{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T18:25:54Z","timestamp":1791483954428,"version":"4.3.4"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031746420","type":"print"},{"value":"9783031746437","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-74643-7_5","type":"book-chapter","created":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T23:21:04Z","timestamp":1735687264000},"page":"51-60","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Neu-BrAuER: A\u00a0Neuromorphic Braille Letters Audio-Reader for\u00a0Commercial Edge Devices"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9175-2838","authenticated-orcid":false,"given":"Vittorio","family":"Fra","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4278-8348","authenticated-orcid":false,"given":"Andrea","family":"Pignata","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6341-8613","authenticated-orcid":false,"given":"Riccardo","family":"Pignari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9046-5618","authenticated-orcid":false,"given":"Enrico","family":"Macii","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2672-7593","authenticated-orcid":false,"given":"Gianvito","family":"Urgese","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,1]]},"reference":[{"key":"5_CR1","unstructured":"https:\/\/freesound.org\/people\/Vladimir_WN\/sounds\/581292\/"},{"key":"5_CR2","unstructured":"https:\/\/www.st.com\/resource\/en\/datasheet\/stm32mp157f.pdf"},{"key":"5_CR3","unstructured":"Alvaria (formerly Aspect Software): Evolution. https:\/\/evolution.voxeo.com\/library\/audio\/prompts\/alphabet\/index.jsp"},{"key":"5_CR4","doi-asserted-by":"publisher","unstructured":"Bach-y-Rita, P.: Tactile sensory substitution studies. Ann. New York Acad. Sci. 1013 (2004). https:\/\/doi.org\/10.1196\/annals.1305.006","DOI":"10.1196\/annals.1305.006"},{"key":"5_CR5","doi-asserted-by":"publisher","unstructured":"Chu, H., et al.: A neuromorphic processing system with spike-driven snn processor for wearable ecg classification. IEEE Trans. Biomed. Circ. Syst. 16 (2022). https:\/\/doi.org\/10.1109\/TBCAS.2022.3189364","DOI":"10.1109\/TBCAS.2022.3189364"},{"key":"5_CR6","doi-asserted-by":"publisher","unstructured":"Datta, G., Kundu, S., Beerel, P.A.: Training energy-efficient deep spiking neural networks with single-spike hybrid input encoding. In: International Joint Conference on Neural Networks (IJCNN) (2021). https:\/\/doi.org\/10.1109\/IJCNN52387.2021.9534306","DOI":"10.1109\/IJCNN52387.2021.9534306"},{"key":"5_CR7","doi-asserted-by":"publisher","unstructured":"Davies, M., et al.: Loihi: a neuromorphic manycore processor with on-chip learning. IEEE Micro 38 (2018). https:\/\/doi.org\/10.1109\/MM.2018.112130359","DOI":"10.1109\/MM.2018.112130359"},{"key":"5_CR8","doi-asserted-by":"publisher","unstructured":"DeWolf, T., Patel, K., Jaworski, P., Leontie, R., Hays, J., Eliasmith, C.: Neuromorphic control of a simulated 7-DOF arm using Loihi. Neuromorphic Comput. Eng. 3 (2023). https:\/\/doi.org\/10.1088\/2634-4386\/acb286","DOI":"10.1088\/2634-4386\/acb286"},{"key":"5_CR9","unstructured":"Eshraghian, J.K., et al.: Training spiking neural networks using lessons from deep learning (2021)"},{"key":"5_CR10","doi-asserted-by":"publisher","unstructured":"Forno, E., Fra, V., Pignari, R., Macii, E., Urgese, G.: Spike encoding techniques for IoT time-varying signals benchmarked on a neuromorphic classification task. Front. Neurosci. 16 (2022). https:\/\/doi.org\/10.3389\/fnins.2022.999029","DOI":"10.3389\/fnins.2022.999029"},{"key":"5_CR11","doi-asserted-by":"publisher","unstructured":"Fra, V., Forno, E., Pignari, R., Stewart, T.C., Macii, E., Urgese, G.: Human activity recognition: suitability of a neuromorphic approach for on-edge AIoT applications. Neuromorphic Comput. Eng. 2 (2022). https:\/\/doi.org\/10.1088\/2634-4386\/ac4c38","DOI":"10.1088\/2634-4386\/ac4c38"},{"key":"5_CR12","doi-asserted-by":"publisher","unstructured":"Frenkel, C., Indiveri, G.: ReckOn: a 28 nm Sub-mm2 task-agnostic spiking recurrent neural network processor enabling on-chip learning over second-long timescales. In: IEEE International Solid- State Circuits Conference (ISSCC) (2022). https:\/\/doi.org\/10.1109\/ISSCC42614.2022.9731734","DOI":"10.1109\/ISSCC42614.2022.9731734"},{"key":"5_CR13","doi-asserted-by":"publisher","unstructured":"Hou, K.M., Diao, X., Shi, H., Ding, H., Zhou, H., de\u00a0Vaulx, C.: Trends and challenges in AIoT\/IIoT\/IoT implementation. Sensors 23 (2023). https:\/\/doi.org\/10.3390\/s23115074","DOI":"10.3390\/s23115074"},{"key":"5_CR14","doi-asserted-by":"publisher","unstructured":"Izhikevich, E.M.: Which model to use for cortical spiking neurons? IEEE Trans. Neural Netw. 15 (2004). https:\/\/doi.org\/10.1109\/TNN.2004.832719","DOI":"10.1109\/TNN.2004.832719"},{"key":"5_CR15","doi-asserted-by":"publisher","unstructured":"Jamali, N., Maggiali, M., Giovannini, F., Metta, G., Natale, L.: A new design of a fingertip for the icub hand. In: IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS) (2015). https:\/\/doi.org\/10.1109\/IROS.2015.7353747","DOI":"10.1109\/IROS.2015.7353747"},{"key":"5_CR16","doi-asserted-by":"publisher","unstructured":"Maass, W.: Networks of spiking neurons: the third generation of neural network models. Neural Netw. 10 (1997). https:\/\/doi.org\/10.1016\/S0893-6080(97)00011-7","DOI":"10.1016\/S0893-6080(97)00011-7"},{"key":"5_CR17","unstructured":"Microsoft: Neural Network Intelligence (2021). https:\/\/github.com\/microsoft\/nni"},{"key":"5_CR18","doi-asserted-by":"publisher","unstructured":"Moradi, S., Qiao, N., Stefanini, F., Indiveri, G.: A scalable multicore architecture with heterogeneous memory structures for dynamic neuromorphic asynchronous processors (DYNAPs). IEEE Trans. Biomed. Circ. Syst. 12 (2018). https:\/\/doi.org\/10.1109\/TBCAS.2017.2759700","DOI":"10.1109\/TBCAS.2017.2759700"},{"key":"5_CR19","doi-asserted-by":"publisher","DOI":"10.5281\/zenodo.7050094","author":"SF M\u00fcller-Cleve","year":"2022","unstructured":"M\u00fcller-Cleve, S.F.: Tactile braille letters dataset (2022). https:\/\/doi.org\/10.5281\/zenodo.7050094","journal-title":"Tactile braille letters dataset"},{"key":"5_CR20","doi-asserted-by":"publisher","unstructured":"M\u00fcller-Cleve, S.F., et al.: Braille letter reading: a benchmark for spatio-temporal pattern recognition on neuromorphic hardware. Front. Neurosci. 16 (2022). https:\/\/doi.org\/10.3389\/fnins.2022.951164","DOI":"10.3389\/fnins.2022.951164"},{"key":"5_CR21","doi-asserted-by":"publisher","unstructured":"Orchard, G., et al.: Efficient neuromorphic signal processing with Loihi 2. In: IEEE Workshop on Signal Processing Systems (SiPS), vol. 2021 (2021). https:\/\/doi.org\/10.1109\/SiPS52927.2021.00053","DOI":"10.1109\/SiPS52927.2021.00053"},{"key":"5_CR22","doi-asserted-by":"publisher","unstructured":"Ou, W., Xiao, S., Zhu, C., Han, W., Zhang, Q.: An overview of brain-like computing: architecture, applications, and future trends. Front. Neurorob. 16 (2022). https:\/\/doi.org\/10.3389\/fnbot.2022.1041108","DOI":"10.3389\/fnbot.2022.1041108"},{"key":"5_CR23","doi-asserted-by":"publisher","unstructured":"Prescott, T.J., Diamond, M.E., Wing, A.M.: Active touch sensing. Phil. Trans. Roy. Soc. B: Biol. Sci. 366 (2011). https:\/\/doi.org\/10.1098\/rstb.2011.0167","DOI":"10.1098\/rstb.2011.0167"},{"key":"5_CR24","doi-asserted-by":"publisher","unstructured":"Putra, R.V.W., Shafique, M.: SpikeDyn: a framework for energy-efficient spiking neural networks with continual and unsupervised learning capabilities in dynamic environments. In: 58th ACM\/IEEE Design Automation Conference (DAC) (2021). https:\/\/doi.org\/10.1109\/DAC18074.2021.9586281","DOI":"10.1109\/DAC18074.2021.9586281"},{"key":"5_CR25","doi-asserted-by":"publisher","unstructured":"Stroobants, S., Dupeyroux, J., de\u00a0Croon, G.C.H.E.: Neuromorphic computing for attitude estimation onboard quadrotors. Neuromorphic Comput. Eng. 2 (2022). https:\/\/doi.org\/10.1088\/2634-4386\/ac7ee0","DOI":"10.1088\/2634-4386\/ac7ee0"},{"key":"5_CR26","doi-asserted-by":"publisher","unstructured":"Teeter, C., et al.: Generalized leaky integrate-and-fire models classify multiple neuron types. Nat. Commun. 9 (2018). https:\/\/doi.org\/10.1038\/s41467-017-02717-4","DOI":"10.1038\/s41467-017-02717-4"},{"key":"5_CR27","doi-asserted-by":"publisher","unstructured":"Tian, F., Yang, J., Zhao, S., Sawan, M.: NeuroCARE: a generic neuromorphic edge computing framework for healthcare applications. Front. Neurosci. 17 (2023). https:\/\/doi.org\/10.3389\/fnins.2023.1093865","DOI":"10.3389\/fnins.2023.1093865"},{"key":"5_CR28","doi-asserted-by":"publisher","unstructured":"Urgese, G., Rios-Navarro, A., Linares-Barranco, A., Stewart, T.C., Michmizos, K.: Editorial: powering the next-generation IoT applications: new tools and emerging technologies for the development of neuromorphic system of systems. Front. Neurosci. 17 (2023). https:\/\/doi.org\/10.3389\/fnins.2023.1197918","DOI":"10.3389\/fnins.2023.1197918"},{"key":"5_CR29","doi-asserted-by":"publisher","unstructured":"Vanarse, A., Osseiran, A., Rassau, A., van\u00a0der Made, P.: A hardware-deployable neuromorphic solution for encoding and classification of electronic nose data. Sensors 19 (2019). https:\/\/doi.org\/10.3390\/s19224831","DOI":"10.3390\/s19224831"},{"key":"5_CR30","doi-asserted-by":"publisher","unstructured":"Vitale, A., Donati, E., Germann, R., Magno, M.: Neuromorphic edge computing for biomedical applications: gesture classification using EMG signals. IEEE Sensors J. 22 (2022). https:\/\/doi.org\/10.1109\/JSEN.2022.3194678","DOI":"10.1109\/JSEN.2022.3194678"},{"key":"5_CR31","doi-asserted-by":"publisher","unstructured":"Vitale, A., Renner, A., Nauer, C., Scaramuzza, D., Sandamirskaya, Y.: Event-driven vision and control for UAVs on a neuromorphic chip. In: IEEE International Conference on Robotics and Automation (ICRA) (2021). https:\/\/doi.org\/10.1109\/ICRA48506.2021.9560881","DOI":"10.1109\/ICRA48506.2021.9560881"}],"container-title":["Communications in Computer and Information Science","Machine Learning and Principles and Practice of Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-74643-7_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T02:33:15Z","timestamp":1735698795000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-74643-7_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031746420","9783031746437"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-74643-7_5","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"1 January 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Turin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2023.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}