• Reduction of threading dislocation density in top-down fabricated GaN nanocolumns via their lateral overgrowth by MOCVD 

  Zubialevich, Vitaly Z.; McLaren, Mathew; Pampili, Pietro; Shen, John; Arredondo-Arechavala, Miryam; Parbrook, Peter J. (AIP Publishing, 2020-01-13)

  Reduction of threading dislocation density in top-down fabricated GaN nanocolumns (NCs) via their successive lateral shrinkage by anisotropic wet etch and lateral overgrowth by metalorganic chemical vapor deposition is ...
• A dual-ISM-band antenna of small size using a spiral structure with parasitic element 

  Buckley, John; McCarthy, Kevin G.; Loizou, Loizos; O'Flynn, Brendan; O'Mathuna, Cian (Institute of Electrical and Electronics Engineers (IEEE), 2015-08-06)

  This letter presents a compact, single-feed, dual-band antenna covering both the 433-MHz and 2.45-GHz Industrial Scientific and Medical (ISM) bands. The antenna has small dimensions of 51 ×28 mm². A square-spiral resonant ...
• Exploring the potential of c-plane indium gallium nitride quantum dots for twin-photon emission 

  Patra, Saroj Kanta; Schulz, Stefan (American Chemical Society, 2019-11-25)

  Nonclassical light emission, such as entangled and single-photon emission, has attracted significant interest because of its importance in future quantum technology applications. In this work, we study the potential of ...
• High dynamic range photo-detection module using on-chip dual avalanche photodiodes 

  Deng, Shijie; Morrison, Alan P.; Liu, Houquan; Deng, Hongcang; Chen, Ming; Yuan, Libo; Ten, Chuanxin (Institute of Electrical and Electronics Engineers (IEEE), 2019-11-01)

  In this work, a high dynamic range APD-based photo-detection module is designed and developed. In the design, on-chip dual avalanche photodiodes (APDs) are fabricated with one biased to work in linear mode and the other ...
• Neonatal seizure detection from raw multi-channel EEG using a fully convolutional architecture 

  O'Shea, Alison; Lightbody, Gordon; Boylan, Geraldine; Temko, Andriy (Elsevier Ltd., 2019-11-30)

  A deep learning classifier for detecting seizures in neonates is proposed. This architecture is designed to detect seizure events from raw electroencephalogram (EEG) signals as opposed to the state-of-the-art hand engineered ...

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