DSpace Collection:
https://hdl.handle.net/10356/79132
2024-03-19T01:37:18ZIntegrated air and space traffic management: an agent-based simulation for analysis of space-launch impact on air traffic
https://hdl.handle.net/10356/174067
Title: Integrated air and space traffic management: an agent-based simulation for analysis of space-launch impact on air traffic
Authors: Wang, Zhengyi; Dhief, Imen; Zhou, Wei; Alam, Sameer; Blom, Henk A. P.; Kaltenhäuser, Sven; Rabus, Tobias
Abstract: The recent surge in space launch activities, driven by the emergence of commercial space launches, has compelled the aviation and space launch sectors to collaborate for the safe and efficient integration of space launch activities.
This paper introduces an agent-based modeling (ABM) and simulation framework designed to assess the impact of spacecraft launches on air traffic within an integrated air and space traffic management system.
The proposed framework incorporates various agents involved in the execution phase of space launches and considers the interactions and coordination between air traffic management and space traffic management.
The paper firstly provides a comprehensive overview of the current state of space launch operations and their effects.
Then, a general agent-based model is developed for space launch execution phase in order to gain an understanding of various entities involved in a space launch activity as well as the interactions among these entities.
Using Monte-Carlo simulations based on the ABM, the paper assesses the impact on air traffic operations in the event of a space launch failure.
In each simulation, various factors are taken into account, including launch site position, launch slot, failure probability during the execution phase, debris dispersion, and time delay in Air Traffic Management (ATM)/Space Traffic Management (STM) coordination.
To demonstrate the practical application of the proposed framework in an operational context, the paper presents a case study of a sea-based space launch in the Singapore FIR.
The paper makes a valuable contribution to the field of air and space traffic management by addressing the need for innovative strategies to ensure the safe sharing ofairspace among different stakeholders.2023-01-01T00:00:00ZLarge area metal surface characterization using plasmonic random laser based imaging technique
https://hdl.handle.net/10356/173893
Title: Large area metal surface characterization using plasmonic random laser based imaging technique
Authors: Gayathri, Radhakrishn; Suchand Sandeep, Chandramathi Sukumaran; Vijayan, C.; Murukeshan, Vadakke Matham
Abstract: We report the use of an incoherent random laser for high resolution, artefact-free wide field microscopic imaging of metal surfaces, enabling large area surface characterization.2022-01-01T00:00:00ZDesign of a passive wearable device using an optimized mechanical metamaterial for mirror therapy
https://hdl.handle.net/10356/172976
Title: Design of a passive wearable device using an optimized mechanical metamaterial for mirror therapy
Authors: Raghavendra Kulkarni, Suhas; Accoto, Dino; Campolo, Domenico
Abstract: Mirror Therapy (MT) is an effective therapeutic method used in the rehabilitation of hemiplegics. The effectiveness of this method is improved by employing a bi-modal approach which requires the synchronous movement of the affected and unaffected arm. For this purpose, we describe the design of a wearable device using a Mechanical Metamaterial (MM) that is optimized for the specific user to provide passive assistance of wrist flexion-extension and enable synchronous motion of the affected and unaffected arm during MT.2023-01-01T00:00:00ZEnhancing airside monitoring: a multi-camera view approach for aircraft position estimation for digital control towers
https://hdl.handle.net/10356/172917
Title: Enhancing airside monitoring: a multi-camera view approach for aircraft position estimation for digital control towers
Authors: Ali, Hasnain; Pham, Duc-Thinh; Alam, Sameer
Abstract: A digital tower offers a cost-efficient substitute for traditional air traffic control towers and is anticipated to deliver video-based surveillance, which is especially beneficial for smaller airports. To fully unlock the potential of digital tower, sophisticated computer vision algorithms are pivotal for efficient surveillance. While current research predominantly concentrates on tracking aircraft movements on the airport surface, an equally crucial aspect lies in real-time monitoring of aircraft as they are are on finals. This capability plays a central role in enhancing both airport and runway operations. In this context, this study introduces a deep learning approach for precise estimation of the position of incoming aircraft, covering distances of up to 10 nautical miles. This approach surpasses the constraints of monoscopic techniques by leveraging multi-view video feeds obtained from digital towers. It combines Yolov7, an advanced real-time object detection model, with auxiliary regression and auto-calibration, allowing real-time tracking and feature extraction from different camera viewpoints. Furthermore, we propose an ensemble approach utilizing an Long Short-Term Memory model to combine input vectors, resulting in precise location estimation. Importantly, this method is designed to seamlessly adapt to different camera setups within digital towers. Its performance is evaluated using simulated video data from Singapore Changi Airport, showcasing stability in various scenarios with minimal predictive errors (Mean Absolute Percentage Error = 0.2%) over a 10 nautical mile range in clear weather conditions. These capabilities, when implemented in a digital tower setting, have the potential to significantly improve the controller's capacity to coordinate runway sequencing and final approach spacing, ultimately enhancing airport efficiency and safety remarkably.2023-01-01T00:00:00Z