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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. Revisiting Food Deserts in North Carolina, USA, Using a Cloud-Based Real-Time Quality Assurance/Quality Control (QA/QC) Tool [Revisiting Food Deserts in North Carolina, USA, Using a Cloud-Based Real-Time Quality Assurance/Quality Control (QA/QC) Tool]

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   Mulrooney, Timothy ; Gutierrez, Isabel ( January 2023 , SCITEPRESS - Science and Technology Publications)
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   Maddipatla, Satya Prasad ; Brennan, Sean ( May 2021 , 2021 American Control Conference (ACC))

   null (Ed.)
4. The largest real eigenvalue in the real Ginibre ensemble and its relation to the Zakharov–Shabat system

   https://doi.org/10.1214/19-AAP1509  

   Baik, Jinho ; Bothner, Thomas ( February 2020 , The Annals of Applied Probability)
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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