Ofertes de projectes

La interacció en entorns de Realitat Virtual és complexa perquè els dispositius amb els quals s'ha d'operar són diferents, i a la vegada permeten obtenir informació sobre la posició de l'usuari, la orientació del seu cap, etc.
L'objectiu del projecte és la implementació i avaluació de diferents mètodes de manipulació i interacció que siguin intuïtius i fàcils d'usar en entorns de realitat virtual per models mèdics. També s'estudiarà la viabilitat i usabilitat de l'aplicació per a ser utilitzada en diagnòstic.

This is precisely why companies such as Google have invested a lot of resources on exploring applications of GNN, some of which are as mediatic as Alpha Fold (recently solving the Protein-Folding problem), or used by Goolge Maps to make predictions of the expected traffic of a road. In this regard, the goal of this thesis is to continue investigating about the potential of GNNs when applied to the field of Computer Networks. Our goal is to provide GNN-based solutions that make computer networks run more autonomously and in a more efficient way.

TinyML aims to do machine learning on microcontrolers. Microcontrollers are sometimes the only choice when the power supply is limited, e.g. in for battery- or solar-powered applications. Application examples are "wildlife" observation, such as: https://mybirdbuddy.com/ https://opencollar.io/ 

Arduino Uno and Mega boards are well known for all kinds of hobbyist microcontroller projects, but there is another kind of more powerful 32 bit microcontrollers and develpment boards which are our target hardware, which 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

We have a couple of these mentioned boards that can be used for the project. 

For the project it would be interesting to develop further ideas from previous work on TinyML (e.g. on-device training, federated learning) which can leverage available code (in C/C++, Python), such as the TFG

https://upcommons.upc.edu/bitstream/handle/2117/353756/160036.pdf

Current quantum hardware, which has on the order of tens to hundreds of physical qubits, is insufficient to run these important quantum algorithms. Scaling these devices even to a moderate size with low error rates has proven extremely challenging. Due to these challenges, as well as developing technology for communicating between different quantum chips, we expect quantum hardware to scale via a modular approach similar to how a classical computer can be scaled increasing the number of processors not just the size of the processors.

Goal:

The goal of the thesis is to understand the communications pattern of a set of quantum computers communicating in a modular approach. This will be achieved by a quantum simulator running a set of relevant quantum algorithms and generating a dataset of the required communications. Then analyzing this dataset and proposing early solutions, both from a computer architecture perspective as well as from the communication technologies needed for this. In addition we will also consider ML-based solution for scheduling, therefore the dataset generated becomes a training set for a  Graph Neural Network-based solution.

The goal of this thesis is to enable the application of Deep Reinforcement Learning techniques for optimization problems in very large and complex scenarios, in this case LEO satellite communication networks. Inspired by the last related work from OpenAI (https://openai.com/blog/evolution-strategies/), we will explore time-efficient alternatives to train a Graph Neural Network based DRL agent on large optimization scenarios in a reasonable amount of time.

The goal of this thesis is to enable the application of Deep Reinforcement Learning techniques for optimization problems in very large and complex scenarios. Inspired by the last related work from OpenAI (https://openai.com/blog/evolution-strategies/), we will explore time-efficient alternatives to train a Graph Neural Network based DRL agent on large optimization scenarios in a reasonable amount of time.

It is known that humans cannot interpret visual depictions equally if some parameters are changed such as color palettes, size of marks, etc. However, it is still unkown the value ranges of certain parameters for a set of commonly used visualization techniques such as heatmaps, for example. The goal of this project is to explore the ability to carry out visualization tasks using different visualization techniques in desktop and Virtual Reality environments according to the visual variables chosen.

The medical records of each patient contain textual information about the clinical evolution of the patient (including drugs, chemicals, diseases, symptoms and body parts). This corpus has already been represented in a structured format as a set of semantic graphs, using Neo4J graph database.

This thesis would aim at the acquisition of patterns of clinical behavior from these graphs. These patterns would be specifically devoted to provide help in diagnosis to the medical community in primary care. For example, we can get to automatically infer that a certain drug has a previously unknown side effect, or that patients suffering from a certain disease develop certain symptoms in a certain period of time. A simple example of pattern might be "patient has fever and is prescribed ibuprofen -(after x days) - patient has fever and soreness - (after y days) - patient has fever and breathing difficulty -> patient diagnosed with COVID".

The thesis would be a continuation of a previous master thesis in which we have built a baseline system that is able to extract simple rules whose elements are generalized codes. However, we want to extend this system to deal with sequence patterns as the ones mentioned before, eventually composed by sentences in Natural Language.

Since we have an annotated corpus of medical records, the project might use supervised and semi-supervised machine learning techniques as well as more standard data mining techniques.

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...

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 another network topology is also possible, to do mesh networking with LoRa among sensor nodes. 

https://ieeexplore.ieee.org/document/9385408

It might be possible to apply LoRa mesh for wildlife preservation:

https://www.hackster.io/mithun-das/mahout-save-the-elephants-819b54

For doing LoRa mesh we have designed a protocol. https://upcommons.upc.edu/bitstream/handle/2117/347444/A_minimalistic_distance_vector_routing_protocol_for_LoRa_mesh_networks.pdf?sequence=1

We have an on-going implementation for the TTGO T-Beam ESP32 LoRa boards (e.g. https://tienda.bricogeek.com/arduino-compatibles/1502-ttgo-t-beam-esp32-wifi-gps-neo-6m-lora-868-mhz.html).  

Technical details. Code is in C++. The implementation leverages RTOS to perform the functions.

The theis can work out specific topics according to interest. For instance protocol design, networking, embedded systems or performance evaluation. It could also be interesting to develop a certain application which integrate the LoRa mesh implementation as a library.

 The goal of this thesis is to enable the application of Deep Reinforcement Learning techniques for optimization problems in very large and complex scenarios, in this case Data Center Networks. Inspired by the last related work from OpenAI (https://openai.com/blog/evolution-strategies/), we will explore time-efficient alternatives to train a Graph Neural Network based DRL agent on large optimization scenarios in a reasonable amount of time.

In many teaching applications such as the study of anatomy, the student is required to obtain a three-dimensional view of the models or structures to be analyzed. This goal is necessary for both individual study and class follow-up. There are some VR-based systems that usually use immersive VR HMD. However, these systems require an infrastructure and cost that is often not affordable.

This master's thesis aims to analyze the feasibility of a remote VR system based on the use of mobile devices with cardboard glasses and low-cost interaction devices. It will start from a system based on HTC-VIVES programmed with Unity. Different portability alternatives to the new platform will be analyzed both in terms of the rendering of the models (locally or on a server) and the limitations of the interaction and connection between students and teacher. Once the advantages/limitations have been analyzed, a prototype will be developed with basic interaction techniques and its usability will be analyzed. The models that will be visualized will be anatomical structures modeled with triangles. For more information contact the directors.

Interested candidates should send a CV and their bachelor and master trancript to pere.barlet@upc.edu