Sussex Research Online: No conditions. Results ordered -Date Deposited. http://sro.sussex.ac.uk/ http://sro.sussex.ac.uk/images/sitelogo.png Sussex Research Online: No conditions. Results ordered -Date Deposited. http://sro.sussex.ac.uk/ Sun, 12 Nov 2023 09:53:31 +0000 Sun, 12 Nov 2023 09:53:31 +0000 en Thu, 30 Nov 2017 06:59:04 +0000 Using GPU acceleration and a novel artificial neural networks approach for ultra-fast fluorescence lifetime imaging microscopy analysis http://sro.sussex.ac.uk/id/eprint/71657/ http://sro.sussex.ac.uk/id/eprint/71657/ Wu, Gang (2017) Using GPU acceleration and a novel artificial neural networks approach for ultra-fast fluorescence lifetime imaging microscopy analysis. Doctoral thesis (PhD), University of Sussex. Mon, 20 Jun 2016 12:28:48 +0100 Artificial neural network approaches for fluorescence lifetime imaging techniques http://sro.sussex.ac.uk/id/eprint/61611/ http://sro.sussex.ac.uk/id/eprint/61611/ Wu, Gang, Nowotny, Thomas, Zhang, Yongliang, Yu, Hong-Qi and Li, David Day-Uei (2016) Artificial neural network approaches for fluorescence lifetime imaging techniques. Optics Letters, 41 (11). pp. 2561-2564. ISSN 0146-9592 Mon, 23 Nov 2015 09:01:12 +0000 GPU acceleration of time-domain fluorescence lifetime imaging http://sro.sussex.ac.uk/id/eprint/58063/ http://sro.sussex.ac.uk/id/eprint/58063/ Wu, Gang, Nowotny, Thomas, Chen, Yu and Li, David Day-Uei (2016) GPU acceleration of time-domain fluorescence lifetime imaging. Journal of Biomedical Optics, 21 (1). 017001. ISSN 1083-3668 Mon, 06 Oct 2014 09:53:57 +0100 GPU enhanced fluorescence lifetime imaging processors with non-iterative and iterative algorithms http://sro.sussex.ac.uk/id/eprint/50484/ http://sro.sussex.ac.uk/id/eprint/50484/ Wu, Gang, Nowotny, Thomas and Li, David (2014) GPU enhanced fluorescence lifetime imaging processors with non-iterative and iterative algorithms. In: The Microscience Microscopy Congress 2014 (MMC2014), 30 June - 3 July, 2014, Manchester, UK.