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1. Novel Physics-Based Machine-Learning Models for Indoor Air Quality Approximations

   Mohammadshirazi, Ahmad; Nadafian, Aida; Monsefi, Amin Karimi; Rafiei, Mohammad; Ramnath, Rajiv (August 2023, https://doi.org/10.48550/arXiv.2308.01438)

   Cost-effective sensors are capable of real-time capturing a variety of air quality-related modalities from different pollutant concentrations to indoor/outdoor humidity and temperature. Machine learning (ML) models are capable of performing air-quality "ahead-of-time" approximations. Undoubtedly, accurate indoor air quality approximation significantly helps provide a healthy indoor environment, optimize associated energy consumption, and offer human comfort. However, it is crucial to design an ML architecture to capture the domain knowledge, so-called problem physics. In this study, we propose six novel physics-based ML models for accurate indoor pollutant concentration approximations. The proposed models include an adroit combination of state-space concepts in physics, Gated Recurrent Units, and Decomposition techniques. The proposed models were illustrated using data collected from five offices in a commercial building in California. The proposed models are shown to be less complex, computationally more efficient, and more accurate than similar state-of-the-art transformer-based models. The superiority of the proposed models is due to their relatively light architecture (computational efficiency) and, more importantly, their ability to capture the underlying highly nonlinear patterns embedded in the often contaminated sensor-collected indoor air quality temporal data. 

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2. Simulating unmanned aerial vehicle swarms with the UB-ANC Emulator

   https://doi.org/10.1177/1756829319837668  

   Modares, Jalil; Mastronarde, Nicholas; Dantu, Karthik (April 2019, International Journal of Micro Air Vehicles)

   Recent advances in multi-rotor vehicle control and miniaturization of hardware, sensing, and battery technologies have enabled cheap, practical design of micro air vehicles for civilian and hobby applications. In parallel, several applications are being envisioned that bring together a swarm of multiple networked micro air vehicles to accomplish large tasks in coordination. However, it is still very challenging to deploy multiple micro air vehicles concurrently. To address this challenge, we have developed an open software/hardware platform called the University at Buffalo’s Airborne Networking and Communications Testbed (UB-ANC), and an associated emulation framework called the UB-ANC Emulator. In this paper, we present the UB-ANC Emulator, which combines multi-micro air vehicle planning and control with high-fidelity network simulation, enables practitioners to design micro air vehicle swarm applications in software and provides seamless transition to deployment on actual hardware. We demonstrate the UB-ANC Emulator’s accuracy against experimental data collected in two mission scenarios: a simple mission with three networked micro air vehicles and a sophisticated coverage path planning mission with a single micro air vehicle. To accurately reflect the performance of a micro air vehicle swarm where communication links are subject to interference and packet losses, and protocols at the data link, network, and transport layers affect network throughput, latency, and reliability, we integrate the open-source discrete-event network simulator ns-3 into the UB-ANC Emulator. We demonstrate through node-to-node and end-to-end measurements how the UB-ANC Emulator can be used to simulate multiple networked micro air vehicles with accurate modeling of mobility, control, wireless channel characteristics, and network protocols defined in ns-3. 

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3. A Personal Visual Comfort Model: Predict Individual’s Visual Comfort Using Occupant Eye pupil Size and Machine Learning

   https://doi.org/10.1088/1757-899X/609/4/042097  

   Cen, Lingkai; Choi, Joon-Ho; Yao, Xiaomeng; Gil, Yolanda; Narayanan, Shrikanth; Pentz, Maryann (September 2019, IOP conference series)

   Lighting, as a significant component of indoor environment quality, was found to be a primary contributor to deficient indoor environments in today’s workplace. This resulted from the fact that current guidelines are derived from empirical values and neglect the prevalence of computer-based tasks in current offices. A personal visual comfort model was designed to predict the degree of an individual’s visual comfort, as a way of evaluating the indoor lighting of the environment. Development of the model relied on experimental data, including individual eye pupil sizes, visual sensations, and visual satisfaction in response to various illuminance levels used for tests of six human subjects. The results showed that (1) A personal comfort model was needed, (2) the personal comfort model produced a median accuracy of 0.7086 for visual sensation and 0.65467 for visual satisfaction for all subjects; (3) To develop a prediction model for the sample group, the Support Vector Machine algorithm,, outperformed the Logistic Regression and the Gaussian Naïve Bayes in terms of prediction accuracy. It was concluded that, a personal visual comfort model can use a building’s occupant’s eye pupil size to generate an accurate prediction of that occupant’s visual sensations and visual satisfaction that can, therefore, be applied with lighting control to improve the indoor environment and energy use in that building. 

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4. COSMOS: Optical Architecture and Prototyping

   Gutterman, Craig; Minakhmetov, Artur; Yu, Jiakai; Chen, Tingjun; Zhu, Shengxiang; Zussman, Gil; Seskar, Ivan; Raychaudhuri, Dipankar; Kilper, Dan (January 2019, ACM SIGCOMM 2019 Workshop on Optical Systems Design)

   ABSTRACT The Cloud Enhanced Open Software Defined Mobile Wireless Testbed for City-Scale Deployment (COSMOS) platform is a programmable city-scale shared multiuser advanced wireless testbed that is being deployed in New York City [1]. Open APIs and programmability across all the technology components and protocol layers in COSMOS will enable researchers to explore 5G technologies in a real world environment. A key feature of COSMOS is its dark fiber based optical x-haul network that enables both highly flexible, user defined network topologies as well as experimentation directly in the optical physical layer. A paper on the COSMOS optical architecture was previously presented in [2]. In this talk, we briefly introduce COSMOS’ optical x-haul network with SDN control, and its integration with the software-defined radio (SDR) and mobile edge cloud. 

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5. A Low-Cost and Scalable Personalized Thermal Comfort Estimation System in Indoor Environments

   https://doi.org/10.1145/3458648.3460006  

   Wei, Peter; Liu, Yanchen; Kang, Hengjiu; Yang, Chenye; Jiang, Xiaofan (May 2021, Proceedings of the First International Workshop on Cyber-Physical-Human System Design and Implementation)

   null (Ed.)

   In commercial buildings, occupant thermal comfort is a key factor that must be optimized to provide a comfortable and productive work environment. However, current methods largely estimate thermal comfort based on preset models which do not incorporate real-time measurements or individual thermal preferences. In this work, we present a scalable system for estimating personalized thermal comfort using low-cost thermal camera based sensor nodes. This system extracts non-intrusive thermal measurements, is robust to different perspectives and environments, is easily deployable and low-cost, and can incorporate individual thermal feedback for more personalized thermal comfort estimates. In comparison with baseline methods, our system is able to improve thermal comfort estimates on the ASHRAE 7-point thermal sensation scale by 64% over baseline methods. 

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