Miracle-dz/UniFAR-FTUs huggingface dataset:
H-Plane Sectoral Horn Antennas in Substrate-Integrated Waveguide Technology

This dissertation aims to study, design and manufacture substrate-integrated waveguide (SIW) Hplane sectoral horn antennas to be used as standard gain antennas in Instituto de Telecomunicações / Instituto Superior Técnico and enhance the existing standard gain antennas collection. In this context, the SIW technology allows to build horns much lighter and as robust as the standard gain pyramidal horns. Moreover, this technology allows to design compact planar structures. In order to fulfill these objectives, one pair of SIW H-plane horns have been designed and manufactured. This pair of antennas have a gain of about 10 dBi in the microwaves L-band. A coaxial-to-waveguide transition have been integrated in the prototypes. The design and the free space simulations were performed using the CST MWS software. After manufacturing the antennas, its performance was measured using an anechoic chamber and a vector network analyzer, and thus allowing a comparison of experimental and simulated results. A good agreement has been obtained.
Automatic Malware Detection in Mobile Apps

 Abstract — Mobile devices have an important role in lives of their users, especially smartphones. The various functionalities offered by these devices have attracted more and more users, who use them in the most varied tasks. With the increasing number of users and the possibility of performing operations such as payments and online bank account management, there has been an increase in the number of cyber-attacks on these devices, especially on the ones based on Android operating system. This project aims to offer a solution to increase the security of these devices, through a system that allows to isolate applications. The proposed system runs applications on a server and virtualizes them on users’ mobile devices through a client application. The server intercepts functions such as the user interface composition and access to other local resources sending this information to the client application on the mobile device, so that it can rebuild the user interface and interact with the user. The client application sends information about user interactions to the server. With this system, personal data stored in mobile devices is isolated from applications. In order to position this solution with other solutions that implement similar systems, a comparison was made between the solutions, which also allowed to understand the important aspects that should be addressed in order to provide a more secure environment to users.
Measuring the Averaged Hausdorff Distance to the Pareto Front of a Multi-Objective Optimization Problem

The Hausdorff distance dH is a widely used tool to measure the distance between different objects in several research fields. Possible reasons for this might be that it is a natural extension of the well-known and intuitive distance between points and/or the fact that dH defines in certain cases a metric in the mathematical sense. Since in evolutionary multi-objective optimization (EMO) the task is typically to compute the entire solution set—the so-called Pareto set—respectively its image, the Pareto front, dH should, at least at first sight, be a natural choice to measure the performance of the outcome set. However, so far, dH does not find the general approval in the EMO community. The main reason for this is that dH penalizes single outliers of the candidate set which does not comply with the use of stochastic search algorithms such as evolutionary strategies. In this work, we define a new performance indicator, ∆p, which can be viewed as an ‘averaged Hausdorff distance’ between the outcome set and the Pareto front and which is composed of (slight modifications of) the well-known indicators Generational Distance (GD) and Inverted Generational Distance (IGD). We will discuss theoretical properties of ∆p (as well as for GD and IGD) such as the metric properties and the compliance with state-of-the-art multi-objective evolutionary algorithms (MOEAs), and will further on demonstrate by empirical results the potential of ∆p as a new performance indicator for the evaluation of MOEAs.
Music Generation Using Generative Adversarial Networks

—The idea of a machine being able to generate music is somehow intriguing. The music composition process implies the manipulation of baseline sounds or notation to create more complex structures. In this thesis a generation system based on raw waveforms representing musical bars is proposed, taking advantage of Machine Learning techniques. A preprocessing of the audio samples is performed, consisting on a transformation of the waveforms into a time-frequency representation, commonly used to deal with music signals. A state-of-the-art generative model was implemented with the purpose of creating music segments similar to those in the dataset, which is composed by 2 second long music bars. The original model is known Generative Adversarial Network (GAN) but the approached variant beneﬁts from convolutional layers in its networks architecture and is called Deep Convolutional Generative Adversarial Network. Several approaches were made regarding different architectures and hyperparameters, in order to evaluate the model’s capability of meeting the proposed objectives. By means of an user study it is concluded that the music segments generated by the implemented system are not noise, and are actually musically pleasing.
Automated Classiﬁcation of Causes of Mortality

This work addresses the automatic assignment of ICD-10 codes for causes of death by analyzing free-text descriptions in death certiﬁcates, together with the associated autopsy reports and clinical bulletins, from the Portuguese Ministry of Health. The proposed method leverages a deep neural network that combines word embeddings, recurrent units, and neural attention as mechanisms for the generation of intermediate representations of the textual contents. The neural network explores the hierarchical nature of the input data, by building representations from the sequences of words within individual ﬁelds, which are then combined according to the sequences of ﬁelds that compose the input. Moreover, innovative mechanisms for initializing the weights of the ﬁnal nodes of the network are explored, leveraging co-occurrences between classes together with the hierarchical structure of ICD-10. Experimental results attest to the contribution of the diﬀerent neural network components. The best model achieves accuracy scores over 89%, 81%, and 76%, respectively for ICD-10 chapters, blocks, and full-codes. Through examples, it is also shown that the proposed method can produce interpretable results, useful for public health surveillance.
Identification of Biomarkers for Deep Brain Stimulation using Electroencephalography and Diffusion Weighted Imaging

Deep brain stimulation (DBS) has been successfully applied to reduce motor symptoms in patients with Parkinson’s disease (PD), by modulating pathological brain networks activity via delivery of high-frequency electrical pulses. Despite the successful identification of anatomical targets and the emergence of innovative electrode technologies for application in PD, setting optimal stimulation parameters is still an elemental procedure, based on manual tuning and visual analysis of the patient’s symptomology. The master project presented here aims to understand some of the changes in both brain structure and function as a consequence of PD, to improve its diagnostic and treatment. In order to find differences in the brain structure, connectivity matrices were computed from diffusion-weighted images, and their predictive power was determined. Classification accuracy of 0.79 was reached with a simple diaglinear discriminant analysis, using a binary connectivity matrix. Optimizing the classification hyperparameters in the future may lead to the development of a new diagnostic tool for PD, or even of a new tool for suitability evaluation of patients for DBS implantation. For understanding the brain function changes with DBS, resting state electroencephalography (rs-EEG) signals were recorded in 4 PD patients before and after their DBS implantation surgery, and then with different stimulation patterns. The variation of synchronized oscillations in the beta frequency range (beta burst) was investigated. Compared with a healthy control group, pre-surgical rs-EEGs show longer beta bursts, and all post-surgical rs-EEGs show a reduction in the duration of the bursts. No significant variability between the different electrical stimulation patterns was found.
Revolutionary Fervor as Contagion: A Network Model of Rebellion∗

In this project, we model revolutionary activism on networks. Revolutionary fervor spreads through the network like a contagion, but depends on the revolutionary thresholds and grievance levels of each node. We provide for nodal removal and rewiring to better capture the underlying dynamics that drive revolutions and endogenize regime legitimacy. Our model deviates from existing work insofar as we arrange actors along a continuum, allowing for counter-revolutionaries and police to eventually be coopted into the revolution. This is a crucial condition for successful revolutions, which is too often gainsaid. Ultimately, we find that initial network structure has a negligible effect on long-run dynamics and that under many parameter settings the system equilibrates to a steady state after a transitory period and phase transition.
Analysis of Spectrum Access Techniques in Unlicensed Frequency Bands

While Short Range Devices (SRDs) already have a relevant presence in networks today, ranging from remote control to wireless sensors, it is expected that in the next decade their number will increase considerably. SRDs operate in unlicensed spectrum, and though there are some rules to control the sharing of radio resources, there is no limit to the geographical density of devices. Therefore, an eﬃciency in radio resource utilization is of paramount importance to guarantee the expected performance, even in high contention situations. It is also necessary to guarantee that the growing number of SRDs operating in the adjacent band of Long Term Evolution (LTE) do not degrade its performance, and vice-versa. In this work, a simulator was developed in order to analyze two mechanisms for channel access – Duty Cycle (DC) and Carrier Sense Multiple Access (CSMA) – in a building automation application scenario (e.g., a room temperature control system) and considering multiple SRD densities. Both these channel access schemes were evaluated independently and in a coexistence scenario. Results have shown that CSMA clearly outperforms DC, yielding lower Packet Loss Rates (PLR). Moreover, CSMA is proven to successfully operate in the presence of dozens of DC sensors, while the contrary is not true. Finally, the results for LTE and SRD coexistence show that LTE User Equipment emissions can cause packet collisions in SRDs for low SRD transmission powers. On the other hand, the impact of SRD emissions on LTE UEs is insigniﬁcant.
Diffusion MRI reconstruction contest / workshop May 02 , 2012

The proposed approach consists in wavelet-based denoising of Diffusion Spectrum Imaging (DSI) data. The data were first reconstructed by inverse Fourier transform and then projected to the multiscale domain by 3D Stationary Wavelet Transform (3D-SWT). Then, denoising was performed by soft and hard thresholding such that a fixed percentage of coefficient was retained. Different families of wavelets and decomposition depths were considered and performance was evaluated according to the Kullbach-Leibler divergence among the reference and the denoised Orientation Distribution Functions (ODFs). Results revealed that the D4 family with a single level of decomposition provides the best solution.
Sparse Predictive Control for Vehicle Motion

This work proposes the development of a control strategy based on the use of `1-norm penalty to the cost of Model Predictive Control (MPC) scheme for autonomous vehicles. The resulting control strategy will then be called sparse predictive control or sparse controller, because of the type of action considered. The vehicle is modeled using a discretized version of single integrator model expressed in Cartesian coordinates for a linear formulation, and a discretized version of unicycle model expressed in polar coordinates for a nonlinear formulation. Simulation results are provided to validate and evaluate the viability of the proposed control strategy in relation to the optimization parameters and constraints as applied in a known environment inhabited with given obstacles. A comparison analysis to quadratic or conventional cost function of MPC is also conducted. Based on the results from simulations, the feasibility of the proposed control strategy for vehicle motion control is shown as well as the effect of the selection of the cost weights that configure the controller. Furthermore, it can suggest that sparse controller is a better alternative to a conventional implementation of MPC, especially on the basis of minimizing the cost incurred during the process, resulting to a minimal fuel consumption.
Optimization Tool for Erbium Doped Fibre Amplifiers

From the moment they were made available for commercialization, in the 1990’s, Erbium Doped Fibre Amplifiers (EDFA) have helped reshape optical communication systems. Their insertion in optical networks allowed longer propagation distance between regenerators and their large bandwidth together with their insensitivity to bit rates and different signal formats enhanced the capacity of WDM systems deployed at the time. As a result, EDFA’s remain, up to this day, a key enabling component of the vast majority of all optical communication systems. The purpose of this paper is to describe EDFA technology, its characteristics and design parameters in order to develop an optimization tool for EDFA design. The paper begins by modelling the EDF and all other devices that compose EDFA architecture, and then goes on discussing optical amplifiers performance metrics (gain, noise, gain flatness, tilt and ripple), and the optimization of some design parameters in response to some predefined requirements. The goal of optimization tool is to find, within a list of possible amplifiers, the one that is both costeffective and fulfils all network requirements.
Development of an Instrumentation System for Small Rockets

The objective is to develop a compact instrumentation system for a model rocket, with both rocket and ground segments, using o -the-shelf components. First the actual parameters to be recorded are discussed and chosen. Four data types were deemed important: time, atmospheric data (pressure and temperature), translational data (position, velocity, and acceleration) and orientation data (attitude, angular velocity, and magnetic eld orientation). Second, the system is designed and the component selected, all while analysing the advantages and limitations of each part, culminating in the selection of the test rocket. Third, the next logical step is to create the software to interface with the system and components, this includes the on board micro-controller programming as well as the telemetry station computer executable. Finally, the system is ight tested, rst only during descent, so as to validate the recovery system, and then in the chosen test rocket. It was concluded that the instrumentation system achieved all the proposed requirements, the data received in the ight-test closely matches the simulation data, with the di erences being more likely due to imperfections in engine construction than due to system error.
Synthetic Data Augmentation for Congenital Heart Disease Cardiac MRI Segmentation

Cardiac MRI provides a non-invasive and non-ionizing assessment of congenital heart disease (CHD). It facilitates a reliable analysis of cardiac function and anatomy by providing diagnostic information via an accurate quantiﬁcation of chamber volume, ejection fraction and other clinically relevant indices. The main challenge in designing a deep learning cardiac MRI segmentation method is the lack of enough annotated data for CHD patients. In this paper, we designed a generative adversarial network to synthetically augment the training set. A fully convolutional network is then proposed for an automated, fast and accurate segmentation and derivation of diagnostic information for this unmet clinical need.
Phase unwrapping using reconfigurable hardware

The most computationally intensive part of the minimum LP Norm phase unwrapping algorithm[1] (its kernel) is the 2D Discrete Cosine Transform(DCT) that computes the variable p in the equation Qp=c using the Preconditioned Conjugate Gradient (PCG) method. The separability of the DCT means that the 2D transform can be decomposed into a series of 1D DCTS that compute the transforms of the rows followed by the transforms of the columns. Furthermore, the DCT can be expressed in terms of a Fast Fourier Transform (FFT), which allows the hardware implementation to use a pre-designed FFT core. This poster presents a design that implements the 1D DCT on a Xilinx FPGA that is part of the Wildstar II Pro board. This implementation performs 1D DCTs on large block sizes using a block floating point format. The DCT was designed to use fewer resources than other popular approaches due to the larger point sizes supported which would otherwise consume all available chip area, but at the cost of higher latency. This latency is similar to that required for an identically sized FFT. A 512-point DCT has been shown to take 1771 cycles or 13.3 us at 133 MHz as compared to a similarly sized FFT that takes 1757 cycles or 13.2 us (including full data load and unload times). Phase unwrapping using Reconfigurable Hardware In order to fully exploit the parallelism exposed in the algorithm, a sufficiently large FPGA with multiple banks of off-chip SRAM is needed. The Wildstar II Pro was selected as the reconfigurable solution that fulfils all these requirements. Reconfigurable Hardware WILDSTARTM-II PRO/PCI Features of the WILDSTARTM-II PRO/PCI boards: • Uses two Xilinx® Virtex-II ProTM FPGAs XC2V70 (33088 slices and 5904Kb BlockRAM) • 12 ports of DDR II SRAM totally 48MBytes, 2 ports of DDR SDRAM totally 256 MBytes • 11 GBytes/sec memory bandwidth This work was supported in part by GordenCenSSIS, the Bernard M. Gordon Center for Subsurface Sensing and Imaging Systems, under the Engineering Research Centers Program of the National Science Foundation (Award Number EEC9986821). State of the art Although no current implementation of a phaseunwrapping algorithm exists on reconfigurable hardware, much work has been done in [1][2] to develop optimal algorithms for single-processor machines. [1] D. C. Ghiglia and M. D. Pritt, Two-Dimensional Phase Unwrapping, John Wiley & Sons, 1998. [2] C.W. Chen and H.A. Zebker, Network approaches to twodimensional phase unwrapping, Journal of the Optical Society of America, Vol. 17 Issue 3, pp. 401-414, 2000. [3] J. Makhoul, A fast Cosine transform in one and two dimensions, Acoustics, Speech and Signal Processing, Volume 28, Issue 1, pp. 27-34, 1980 This work is a part of the CenSSIS BioBed effort under R1, R3 and S1. Calculating the minimum LP norm phase unwrap takes several minutes per frame to process. This prevents the streaming of live video data from the microscope. Our goal is to develop a hardware/software implementation of phase unwrapping to achieve near real-time performance. R2 Fundamental Science Validating TestBEDs
An Automatic Portuguese Tile Panel Jigsaw Puzzle Solver

—The main goal of this work is to assemble Portuguese tile panels, from the individual tile images and no other information. Using the color of the tile images as a feature, multiple compatibility functions were tested: Euclidean distance, l-½ norm, ( L p ) q norm (with p and q rational), Pomeranz prediction based distance, Mahalanobis Gradient distance and a distance function learned via KLFDA. After that, the Portuguese tile was assembled in two different ways: ﬁrstly knowing the ﬁnal orientation of each tile and secondly without any kind of information. Fortheﬁrst problem two different assembly methods were proposed: one approaching the problem via an Augmented Lagrange Method and one using a greedy approach based on a modiﬁed Nearest Neighbor rule. For the second problem, only the second approach proved to be useful. Two different image databases were used, a 20 natural images database and a 12 Portuguese tile database. The greedy method solved both settings with performance comparable to the state-of-the-art; surpassing it in the most challenging case, where the orientations of the pieces is unknown. In particular, the proposed system accurately reconstructs 92.63% of the natural image puzzles and 35.91% of the Portuguese tiles panels. Itwas also developed a speciﬁc software for image capturing and assembly testing platform.
Structured Procedural Knowledge Extraction from Cooking Videos

Procedural knowledge, which we deﬁne as concrete information about the sequence of actions that go into performing a particular procedure, plays an important role in understanding real world tasks and actions. Humans often learn this knowledge from instructional text and video, and in this paper we aim to perform automatic extraction of this knowledge in a similar way. As a concrete step in this direction, we propose the new task of inferring procedures in a structured form (a data structure containing verbs and arguments) from multimodal instructional video contents and their corresponding transcripts. We ﬁrst create a manually annotated, large evaluation dataset including over 350 instructional cooking videos along with over 15,000 English sentences in transcripts spanning 89 recipes. We conduct analysis of the challenges posed by this task and dataset with experiments with unsupervised segmentation, semantic role labeling
The 2019 NIST Speaker Recognition Evaluation CTS Challenge

In 2019, the U.S. National Institute of Standards and Technology (NIST) conducted a leaderboard style speaker recognition challenge using conversational telephone speech (CTS) data extracted from the unexposed portion of the Call My Net 2 (CMN2) corpus previously used in the 2018 Speaker Recognition Evaluation (SRE). The SRE19 CTS Challenge was organized in a similar manner to SRE18, except it offered only the open training condition. In addition, similar to the NIST i-vector challenge, the evaluation set consisted of two subsets: a progress subset, and a test subset. Trials for the progress subset comprised 30% of the target speakers from the unexposed portion of the CMN2 corpus and was used to monitor progress on the leaderboard, while trials from the remaining 70% of the speakers were allocated for the test subset, which was used to generate the ofﬁcial ﬁnal results determined at the end of the challenge. Which subset (i.e., progress or test ) a trial belonged to was unknown to challenge participants, and each system submission had to contain outputs for all of the trials. The SRE19 CTS Challenge also served as a prerequisite for entrance to the main SRE19 whose primary task was audio-visual person recognition. A total of 67 organizations (forming 51 teams) from academia and industry participated in the CTS Challenge and submitted 1347 valid system outputs. This paper presents an overview of the evaluation and several analyses of system performance for all primary conditions in the CTS Challenge. Compared to the CTS track of SRE18, the SRE19 CTS Challenge results indicate remarkable improvements in performance which are mainly attributed to 1) the availability of large amounts of in-domain development data