Thesis offers

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.

Cybersecurity is becoming an increasingly important challenge for all companies and individuals alike. While big names used to be the main targets in the past, as people's lives move online, anyone is nowadays a potential target for any kind of cyber-attack, ranging from phishing to ransomware or serious privacy issues. In order to fight against those ever-evolving threats, Machine Learning is increasingly being used behind the scenes to design better systems that are able of self-learning to boost detection rates and boost overall resilience to unknown attacks. As AI-based solutions penetrate products across the industry, a new kind of threat that is often overlooked is becoming more and more prominent and dangerous: adversarial machine learning (AML).

AML focuses on designing specific inputs to deceive a previously trained Machine Learning models into misclassifying them for a specific purpose. One of the main flaws of any state-of-the-art Machine Learning or Deep Learning algorithms is that they assume that the nature of the data they receive is systematically benign, which is generally the case but does not hold true when an adversarial input is received. The motivation behind altering a ML model into thinking that, for example, a new sample is benign when in fact is malicious can range from pure research to more serious real-life issues such as an autonomous car wrongly classifying a stop sign (and thus provoking a fatal accident) or a wrongly diagnosed disease because of a slightly manipulated magnetic resonance image.

This problem is no exception for Cybersecurity where companies wrongly assume that once the last AI-based product is deployed in their network, their employees are safe...

ZeOS és un sistema operatiu monolític mínim amb suport de diferents nivells de privilegi, gestió de memòria, i gestió de processos per plataformes intel x86. En aquest projecte ens plantegem portar el codi existent a la plataforma RISC-V. En particular, adaptar:
- el sistema de càrrega del sistema operatiu,
- la gestió de dispositius rellotge, pantalla i teclat,
- els mecanismes de gestió de memòria,
- i els mecanismes de gestió de processos.

This proposal explores a specific case of the simulation of weathering effects in computer graphics. Weathering refers to the process of deterioration and transformation that occurs on surfaces over time due to exposure to various environmental factors. In this project, the main focus will be on simulating and visualizing the growth of lichens on cultural heritage 3D models. Lichens are unique organisms that colonize surfaces and contribute to changes in the appearance and degradation of materials. This project aims to accurately model the growth patterns of lichens on the surface of the models and render the changes in the appearance of the materials in terms of color and/or shape. The project will involve implementing algorithms that simulate the growth of lichens based on environmental conditions and surface characteristics. The final outcome will be a visually compelling and scientifically accurate representation of the weathering process caused by lichens on cultural heritage 3D models, contributing to the preservation and conservation of these invaluable artifacts.

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