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In-Materio Extreme Learning Machines (2022)
Book Chapter
Jones, B. A., Al Moubayed, N., Zeze, D. A., & Groves, C. (2022). In-Materio Extreme Learning Machines. In G. Rudolph, A. V. Kononova, H. Aguirre, P. Kerschke, G. Ochoa, & T. Tušar (Eds.), Parallel Problem Solving from Nature – PPSN XVII (505-519). Springer Verlag. https://doi.org/10.1007/978-3-031-14714-2_35

Nanomaterial networks have been presented as a building block for unconventional in-Materio processors. Evolution in-Materio (EiM) has previously presented a way to congure and exploit physical materials for computation, but their ability to scale as... Read More about In-Materio Extreme Learning Machines.

The seven liberal arts: Commentary and Analysis (2019)
Book Chapter
Thomson, D., & Tanner, B. (2019). The seven liberal arts: Commentary and Analysis. In G. Gasper, C. Panti, T. McLeish, & H. Smithson (Eds.), Knowing and speaking: Robert Grosseteste's De artibus liberalibus 'On the liberal arts' and De generatione sonorum 'On the generation of sounds' (448-482). Oxford University Press

Solving Binary Classification Problems with Carbon Nanotube / Liquid Crystal Composites and Evolutionary Algorithms (2017)
Book Chapter
Vissol-Gaudin, E., Kotsialos, A., Massey, M. K., Groves, C., Pearson, C., Zeze, D. A., & Petty, M. C. (2017). Solving Binary Classification Problems with Carbon Nanotube / Liquid Crystal Composites and Evolutionary Algorithms. In 2017 IEEE Congress on Evolutionary Computation (CEC) : 5-8 June 2017, Donostia-San Sebastián, Spain ; proceedings (1924-1931). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/cec.2017.7969536

This paper presents a series of experiments demonstrating the capacity of single-walled carbon-nanotube (SWCNT)/liquid crystal (LC) mixtures to be trained by evolutionary algorithms to act as classifiers on linear and nonlinear binary datasets. The t... Read More about Solving Binary Classification Problems with Carbon Nanotube / Liquid Crystal Composites and Evolutionary Algorithms.

Organic Floating-Gate Memory Structures (2017)
Book Chapter
Fakher, S., Sleiman, A., Ayesh, A., Al-Ghaferi, A., Petty, M., Zeze, D., & Mabrook, M. (2017). Organic Floating-Gate Memory Structures. In P. Dimitrakis (Ed.), Emerging Materials and Structures (123-156). Springer Verlag

Molecular Electronics (2017)
Book Chapter
Petty, M. C., Nagase, T., Suzuki, H., & Naito, H. (2017). Molecular Electronics. In S. Kasap, & P. Capper (Eds.), Springer Handbook of Electronic and Photonic Materials (1257-1279). Springer Verlag. https://doi.org/10.1007/978-3-319-48933-9_51

The prospects of using organic materials in electronics and optoelectronics applications have attracted scientists and technologists since the 1970s. This field has become known as molecular electronics. Some successes have already been achieved, for... Read More about Molecular Electronics.

X-ray Scattering from Spintronic Structures (2016)
Book Chapter
Tanner, B. (2016). X-ray Scattering from Spintronic Structures. In Y. Xu, D. D. Awschalom, & J. Nitta (Eds.), Handbook of Spintronics (919-945). Springer Netherlands. https://doi.org/10.1007/978-94-007-6892-5_33

The principles and underlying physics of grazing-incidence X-ray scattering are outlined in the context of application to the study of room temperature spintronic systems. Examples are presented showing the precision and reliability of analysis. The... Read More about X-ray Scattering from Spintronic Structures.

CNT-based two terminal organic nonvolatile memory devices. (2015)
Book Chapter
Sleiman, A., Mabrook, M., Sayers, P., & Zeze, D. (2015). CNT-based two terminal organic nonvolatile memory devices. In S. Logothetidis (Ed.), Handbook of Flexible Organic Electronics: Materials, Manufacturing and Applications (413-428). Woodhead Publishing Series