Tyndall National Institute - Conference Items
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Item Novel optical delay line overcomes bandwidth-delay limit for enhanced beamforming performance(Institute of Electrical and Electronics Engineers (IEEE), 2024-12-20) Waqas, Abi; Khani, Talha Kaim; Roycroft, Brendan; Corbett, Brian; Horizon 2020; Science Foundation IrelandWe propose a novel, reconfigurable delay line architecture that achieves a (2N-1) times greater tunability range compared to previously reported architectures. This design overcomes the bandwidth-delay product constraint and surpasses the tunability range by 67% and offers a 1.5x reduction in controllers and power consumption.Item Radio frequency generation using transfer-printed uni-traveling carrier photodiode for microwave photonics applications(Institute of Electrical and Electronics Engineers (IEEE), 2024-12-20) Mishra, Darpan; Atar, Fatih Bilge; Moynihan, Owen; Arafat, Yeasir; Waqas, Abi; O’Callaghan, James; Piwonski, Tomasz; Thomas, Kevin; Pelucchi, Emanuele; Corbett, Brian; Science Foundation Ireland; Horizon 2020We report RF signal generation up to 50 GHz by beating two lasers on an in-house fabricated transfer-printed uni-traveling carrier photodiode. A 10 dB improvement in the RF signal at 40 GHz is observed with -2V bias compared to zero-bias condition of the photodiode.Item Extended InGaAs photodiode integrated on SOI waveguide circuit for 2 µm waveband(Institute of Electrical and Electronics Engineers (IEEE), 2024-06-10) Arafat, Yeasir; Justice, John; Gocalińska, Agnieszka; Atar, Fatih; Russel, Eoin; Roycroft, Brendan; O’Faolain, Liam; Pelucchi, Emanuele; Gunning, Fatima; Corbett, BrianWe demonstrate micro transfer printing of 2 µm bandgap photodiodes onto an SOI circuit. The integrated photodiode has a dark current below 15 nA and responsivity-0.45 A/W at a reverse bias voltage of 2 V.Item Assessing latency cascades: Quantify time-to-respond dynamics in human-robot collaboration for speed and separation monitoring(Institute of Electrical and Electronics Engineers (IEEE), 2024-11-05) Rinaldi, Alessandra; Menolotto, Matteo; Kelly, David; Torres-Sanchez, Javier; O’Flynn, Brendan; Chiaberge, Marcello; Enterprise Ireland; Science Foundation Ireland; Politecnico di TorinoAdvancements in sensing technology and artificial intelligence have revolutionized industrial settings by introducing robots that work alongside humans, enhancing productivity and flexibility. However, ensuring safety in human-robot interactions has become more challenging. Established safety standards emphasize risk assessment, protective measures, and real-time monitoring systems, where safety complexities arise from intricate industrial interactions. The study focuses on “Speed and Separation Monitoring” (SSM), a collaborative type defined by ISO/TS 15066. The research addresses unknowns within SSM, particularly on the parameter accounting for the robot system to respond to the operator’s presence, crucial for decision-making on speed and separation limits. A proximity sensor was utilized to assess the overall delay of a classic industrial network between the sensing node for the operator detection (AI-based vision system) and the triggering of the safety node to the robot. The methodology was tested on a cohort of 23 subjects and evaluated under various lighting conditions. The study identified bottlenecks and the impact of each subsystem composing typical industrial control networks, highlighting the need for precise methodologies to assess latency as a critical factor in safety and productivity as sensing technology, collaborative robots and safety networks keep evolving.Item UAV trajectory optimization based on predicted user locations(Institute of Electrical and Electronics Engineers (IEEE), 2024-07-03) Ho, Lester; Jangsher, Sobia; Science Foundation IrelandUnmanned aerial vehicles (UAVs) can extend the coverage of wireless networks due to their high mobility and the favorable radio propagation characteristics. This paper studies the trajectory optimization of UAV that are acting as radio relays. The optimization is based on predicted user locations (UTO-PUL) to assist communication to the ground users who are unable to get coverage from the base station (BS). The existing work on trajectory design has considered several optimization approaches as well as reinforcement learning (RL) algorithms. All the algorithm takes into consideration the existing state of the network such as the channel conditions, initial positions and computes the destination of the UAV based on it. The proposed algorithm is designed to also consider the predicted user mobility of the future instances. The objective is to ensure the ground users are connected to the BS. The proposed UTO-PUL algorithm's performance is evaluated using simulations in a scenario with challenging terrain, where the proposed algorithm reduced the probability of users having no coverage by between 45% to 85% compared to non-predictive approaches, and achieved gains in median downlink signal power of 14 dB compared with a deep reinforcement learning (DRL) algorithm.