Ofertes de projectes

Objectives:

- Understand the challenges of quantum communication for quantum computers.

- Develop new architectural methods to extend single-chip quantum computers to the multi-core paradigm.

- Model quantum interconnects within NetSquid, a simulator for quantum networks.

- Simulate different protocols for quantum communication within quantum computers.

El objetivo principal es diseñar, desarrolar e implementar un producto mínimo viable. Juntamente con los directores se tendran que: 

• Elegir la pila de tecnología
• Diseñar y configurar la infraestructura
• Seleccionar kit de herramientas de desarrollo
• Diseñar e implementar el diseño de la base de datos
• Crear la arquitectura de aplicaciones
• Garantizar la escalabilidad de la aplicación
• Garantizar la escalabilidad de la infraestructura
• Asegurar la calidad
• Garantizar la ciberseguridad
• Explorar nuevas tecnologías para implementar
• Garantizar el uso eficiente de las tecnologías.
• Evaluar la implementación

 L'algorisme d'encaminament Babel[1] s'ha desenvolupat per a xarxes mesh. El protocol està estandarditzat per l'IETF [2] i existeixen implementacions de lliure distribució i codi obert per linux, i en particular, per a la distribució OpenWRT [3].. L'objectiu del PFC és desenvolupar un sistema de monitorització de Babel en una xarxa mesh OpenWRT amb Babel. El sistema de monitorització es provarà en una xarxa virtual.

[1] https://en.wikipedia.org/wiki/Babel_(protocol)
[2] https://datatracker.ietf.org/doc/html/rfc8966
[3] https://openwrt.org/

Machine learning is a branch of artificial intelligence which focuses on the use of data and algorithms to produce machine learning models to imitate intelligent human behaviour. A machine learning pipeline is the code that builds a machine learning model. Usually, machine learning pipelines (defined in the experimentation stage) are written in Python using Jupiter Notebook (a server-client application that allows editing and running notebook documents via a web browser). The rapid development of these pipelines in an experimental stage together with the lack of application of software engineering best practices make it difficult to maintain, evolve and replicate the pipeline for the construction of the corresponding machine learning models.  Therefore, the transition of machine learning pipelines from experimentation to production is currently anecdotal. This Final Degree Project (TFG) aims to improve machine learning pipelines maintainability, evolvability and replication, and ease their transition from experimentation to production. To do so, it proposes the use of object-oriented concepts to define suitable architectural elements needed to design Python machine learning pipelines in Jupiter Notebook.  Such architectural elements will support the design of mantenible, evolvable and replicable Python machine learning pipelines in Jupiter Notebook, easing their transition from experimentation to production.  To demonstrate the feasibility of the proposal, an example in the domain of tweet sentiment analysis is presented.

El objetivo principal del TFG es la implementación de un sistema de directorio (estructura de datos para saber donde estan los datos en un modelo paralelo basado en tareas) en la libreria de "runtime" del modelo de programación paralelo del BSC (OmpSs) que trabaja con "tasks", muy parecidas a las de OpenMP, pero con soporte de diferentes dispositivos. Este directorio deberá ayudar a poder realizar la ejecución de tareas en sistemas heterogeneos y distribuidos con diferentes espacios de direcciones. El trabajo se realizará con el soporte de una beca de cooperación a media jornada en el BSC e integrado en el grupo de trabajo de modelos de programación paralela del BSC y de la UPC.

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.

TinyML is a growing community that does machine learning on microcontrollers https://www.tinyml.org. 

Microcontrollers are sometimes the only choice when the power supply is limited, e.g. in for battery- or solar-powered applications. Application examples of TinyML are "wildlife" observation, such as: https://mybirdbuddy.com/ https://opencollar.io/, but tiny machine learning is also in domestic, healthcare and industrial applications.

While Arduino Uno boards are well known for all kinds of hobbyist microcontroller projects, for machine learning the more powerful 32 bit microcontrollers and development boards are used, such as the Arduino Portenta H7 or Arduino Nano 33 BLE Sense. These are able to run machine learning applications. To get the first information on this topic have a look at TensorFlow Lite for Microcontrollers: https://www.tensorflow.org/lite/microcontrollers.

In this project we would like to explore on-device machine learning model training specifically doing it by federated learning. We would like to use the Arduino Portenta H7, the most powerful microcontroller board from the Arduino family. We have Arduino Portenta H7 that can be used for the project. 

We have done previous work and code available where we showed that it is feasible.

https://www.mdpi.com/2079-9292/11/4/573

In the project it would be interesting to explore the capacities of the Portant H7, like using the two cores of the board, use at least one the sensors (camera, accelaration, microphone,...), and use the network connectivity (Wifi, BLE, LoRa), try with Convolutionary Neural Networks, ...

One concrete case would be to use for the communication part of federated learning the Wifi connectivity of the Arduino Portenta H7 to build a "cluster" (inspired by clusters of Raspberry Pi) where all the nodes are interconnected and can participate in federated learning. 

The project can start with already working code (in C/C++, Python) that we have from previous work on TinyML with on-device training and federated learning. https://upcommons.upc.edu/bitstream/handle/2117/353756/160036.pdf