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1. An Evaluation of Three Distance Measurement Technologies for Flying Light Specks

   Phan Trung; Alimohammadzadeh Hamed; Culbertson Heather; Ghandeharizadeh Shahram (September 2023, iMETA 2023 : International Conference on Intelligent Metaverse Technologies & Applications)

   Awaysheh Feras; Srivastava Gautam; Wu Jun; Aloqaily Moayad (Ed.)

   This study evaluates the accuracy of three different types of time-of-flight sensors to measure distance. We envision the possible use of these sensors to localize swarms of flying light specks (FLSs) to illuminate objects and avatars of a metaverse. An FLS is a miniature-sized drone configured with RGB light sources. It is unable to illuminate a point cloud by itself. However, the inter-FLS relationship effect of an organizational framework will compensate for the simplicity of each individual FLS, enabling a swarm of cooperating FLSs to illuminate complex shapes and render haptic interactions. Distance between FLSs is an important criterion of the inter-FLS relationship. We consider sensors that use radio frequency (UWB), infrared light (IR), and sound (ultrasonic) to quantify this metric. Obtained results show only one sensor is able to measure distances as small as 1 cm with a high accuracy. A sensor may require a calibration process that impacts its accuracy in measuring distance. 

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2. An Evaluation of Decentralized Group Formation Techniques for Flying Light Specks

   https://doi.org/10.1145/3595916.3626460  

   Alimohammadzadeh, Hamed; Culbertson, Heather; Ghandeharizadeh, Shahram (December 2023, ACM)

   Group formation is fundamental for 3D displays that use Flying Light Specks, FLSs, to illuminate shapes and provide haptic interactions. An FLS is a drone with light sources that illuminates a shape. Groups of G FLSs may implement reliability techniques to tolerate FLS failures, provide kinesthetic haptic feedback in response to a user’s touch, and facilitate a divide and conquer approach to challenges such as localizing FLSs to render a shape. This paper evaluates four decentralized techniques to form groups. An FLS implements a technique autonomously using asynchronous communication and without a global clock. We evaluate these techniques using synthetic point clouds with known optimal solutions and real point clouds. Obtained results show a technique named Random Subset (RS) is superior when constructing small groups (G ≤ 5) while a different technique named Closest Available Neighbor First (CANF) is superior when constructing large groups (G ≥ 10). 

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3. An Evaluation of Decentralized Group Formation Techniques for Flying Light Specks

   Alimohammadzadeh Hamed; Culbertson Heather; Ghandeharizadeh Shahram (December 2023, ACM Multimedia Asia)

   Hu Min-Chun; Liu Jiaying; Kim Munchurl; Zhang Wei (Ed.)

   Group formation is fundamental for 3D displays that use Flying Light Specks, FLSs, to illuminate shapes and provide haptic interactions. An FLS is a drone with light sources that illuminates a shape. Groups of $$G$$ FLSs may implement reliability techniques to tolerate FLS failures, provide kinesthetic haptic feedback in response to a user's touch, and facilitate a divide and conquer approach to challenges such as localizing FLSs to render a shape. This paper evaluates four decentralized techniques to form groups. An FLS implements a technique autonomously using asynchronous communication and without a global clock. We evaluate these techniques using synthetic point clouds with known optimal solutions and real point clouds. Obtained results show a technique named Random Subset (RS) is superior when constructing small groups (G$$\leq$$5) while a different technique named Closest Available Neighbor First (CANF) is superior when constructing large groups (G$$\geq$$10). 

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4. Swarical: An Integrated Hierarchical Approach to Localizing Flying Light Specks

   https://doi.org/10.1145/3664647.3681080  

   Alimohammadzadeh, Hamed; Ghandeharizadeh, Shahram (October 2024, ACM)

   Swarical, a Swarm-based hierarchical localization technique, enables miniature drones, Flying Light Specks (FLSs), to accurately and efficiently localize and illuminate complex 2D and 3D shapes. Its accuracy depends on the physical hardware (sensors) of FLSs used to track neighboring FLSs to localize themselves. It uses the specification of the sensors to convert mesh files into point clouds that enable a swarm of FLSs to localize at the highest accuracy afforded by their sensors. Swarical considers a heterogeneous mix of FLSs with different orientations for their tracking sensors, ensuring a line of sight between a localizing FLS and its anchor FLS. We present an implementation using Raspberry cameras and ArUco markers. A comparison of Swarical with a state of the art decentralized localization technique shows that it is as accurate and more than 2x faster. 

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5. Reliability Groups with Standby Flying Light Specks

   https://doi.org/10.1145/3625468.3647606  

   Alimohammadzadeh, Hamed; Zhu, Shuqin; Bai, Jiadong; Ghandeharizadeh, Shahram (April 2024, ACM)

   A Flying Light Speck, FLS, is a miniature sized drone configured with light sources to illuminate different colors and textures. A swarm of FLSs illuminates complex 3D multimedia shapes in a fixed volume, a 3D display. An FLS is a mechanical device. Its failure is the norm rather than an exception, causing a point of an illumination to go dark. In this paper, we use reliability groups with dark standby FLSs to minimize the duration of time a point remains dark. This study makes two novel contributions. First, it compares a centralized and a decentralized algorithm to form groups, demonstrating the superiority of the centralized technique. Second, it detects when the dark standby FLSs may obstruct the user's field of view and relocates them with minimal impact on their provided benefit. 

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