and Gianaurelio Cuniberti. “Carbon nanotube neurotransistors with ambipolar memory and learning functions.” MRS Bulletin 2023.
- S. Huang, A. Croy, A. L. Bierling, V. Khavrus, L. A. Panes-Ruiz, A. Dianat, B Ibarlucea, G. Cuniberti, “Machine Learning-enabled Graphene-based Electronic Olfaction Sensors and Their Olfactory Performance Assessment,” Applied Physics Reviews 2023.
- S. Huang, A. Croy, B. Ibarlucea, G. Cuniberti, “Odor discrimination and identification by graphene-based electronic nose system,” APS March Meeting 2023.
- Y. Li, S. Huang, S. Peng, H. Jia, J. Pang, B. Ibarlucea, C. Hou, Y. Cao, W. Zhou, H. Liu, G. Cuniberti, “Toward smart sensing by MXene,” Small 2023.
- Ibarlucea, Bergoi, Ertürk Enver Yildirim, Ronald Tetzlaff, Alon Ascoli, Luis-Antonio Panes-Ruiz, and Gianaurelio Cuniberti. “Nanoscale Mem-Devices for Chemical Sensing.” In 2023 30th IEEE International Conference on Electronics, Circuits and Systems (ICECS), pp. 1-5. IEEE, 2023.
- L. A. Panes-Ruiz, S. Huang, L. Riemenschneider, A. Croy, B. Ibarlucea, G. Cuniberti, “Surface-Functionalized Multichannel Nanosensors and Machine Learning Analysis for Improved Sensitivity and Selectivity in Gas Sensing Applications,” Advances in System-Integrated Intelligence. SYSINT 2022.
- S. Huang, A. Croy, L. A. Panes-Ruiz, V. Khavrus, V. Bezugly, B. Ibarlucea, G. Cuniberti, “Machine Learning-Enabled Smart Gas Sensing Platform for Identification of Industrial Gases,” Adv. Intell. Syst. 2022.
- S. Huang; A. Croy, A. Bierling, L. A. Panes-Ruiz; B. Ibarlucea, G. Cuniberti, “Machine Learning-enabled Biomimetic Electronic Olfaction Using Graphene Single-channel Sensors,” 2022 IEEE ISOEN.
- Aßmann, Uwe, Mikhail Belov, Thanh-Tien Tenh Cong, Waltenegus Dargie, Jianjun Wen, Leon Urbas, Candy Lohse et al. “Sniffbots to the rescue–fog services for a gas-sniffing immersive robot collective.” In European Conference on Service-Oriented and Cloud Computing.
- S. Huang, A. Croy, L. A. Panes-Ruiz, V. Khavrus, B. Ibarlucea, G. Cuniberti, “Industrial Gases Identification Using Graphene-based Gas Sensors: NH3 and PH3 as an Example,” 2022 IEEE ISOEN.
- S.Huang., Croy, A., Ibarlucea, B., & Cuniberti, G. (2023). Machine Learning-Driven Gas Identification in Gas Sensors. In Machine Learning for Advanced Functional Materials (pp. 21-41). Singapore: Springer Nature Singapore.
- Parichenko, A., S. Huang., Pang, J., Ibarlucea, B., & Cuniberti, G. (2023). Recent advances in technologies toward the development of 2D materials-based electronic noses. TrAC Trends in Analytical Chemistry, 117185.
