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1. Self-triggered MPC with Performance Guarantee for Tracking Piecewise Constant Reference Signals

   https://doi.org/10.23919/ECC54610.2021.9655178  

   Lu, Liang ; Limon, Daniel ; Kolmanovsky, Ilya ( June 2021 , Proceedings of European Control Conference, Doelen ICC Rotterdam, Netherlands, July 29-July 2, 2021)

   This paper considers a self-triggered MPC controller design strategy for tracking piecewise constant reference signals. The proposed triggering scheme is based on the relaxed dynamic programming inequality and the idea of reference governor; such a scheme computes both the updated control action and the next triggering time. The resulting self-triggered tracking MPC control law preserves stability and constraint satisfaction and also satisfies certain a priori chosen performance requirements without the need to impose stabilizing terminal conditions. An illustrative example shows the effectiveness of this self-triggered tracking MPC implementation. 

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2. RT-TRAQ: An algorithm for real-time tracking of faint quasi-periodic signals in noisy time series

   https://doi.org/10.1016/j.smhl.2023.100392  

   Joarder, Rishad ; Kasap, Begum ; Ghiasi, Soheil ( June 2023 , Smart Health)
3. The First Carrier Phase Tracking and Positioning Results With Starlink LEO Satellite Signals

   https://doi.org/10.1109/TAES.2021.3113880  

   Khalife, Joe ; Neinavaie, Mohammad ; Kassas, Zaher Zak ( April 2022 , IEEE Transactions on Aerospace and Electronic Systems)
4. The times, they are a-changin’: tracking shifts in mental health signals from early phase to later phase of the COVID-19 pandemic in Australia

   https://doi.org/10.1136/bmjgh-2021-007081  

   Wang, Siqin ; Huang, Xiao ; Hu, Tao ; Zhang, Mengxi ; Li, Zhenlong ; Ning, Huan ; Corcoran, Jonathan ; Khan, Asaduzzaman ; Liu, Yan ; Zhang, Jiajia ; et al ( January 2022 , BMJ Global Health)

   Introduction Widespread problems of psychological distress have been observed in many countries following the outbreak of COVID-19, including Australia. What is lacking from current scholarship is a national-scale assessment that tracks the shifts in mental health during the pandemic timeline and across geographic contexts. Methods Drawing on 244 406 geotagged tweets in Australia from 1 January 2020 to 31 May 2021, we employed machine learning and spatial mapping techniques to classify, measure and map changes in the Australian public’s mental health signals, and track their change across the different phases of the pandemic in eight Australian capital cities. Results Australians’ mental health signals, quantified by sentiment scores, have a shift from pessimistic (early pandemic) to optimistic (middle pandemic), reflected by a 174.1% (95% CI 154.8 to 194.5) increase in sentiment scores. However, the signals progressively recessed towards a more pessimistic outlook (later pandemic) with a decrease in sentiment scores by 48.8% (95% CI 34.7 to 64.9). Such changes in mental health signals vary across capital cities. Conclusion We set out a novel empirical framework using social media to systematically classify, measure, map and track the mental health of a nation. Our approach is designed in a manner that can readily be augmented into an ongoing monitoring capacity and extended to other nations. Tracking locales where people are displaying elevated levels of pessimistic mental health signals provide important information for the smart deployment of finite mental health services. This is especially critical in a time of crisis during which resources are stretched beyond normal bounds. 

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5. Orbit Modeling for Simultaneous Tracking and Navigation using LEO Satellite Signals

   https://doi.org/10.33012/2019.17029  

   Morales, Joshua J. ; Khalife, Joe ; Cruz, Ulices Santa ; Kassas, Zaher M. ( October 2019 , ION GNSS+, The International Technical Meeting of the Satellite Division of The Institute of Navigation)