Difference between revisions of "Resource:Previous Seminars"

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=== History ===
=== History ===
====2024====
====2024====
{{Hist_seminar
|abstract = Recent advances in quantum information science enabled the development of quantum communication network prototypes and created an opportunity to study full-stack quantum network architectures. This work develops SeQUeNCe, a comprehensive, customizable quantum network simulator. Our simulator consists of five modules: hardware models, entanglement management protocols, resource management, network management, and application. This framework is suitable for simulation of quantum network prototypes that capture the breadth of current and future hardware technologies and protocols. We implement a comprehensive suite of network protocols and demonstrate the use of SeQUeNCe by simulating a photonic quantum network with nine routers equipped with quantum memories. The simulation capabilities are illustrated in three use cases. We show the dependence of quantum network throughput on several key hardware parameters and study the impact of classical control message latency. We also investigate quantum memory usage efficiency in routers and demonstrate that redistributing memory according to anticipated load increases network capacity by 69.1% and throughput by 6.8%. We design SeQUeNCe to enable comparisons of alternative quantum network technologies, experiment planning, and validation and to aid with new protocol design. We are releasing SeQUeNCe as an open source tool and aim to generate community interest in extending it.
|confname =IOPSCIENCE'21
|link = https://iopscience.iop.org/article/10.1088/2058-9565/ac22f6/meta
|title= SeQUeNCe: a customizable discrete-event simulator of quantum networks
|speaker=Junzhe
|date=2025-02-21
}}{{Hist_seminar
|abstract = This article proposes a remote environmental monitoring system based on low-power Internet of Things, which is applied in smart agriculture to achieve remote and real-time measurement of temperature, humidity, and light intensity parameters in the crop growth environment within the coverage range of the device The system adopts low-power Internet of Things technology, which has the characteristics of wide coverage, multiple connections, fast speed, low cost, low power consumption, and excellent architecture. The overall design of the system includes multiple environmental monitoring nodes, a LoRa gateway, and corresponding environmental monitoring upper computer software. In terms of system software, it involves programming of node MCU and client upper computer software. The key technology implementation includes the hardware design and implementation of low-power sensor nodes and the development of LoRa protocol. System testing and performance analysis show that the optimized LoRa protocol performs well in communication distance, power consumption, stability, and other aspects, laying the foundation for the efficient operation of the system. This study provides a powerful tool for sustainable resource management, which helps to promote agricultural modernization and rural revitalization.
|confname =CISCE'24
|link = https://ieeexplore.ieee.org/abstract/document/10653076
|title= A Long Distance Environmental Monitoring System Based on Low Power IoT
|speaker= Ayesha Rasool
|date=2025-02-21
}}
{{Hist_seminar
{{Hist_seminar
|abstract = Recently, smart roadside infrastructure (SRI) has demonstrated the potential of achieving fully autonomous driving systems. To explore the potential of infrastructure-assisted autonomous driving, this paper presents the design and deployment of Soar, the first end-to-end SRI system specifically designed to support autonomous driving systems. Soar consists of both software and hardware components carefully designed to overcome various system and physical challenges. Soar can leverage the existing operational infrastructure like street lampposts for a lower barrier of adoption. Soar adopts a new communication architecture that comprises a bi-directional multi-hop I2I network and a downlink I2V broadcast service, which are designed based on off-the-shelf 802.11ac interfaces in an integrated manner. Soar also features a hierarchical DL task management framework to achieve desirable load balancing among nodes and enable them to collaborate efficiently to run multiple data-intensive autonomous driving applications. We deployed a total of 18 Soar nodes on existing lampposts on campus, which have been operational for over two years. Our real-world evaluation shows that Soar can support a diverse set of autonomous driving applications and achieve desirable real-time performance and high communication reliability. Our findings and experiences in this work offer key insights into the development and deployment of next-generation smart roadside infrastructure and autonomous driving systems.
|abstract = Recently, smart roadside infrastructure (SRI) has demonstrated the potential of achieving fully autonomous driving systems. To explore the potential of infrastructure-assisted autonomous driving, this paper presents the design and deployment of Soar, the first end-to-end SRI system specifically designed to support autonomous driving systems. Soar consists of both software and hardware components carefully designed to overcome various system and physical challenges. Soar can leverage the existing operational infrastructure like street lampposts for a lower barrier of adoption. Soar adopts a new communication architecture that comprises a bi-directional multi-hop I2I network and a downlink I2V broadcast service, which are designed based on off-the-shelf 802.11ac interfaces in an integrated manner. Soar also features a hierarchical DL task management framework to achieve desirable load balancing among nodes and enable them to collaborate efficiently to run multiple data-intensive autonomous driving applications. We deployed a total of 18 Soar nodes on existing lampposts on campus, which have been operational for over two years. Our real-world evaluation shows that Soar can support a diverse set of autonomous driving applications and achieve desirable real-time performance and high communication reliability. Our findings and experiences in this work offer key insights into the development and deployment of next-generation smart roadside infrastructure and autonomous driving systems.

Revision as of 03:05, 28 February 2025

History

2024

2023

2022

2021

2020

  • [Topic] [ The path planning algorithm for multiple mobile edge servers in EdgeGO], Rong Cong, 2020-11-18

2019

2018

2017

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