Browsing by Author "Katumba, A."

Now showing 1 - 5 of 5
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    Analog radio-over-fiber transceivers based on III-V-on-silicon photonics
    (IEEE Photonics Technology Letters, 2018) Gasse, K. Van; Bogaert, L.; Breyne, L.; Kerrebrouck, J. Van; Dhoore, S.; Op de Beeck, C.; Katumba, A.; Wu, Y.; Li, H.; Morthier, G.; Bauwelinck, J.; Torfs, G.; Roelkens, G.
    Analog radio-over-fiber transceivers allow a substantial reduction in the complexity of the remote radio heads in the wireless network of the future. In this paper we discuss the building blocks for such a transceiver implemented on a silicon photonics platform, with the heterogeneous integration of III-V devices and the co-integration with electronics. Transmission experiments that demonstrate the viability of such integrated analog transceivers are described.
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    New Dimensions in Teaching Digital Electronics: A Multimode Laboratory Utilizing NI ELVIS IITM, LabVIEW and NI Multisim
    (International Journal of Online Engineering, 2010) Mwikirize, C.; Asiimwe, A.T.; Musasizi, P.I.; Tickodri- Togboa, S.S.; Katumba, A.; Butime, J.
    Over the years, conventional Laboratories in African Universities have been hampered by inadequate resources in terms of the required hardware, space and skilled personnel to administer them. This paper describes a multidimensional approach to experimentation, developed by the Makerere University iLabs Project Team, hereafter referred to as iLABS@MAK. The two dimensional approach involves both Virtual Labs and Online Laboratories designed to address laboratory deficiencies in Digital Electronics, encompassing five courses in the curricula of the Bachelor of Science (B.Sc) in Computer, Electrical and Telecommunication Engineering Programmes. A digital Online Laboratory, the Makerere University Digital iLab (MDEi) supporting experiments in the fields of combinational logic circuits and asynchronous sequential logic circuits has been developed. The laboratory utilises the National Instruments Educational Laboratory Virtual Instrumentation Suite (NI ELVIS II™) platform, the Laboratory Virtual Instrument Engineering Workbench (LabVIEW) graphical programming environment and NI Multisim. Typical experiment setups supported by the MDEi are presented.
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    PAM-4 and Duobinary Direct Modulation of a Hybrid InP/SOI DFB Laser for 40 Gb/s Transmission over 2 km Single Mode Fiber
    (In Optical Fiber Communication Conference, 2016) Abbasi, A.; Spatharakis, C.; Kanakis, G.; André, N. S.; Louchet, H.; Katumba, A.; Verbist, J.; Yin, X.; Bauwelinck, J.; Avramopoulos, H.; Roelkens, G.; Morthier, G.
    We demonstrate 40 Gb/s PAM-4 and Duobinary direct modulation of a heterogeneously integrated InP on SOI DFB laser. Transmission measurement was performed using a 2 km NZ-DSF with a PRBS 215 and 1.5 Vpp swing voltage.
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    Photonic Delay-based Reservoir Computing Integrated on InP Chip
    (Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference, 2019) Harkhoe, K.; Verschaffelt, G.; Katumba, A.; Bienstman, P.; Van der Sande, G.
    Delay-based reservoir computing (RC) offers a simple technological route to implement photonic neuromorphic computation. Its operation boils down to a time-multiplexing with the delay limiting the processing speed. As most optical setups end up to be bulky employing long fiber loops or free-space optics, the processing speeds are limited in the range of kSa/s to tens of MSa/s [1]. In this work, we focus on external cavities which are far shorter than what has been realized before in experiment. We present the results of an experimental validation of reservoir computing based on a semiconductor laser with a 10.8 cm delay line, both integrated on an active/passive InP photonic chip built on the Jeppix platform [3]. The single mode laser operates around 1550nm with a side mode suppression of larger than 20dB.
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    Photonic neuromorphic information processing and reservoir computing
    (APL Photonics, 2020) Lugnan, A.; Katumba, A.; Laporte, F.; Freiberger, M.; Sackesyn, S.; Ma, C.; Gooskens, E.; Dambre, J.; Bienstman, P.
    Photonic neuromorphic computing is attracting tremendous research interest now, catalyzed in no small part by the rise of deep learning in many applications. In this paper, we will review some of the exciting work that has been going in this area and then focus on one particular technology, namely, photonic reservoir computing.