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1. Real-time MLton: A Standard ML runtime for real-time functional programs

   https://doi.org/10.1017/S0956796821000174  

   SHIVKUMAR, BHARGAV; MURPHY, JEFFREY; ZIAREK, LUKASZ (January 2021, Journal of Functional Programming)

   Abstract There is a growing interest in leveraging functional programming languages in real-time and embedded contexts. Functional languages are appealing as many are strictly typed, amenable to formal methods, have limited mutation, and have simple but powerful concurrency control mechanisms. Although there have been many recent proposals for specialized domain-specific languages for embedded and real-time systems, there has been relatively little progress on adapting more general purpose functional languages for programming embedded and real-time systems. In this paper, we present our current work on leveraging Standard ML (SML) in the embedded and real-time domains. Specifically, we detail our experiences in modifying MLton, a whole-program optimizing compiler for SML, for use in such contexts. We focus primarily on the language runtime, reworking the threading subsystem, object model, and garbage collector. We provide preliminary results over a radar-based aircraft collision detector ported to SML. 

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2. Real-Time LaCAM for Real-Time MAPF

   https://doi.org/10.1609/socs.v18i1.35993  

   Liang, Runzhe; Veerapaneni, Rishi; Harabor, Daniel; Li, Jiaoyang; Likhachev, Maxim (July 2025, Proceedings of the International Symposium on Combinatorial Search)

   The vast majority of Multi-Agent Path Finding (MAPF) methods with completeness guarantees require planning full-horizon paths. However, planning full-horizon paths can take too long and be impractical in real-world applications. Instead, real-time planning and execution, which only allows the planner a finite amount of time before executing and replanning, is more practical for real-world multi-agent systems. Several methods utilize real-time planning schemes but none are provably complete, which leads to livelock or deadlock. Our main contribution is Real-Time LaCAM, the first Real-Time MAPF method with provable completeness guarantees. We do this by leveraging LaCAM in an incremental fashion. Our results show how we can iteratively plan for congested environments with a cutoff time of milliseconds while still maintaining the same success rate as full-horizon LaCAM. We also show how it can be used with a single-step learned MAPF policy. 

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3. ARrow: A Real-Time AR Rowing Coach

   Iannucci, E.; Chen, Z.; Armeni, I.; Pollefeys, M.; Pfister, H.; Beyer, J. (January 2023, EuroVis - Short Papers)

   Rowing requires physical strength and endurance in athletes as well as a precise rowing technique. The ideal rowing stroke is based on biomechanical principles and typically takes years to master. Except for time-consuming video analysis after practice, coaches currently have no means to quantitatively analyze a rower’s stroke sequence and body movement. We propose ARrow, an AR application for coaches and athletes that provides real-time and situated feedback on a rower’s body position and stroke. We use computer vision techniques to extract the rower’s 3D skeleton and to detect the rower’s stroke cycle. ARrow provides visual feedback on three levels: Tracking of basic performance metrics over time, visual feedback and guidance on a rower’s stroke sequence, and a rowing ghost view that helps synchronize the body movement of two rowers. We developed ARrow in close collaboration with international rowing coaches and demonstrated its usefulness in a user study with athletes and coaches. 

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4. A Real Time RF Analog Signal Processor for Time Delay Estimation

   https://doi.org/10.1109/WAMICON53991.2022.9786168  

   Zhang, Hanxiang; Arigong, Bayaner (April 2022, 2022 IEEE 22nd Annual Wireless and Microwave Technology Conference (WAMICON))
5. Real-Time 3D Object Detection and Recognition using a Smartphone [Real-Time 3D Object Detection and Recognition using a Smartphone]

   https://doi.org/10.5220/0011060600003209  

   Chen, Jin; Zhu, Zhigang (January 2022, Proceedings of the 2nd International Conference on Image Processing and Vision Engineering - IMPROVE)

   Real-time detection of 3D obstacles and recognition of humans and other objects is essential for blind or low- vision people to travel not only safely and independently but also confidently and interactively, especially in a cluttered indoor environment. Most existing 3D obstacle detection techniques that are widely applied in robotic applications and outdoor environments often require high-end devices to ensure real-time performance. There is a strong need to develop a low-cost and highly efficient technique for 3D obstacle detection and object recognition in indoor environments. This paper proposes an integrated 3D obstacle detection system implemented on a smartphone, by utilizing deep-learning-based pre-trained 2D object detectors and ARKit- based point cloud data acquisition to predict and track the 3D positions of multiple objects (obstacles, humans, and other objects), and then provide alerts to users in real time. The system consists of four modules: 3D obstacle detection, 3D object tracking, 3D object matching, and information filtering. Preliminary tests in a small house setting indicated that this application could reliably detect large obstacles and their 3D positions and sizes in the real world and small obstacles’ positions, without any expensive devices besides an iPhone. 

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