Consulta ofertes d'altres estudis i especialitats
The domain of Software Analytics is broad and can be applied to various environments. In the context of a UPC granted project, the GESSI group has developed a first prototype of a Strategic Dashboard for monitoring the progress of software projects developed by student teams, which is called Learning Dashboard. The goal of this project is to add new capabilities to the current implementation of the Learning Dashboard to allow it to cover other scenarios where teams of software developers are involved. For instance, the onboarding of junior developers.
Remuneration: Possibility of making an Educational Cooperative Agreement (Conveni de Cooperació Educativa)
We have developed LoRaMesher, an on-going implementation for doing mesh networking with LoRa nodes. https://github.com/LoRaMesher/LoRaMesher The TFM will develop LoRaMesher further on a specific topic, such as embedded systems, network level, machine learning or application level, according to the interest.
We have developed LoRaMesher, an on-going implementation for doing mesh networking with LoRa nodes.
https://github.com/LoRaMesher/LoRaMesher
LoRaMesher is base on this protocol design: https://upcommons.upc.edu/bitstream/handle/2117/347444/A_minimalistic_distance_vector_routing_protocol_for_LoRa_mesh_networks.pdf?sequence=1
LoRa is a communication technology for IoT applications. It is typically used to send small amounts of data from the sensor node to a gateway (this is the so-called LoRaWAN architecture). But with LoRaMesher another network topology is also possible: to do mesh networking with LoRa among sensor nodes.
What for? E.g. for wildlife preservation:
https://www.hackster.io/mithun-das/mahout-save-the-elephants-819b54
Technical details about LoRaMesher: Code is in C++. The implementation leverages RTOS to perform the functions.
We deploy LoRaMesher on ESP32 LoRa boards, such as the T-Beam (e.g. https://tienda.bricogeek.com/arduino-compatibles/1502-ttgo-t-beam-esp32-wifi-gps-neo-6m-lora-868-mhz.html).
A project with LoRaMesher can focus on a specific topic (embedded systems, network level, machine learning or application level) according to interest.
* improving the LoRaMesher protocoll design and implementation, e.g.
- design, implementation and evaluation of different protocol alternatives for reliable messaging with LoRaMesher
- explore different metrics for the routing table in LoRaMesher (currently we use hops, but it is not reflecting the link quality, leading to suboptimal performance in heterogeneous links)
* machine learning.
- We have an initial federated learning prototype application that runs over LoRaMesher, a first steo to go further.
* applications that use LoRaMesher as a communication layer
- We have already developed a demo application called LoRaChat. A user connects the Android bluetooth terminal and can chat with other users in the LoRaMesh network.
https://github.com/Jaimi5/LoRaChat
A similar application is that of Meshtastic. https://meshtastic.org
- We currently work on an IoT monitoring application which spans over the LoRa mesh network and services in Internet.
It could be very interesting to demonstrate this concept of interconnected LoRa and Internet services with a new application. We would like to create and test new applications which (in part) use the LoRaMesh network. These application can have an Android app (if the application is end user oriented) or the code can run entirely on the IoT board (maybe a "tracker" for a Peer-to-Peer system, and then integrate the LoRa mesh implementation as a library.
* embedded systems: We would like to port LoRaMesher to the Arduino Portenta board (LoRaMesher currently runs only on ESP32 boards). We have the board ready with LoRa radio. But the FreeRTOS usage that we make in LoRaMesher has to be "translated" into the equivalent in MBedOS.
https://people.ac.upc.es/felix/2022_CR_LANMAN_Portenta.pdf
Doctors take notes of the evolution of patients during the length of their treatment. Once it is done, it is important to summarize all this information in a concise way to be included in their medical history. The aim of this project is to develop an end-to-end summarization model that is able to produce coherent medical summaries given medical reports.
Doctors take notes of the evolution of patients during the length of their treatment. Once it is done, it is important to summarize all this information in a concise way to be included in their medical history.
The aim of this project is to develop an end-to-end summarization model that is able to produce coherent medical summaries given medical reports.
The project can be defined in the following objectives:
- Study the current state-of-the-art summarization techniques and develop a competitive baseline system.
- Propose a modification of such baseline in terms of performance or data efficiency.
If those objectives are succesful, the work could be presented at the BioNLP workshops share task for publication.
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