The JPEG standardization committee (ISO/IEC JTC1 SC29/WG1) has developed a new part of "JPEG Systems". This Part 4 standardizes some mechanisms for adding privacy and security to standard JPEG images. The DMAG (Distributed Multimedia Applications Group) of the Computer Architecture Department of the UPC has contributed to the specification of this new standard. Most JPEG standards are complemented with "Reference Software", which implements most of the features of the standard in order to demonstrate its feasibility and give hints on how to implement it. The objective of this project is to produce a first draft version of a partial implementation of Reference Software for the new JPEG Privacy and Security (JPEG P&S) specification. Since JPEG P&S is based on the concept of the JPEG Universal Metadata Box Format (JUMBF), specified in the new part 5 of JPEG Systems, it might be necessary to also implement some elements of JUMBF. The results of this work could be contributed to the JPEG standardization committee for its approval.
Companies and scientists working in areas such as finance or genomics are generating enormously large datasets (in the order of petabytes) commonly referred as Big Data. How to efficiently and effectively process such large amounts of data is an open research problem. Since communication is involved in Big Data processing at many levels, at the NaNoNetworking Center in Catalunya (N3Cat) we are currently investigating the potential role of wireless communications in the Big Data scenario. The main focus of the project is to evaluate the impact of applying wireless communications and networking methods to processors and data centers oriented to the management of Big Data. OBJECTIVES =========== N3Cat is looking for students wanting to work in the area of wireless communications for Big Data. To this end, the candidate will work on one of the following areas: - Traffic analysis of Big Data frameworks and applications, as well as in smaller manycore systems. - Channel characterization in Big Data environments: indoor, within the racks of a data center, within the package of CPU, within a chip. - Design of wireless communication protocols for computing systems from the processor level to the data center level.
Machine Learning (ML) has taken the world by storm and has become a fundamental pillar of engineering. As a result, the last decade has witnessed an explosive growth in the use of deep neural networks (DNNs) in pursuit of exploiting the advantages of ML in virtually every aspect of our lives: computer vision, natural language processing, medicine or economics are just a few examples. However, NOT all DNNs fit to all problems: convolutional NNs are good for computer vision, recurrent NNs are good for temporal analysis, and so on. In this context, the main focus of N3Cat and BNN-UPC is to explore the possibilities of the new and less explored variant called Graph Neural Networks (GNNs), whose aim is to learn and model graph-structured data. This has huge implications in fields such as quantum chemistry, computer networks, or social networks among others. OBJECTIVES =========== N3Cat and BNN-UPC are looking for students wanting to work in the area of Graph Neural Networks studying their uses, processing architectures, and algorithms. To this end, the candidate will work on ONE of the following areas: - Investigating the state of the art on this area, surveying the different works done in terms of applications, processing frameworks, algorithms, benchmarks, datasets. This can be taken from a hardware or software perspective. - Helping to build a testbed formed by a cluster of GPUs that will be running pyTorch or Tensorflow. We will instrument the testbed to measure the computation workload and communication flows between GPUs. - Analyzing the communication workload of running a GNN either in the testbed or by means of architectural simulations. - Developing means of accelerating GNN processing in software (e.g., improving scheduling of the message passing) or hardware (e.g. designing a domain-specific architecture).
Robotic Process Automation is receiving significant attention, due to the promise of improving the performance of the main processes of an organization by incorporating robots that partially perform repetitive tasks. In this project, we will consider how Process Mining can help into finding opportunities to apply Robotic Process Automation for a real case study.
Recently, one of the leaders in Robotic Process Automation has adquired one of the main process mining tools (https://www.uipath.com/newsroom/uipath-acquires-process-gold-unparalleled-process-understanding). This is a confirmation of the potential link between the field of process mining and the field of robotic process automation.
In this project we will try to find out how strong is this link. By using real data from a company that is in trying to automate its processes, the student will dig into the field of process mining to propose a methodology to unleash the application of RPA.
In this project, there is a possibility to have a grant that covers the time invested.
In multiple information technology scenarios, it is of interest to be able to specify who is allowed to perform a given action, and under which circumstances. XACML is a way to represent this type of rules in an XML file: it defines policies applying to specified resources which are evaluated against requests. There are multiple implementations of XACML, but none is available as an open-source C or C++ native library: they are only available in JAVA or Python, thus adding overhead when attempting to use XACML from a C/C++ project. This project aims at implementing XACML in C or C++ (C++ is advised). As the entire XACML specification is likely a too large target, the project will focus on what is specified as the core. The student will use the specification as starting point for the implementation, and the use of the existing libraries' source code as a guideline is strongly encouraged and advised. Experience in C++ is required (or C if the student so prefers). This project will have a very important software engineering aspect, and will require to be carefully tested. A decision can be taken to further restrict the scope of the project: e.g. functions such as add time to a date can be skipped, as long as it is clear that the project architecture could support it, and implementing it would be only a time issue. Through this project, the student will be able to show skills, and gain experience, in software engineering and test driven development alongside proving the ability to work autonomously.
Mesh networking with LoRa nodes Meshtastic (https://www.meshtastic.org/) is an open source project which builds a mesh network between LoRa nodes. The LoRa nodes are coupled via Bluetooth to an Android application which implements a messaging service. Text messages are spread over the LoRa network to the other Meshtastic nodes. This is a good place to start reading: https://meshtastic.letstalkthis.com/ We have a couple of TTGO ESP32 LoRa nodes (e.g. http://www.lilygo.cn/prod_view.aspx?TypeId=50003&Id=1271&FId=t3:50003:3) on which Meshtastic can be flashed. Code and further information can be found here: https://github.com/meshtastic/Meshtastic-device More advanced topics: https://github.com/meshtastic/Meshtastic-device/blob/master/docs/software/mesh-alg.md Depending on the scope of the project, the initial work could focus getting familiar with microcontrolers and install and deploy a testbed of a few Meshtastic nodes. Some evaluations and an assessment could be carried out. More advanced work could look at the design of the mesh protocol in Meshtastic, analyze design parameters and propose and evaluate changes/alternative options. If there is a strong interest in the topic, the project could be connected to the work on LoRa mesh networking by Roger Pueyo, member of our research group (https://futur.upc.edu/RogerPueyoCentelles), . https://www.sciencedirect.com/science/article/abs/pii/S0167739X20306063 Note: We have also a gateway connect to The Things Network https://www.thethingsnetwork.org/
In this project the aim is to implement and evaluate some agile optimization methods for city logistics that meet real time and large scale requirements.
City logistics is benefiting significantly by using big data analytics (based on IoT data) to improve the performance and sustainability in modern large cities. However, smart city platforms distinguish for their dynamics and large scale making it difficult to take real time decisions. Therefore agile optimization methods have emerged as a way to cope with such demanding requirements.
The project will seek large scale distributed implementations using real life data sets.
La situació d'emergència climàtica que estem vivint implica el desenvolupament d'un seguit d'accions multi i transdisciplinars que intentaran mitigar els efectes negatius de la modificació dels patrons climàtics. El projecte, desenvolupat en el marc del grup de treball d'emergència climàtica de la UPC per edificació, busca mitigar aquests efectes en l'àmbit de l'edificació.
El consum energètic en edificació representa més d'un 40% del total i, sabem que els edificis actualment no estan preparats per situacions de manca d'energia o de climatologia adversa.
Si a això se li afegeix els canvis en l'ús dels edificis (teletreball, canvis en la mobilitat amb vehicles que es carreguen a casa, etc.), cal començar a repensar tot el part edificat per buscar mecanismes de millora.
El projecte, que es desenvolupa amb altres estudiants de titulacions diferents (arquitectura, urbanisme, etc), cerca definir un model comú per poder detallar les accions a emprendre. El model, de simulació, ha de permetre establir les bases de comunicació per tal de poder implementar una solució informàtica que permeti optimitzar l'ús o la definició d'un edifici.
El projecte no parteix de zero i es basa en la tasca feta a través del projecte NECADA (https://necada.com)
The aim of the project is to study the properties of quantum particles in an external field reproducing a fractal structure. A possible realization is an ultracold quantum gas confined to two dimensions with a superimposed external field, which will create the fractal geometry. As a model for the fractal, we will consider Sierpi¿ski carpet. The main properties of interest in the ground state energy and the density profile. Such properties are to be calculated as a function of the recursion number and a possible existence of a simple scaling law has to be verified.
This interdisciplinary problem is based on application of mathematical concepts to the field of quantum physics and relies on use of numerical methods. This project requires carrying out a scientific investigation and a priori it is not clear which method will work the best. In particular, possible approaches to address the problem are
1) to solve Schrödinger equation in imaginary time for one particle
2) to solve Gross-Pitaevskii equation to solve Schroedinger equation in imaginary time for many bosons
3) do exact diagonalization of a discretized Hamiltonian for one particle and few fermions
4) random walks solution for the diffusion equation (i.e. Schroedinger equation in imaginary time) for one particle
 Simulating the 1D GPE http://link.springer.com/content/pdf/bbm%3A978-3-319-42476-7%2F1.pdf
 Solving Hydrogen atom numerically with 14 lines of Matlab code https://timqian.com/2015/03/04/H-atom-numerical/
Web tracking technologies are extensively used to collect large amounts of personal information (PI), including the things we search, the sites we visit, the people we contact, or the products we buy. Although it is commonly believed that this data is mainly used for targeted advertising, some recent works revealed that it is exploited for many other purposes, such price discrimination, financial credibility, insurance coverage, government surveillance, background scanning or identity theft. The main objective of this project is to apply network traffic monitoring and analysis technologies to uncover the particular methods used to track Internet users and collect PI. This project will be useful for both Internet users and the research community, and will produce open source tools, real data sets, and publications revealing most privacy attempting practices. Some preliminary results of our work in this area were recently published in Proceedings of the IEEE (IF: 9.237) and featured in a Wall Street Journal article.
More info at:
The main goal of this project is to develop a network monitoring system that can be used by network operators to detect bitcoin miners (or miners from other blockchain technologies) in their network. The system will rely only on network measurements obtained by standard network measurement tools and estimate interesting characteristics of detected miners, such as power consumption. How to apply: Please send an email to with your CV and academic file (pdf can be generated from the Raco).
Machine Learning with TinyML TinyML aims to do machine learning on microcontrolers. Microcontrolers are sometimes the only choice when the power supply is limited, e.g. in for battery-operated 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 microcontroler projects, but there is another kind of more powerful 32 bit microcontrolers and develpment boards, 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 the mentioned boards like the Arduino Nano 33 BLE Sense, STM32F746 Discovery kit and Espressif ESP32 microcontrolers which can be used for this project. The project will in a first phase explore with practically example applications the topic and do some reading to get a basic understanding of the background. Then the second phase can be shaped accroding to the interest: The project could either develop and deploy a specific applications of interest or focus on analyzing and experimenting a specific step of the machine learning (ML) pipeline which starts at data acquisition and building a machine learning model until doing the deployment and evaluating the application. Other ideas can be suggested, also the integrating an ML-component running on a microcontroler into a distributed application can be discussed. You can find several TinyML examples in Tensorflow, medium or towardsdatascience webs, which people have already tried with code in github repositories e.g. https://codelabs.developers.google.com/codelabs/ai-magicwand/#0 https://www.digikey.es/en/maker/projects/intro-to-tinyml-part-1-training-a-model-for-arduino-in-tensorflow/8f1fc8c0b83d417ab521c48864d2a8ec https://towardsdatascience.com/tensorflow-meet-the-esp32-3ac36d7f32c7
Applications are invited for a Final Degree Thesis (TFG) position in High Performance Computing (HPC) architectures at the Barcelona Supercomputing Center (BSC). BSC intends to pave the way to the future low-power European processor for Exascale in the context of multiple architecture initiatives (EPI, DRAC, MEEP, eProcessor, etc.) in collaboration with multiple companies (Arm, Intel, Lenovo, etc.). The main task of this TFG is to analyze the space design of the Integer Division Hardware. The student will start writing different Division Units in System Verilog, a Hardware Description Language (HDL). Next he will evaluate the area, frequency and number of stages to select the best design. This design will be adapted and integrated into System on Chip (SoC) based on RISC-V Instruction Set Unit (ISA).
About RISC-V: RISC-V is an open instruction set architecture defined by the RISC-V Foundation. This foundation has attracted more than 100 international institutions including universities, research centers and companies worldwide. RISC-V is modular and extensible, and multiple accelerators can be incorporated to the design.
About the DRAC project: The DRAC (Designing RISC-V-based Accelerators for next generation Computers) project will design, verify, implement and fabricate a high performance general purpose processor that will incorporate different accelerators based on the RISC-V technology, with specific applications in the field of security, genomics and autonomous navigation. RISC-V is an open instruction set architecture defined by the RISC-V Foundation. This foundation has attracted more than 100 international institutions including universities, research centers and companies worldwide. RISC-V is modular and extensible, and multiple accelerators can be incorporated to the design.
About BSC-CNS: BSC-CNS is the National Supercomputing Facility in Spain, located in the city of Barcelona, and was officially constituted in April 2005. BSC-CNS manages MareNostrum, one of the most powerful supercomputers in Europe. The mission of BSC-CNS is to investigate, develop and manage information technology in order to facilitate scientific progress. With this aim, special dedication has been taken to areas such as Computational Sciences, Life Sciences and Earth Sciences. All these activities are complementary to each other and very tightly related. In this way, a multidisciplinary loop is set up: our exposure to industrial and non-computer science academic practices improves our understanding of the needs and helps us focusing our basic research towards improving those practices. The result is very positive both for our research work as well as for improving the way we service our society.
- EPI: https://www.european-processor-initiative.eu/
- DRAC: https://drac.bsc.es/
- RISC-V: https://riscv.org/
- BSC: http://www.bsc.es
The identification of the applications behind the network traffic (i.e. traffic classification) is crucial for ISPs and network operators to better manage and control their networks. However, the increasing use of encryption and web-based applications makes this identification very challenging. This problem is exacerbated with the widespread deployment of content distribution networks (e.g. Akamai) and cloud-based services (e.g. Amazon AWS). The goal of this project is to develop a traffic monitoring tool to accurately identify web services from HTTPS traffic, including Google, YouTube, Facebook, Twitter among others. The tool will combine the information from IP addresses and DNS, with novel classification methods inspired on the Google PageRank algorithm to identify encrypted traffic, even if served from Akamai, AWS or Google infrastructures. This project will be carried out in collaboration with the tech-based company Talaia Networks (https://www.talaia.io), which develops cloud-based network monitoring solutions.
How to apply: Please send an email to email@example.com with your CV and academic file (pdf can be generated from the Raco).
One of the unusual effects observed in non-linear optics is a self-focusing leading to creation of stable propagating wave in a shape of a soliton. A similar state, known as a bright soliton, can be created in ultracold atomic gases in a tight waveguide for attractive interaction between bosons. Such states are commonly described within the mean-field theory in terms of a one-dimensional non-linear Gross-Pitaevskii equation. This equation permits an explicit solution for the bright soliton which is applicable when the interactions between particles are weak. For arbitrary interaction strength there is an exact solution by McGuire describing a one-dimensional system with a contact interaction. Nevertheless, a full description of the soliton as a three-dimensional object confined to a waveguide is still missing. This project aims at providing a full three-dimensional description of a bright soliton in a waveguide by means of Variational and Diffusion Monte Carlo methods.
One of the unusual effects observed in non-linear optics is a self-focusing leading to creation of stable propagating wave in a shape of a soliton. A similar state, known as a bright soliton, can be created in ultracold atomic gases in a tight waveguide for attractive interaction between bosons. Such states are commonly described within the mean-field theory in terms of a one-dimensional non-linear Gross-Pitaevskii equation. This equation permits an explicit solution for the bright soliton which is applicable when the interactions between particles are weak[2,3]. For arbitrary interaction strength there is an exact solution by McGuire describing a one-dimensional system with a contact interaction[4,5]. Nevertheless, a full description of the soliton as a three-dimensional object confined to a waveguide is still missing. This project aims at providing a full three-dimensional description of a bright soliton in a waveguide by means of Variational and Diffusion Monte Carlo methods.
During the project it will be necessary to develop a code (preferably in c programming language), test it and perform simulations of the soliton in quasi-one-dimensional geometry. In case of a positive outcome, a scientific publication will be prepared.
 V. E. Zakharov, A. B. Shabat "Integration of nonlinear equations of mathematical physics by the method of inverse scattering II" Funktsional. Anal. i Prilozhen., 13, 13 (1979)
 L. Salasnich "Bright solitons in ultracold atoms" Optical and Quantum Electronics, 49, 409 (2017)
 J. B. McGuire "Study of Exactly Soluble One-Dimensional N-Body Problems" J. Math. Phys. 5, 622 (1964)
 F. Calogero, A. Degasperis "Comparison between the exact and Hartree solutions of a one-dimensional many-body problem" Phys. Rev. A 11, 265 (1975)
 G. E. Astrakharchik and S. Giorgini, "Correlation functions and momentum distribution of one-dimensional Bose systems",
hys. Rev. A 68, 031602(R) (2003)
UPC is offering a new position to develop the TFG/TFM in the field of Machine Learning and Cybersecurity. This TFM will be fully funded (internship) and carried out in collaboration with the Global Security Operations Center of Nestlé and UPC.
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...
In a recent experiment (https://arxiv.org/abs/2002.10475) with ultracold dysprosium atoms, it was possible to realize a dipolar gas in one-dimensional geometry at low temperature. The goal of the project is to provide realistic simulation of such a system. To do so, quantum Monte Carlo code has to be developed.
Variational and Diffusion Monte Carlo codes will be implemented. One-dimensional geometry makes it easier to write the code. In case of a sussesfull simulation of the experiment, a common article with some of the authors of the experiment is possible.